Tensorflow Python API

Building Graphs > Core graph data structures

Members
class tf.GraphA TensorFlow computation, represented as a dataflow graph.
tf.Graph.__init__()Creates a new, empty Graph.
tf.Graph.as_default()Returns a context manager that makes this Graph the default graph.
tf.Graph.as_graph_def(from_version=None, add_shapes=False)Returns a serialized GraphDef representation of this graph.
tf.Graph.finalize()Finalizes this graph, making it read-only.
tf.Graph.finalizedTrue if this graph has been finalized.
tf.Graph.control_dependencies(control_inputs)Returns a context manager that specifies control dependencies.
tf.Graph.device(device_name_or_function)Returns a context manager that specifies the default device to use.
tf.Graph.name_scope(name)Returns a context manager that creates hierarchical names for operations.
tf.Graph.add_to_collection(name, value)Stores value in the collection with the given name.
tf.Graph.add_to_collections(names, value)Stores value in the collections given by names.
tf.Graph.get_collection(name, scope=None)Returns a list of values in the collection with the given name.
tf.Graph.get_collection_ref(name)Returns a list of values in the collection with the given name.
tf.Graph.as_graph_element(obj, allow_tensor=True, allow_operation=True)Returns the object referred to by obj, as an Operation or Tensor.
tf.Graph.get_operation_by_name(name)Returns the Operation with the given name.
tf.Graph.get_tensor_by_name(name)Returns the Tensor with the given name.
tf.Graph.get_operations()Return the list of operations in the graph.
tf.Graph.seedThe graph-level random seed of this graph.
tf.Graph.unique_name(name, mark_as_used=True)Return a unique operation name for name.
tf.Graph.versionReturns a version number that increases as ops are added to the graph.
tf.Graph.graph_def_versionsThe GraphDef version information of this graph.
tf.Graph.create_op(op_type, inputs, dtypes, input_types=None, name=None, attrs=None, op_def=None, compute_shapes=True, compute_device=True)Creates an Operation in this graph.
tf.Graph.gradient_override_map(op_type_map)EXPERIMENTAL: A context manager for overriding gradient functions.
tf.Graph.colocate_with(op, ignore_existing=False)Returns a context manager that specifies an op to colocate with.
tf.Graph.container(container_name)Returns a context manager that specifies the resource container to use.
tf.Graph.get_all_collection_keys()Returns a list of collections used in this graph.
tf.Graph.is_feedable(tensor)Returns True if and only if tensor is feedable.
tf.Graph.is_fetchable(tensor_or_op)Returns True if and only if tensor_or_op is fetchable.
tf.Graph.prevent_feeding(tensor)Marks the given tensor as unfeedable in this graph.
tf.Graph.prevent_fetching(op)Marks the given op as unfetchable in this graph.
class tf.OperationRepresents a graph node that performs computation on tensors.
tf.Operation.nameThe full name of this operation.
tf.Operation.typeThe type of the op (e.g. "MatMul").
tf.Operation.inputsThe list of Tensor objects representing the data inputs of this op.
tf.Operation.control_inputsThe Operation objects on which this op has a control dependency.
tf.Operation.outputsThe list of Tensor objects representing the outputs of this op.
tf.Operation.deviceThe name of the device to which this op has been assigned, if any.
tf.Operation.graphThe Graph that contains this operation.
tf.Operation.run(feed_dict=None, session=None)Runs this operation in a Session.
tf.Operation.get_attr(name)Returns the value of the attr of this op with the given name.
tf.Operation.tracebackReturns the call stack from when this operation was constructed.
tf.Operation.__init__(node_def, g, inputs=None, output_types=None, control_inputs=None, input_types=None, original_op=None, op_def=None)Creates an Operation.
tf.Operation.__str__()
tf.Operation.colocation_groups()Returns the list of colocation groups of the op.
tf.Operation.node_defReturns a serialized NodeDef representation of this operation.
tf.Operation.op_defReturns the OpDef proto that represents the type of this op.
tf.Operation.values()DEPRECATED: Use outputs.
class tf.TensorRepresents one of the outputs of an Operation.
tf.Tensor.dtypeThe DType of elements in this tensor.
tf.Tensor.nameThe string name of this tensor.
tf.Tensor.value_indexThe index of this tensor in the outputs of its Operation.
tf.Tensor.graphThe Graph that contains this tensor.
tf.Tensor.opThe Operation that produces this tensor as an output.
tf.Tensor.consumers()Returns a list of Operations that consume this tensor.
tf.Tensor.eval(feed_dict=None, session=None)Evaluates this tensor in a Session.
tf.Tensor.get_shape()Returns the TensorShape that represents the shape of this tensor.
tf.Tensor.set_shape(shape)Updates the shape of this tensor.
tf.Tensor.__abs__(x, name=None)Computes the absolute value of a tensor.
tf.Tensor.__add__(x, y)Returns x + y element-wise.
tf.Tensor.__and__(x, y)Returns the truth value of x AND y element-wise.
tf.Tensor.__bool__()Dummy method to prevent a tensor from being used as a Python bool.
tf.Tensor.__div__(x, y)Returns x / y element-wise.
tf.Tensor.__eq__(other)
tf.Tensor.__floordiv__(x, y)Divides x / y elementwise, rounding down for floating point.
tf.Tensor.__ge__(x, y, name=None)Returns the truth value of (x >= y) element-wise.
tf.Tensor.__getitem__(tensor, slice_spec, var=None)Overload for Tensor.__getitem__.
tf.Tensor.__gt__(x, y, name=None)Returns the truth value of (x > y) element-wise.
tf.Tensor.__hash__()
tf.Tensor.__init__(op, value_index, dtype)Creates a new Tensor.
tf.Tensor.__invert__(x, name=None)Returns the truth value of NOT x element-wise.
tf.Tensor.__iter__()Dummy method to prevent iteration. Do not call.
tf.Tensor.__le__(x, y, name=None)Returns the truth value of (x <= y) element-wise.
tf.Tensor.__lt__(x, y, name=None)Returns the truth value of (x < y) element-wise.
tf.Tensor.__mod__(x, y)Returns element-wise remainder of division.
tf.Tensor.__mul__(x, y)Dispatches cwise mul for "Dense*Dense" and "Dense*Sparse".
tf.Tensor.__neg__(x, name=None)Computes numerical negative value element-wise.
tf.Tensor.__nonzero__()Dummy method to prevent a tensor from being used as a Python bool.
tf.Tensor.__or__(x, y)Returns the truth value of x OR y element-wise.
tf.Tensor.__pow__(x, y)Computes the power of one value to another.
tf.Tensor.__radd__(y, x)Returns x + y element-wise.
tf.Tensor.__rand__(y, x)Returns the truth value of x AND y element-wise.
tf.Tensor.__rdiv__(y, x)Returns x / y element-wise.
tf.Tensor.__repr__()
tf.Tensor.__rfloordiv__(y, x)Divides x / y elementwise, rounding down for floating point.
tf.Tensor.__rmod__(y, x)Returns element-wise remainder of division.
tf.Tensor.__rmul__(y, x)Dispatches cwise mul for "Dense*Dense" and "Dense*Sparse".
tf.Tensor.__ror__(y, x)Returns the truth value of x OR y element-wise.
tf.Tensor.__rpow__(y, x)Computes the power of one value to another.
tf.Tensor.__rsub__(y, x)Returns x - y element-wise.
tf.Tensor.__rtruediv__(y, x)Divides x / y elementwise, always producing floating point results.
tf.Tensor.__rxor__(y, x)x ^ y = (x | y) & ~(x & y).
tf.Tensor.__str__()
tf.Tensor.__sub__(x, y)Returns x - y element-wise.
tf.Tensor.__truediv__(x, y)Divides x / y elementwise, always producing floating point results.
tf.Tensor.__xor__(x, y)x ^ y = (x | y) & ~(x & y).
tf.Tensor.deviceThe name of the device on which this tensor will be produced, or None.

Building Graphs > Tensor types

Members
class tf.DTypeRepresents the type of the elements in a Tensor.
tf.DType.is_compatible_with(other)Returns True if the other DType will be converted to this DType.
tf.DType.nameReturns the string name for this DType.
tf.DType.base_dtypeReturns a non-reference DType based on this DType.
tf.DType.real_dtypeReturns the dtype correspond to this dtype's real part.
tf.DType.is_ref_dtypeReturns True if this DType represents a reference type.
tf.DType.as_refReturns a reference DType based on this DType.
tf.DType.is_floatingReturns whether this is a (real) floating point type.
tf.DType.is_complexReturns whether this is a complex floating point type.
tf.DType.is_integerReturns whether this is a (non-quantized) integer type.
tf.DType.is_quantizedReturns whether this is a quantized data type.
tf.DType.is_unsignedReturns whether this type is unsigned.
tf.DType.as_numpy_dtypeReturns a numpy.dtype based on this DType.
tf.DType.as_datatype_enumReturns a types_pb2.DataType enum value based on this DType.
tf.DType.__eq__(other)Returns True iff this DType refers to the same type as other.
tf.DType.__hash__()
tf.DType.__init__(type_enum)Creates a new DataType.
tf.DType.__ne__(other)Returns True iff self != other.
tf.DType.__repr__()
tf.DType.__str__()
tf.DType.maxReturns the maximum representable value in this data type.
tf.DType.minReturns the minimum representable value in this data type.
tf.DType.size
tf.as_dtype(type_value)Converts the given type_value to a DType.

Building Graphs > Utility functions

Members
tf.device(device_name_or_function)Wrapper for Graph.device() using the default graph.
tf.container(container_name)Wrapper for Graph.container() using the default graph.
tf.name_scope(name, default_name=None, values=None)Returns a context manager for use when defining a Python op.
tf.control_dependencies(control_inputs)Wrapper for Graph.control_dependencies() using the default graph.
tf.convert_to_tensor(value, dtype=None, name=None, as_ref=False, preferred_dtype=None)Converts the given value to a Tensor.
tf.convert_to_tensor_or_indexed_slices(value, dtype=None, name=None, as_ref=False)Converts the given object to a Tensor or an IndexedSlices.
tf.get_default_graph()Returns the default graph for the current thread.
tf.reset_default_graph()Clears the default graph stack and resets the global default graph.
tf.import_graph_def(graph_def, input_map=None, return_elements=None, name=None, op_dict=None, producer_op_list=None)Imports the TensorFlow graph in graph_def into the Python Graph.
tf.load_file_system_library(library_filename)Loads a TensorFlow plugin, containing file system implementation.
tf.load_op_library(library_filename)Loads a TensorFlow plugin, containing custom ops and kernels.

Building Graphs > Graph collections

Members
tf.add_to_collection(name, value)Wrapper for Graph.add_to_collection() using the default graph.
tf.get_collection(key, scope=None)Wrapper for Graph.get_collection() using the default graph.
tf.get_collection_ref(key)Wrapper for Graph.get_collection_ref() using the default graph.
class tf.GraphKeysStandard names to use for graph collections.

Building Graphs > Defining new operations

Members
class tf.RegisterGradientA decorator for registering the gradient function for an op type.
tf.RegisterGradient.__init__(op_type)Creates a new decorator with op_type as the Operation type.
tf.RegisterGradient.__call__(f)Registers the function f as gradient function for op_type.
tf.NotDifferentiable(op_type)Specifies that ops of type op_type is not differentiable.
tf.NoGradient(op_type)Specifies that ops of type op_type is not differentiable.
class tf.RegisterShapeA decorator for registering the shape function for an op type.
tf.RegisterShape.__call__(f)Registers "f" as the shape function for "op_type".
tf.RegisterShape.__init__(op_type)Saves the op_type as the Operation type.
class tf.TensorShapeRepresents the shape of a Tensor.
tf.TensorShape.merge_with(other)Returns a TensorShape combining the information in self and other.
tf.TensorShape.concatenate(other)Returns the concatenation of the dimension in self and other.
tf.TensorShape.ndimsReturns the rank of this shape, or None if it is unspecified.
tf.TensorShape.dimsReturns a list of Dimensions, or None if the shape is unspecified.
tf.TensorShape.as_list()Returns a list of integers or None for each dimension.
tf.TensorShape.as_proto()Returns this shape as a TensorShapeProto.
tf.TensorShape.is_compatible_with(other)Returns True iff self is compatible with other.
tf.TensorShape.is_fully_defined()Returns True iff self is fully defined in every dimension.
tf.TensorShape.with_rank(rank)Returns a shape based on self with the given rank.
tf.TensorShape.with_rank_at_least(rank)Returns a shape based on self with at least the given rank.
tf.TensorShape.with_rank_at_most(rank)Returns a shape based on self with at most the given rank.
tf.TensorShape.assert_has_rank(rank)Raises an exception if self is not compatible with the given rank.
tf.TensorShape.assert_same_rank(other)Raises an exception if self and other do not have compatible ranks.
tf.TensorShape.assert_is_compatible_with(other)Raises exception if self and other do not represent the same shape.
tf.TensorShape.assert_is_fully_defined()Raises an exception if self is not fully defined in every dimension.
tf.TensorShape.__bool__()Returns True if this shape contains non-zero information.
tf.TensorShape.__eq__(other)Returns True if self is equivalent to other.
tf.TensorShape.__getitem__(key)Returns the value of a dimension or a shape, depending on the key.
tf.TensorShape.__init__(dims)Creates a new TensorShape with the given dimensions.
tf.TensorShape.__iter__()Returns self.dims if the rank is known, otherwise raises ValueError.
tf.TensorShape.__len__()Returns the rank of this shape, or raises ValueError if unspecified.
tf.TensorShape.__ne__(other)Returns True if self is known to be different from other.
tf.TensorShape.__nonzero__()Returns True if this shape contains non-zero information.
tf.TensorShape.__repr__()
tf.TensorShape.__str__()
tf.TensorShape.num_elements()Returns the total number of elements, or none for incomplete shapes.
class tf.DimensionRepresents the value of one dimension in a TensorShape.
tf.Dimension.__add__(other)Returns the sum of self and other.
tf.Dimension.__div__(other)DEPRECATED: Use __floordiv__ via x // y instead.
tf.Dimension.__eq__(other)Returns true if other has the same known value as this Dimension.
tf.Dimension.__floordiv__(other)Returns the quotient of self and other rounded down.
tf.Dimension.__ge__(other)Returns True if self is known to be greater than or equal to other.
tf.Dimension.__gt__(other)Returns True if self is known to be greater than other.
tf.Dimension.__index__()
tf.Dimension.__init__(value)Creates a new Dimension with the given value.
tf.Dimension.__int__()
tf.Dimension.__le__(other)Returns True if self is known to be less than or equal to other.
tf.Dimension.__lt__(other)Returns True if self is known to be less than other.
tf.Dimension.__mod__(other)Returns self modulo other.
tf.Dimension.__mul__(other)Returns the product of self and other.
tf.Dimension.__ne__(other)Returns true if other has a different known value from self.
tf.Dimension.__repr__()
tf.Dimension.__str__()
tf.Dimension.__sub__(other)Returns the subtraction of other from self.
tf.Dimension.assert_is_compatible_with(other)Raises an exception if other is not compatible with this Dimension.
tf.Dimension.is_compatible_with(other)Returns true if other is compatible with this Dimension.
tf.Dimension.merge_with(other)Returns a Dimension that combines the information in self and other.
tf.Dimension.valueThe value of this dimension, or None if it is unknown.
tf.op_scope(values, name, default_name=None)DEPRECATED. Same as name_scope above, just different argument order.
tf.get_seed(op_seed)Returns the local seeds an operation should use given an op-specific seed.

Building Graphs > For libraries building on TensorFlow

Members
tf.register_tensor_conversion_function(base_type, conversion_func, priority=100)Registers a function for converting objects of base_type to Tensor.

Building Graphs > Other Functions and Classes

Members
class tf.DeviceSpecRepresents a (possibly partial) specification for a TensorFlow device.
tf.DeviceSpec.__init__(job=None, replica=None, task=None, device_type=None, device_index=None)Create a new DeviceSpec object.
tf.DeviceSpec.from_string(spec)Construct a DeviceSpec from a string.
tf.DeviceSpec.job
tf.DeviceSpec.merge_from(dev)Merge the properties of "dev" into this DeviceSpec.
tf.DeviceSpec.parse_from_string(spec)Parse a DeviceSpec name into its components.
tf.DeviceSpec.replica
tf.DeviceSpec.task
tf.DeviceSpec.to_string()Return a string representation of this DeviceSpec.

Asserts and boolean checks. > Asserts and Boolean Checks

Members
tf.assert_negative(x, data=None, summarize=None, message=None, name=None)Assert the condition x < 0 holds element-wise.
tf.assert_positive(x, data=None, summarize=None, message=None, name=None)Assert the condition x > 0 holds element-wise.
tf.assert_proper_iterable(values)Static assert that values is a "proper" iterable.
tf.assert_non_negative(x, data=None, summarize=None, message=None, name=None)Assert the condition x >= 0 holds element-wise.
tf.assert_non_positive(x, data=None, summarize=None, message=None, name=None)Assert the condition x <= 0 holds element-wise.
tf.assert_equal(x, y, data=None, summarize=None, message=None, name=None)Assert the condition x == y holds element-wise.
tf.assert_integer(x, message=None, name=None)Assert that x is of integer dtype.
tf.assert_less(x, y, data=None, summarize=None, message=None, name=None)Assert the condition x < y holds element-wise.
tf.assert_less_equal(x, y, data=None, summarize=None, message=None, name=None)Assert the condition x <= y holds element-wise.
tf.assert_greater(x, y, data=None, summarize=None, message=None, name=None)Assert the condition x > y holds element-wise.
tf.assert_greater_equal(x, y, data=None, summarize=None, message=None, name=None)Assert the condition x >= y holds element-wise.
tf.assert_rank(x, rank, data=None, summarize=None, message=None, name=None)Assert x has rank equal to rank.
tf.assert_rank_at_least(x, rank, data=None, summarize=None, message=None, name=None)Assert x has rank equal to rank or higher.
tf.assert_type(tensor, tf_type, message=None, name=None)Statically asserts that the given Tensor is of the specified type.
tf.is_non_decreasing(x, name=None)Returns True if x is non-decreasing.
tf.is_numeric_tensor(tensor)
tf.is_strictly_increasing(x, name=None)Returns True if x is strictly increasing.

Constants, Sequences, and Random Values > Constant Value Tensors

Members
tf.zeros(shape, dtype=tf.float32, name=None)Creates a tensor with all elements set to zero.
tf.zeros_like(tensor, dtype=None, name=None, optimize=True)Creates a tensor with all elements set to zero.
tf.ones(shape, dtype=tf.float32, name=None)Creates a tensor with all elements set to 1.
tf.ones_like(tensor, dtype=None, name=None, optimize=True)Creates a tensor with all elements set to 1.
tf.fill(dims, value, name=None)Creates a tensor filled with a scalar value.
tf.constant(value, dtype=None, shape=None, name='Const')Creates a constant tensor.

Constants, Sequences, and Random Values > Sequences

Members
tf.linspace(start, stop, num, name=None)Generates values in an interval.
tf.range(start, limit=None, delta=1, name='range')Creates a sequence of integers.

Constants, Sequences, and Random Values > Random Tensors

Members
tf.random_normal(shape, mean=0.0, stddev=1.0, dtype=tf.float32, seed=None, name=None)Outputs random values from a normal distribution.
tf.truncated_normal(shape, mean=0.0, stddev=1.0, dtype=tf.float32, seed=None, name=None)Outputs random values from a truncated normal distribution.
tf.random_uniform(shape, minval=0, maxval=None, dtype=tf.float32, seed=None, name=None)Outputs random values from a uniform distribution.
tf.random_shuffle(value, seed=None, name=None)Randomly shuffles a tensor along its first dimension.
tf.random_crop(value, size, seed=None, name=None)Randomly crops a tensor to a given size.
tf.multinomial(logits, num_samples, seed=None, name=None)Draws samples from a multinomial distribution.
tf.random_gamma(shape, alpha, beta=None, dtype=tf.float32, seed=None, name=None)Draws shape samples from each of the given Gamma distribution(s).
tf.set_random_seed(seed)Sets the graph-level random seed.

Variables > Variables

Members
class tf.VariableSee the [Variables How To](../../how_tos/variables/index.md) for a high
tf.Variable.__init__(initial_value=None, trainable=True, collections=None, validate_shape=True, caching_device=None, name=None, variable_def=None, dtype=None)Creates a new variable with value initial_value.
tf.Variable.initialized_value()Returns the value of the initialized variable.
tf.Variable.assign(value, use_locking=False)Assigns a new value to the variable.
tf.Variable.assign_add(delta, use_locking=False)Adds a value to this variable.
tf.Variable.assign_sub(delta, use_locking=False)Subtracts a value from this variable.
tf.Variable.scatter_sub(sparse_delta, use_locking=False)Subtracts IndexedSlices from this variable.
tf.Variable.count_up_to(limit)Increments this variable until it reaches limit.
tf.Variable.eval(session=None)In a session, computes and returns the value of this variable.
tf.Variable.nameThe name of this variable.
tf.Variable.dtypeThe DType of this variable.
tf.Variable.get_shape()The TensorShape of this variable.
tf.Variable.deviceThe device of this variable.
tf.Variable.initializerThe initializer operation for this variable.
tf.Variable.graphThe Graph of this variable.
tf.Variable.opThe Operation of this variable.
tf.Variable.__abs__(a, *args)Computes the absolute value of a tensor.
tf.Variable.__add__(a, *args)Returns x + y element-wise.
tf.Variable.__and__(a, *args)Returns the truth value of x AND y element-wise.
tf.Variable.__div__(a, *args)Returns x / y element-wise.
tf.Variable.__floordiv__(a, *args)Divides x / y elementwise, rounding down for floating point.
tf.Variable.__ge__(a, *args)Returns the truth value of (x >= y) element-wise.
tf.Variable.__getitem__(var, slice_spec)Creates a slice helper object given a variable.
tf.Variable.__gt__(a, *args)Returns the truth value of (x > y) element-wise.
tf.Variable.__invert__(a, *args)Returns the truth value of NOT x element-wise.
tf.Variable.__iter__()Dummy method to prevent iteration. Do not call.
tf.Variable.__le__(a, *args)Returns the truth value of (x <= y) element-wise.
tf.Variable.__lt__(a, *args)Returns the truth value of (x < y) element-wise.
tf.Variable.__mod__(a, *args)Returns element-wise remainder of division.
tf.Variable.__mul__(a, *args)Dispatches cwise mul for "Dense*Dense" and "Dense*Sparse".
tf.Variable.__neg__(a, *args)Computes numerical negative value element-wise.
tf.Variable.__or__(a, *args)Returns the truth value of x OR y element-wise.
tf.Variable.__pow__(a, *args)Computes the power of one value to another.
tf.Variable.__radd__(a, *args)Returns x + y element-wise.
tf.Variable.__rand__(a, *args)Returns the truth value of x AND y element-wise.
tf.Variable.__rdiv__(a, *args)Returns x / y element-wise.
tf.Variable.__rfloordiv__(a, *args)Divides x / y elementwise, rounding down for floating point.
tf.Variable.__rmod__(a, *args)Returns element-wise remainder of division.
tf.Variable.__rmul__(a, *args)Dispatches cwise mul for "Dense*Dense" and "Dense*Sparse".
tf.Variable.__ror__(a, *args)Returns the truth value of x OR y element-wise.
tf.Variable.__rpow__(a, *args)Computes the power of one value to another.
tf.Variable.__rsub__(a, *args)Returns x - y element-wise.
tf.Variable.__rtruediv__(a, *args)Divides x / y elementwise, always producing floating point results.
tf.Variable.__rxor__(a, *args)x ^ y = (x | y) & ~(x & y).
tf.Variable.__sub__(a, *args)Returns x - y element-wise.
tf.Variable.__truediv__(a, *args)Divides x / y elementwise, always producing floating point results.
tf.Variable.__xor__(a, *args)x ^ y = (x | y) & ~(x & y).
tf.Variable.from_proto(variable_def)Returns a Variable object created from variable_def.
tf.Variable.initial_valueReturns the Tensor used as the initial value for the variable.
tf.Variable.ref()Returns a reference to this variable.
tf.Variable.to_proto()Converts a Variable to a VariableDef protocol buffer.
tf.Variable.value()Returns the last snapshot of this variable.

Variables > Variable helper functions

Members
tf.all_variables()Returns all variables that must be saved/restored.
tf.trainable_variables()Returns all variables created with trainable=True.
tf.local_variables()Returns all variables created with collection=[LOCAL_VARIABLES].
tf.model_variables()Returns all variables in the MODEL_VARIABLES collection.
tf.moving_average_variables()Returns all variables that maintain their moving averages.
tf.initialize_all_variables()Returns an Op that initializes all variables.
tf.initialize_variables(var_list, name='init')Returns an Op that initializes a list of variables.
tf.initialize_local_variables()Returns an Op that initializes all local variables.
tf.is_variable_initialized(variable)Tests if a variable has been initialized.
tf.report_uninitialized_variables(var_list=None, name='report_uninitialized_variables')Adds ops to list the names of uninitialized variables.
tf.assert_variables_initialized(var_list=None)Returns an Op to check if variables are initialized.
tf.assign(ref, value, validate_shape=None, use_locking=None, name=None)Update 'ref' by assigning 'value' to it.
tf.assign_add(ref, value, use_locking=None, name=None)Update 'ref' by adding 'value' to it.
tf.assign_sub(ref, value, use_locking=None, name=None)Update 'ref' by subtracting 'value' from it.

Variables > Saving and Restoring Variables

Members
class tf.train.SaverSaves and restores variables.
tf.train.Saver.__init__(var_list=None, reshape=False, sharded=False, max_to_keep=5, keep_checkpoint_every_n_hours=10000.0, name=None, restore_sequentially=False, saver_def=None, builder=None, defer_build=False, allow_empty=False, write_version=1)Creates a Saver.
tf.train.Saver.save(sess, save_path, global_step=None, latest_filename=None, meta_graph_suffix='meta', write_meta_graph=True)Saves variables.
tf.train.Saver.restore(sess, save_path)Restores previously saved variables.
tf.train.Saver.last_checkpointsList of not-yet-deleted checkpoint filenames.
tf.train.Saver.set_last_checkpoints_with_time(last_checkpoints_with_time)Sets the list of old checkpoint filenames and timestamps.
tf.train.Saver.recover_last_checkpoints(checkpoint_paths)Recovers the internal saver state after a crash.
tf.train.Saver.as_saver_def()Generates a SaverDef representation of this saver.
tf.train.Saver.build()Builds saver_def.
tf.train.Saver.export_meta_graph(filename=None, collection_list=None, as_text=False)Writes MetaGraphDef to save_path/filename.
tf.train.Saver.from_proto(saver_def)Returns a Saver object created from saver_def.
tf.train.Saver.set_last_checkpoints(last_checkpoints)DEPRECATED: Use set_last_checkpoints_with_time.
tf.train.Saver.to_proto()Converts this Saver to a SaverDef protocol buffer.
tf.train.latest_checkpoint(checkpoint_dir, latest_filename=None)Finds the filename of latest saved checkpoint file.
tf.train.get_checkpoint_state(checkpoint_dir, latest_filename=None)Returns CheckpointState proto from the "checkpoint" file.
tf.train.update_checkpoint_state(save_dir, model_checkpoint_path, all_model_checkpoint_paths=None, latest_filename=None)Updates the content of the 'checkpoint' file.

Variables > Sharing Variables

Members
tf.get_variable(name, shape=None, dtype=None, initializer=None, regularizer=None, trainable=True, collections=None, caching_device=None, partitioner=None, validate_shape=True, custom_getter=None)Gets an existing variable with these parameters or create a new one.
class tf.VariableScopeVariable scope object to carry defaults to provide to get_variable.
tf.VariableScope.__init__(reuse, name='', initializer=None, regularizer=None, caching_device=None, partitioner=None, custom_getter=None, name_scope='', dtype=tf.float32)Creates a new VariableScope with the given properties.
tf.VariableScope.caching_device
tf.VariableScope.custom_getter
tf.VariableScope.dtype
tf.VariableScope.get_variable(var_store, name, shape=None, dtype=None, initializer=None, regularizer=None, trainable=True, collections=None, caching_device=None, partitioner=None, validate_shape=True, custom_getter=None)Gets an existing variable with this name or create a new one.
tf.VariableScope.initializer
tf.VariableScope.name
tf.VariableScope.original_name_scope
tf.VariableScope.partitioner
tf.VariableScope.regularizer
tf.VariableScope.reuse
tf.VariableScope.reuse_variables()Reuse variables in this scope.
tf.VariableScope.set_caching_device(caching_device)Set caching_device for this scope.
tf.VariableScope.set_custom_getter(custom_getter)Set custom getter for this scope.
tf.VariableScope.set_dtype(dtype)Set data type for this scope.
tf.VariableScope.set_initializer(initializer)Set initializer for this scope.
tf.VariableScope.set_partitioner(partitioner)Set partitioner for this scope.
tf.VariableScope.set_regularizer(regularizer)Set regularizer for this scope.
tf.variable_scope(name_or_scope, default_name=None, values=None, initializer=None, regularizer=None, caching_device=None, partitioner=None, custom_getter=None, reuse=None, dtype=None)Returns a context manager for defining ops that creates variables (layers).
tf.variable_op_scope(values, name_or_scope, default_name=None, initializer=None, regularizer=None, caching_device=None, partitioner=None, custom_getter=None, reuse=None, dtype=None)Deprecated: context manager for defining an op that creates variables.
tf.get_variable_scope()Returns the current variable scope.
tf.make_template(name_, func_, create_scope_now_=False, unique_name_=None, **kwargs)Given an arbitrary function, wrap it so that it does variable sharing.
tf.no_regularizer(_)Use this function to prevent regularization of variables.
tf.constant_initializer(value=0, dtype=tf.float32)Returns an initializer that generates tensors with constant values.
tf.random_normal_initializer(mean=0.0, stddev=1.0, seed=None, dtype=tf.float32)Returns an initializer that generates tensors with a normal distribution.
tf.truncated_normal_initializer(mean=0.0, stddev=1.0, seed=None, dtype=tf.float32)Returns an initializer that generates a truncated normal distribution.
tf.random_uniform_initializer(minval=0, maxval=None, seed=None, dtype=tf.float32)Returns an initializer that generates tensors with a uniform distribution.
tf.uniform_unit_scaling_initializer(factor=1.0, seed=None, dtype=tf.float32)Returns an initializer that generates tensors without scaling variance.
tf.zeros_initializer(shape, dtype=tf.float32, partition_info=None)An adaptor for zeros() to match the Initializer spec.
tf.ones_initializer(shape, dtype=tf.float32, partition_info=None)An adaptor for ones() to match the Initializer spec.

Variables > Variable Partitioners for Sharding

Members
tf.fixed_size_partitioner(num_shards, axis=0)Partitioner to specify a fixed number of shards along given axis.
tf.variable_axis_size_partitioner(max_shard_bytes, axis=0, bytes_per_string_element=16, max_shards=None)Get a partitioner for VariableScope to keep shards below max_shard_bytes.
tf.min_max_variable_partitioner(max_partitions=1, axis=0, min_slice_size=262144, bytes_per_string_element=16)Partitioner to allocate minimum size per slice.

Variables > Sparse Variable Updates

Members
tf.scatter_update(ref, indices, updates, use_locking=None, name=None)Applies sparse updates to a variable reference.
tf.scatter_add(ref, indices, updates, use_locking=None, name=None)Adds sparse updates to a variable reference.
tf.scatter_sub(ref, indices, updates, use_locking=None, name=None)Subtracts sparse updates to a variable reference.
tf.scatter_mul(ref, indices, updates, use_locking=None, name=None)Multiplies sparse updates into a variable reference.
tf.scatter_div(ref, indices, updates, use_locking=None, name=None)Divides a variable reference by sparse updates.
tf.sparse_mask(a, mask_indices, name=None)Masks elements of IndexedSlices.
class tf.IndexedSlicesA sparse representation of a set of tensor slices at given indices.
tf.IndexedSlices.__init__(values, indices, dense_shape=None)Creates an IndexedSlices.
tf.IndexedSlices.valuesA Tensor containing the values of the slices.
tf.IndexedSlices.indicesA 1-D Tensor containing the indices of the slices.
tf.IndexedSlices.dense_shapeA 1-D Tensor containing the shape of the corresponding dense tensor.
tf.IndexedSlices.nameThe name of this IndexedSlices.
tf.IndexedSlices.dtypeThe DType of elements in this tensor.
tf.IndexedSlices.deviceThe name of the device on which values will be produced, or None.
tf.IndexedSlices.opThe Operation that produces values as an output.
tf.IndexedSlices.__neg__()
tf.IndexedSlices.__str__()
tf.IndexedSlices.graphThe Graph that contains the values, indices, and shape tensors.

Variables > Exporting and Importing Meta Graphs

Members
tf.train.export_meta_graph(filename=None, meta_info_def=None, graph_def=None, saver_def=None, collection_list=None, as_text=False)Returns MetaGraphDef proto. Optionally writes it to filename.
tf.train.import_meta_graph(meta_graph_or_file, clear_devices=False)Recreates a Graph saved in a MetaGraphDef proto.

Tensor Transformations > Casting

Members
tf.string_to_number(string_tensor, out_type=None, name=None)Converts each string in the input Tensor to the specified numeric type.
tf.to_double(x, name='ToDouble')Casts a tensor to type float64.
tf.to_float(x, name='ToFloat')Casts a tensor to type float32.
tf.to_bfloat16(x, name='ToBFloat16')Casts a tensor to type bfloat16.
tf.to_int32(x, name='ToInt32')Casts a tensor to type int32.
tf.to_int64(x, name='ToInt64')Casts a tensor to type int64.
tf.cast(x, dtype, name=None)Casts a tensor to a new type.
tf.bitcast(input, type, name=None)Bitcasts a tensor from one type to another without copying data.
tf.saturate_cast(value, dtype, name=None)Performs a safe saturating cast of value to dtype.

Tensor Transformations > Shapes and Shaping

Members
tf.shape(input, name=None, out_type=tf.int32)Returns the shape of a tensor.
tf.shape_n(input, out_type=None, name=None)Returns shape of tensors.
tf.size(input, name=None, out_type=tf.int32)Returns the size of a tensor.
tf.rank(input, name=None)Returns the rank of a tensor.
tf.reshape(tensor, shape, name=None)Reshapes a tensor.
tf.squeeze(input, squeeze_dims=None, name=None)Removes dimensions of size 1 from the shape of a tensor.
tf.expand_dims(input, dim, name=None)Inserts a dimension of 1 into a tensor's shape.
tf.meshgrid(*args, **kwargs)Broadcasts parameters for evaluation on an N-D grid.

Tensor Transformations > Slicing and Joining

Members
tf.slice(input_, begin, size, name=None)Extracts a slice from a tensor.
tf.strided_slice(input_, begin, end, strides, begin_mask=0, end_mask=0, ellipsis_mask=0, new_axis_mask=0, shrink_axis_mask=0, var=None, name=None)Extracts a strided slice from a tensor.
tf.split(split_dim, num_split, value, name='split')Splits a tensor into num_split tensors along one dimension.
tf.tile(input, multiples, name=None)Constructs a tensor by tiling a given tensor.
tf.pad(tensor, paddings, mode='CONSTANT', name=None)Pads a tensor.
tf.concat(concat_dim, values, name='concat')Concatenates tensors along one dimension.
tf.pack(values, axis=0, name='pack')Packs a list of rank-R tensors into one rank-(R+1) tensor.
tf.unpack(value, num=None, axis=0, name='unpack')Unpacks the given dimension of a rank-R tensor into rank-(R-1) tensors.
tf.reverse_sequence(input, seq_lengths, seq_dim, batch_dim=None, name=None)Reverses variable length slices.
tf.reverse(tensor, dims, name=None)Reverses specific dimensions of a tensor.
tf.transpose(a, perm=None, name='transpose')Transposes a. Permutes the dimensions according to perm.
tf.extract_image_patches(images, ksizes, strides, rates, padding, name=None)Extract patches from images and put them in the "depth" output dimension.
tf.space_to_batch_nd(input, block_shape, paddings, name=None)SpaceToBatch for N-D tensors of type T.
tf.space_to_batch(input, paddings, block_size, name=None)SpaceToBatch for 4-D tensors of type T.
tf.required_space_to_batch_paddings(input_shape, block_shape, base_paddings=None, name=None)Calculate padding required to make block_shape divide input_shape.
tf.batch_to_space_nd(input, block_shape, crops, name=None)BatchToSpace for N-D tensors of type T.
tf.batch_to_space(input, crops, block_size, name=None)BatchToSpace for 4-D tensors of type T.
tf.space_to_depth(input, block_size, name=None)SpaceToDepth for tensors of type T.
tf.depth_to_space(input, block_size, name=None)DepthToSpace for tensors of type T.
tf.gather(params, indices, validate_indices=None, name=None)Gather slices from params according to indices.
tf.gather_nd(params, indices, name=None)Gather values or slices from params according to indices.
tf.unique_with_counts(x, out_idx=None, name=None)Finds unique elements in a 1-D tensor.
tf.dynamic_partition(data, partitions, num_partitions, name=None)Partitions data into num_partitions tensors using indices from partitions.
tf.dynamic_stitch(indices, data, name=None)Interleave the values from the data tensors into a single tensor.
tf.boolean_mask(tensor, mask, name='boolean_mask')Apply boolean mask to tensor. Numpy equivalent is tensor[mask].
tf.one_hot(indices, depth, on_value=None, off_value=None, axis=None, dtype=None, name=None)Returns a one-hot tensor.
tf.sequence_mask(lengths, maxlen=None, dtype=tf.bool, name=None)Return a mask tensor representing the first N positions of each row.

Math > Arithmetic Operators

Members
tf.add(x, y, name=None)Returns x + y element-wise.
tf.sub(x, y, name=None)Returns x - y element-wise.
tf.mul(x, y, name=None)Returns x * y element-wise.
tf.scalar_mul(scalar, x)Multiplies a scalar times a Tensor or IndexedSlices object.
tf.div(x, y, name=None)Returns x / y element-wise.
tf.truediv(x, y, name=None)Divides x / y elementwise, always producing floating point results.
tf.floordiv(x, y, name=None)Divides x / y elementwise, rounding down for floating point.
tf.mod(x, y, name=None)Returns element-wise remainder of division.
tf.cross(a, b, name=None)Compute the pairwise cross product.

Math > Basic Math Functions

Members
tf.add_n(inputs, name=None)Adds all input tensors element-wise.
tf.abs(x, name=None)Computes the absolute value of a tensor.
tf.neg(x, name=None)Computes numerical negative value element-wise.
tf.sign(x, name=None)Returns an element-wise indication of the sign of a number.
tf.inv(x, name=None)Computes the reciprocal of x element-wise.
tf.square(x, name=None)Computes square of x element-wise.
tf.round(x, name=None)Rounds the values of a tensor to the nearest integer, element-wise.
tf.sqrt(x, name=None)Computes square root of x element-wise.
tf.rsqrt(x, name=None)Computes reciprocal of square root of x element-wise.
tf.pow(x, y, name=None)Computes the power of one value to another.
tf.exp(x, name=None)Computes exponential of x element-wise. \\(y = e^x\\).
tf.log(x, name=None)Computes natural logarithm of x element-wise.
tf.ceil(x, name=None)Returns element-wise smallest integer in not less than x.
tf.floor(x, name=None)Returns element-wise largest integer not greater than x.
tf.maximum(x, y, name=None)Returns the max of x and y (i.e. x > y ? x : y) element-wise.
tf.minimum(x, y, name=None)Returns the min of x and y (i.e. x < y ? x : y) element-wise.
tf.cos(x, name=None)Computes cos of x element-wise.
tf.sin(x, name=None)Computes sin of x element-wise.
tf.lbeta(x, name='lbeta')Computes ln(|Beta(x)|), reducing along the last dimension.
tf.tan(x, name=None)Computes tan of x element-wise.
tf.acos(x, name=None)Computes acos of x element-wise.
tf.asin(x, name=None)Computes asin of x element-wise.
tf.atan(x, name=None)Computes atan of x element-wise.
tf.lgamma(x, name=None)Computes the log of the absolute value of Gamma(x) element-wise.
tf.digamma(x, name=None)Computes Psi, the derivative of Lgamma (the log of the absolute value of
tf.erf(x, name=None)Computes the Gauss error function of x element-wise.
tf.erfc(x, name=None)Computes the complementary error function of x element-wise.
tf.squared_difference(x, y, name=None)Returns (x - y)(x - y) element-wise.
tf.igamma(a, x, name=None)Compute the lower regularized incomplete Gamma function Q(a, x).
tf.igammac(a, x, name=None)Compute the upper regularized incomplete Gamma function Q(a, x).
tf.zeta(x, q, name=None)Compute the Hurwitz zeta function \\(\zeta(x, q)\\).
tf.polygamma(a, x, name=None)Compute the polygamma function \\(\psi^{(n)}(x)\\).
tf.betainc(a, b, x, name=None)Compute the regularized incomplete beta integral \\(I_x(a, b)\\).

Math > Matrix Math Functions

Members
tf.diag(diagonal, name=None)Returns a diagonal tensor with a given diagonal values.
tf.diag_part(input, name=None)Returns the diagonal part of the tensor.
tf.trace(x, name=None)Compute the trace of a tensor x.
tf.transpose(a, perm=None, name='transpose')Transposes a. Permutes the dimensions according to perm.
tf.matrix_diag(diagonal, name=None)Returns a batched diagonal tensor with a given batched diagonal values.
tf.matrix_diag_part(input, name=None)Returns the batched diagonal part of a batched tensor.
tf.matrix_band_part(input, num_lower, num_upper, name=None)Copy a tensor setting everything outside a central band in each innermost matrix
tf.matrix_set_diag(input, diagonal, name=None)Returns a batched matrix tensor with new batched diagonal values.
tf.matrix_transpose(a, name='matrix_transpose')Transposes last two dimensions of tensor a.
tf.matmul(a, b, transpose_a=False, transpose_b=False, a_is_sparse=False, b_is_sparse=False, name=None)Multiplies matrix a by matrix b, producing a * b.
tf.batch_matmul(x, y, adj_x=None, adj_y=None, name=None)Multiplies slices of two tensors in batches.
tf.matrix_determinant(input, name=None)Computes the determinant of one ore more square matrices.
tf.matrix_inverse(input, adjoint=None, name=None)Computes the inverse of one or more square invertible matrices or their
tf.cholesky(input, name=None)Computes the Cholesky decomposition of one or more square matrices.
tf.cholesky_solve(chol, rhs, name=None)Solves systems of linear eqns A X = RHS, given Cholesky factorizations.
tf.matrix_solve(matrix, rhs, adjoint=None, name=None)Solves systems of linear equations.
tf.matrix_triangular_solve(matrix, rhs, lower=None, adjoint=None, name=None)Solves systems of linear equations with upper or lower triangular matrices by
tf.matrix_solve_ls(matrix, rhs, l2_regularizer=0.0, fast=True, name=None)Solves one or more linear least-squares problems.
tf.self_adjoint_eig(tensor, name=None)Computes the eigen decomposition of a batch of self-adjoint matrices.
tf.self_adjoint_eigvals(tensor, name=None)Computes the eigenvalues of one or more self-adjoint matrices.
tf.svd(tensor, compute_uv=True, full_matrices=False, name=None)Computes the singular value decompositions of one or more matrices.

Math > Complex Number Functions

Members
tf.complex(real, imag, name=None)Converts two real numbers to a complex number.
tf.complex_abs(x, name=None)Computes the complex absolute value of a tensor.
tf.conj(x, name=None)Returns the complex conjugate of a complex number.
tf.imag(input, name=None)Returns the imaginary part of a complex number.
tf.real(input, name=None)Returns the real part of a complex number.

Math > Fourier Transform Functions

Members
tf.fft(input, name=None)Compute the 1-dimensional discrete Fourier Transform over the inner-most
tf.ifft(input, name=None)Compute the inverse 1-dimensional discrete Fourier Transform over the inner-most
tf.fft2d(input, name=None)Compute the 2-dimensional discrete Fourier Transform over the inner-most
tf.ifft2d(input, name=None)Compute the inverse 2-dimensional discrete Fourier Transform over the inner-most
tf.fft3d(input, name=None)Compute the 3-dimensional discrete Fourier Transform over the inner-most 3
tf.ifft3d(input, name=None)Compute the inverse 3-dimensional discrete Fourier Transform over the inner-most

Math > Reduction

Members
tf.reduce_sum(input_tensor, reduction_indices=None, keep_dims=False, name=None)Computes the sum of elements across dimensions of a tensor.
tf.reduce_prod(input_tensor, reduction_indices=None, keep_dims=False, name=None)Computes the product of elements across dimensions of a tensor.
tf.reduce_min(input_tensor, reduction_indices=None, keep_dims=False, name=None)Computes the minimum of elements across dimensions of a tensor.
tf.reduce_max(input_tensor, reduction_indices=None, keep_dims=False, name=None)Computes the maximum of elements across dimensions of a tensor.
tf.reduce_mean(input_tensor, reduction_indices=None, keep_dims=False, name=None)Computes the mean of elements across dimensions of a tensor.
tf.reduce_all(input_tensor, reduction_indices=None, keep_dims=False, name=None)Computes the "logical and" of elements across dimensions of a tensor.
tf.reduce_any(input_tensor, reduction_indices=None, keep_dims=False, name=None)Computes the "logical or" of elements across dimensions of a tensor.
tf.reduce_logsumexp(input_tensor, reduction_indices=None, keep_dims=False, name=None)Computes log(sum(exp(elements across dimensions of a tensor))).
tf.accumulate_n(inputs, shape=None, tensor_dtype=None, name=None)Returns the element-wise sum of a list of tensors.
tf.einsum(axes, *inputs)A generalized contraction between tensors of arbitrary dimension.

Math > Scan

Members
tf.cumsum(x, axis=0, exclusive=False, reverse=False, name=None)Compute the cumulative sum of the tensor x along axis.
tf.cumprod(x, axis=0, exclusive=False, reverse=False, name=None)Compute the cumulative product of the tensor x along axis.

Math > Segmentation

Members
tf.segment_sum(data, segment_ids, name=None)Computes the sum along segments of a tensor.
tf.segment_prod(data, segment_ids, name=None)Computes the product along segments of a tensor.
tf.segment_min(data, segment_ids, name=None)Computes the minimum along segments of a tensor.
tf.segment_max(data, segment_ids, name=None)Computes the maximum along segments of a tensor.
tf.segment_mean(data, segment_ids, name=None)Computes the mean along segments of a tensor.
tf.unsorted_segment_sum(data, segment_ids, num_segments, name=None)Computes the sum along segments of a tensor.
tf.sparse_segment_sum(data, indices, segment_ids, name=None)Computes the sum along sparse segments of a tensor.
tf.sparse_segment_mean(data, indices, segment_ids, name=None)Computes the mean along sparse segments of a tensor.
tf.sparse_segment_sqrt_n(data, indices, segment_ids, name=None)Computes the sum along sparse segments of a tensor divided by the sqrt of N.

Math > Sequence Comparison and Indexing

Members
tf.argmin(input, dimension, name=None)Returns the index with the smallest value across dimensions of a tensor.
tf.argmax(input, dimension, name=None)Returns the index with the largest value across dimensions of a tensor.
tf.listdiff(x, y, out_idx=None, name=None)Computes the difference between two lists of numbers or strings.
tf.where(input, name=None)Returns locations of true values in a boolean tensor.
tf.unique(x, out_idx=None, name=None)Finds unique elements in a 1-D tensor.
tf.edit_distance(hypothesis, truth, normalize=True, name='edit_distance')Computes the Levenshtein distance between sequences.
tf.invert_permutation(x, name=None)Computes the inverse permutation of a tensor.

Strings > Hashing

Members
tf.string_to_hash_bucket_fast(input, num_buckets, name=None)Converts each string in the input Tensor to its hash mod by a number of buckets.
tf.string_to_hash_bucket_strong(input, num_buckets, key, name=None)Converts each string in the input Tensor to its hash mod by a number of buckets.
tf.string_to_hash_bucket(string_tensor, num_buckets, name=None)Converts each string in the input Tensor to its hash mod by a number of buckets.

Strings > Joining

Members
tf.reduce_join(inputs, reduction_indices, keep_dims=None, separator=None, name=None)Joins a string Tensor across the given dimensions.
tf.string_join(inputs, separator=None, name=None)Joins the strings in the given list of string tensors into one tensor;

Strings > Splitting

Members
tf.string_split(source, delimiter=' ')Split elements of source based on delimiter into a SparseTensor.

Strings > Conversion

Members
tf.as_string(input, precision=None, scientific=None, shortest=None, width=None, fill=None, name=None)Converts each entry in the given tensor to strings. Supports many numeric
tf.encode_base64(input, pad=None, name=None)Encode strings into web-safe base64 format.
tf.decode_base64(input, name=None)Decode web-safe base64-encoded strings.

Histograms > Histograms

Members
tf.histogram_fixed_width(values, value_range, nbins=100, dtype=tf.int32, name=None)Return histogram of values.

Control Flow > Control Flow Operations

Members
tf.identity(input, name=None)Return a tensor with the same shape and contents as the input tensor or value.
tf.tuple(tensors, name=None, control_inputs=None)Group tensors together.
tf.group(*inputs, **kwargs)Create an op that groups multiple operations.
tf.no_op(name=None)Does nothing. Only useful as a placeholder for control edges.
tf.count_up_to(ref, limit, name=None)Increments 'ref' until it reaches 'limit'.
tf.cond(pred, fn1, fn2, name=None)Return either fn1() or fn2() based on the boolean predicate pred.
tf.case(pred_fn_pairs, default, exclusive=False, name='case')Create a case operation.
tf.while_loop(cond, body, loop_vars, shape_invariants=None, parallel_iterations=10, back_prop=True, swap_memory=False, name=None)Repeat body while the condition cond is true.

Control Flow > Logical Operators

Members
tf.logical_and(x, y, name=None)Returns the truth value of x AND y element-wise.
tf.logical_not(x, name=None)Returns the truth value of NOT x element-wise.
tf.logical_or(x, y, name=None)Returns the truth value of x OR y element-wise.
tf.logical_xor(x, y, name='LogicalXor')x ^ y = (x | y) & ~(x & y).

Control Flow > Comparison Operators

Members
tf.equal(x, y, name=None)Returns the truth value of (x == y) element-wise.
tf.not_equal(x, y, name=None)Returns the truth value of (x != y) element-wise.
tf.less(x, y, name=None)Returns the truth value of (x < y) element-wise.
tf.less_equal(x, y, name=None)Returns the truth value of (x <= y) element-wise.
tf.greater(x, y, name=None)Returns the truth value of (x > y) element-wise.
tf.greater_equal(x, y, name=None)Returns the truth value of (x >= y) element-wise.
tf.select(condition, t, e, name=None)Selects elements from t or e, depending on condition.
tf.where(input, name=None)Returns locations of true values in a boolean tensor.

Control Flow > Debugging Operations

Members
tf.is_finite(x, name=None)Returns which elements of x are finite.
tf.is_inf(x, name=None)Returns which elements of x are Inf.
tf.is_nan(x, name=None)Returns which elements of x are NaN.
tf.verify_tensor_all_finite(t, msg, name=None)Assert that the tensor does not contain any NaN's or Inf's.
tf.check_numerics(tensor, message, name=None)Checks a tensor for NaN and Inf values.
tf.add_check_numerics_ops()Connect a check_numerics to every floating point tensor.
tf.Assert(condition, data, summarize=None, name=None)Asserts that the given condition is true.
tf.Print(input_, data, message=None, first_n=None, summarize=None, name=None)Prints a list of tensors.

Higher Order Functions > Higher Order Operators

Members
tf.map_fn(fn, elems, dtype=None, parallel_iterations=10, back_prop=True, swap_memory=False, infer_shape=True, name=None)map on the list of tensors unpacked from elems on dimension 0.
tf.foldl(fn, elems, initializer=None, parallel_iterations=10, back_prop=True, swap_memory=False, name=None)foldl on the list of tensors unpacked from elems on dimension 0.
tf.foldr(fn, elems, initializer=None, parallel_iterations=10, back_prop=True, swap_memory=False, name=None)foldr on the list of tensors unpacked from elems on dimension 0.
tf.scan(fn, elems, initializer=None, parallel_iterations=10, back_prop=True, swap_memory=False, infer_shape=True, name=None)scan on the list of tensors unpacked from elems on dimension 0.

TensorArray Operations > Classes containing dynamically sized arrays of Tensors.

Members
class tf.TensorArrayClass wrapping dynamic-sized, per-time-step, write-once Tensor arrays.
tf.TensorArray.handleThe reference to the TensorArray.
tf.TensorArray.flowThe flow Tensor forcing ops leading to this TensorArray state.
tf.TensorArray.read(index, name=None)Read the value at location index in the TensorArray.
tf.TensorArray.gather(indices, name=None)Return selected values in the TensorArray as a packed Tensor.
tf.TensorArray.pack(name=None)Return the values in the TensorArray as a packed Tensor.
tf.TensorArray.concat(name=None)Return the values in the TensorArray as a concatenated Tensor.
tf.TensorArray.write(index, value, name=None)Write value into index index of the TensorArray.
tf.TensorArray.scatter(indices, value, name=None)Scatter the values of a Tensor in specific indices of a TensorArray.
tf.TensorArray.unpack(value, name=None)Pack the values of a Tensor in the TensorArray.
tf.TensorArray.split(value, lengths, name=None)Split the values of a Tensor into the TensorArray.
tf.TensorArray.grad(source, flow=None, name=None)
tf.TensorArray.__init__(dtype, size=None, dynamic_size=None, clear_after_read=None, tensor_array_name=None, handle=None, flow=None, infer_shape=True, name=None)Construct a new TensorArray or wrap an existing TensorArray handle.
tf.TensorArray.close(name=None)Close the current TensorArray.
tf.TensorArray.dtypeThe data type of this TensorArray.
tf.TensorArray.size(name=None)Return the size of the TensorArray.

Tensor Handle Operations > Tensor Handle Operations.

Members
tf.get_session_handle(data, name=None)Return the handle of data.
tf.get_session_tensor(handle, dtype, name=None)Get the tensor of type dtype by feeding a tensor handle.
tf.delete_session_tensor(handle, name=None)Delete the tensor for the given tensor handle.

Images > Encoding and Decoding

Members
tf.image.decode_jpeg(contents, channels=None, ratio=None, fancy_upscaling=None, try_recover_truncated=None, acceptable_fraction=None, name=None)Decode a JPEG-encoded image to a uint8 tensor.
tf.image.encode_jpeg(image, format=None, quality=None, progressive=None, optimize_size=None, chroma_downsampling=None, density_unit=None, x_density=None, y_density=None, xmp_metadata=None, name=None)JPEG-encode an image.
tf.image.decode_png(contents, channels=None, dtype=None, name=None)Decode a PNG-encoded image to a uint8 or uint16 tensor.
tf.image.encode_png(image, compression=None, name=None)PNG-encode an image.

Images > Resizing

Members
tf.image.resize_images(images, size, method=0, align_corners=False)Resize images to size using the specified method.
tf.image.resize_area(images, size, align_corners=None, name=None)Resize images to size using area interpolation.
tf.image.resize_bicubic(images, size, align_corners=None, name=None)Resize images to size using bicubic interpolation.
tf.image.resize_bilinear(images, size, align_corners=None, name=None)Resize images to size using bilinear interpolation.
tf.image.resize_nearest_neighbor(images, size, align_corners=None, name=None)Resize images to size using nearest neighbor interpolation.

Images > Cropping

Members
tf.image.resize_image_with_crop_or_pad(image, target_height, target_width)Crops and/or pads an image to a target width and height.
tf.image.central_crop(image, central_fraction)Crop the central region of the image.
tf.image.pad_to_bounding_box(image, offset_height, offset_width, target_height, target_width)Pad image with zeros to the specified height and width.
tf.image.crop_to_bounding_box(image, offset_height, offset_width, target_height, target_width)Crops an image to a specified bounding box.
tf.image.extract_glimpse(input, size, offsets, centered=None, normalized=None, uniform_noise=None, name=None)Extracts a glimpse from the input tensor.
tf.image.crop_and_resize(image, boxes, box_ind, crop_size, method=None, extrapolation_value=None, name=None)Extracts crops from the input image tensor and bilinearly resizes them (possibly

Images > Flipping Rotating and Transposing

Members
tf.image.flip_up_down(image)Flip an image horizontally (upside down).
tf.image.random_flip_up_down(image, seed=None)Randomly flips an image vertically (upside down).
tf.image.flip_left_right(image)Flip an image horizontally (left to right).
tf.image.random_flip_left_right(image, seed=None)Randomly flip an image horizontally (left to right).
tf.image.transpose_image(image)Transpose an image by swapping the first and second dimension.
tf.image.rot90(image, k=1, name=None)Rotate an image counter-clockwise by 90 degrees.

Images > Converting Between Colorspaces.

Members
tf.image.rgb_to_grayscale(images, name=None)Converts one or more images from RGB to Grayscale.
tf.image.grayscale_to_rgb(images, name=None)Converts one or more images from Grayscale to RGB.
tf.image.hsv_to_rgb(images, name=None)Convert one or more images from HSV to RGB.
tf.image.rgb_to_hsv(images, name=None)Converts one or more images from RGB to HSV.
tf.image.convert_image_dtype(image, dtype, saturate=False, name=None)Convert image to dtype, scaling its values if needed.

Images > Image Adjustments

Members
tf.image.adjust_brightness(image, delta)Adjust the brightness of RGB or Grayscale images.
tf.image.random_brightness(image, max_delta, seed=None)Adjust the brightness of images by a random factor.
tf.image.adjust_contrast(images, contrast_factor)Adjust contrast of RGB or grayscale images.
tf.image.random_contrast(image, lower, upper, seed=None)Adjust the contrast of an image by a random factor.
tf.image.adjust_hue(image, delta, name=None)Adjust hue of an RGB image.
tf.image.random_hue(image, max_delta, seed=None)Adjust the hue of an RGB image by a random factor.
tf.image.adjust_saturation(image, saturation_factor, name=None)Adjust saturation of an RGB image.
tf.image.random_saturation(image, lower, upper, seed=None)Adjust the saturation of an RGB image by a random factor.
tf.image.per_image_whitening(image)Linearly scales image to have zero mean and unit norm.

Images > Working with Bounding Boxes

Members
tf.image.draw_bounding_boxes(images, boxes, name=None)Draw bounding boxes on a batch of images.
tf.image.non_max_suppression(boxes, scores, max_output_size, iou_threshold=None, name=None)Greedily selects a subset of bounding boxes in descending order of score,
tf.image.sample_distorted_bounding_box(image_size, bounding_boxes, seed=None, seed2=None, min_object_covered=None, aspect_ratio_range=None, area_range=None, max_attempts=None, use_image_if_no_bounding_boxes=None, name=None)Generate a single randomly distorted bounding box for an image.

Sparse Tensors > Sparse Tensor Representation

Members
class tf.SparseTensorRepresents a sparse tensor.
tf.SparseTensor.__init__(indices, values, shape)Creates a SparseTensor.
tf.SparseTensor.indicesThe indices of non-zero values in the represented dense tensor.
tf.SparseTensor.valuesThe non-zero values in the represented dense tensor.
tf.SparseTensor.shapeA 1-D Tensor of int64 representing the shape of the dense tensor.
tf.SparseTensor.dtypeThe DType of elements in this tensor.
tf.SparseTensor.opThe Operation that produces values as an output.
tf.SparseTensor.graphThe Graph that contains the index, value, and shape tensors.
tf.SparseTensor.__div__(sp_x, y)Component-wise divides a SparseTensor by a dense Tensor.
tf.SparseTensor.__mul__(sp_x, y)Component-wise multiplies a SparseTensor by a dense Tensor.
tf.SparseTensor.__str__()
tf.SparseTensor.__truediv__(sp_x, y)Internal helper function for 'sp_t / dense_t'.
tf.SparseTensor.eval(feed_dict=None, session=None)Evaluates this sparse tensor in a Session.
tf.SparseTensor.from_value(cls, sparse_tensor_value)
class tf.SparseTensorValueSparseTensorValue(indices, values, shape)
tf.SparseTensorValue.__getnewargs__()Return self as a plain tuple. Used by copy and pickle.
tf.SparseTensorValue.__getstate__()Exclude the OrderedDict from pickling
tf.SparseTensorValue.__new__(_cls, indices, values, shape)Create new instance of SparseTensorValue(indices, values, shape)
tf.SparseTensorValue.__repr__()Return a nicely formatted representation string
tf.SparseTensorValue.indicesAlias for field number 0
tf.SparseTensorValue.shapeAlias for field number 2
tf.SparseTensorValue.valuesAlias for field number 1

Sparse Tensors > Conversion

Members
tf.sparse_to_dense(sparse_indices, output_shape, sparse_values, default_value=0, validate_indices=True, name=None)Converts a sparse representation into a dense tensor.
tf.sparse_tensor_to_dense(sp_input, default_value=0, validate_indices=True, name=None)Converts a SparseTensor into a dense tensor.
tf.sparse_to_indicator(sp_input, vocab_size, name=None)Converts a SparseTensor of ids into a dense bool indicator tensor.
tf.sparse_merge(sp_ids, sp_values, vocab_size, name=None, already_sorted=False)Combines a batch of feature ids and values into a single SparseTensor.

Sparse Tensors > Manipulation

Members
tf.sparse_concat(concat_dim, sp_inputs, name=None, expand_nonconcat_dim=False)Concatenates a list of SparseTensor along the specified dimension.
tf.sparse_reorder(sp_input, name=None)Reorders a SparseTensor into the canonical, row-major ordering.
tf.sparse_reshape(sp_input, shape, name=None)Reshapes a SparseTensor to represent values in a new dense shape.
tf.sparse_split(split_dim, num_split, sp_input, name=None)Split a SparseTensor into num_split tensors along split_dim.
tf.sparse_retain(sp_input, to_retain)Retains specified non-empty values within a SparseTensor.
tf.sparse_reset_shape(sp_input, new_shape=None)Resets the shape of a SparseTensor with indices and values unchanged.
tf.sparse_fill_empty_rows(sp_input, default_value, name=None)Fills empty rows in the input 2-D SparseTensor with a default value.
tf.sparse_transpose(sp_input, perm=None, name=None)Transposes a SparseTensor

Sparse Tensors > Reduction

Members
tf.sparse_reduce_sum(sp_input, reduction_axes=None, keep_dims=False)Computes the sum of elements across dimensions of a SparseTensor.
tf.sparse_reduce_sum_sparse(sp_input, reduction_axes=None, keep_dims=False)Computes the sum of elements across dimensions of a SparseTensor.

Sparse Tensors > Math Operations

Members
tf.sparse_add(a, b, thresh=0)Adds two tensors, at least one of each is a SparseTensor.
tf.sparse_softmax(sp_input, name=None)Applies softmax to a batched N-D SparseTensor.
tf.sparse_tensor_dense_matmul(sp_a, b, adjoint_a=False, adjoint_b=False, name=None)Multiply SparseTensor (of rank 2) "A" by dense matrix "B".
tf.sparse_maximum(sp_a, sp_b, name=None)Returns the element-wise max of two SparseTensors.
tf.sparse_minimum(sp_a, sp_b, name=None)Returns the element-wise min of two SparseTensors.

Inputs and Readers > Placeholders

Members
tf.placeholder(dtype, shape=None, name=None)Inserts a placeholder for a tensor that will be always fed.
tf.placeholder_with_default(input, shape, name=None)A placeholder op that passes though input when its output is not fed.
tf.sparse_placeholder(dtype, shape=None, name=None)Inserts a placeholder for a sparse tensor that will be always fed.

Inputs and Readers > Readers

Members
class tf.ReaderBaseBase class for different Reader types, that produce a record every step.
tf.ReaderBase.__init__(reader_ref, supports_serialize=False)Creates a new ReaderBase.
tf.ReaderBase.num_records_produced(name=None)Returns the number of records this reader has produced.
tf.ReaderBase.num_work_units_completed(name=None)Returns the number of work units this reader has finished processing.
tf.ReaderBase.read(queue, name=None)Returns the next record (key, value pair) produced by a reader.
tf.ReaderBase.read_up_to(queue, num_records, name=None)Returns up to num_records (key, value pairs) produced by a reader.
tf.ReaderBase.reader_refOp that implements the reader.
tf.ReaderBase.reset(name=None)Restore a reader to its initial clean state.
tf.ReaderBase.restore_state(state, name=None)Restore a reader to a previously saved state.
tf.ReaderBase.serialize_state(name=None)Produce a string tensor that encodes the state of a reader.
tf.ReaderBase.supports_serializeWhether the Reader implementation can serialize its state.
class tf.TextLineReaderA Reader that outputs the lines of a file delimited by newlines.
tf.TextLineReader.__init__(skip_header_lines=None, name=None)Create a TextLineReader.
tf.TextLineReader.num_records_produced(name=None)Returns the number of records this reader has produced.
tf.TextLineReader.num_work_units_completed(name=None)Returns the number of work units this reader has finished processing.
tf.TextLineReader.read(queue, name=None)Returns the next record (key, value pair) produced by a reader.
tf.TextLineReader.read_up_to(queue, num_records, name=None)Returns up to num_records (key, value pairs) produced by a reader.
tf.TextLineReader.reader_refOp that implements the reader.
tf.TextLineReader.reset(name=None)Restore a reader to its initial clean state.
tf.TextLineReader.restore_state(state, name=None)Restore a reader to a previously saved state.
tf.TextLineReader.serialize_state(name=None)Produce a string tensor that encodes the state of a reader.
tf.TextLineReader.supports_serializeWhether the Reader implementation can serialize its state.
class tf.WholeFileReaderA Reader that outputs the entire contents of a file as a value.
tf.WholeFileReader.__init__(name=None)Create a WholeFileReader.
tf.WholeFileReader.num_records_produced(name=None)Returns the number of records this reader has produced.
tf.WholeFileReader.num_work_units_completed(name=None)Returns the number of work units this reader has finished processing.
tf.WholeFileReader.read(queue, name=None)Returns the next record (key, value pair) produced by a reader.
tf.WholeFileReader.read_up_to(queue, num_records, name=None)Returns up to num_records (key, value pairs) produced by a reader.
tf.WholeFileReader.reader_refOp that implements the reader.
tf.WholeFileReader.reset(name=None)Restore a reader to its initial clean state.
tf.WholeFileReader.restore_state(state, name=None)Restore a reader to a previously saved state.
tf.WholeFileReader.serialize_state(name=None)Produce a string tensor that encodes the state of a reader.
tf.WholeFileReader.supports_serializeWhether the Reader implementation can serialize its state.
class tf.IdentityReaderA Reader that outputs the queued work as both the key and value.
tf.IdentityReader.__init__(name=None)Create a IdentityReader.
tf.IdentityReader.num_records_produced(name=None)Returns the number of records this reader has produced.
tf.IdentityReader.num_work_units_completed(name=None)Returns the number of work units this reader has finished processing.
tf.IdentityReader.read(queue, name=None)Returns the next record (key, value pair) produced by a reader.
tf.IdentityReader.read_up_to(queue, num_records, name=None)Returns up to num_records (key, value pairs) produced by a reader.
tf.IdentityReader.reader_refOp that implements the reader.
tf.IdentityReader.reset(name=None)Restore a reader to its initial clean state.
tf.IdentityReader.restore_state(state, name=None)Restore a reader to a previously saved state.
tf.IdentityReader.serialize_state(name=None)Produce a string tensor that encodes the state of a reader.
tf.IdentityReader.supports_serializeWhether the Reader implementation can serialize its state.
class tf.TFRecordReaderA Reader that outputs the records from a TFRecords file.
tf.TFRecordReader.__init__(name=None, options=None)Create a TFRecordReader.
tf.TFRecordReader.num_records_produced(name=None)Returns the number of records this reader has produced.
tf.TFRecordReader.num_work_units_completed(name=None)Returns the number of work units this reader has finished processing.
tf.TFRecordReader.read(queue, name=None)Returns the next record (key, value pair) produced by a reader.
tf.TFRecordReader.read_up_to(queue, num_records, name=None)Returns up to num_records (key, value pairs) produced by a reader.
tf.TFRecordReader.reader_refOp that implements the reader.
tf.TFRecordReader.reset(name=None)Restore a reader to its initial clean state.
tf.TFRecordReader.restore_state(state, name=None)Restore a reader to a previously saved state.
tf.TFRecordReader.serialize_state(name=None)Produce a string tensor that encodes the state of a reader.
tf.TFRecordReader.supports_serializeWhether the Reader implementation can serialize its state.
class tf.FixedLengthRecordReaderA Reader that outputs fixed-length records from a file.
tf.FixedLengthRecordReader.__init__(record_bytes, header_bytes=None, footer_bytes=None, name=None)Create a FixedLengthRecordReader.
tf.FixedLengthRecordReader.num_records_produced(name=None)Returns the number of records this reader has produced.
tf.FixedLengthRecordReader.num_work_units_completed(name=None)Returns the number of work units this reader has finished processing.
tf.FixedLengthRecordReader.read(queue, name=None)Returns the next record (key, value pair) produced by a reader.
tf.FixedLengthRecordReader.read_up_to(queue, num_records, name=None)Returns up to num_records (key, value pairs) produced by a reader.
tf.FixedLengthRecordReader.reader_refOp that implements the reader.
tf.FixedLengthRecordReader.reset(name=None)Restore a reader to its initial clean state.
tf.FixedLengthRecordReader.restore_state(state, name=None)Restore a reader to a previously saved state.
tf.FixedLengthRecordReader.serialize_state(name=None)Produce a string tensor that encodes the state of a reader.
tf.FixedLengthRecordReader.supports_serializeWhether the Reader implementation can serialize its state.

Inputs and Readers > Converting

Members
tf.decode_csv(records, record_defaults, field_delim=None, name=None)Convert CSV records to tensors. Each column maps to one tensor.
tf.decode_raw(bytes, out_type, little_endian=None, name=None)Reinterpret the bytes of a string as a vector of numbers.
class tf.VarLenFeatureConfiguration for parsing a variable-length input feature.
tf.VarLenFeature.__getnewargs__()Return self as a plain tuple. Used by copy and pickle.
tf.VarLenFeature.__getstate__()Exclude the OrderedDict from pickling
tf.VarLenFeature.__new__(_cls, dtype)Create new instance of VarLenFeature(dtype,)
tf.VarLenFeature.__repr__()Return a nicely formatted representation string
tf.VarLenFeature.dtypeAlias for field number 0
class tf.FixedLenFeatureConfiguration for parsing a fixed-length input feature.
tf.FixedLenFeature.__getnewargs__()Return self as a plain tuple. Used by copy and pickle.
tf.FixedLenFeature.__getstate__()Exclude the OrderedDict from pickling
tf.FixedLenFeature.__new__(_cls, shape, dtype, default_value=None)Create new instance of FixedLenFeature(shape, dtype, default_value)
tf.FixedLenFeature.__repr__()Return a nicely formatted representation string
tf.FixedLenFeature.default_valueAlias for field number 2
tf.FixedLenFeature.dtypeAlias for field number 1
tf.FixedLenFeature.shapeAlias for field number 0
class tf.FixedLenSequenceFeatureConfiguration for a dense input feature in a sequence item.
tf.FixedLenSequenceFeature.__getnewargs__()Return self as a plain tuple. Used by copy and pickle.
tf.FixedLenSequenceFeature.__getstate__()Exclude the OrderedDict from pickling
tf.FixedLenSequenceFeature.__new__(_cls, shape, dtype, allow_missing=False)Create new instance of FixedLenSequenceFeature(shape, dtype, allow_missing)
tf.FixedLenSequenceFeature.__repr__()Return a nicely formatted representation string
tf.FixedLenSequenceFeature.allow_missingAlias for field number 2
tf.FixedLenSequenceFeature.dtypeAlias for field number 1
tf.FixedLenSequenceFeature.shapeAlias for field number 0
tf.parse_example(serialized, features, name=None, example_names=None)Parses Example protos into a dict of tensors.
tf.parse_single_example(serialized, features, name=None, example_names=None)Parses a single Example proto.
tf.parse_tensor(serialized, out_type, name=None)Transforms a serialized tensorflow.TensorProto proto into a Tensor.
tf.decode_json_example(json_examples, name=None)Convert JSON-encoded Example records to binary protocol buffer strings.

Inputs and Readers > Queues

Members
class tf.QueueBaseBase class for queue implementations.
tf.QueueBase.enqueue(vals, name=None)Enqueues one element to this queue.
tf.QueueBase.enqueue_many(vals, name=None)Enqueues zero or more elements to this queue.
tf.QueueBase.dequeue(name=None)Dequeues one element from this queue.
tf.QueueBase.dequeue_many(n, name=None)Dequeues and concatenates n elements from this queue.
tf.QueueBase.size(name=None)Compute the number of elements in this queue.
tf.QueueBase.close(cancel_pending_enqueues=False, name=None)Closes this queue.
tf.QueueBase.__init__(dtypes, shapes, names, queue_ref)Constructs a queue object from a queue reference.
tf.QueueBase.dequeue_up_to(n, name=None)Dequeues and concatenates n elements from this queue.
tf.QueueBase.dtypesThe list of dtypes for each component of a queue element.
tf.QueueBase.from_list(index, queues)Create a queue using the queue reference from queues[index].
tf.QueueBase.nameThe name of the underlying queue.
tf.QueueBase.namesThe list of names for each component of a queue element.
tf.QueueBase.queue_refThe underlying queue reference.
tf.QueueBase.shapesThe list of shapes for each component of a queue element.
class tf.FIFOQueueA queue implementation that dequeues elements in first-in first-out order.
tf.FIFOQueue.__init__(capacity, dtypes, shapes=None, names=None, shared_name=None, name='fifo_queue')Creates a queue that dequeues elements in a first-in first-out order.
class tf.PaddingFIFOQueueA FIFOQueue that supports batching variable-sized tensors by padding.
tf.PaddingFIFOQueue.__init__(capacity, dtypes, shapes, names=None, shared_name=None, name='padding_fifo_queue')Creates a queue that dequeues elements in a first-in first-out order.
class tf.RandomShuffleQueueA queue implementation that dequeues elements in a random order.
tf.RandomShuffleQueue.__init__(capacity, min_after_dequeue, dtypes, shapes=None, names=None, seed=None, shared_name=None, name='random_shuffle_queue')Create a queue that dequeues elements in a random order.
class tf.PriorityQueueA queue implementation that dequeues elements in prioritized order.
tf.PriorityQueue.__init__(capacity, types, shapes=None, names=None, shared_name=None, name='priority_queue')Creates a queue that dequeues elements in a first-in first-out order.

Inputs and Readers > Dealing with the filesystem

Members
tf.matching_files(pattern, name=None)Returns the set of files matching a pattern.
tf.read_file(filename, name=None)Reads and outputs the entire contents of the input filename.

Inputs and Readers > Input pipeline

Members
tf.train.match_filenames_once(pattern, name=None)Save the list of files matching pattern, so it is only computed once.
tf.train.limit_epochs(tensor, num_epochs=None, name=None)Returns tensor num_epochs times and then raises an OutOfRange error.
tf.train.input_producer(input_tensor, element_shape=None, num_epochs=None, shuffle=True, seed=None, capacity=32, shared_name=None, summary_name=None, name=None)Output the rows of input_tensor to a queue for an input pipeline.
tf.train.range_input_producer(limit, num_epochs=None, shuffle=True, seed=None, capacity=32, shared_name=None, name=None)Produces the integers from 0 to limit-1 in a queue.
tf.train.slice_input_producer(tensor_list, num_epochs=None, shuffle=True, seed=None, capacity=32, shared_name=None, name=None)Produces a slice of each Tensor in tensor_list.
tf.train.string_input_producer(string_tensor, num_epochs=None, shuffle=True, seed=None, capacity=32, shared_name=None, name=None)Output strings (e.g. filenames) to a queue for an input pipeline.
tf.train.batch(tensors, batch_size, num_threads=1, capacity=32, enqueue_many=False, shapes=None, dynamic_pad=False, allow_smaller_final_batch=False, shared_name=None, name=None)Creates batches of tensors in tensors.
tf.train.batch_join(tensors_list, batch_size, capacity=32, enqueue_many=False, shapes=None, dynamic_pad=False, allow_smaller_final_batch=False, shared_name=None, name=None)Runs a list of tensors to fill a queue to create batches of examples.
tf.train.shuffle_batch(tensors, batch_size, capacity, min_after_dequeue, num_threads=1, seed=None, enqueue_many=False, shapes=None, allow_smaller_final_batch=False, shared_name=None, name=None)Creates batches by randomly shuffling tensors.
tf.train.shuffle_batch_join(tensors_list, batch_size, capacity, min_after_dequeue, seed=None, enqueue_many=False, shapes=None, allow_smaller_final_batch=False, shared_name=None, name=None)Create batches by randomly shuffling tensors.

Data IO (Python functions) > Data IO (Python Functions)

Members
class tf.python_io.TFRecordWriterA class to write records to a TFRecords file.
tf.python_io.TFRecordWriter.__init__(path, options=None)Opens file path and creates a TFRecordWriter writing to it.
tf.python_io.TFRecordWriter.write(record)Write a string record to the file.
tf.python_io.TFRecordWriter.close()Close the file.
tf.python_io.TFRecordWriter.__enter__()Enter a with block.
tf.python_io.TFRecordWriter.__exit__(unused_type, unused_value, unused_traceback)Exit a with block, closing the file.
tf.python_io.tf_record_iterator(path, options=None)An iterator that read the records from a TFRecords file.

Neural Network > Activation Functions.

Members
tf.nn.relu(features, name=None)Computes rectified linear: max(features, 0).
tf.nn.relu6(features, name=None)Computes Rectified Linear 6: min(max(features, 0), 6).
tf.nn.crelu(features, name=None)Computes Concatenated ReLU.
tf.nn.elu(features, name=None)Computes exponential linear: exp(features) - 1 if < 0, features otherwise.
tf.nn.softplus(features, name=None)Computes softplus: log(exp(features) + 1).
tf.nn.softsign(features, name=None)Computes softsign: features / (abs(features) + 1).
tf.nn.dropout(x, keep_prob, noise_shape=None, seed=None, name=None)Computes dropout.
tf.nn.bias_add(value, bias, data_format=None, name=None)Adds bias to value.
tf.sigmoid(x, name=None)Computes sigmoid of x element-wise.
tf.tanh(x, name=None)Computes hyperbolic tangent of x element-wise.

Neural Network > Convolution

Members
tf.nn.conv2d(input, filter, strides, padding, use_cudnn_on_gpu=None, data_format=None, name=None)Computes a 2-D convolution given 4-D input and filter tensors.
tf.nn.depthwise_conv2d(input, filter, strides, padding, name=None)Depthwise 2-D convolution.
tf.nn.separable_conv2d(input, depthwise_filter, pointwise_filter, strides, padding, name=None)2-D convolution with separable filters.
tf.nn.atrous_conv2d(value, filters, rate, padding, name=None)Atrous convolution (a.k.a. convolution with holes or dilated convolution).
tf.nn.conv2d_transpose(value, filter, output_shape, strides, padding='SAME', name=None)The transpose of conv2d.
tf.nn.conv1d(value, filters, stride, padding, use_cudnn_on_gpu=None, data_format=None, name=None)Computes a 1-D convolution given 3-D input and filter tensors.
tf.nn.conv3d(input, filter, strides, padding, name=None)Computes a 3-D convolution given 5-D input and filter tensors.
tf.nn.conv3d_transpose(value, filter, output_shape, strides, padding='SAME', name=None)The transpose of conv3d.

Neural Network > Pooling

Members
tf.nn.avg_pool(value, ksize, strides, padding, data_format='NHWC', name=None)Performs the average pooling on the input.
tf.nn.max_pool(value, ksize, strides, padding, data_format='NHWC', name=None)Performs the max pooling on the input.
tf.nn.max_pool_with_argmax(input, ksize, strides, padding, Targmax=None, name=None)Performs max pooling on the input and outputs both max values and indices.
tf.nn.avg_pool3d(input, ksize, strides, padding, name=None)Performs 3D average pooling on the input.
tf.nn.max_pool3d(input, ksize, strides, padding, name=None)Performs 3D max pooling on the input.
tf.nn.fractional_avg_pool(value, pooling_ratio, pseudo_random=None, overlapping=None, deterministic=None, seed=None, seed2=None, name=None)Performs fractional average pooling on the input.
tf.nn.fractional_max_pool(value, pooling_ratio, pseudo_random=None, overlapping=None, deterministic=None, seed=None, seed2=None, name=None)Performs fractional max pooling on the input.

Neural Network > Morphological filtering

Members
tf.nn.dilation2d(input, filter, strides, rates, padding, name=None)Computes the grayscale dilation of 4-D input and 3-D filter tensors.
tf.nn.erosion2d(value, kernel, strides, rates, padding, name=None)Computes the grayscale erosion of 4-D value and 3-D kernel tensors.

Neural Network > Normalization

Members
tf.nn.l2_normalize(x, dim, epsilon=1e-12, name=None)Normalizes along dimension dim using an L2 norm.
tf.nn.local_response_normalization(input, depth_radius=None, bias=None, alpha=None, beta=None, name=None)Local Response Normalization.
tf.nn.sufficient_statistics(x, axes, shift=None, keep_dims=False, name=None)Calculate the sufficient statistics for the mean and variance of x.
tf.nn.normalize_moments(counts, mean_ss, variance_ss, shift, name=None)Calculate the mean and variance of based on the sufficient statistics.
tf.nn.moments(x, axes, shift=None, name=None, keep_dims=False)Calculate the mean and variance of x.

Neural Network > Losses

Members
tf.nn.l2_loss(t, name=None)L2 Loss.
tf.nn.log_poisson_loss(log_input, targets, compute_full_loss=False, name=None)Computes log poisson loss given log_input.

Neural Network > Classification

Members
tf.nn.sigmoid_cross_entropy_with_logits(logits, targets, name=None)Computes sigmoid cross entropy given logits.
tf.nn.softmax(logits, dim=-1, name=None)Computes log softmax activations.
tf.nn.log_softmax(logits, dim=-1, name=None)Computes log softmax activations.
tf.nn.softmax_cross_entropy_with_logits(logits, labels, dim=-1, name=None)Computes softmax cross entropy between logits and labels.
tf.nn.sparse_softmax_cross_entropy_with_logits(logits, labels, name=None)Computes sparse softmax cross entropy between logits and labels.
tf.nn.weighted_cross_entropy_with_logits(logits, targets, pos_weight, name=None)Computes a weighted cross entropy.

Neural Network > Embeddings

Members
tf.nn.embedding_lookup(params, ids, partition_strategy='mod', name=None, validate_indices=True)Looks up ids in a list of embedding tensors.
tf.nn.embedding_lookup_sparse(params, sp_ids, sp_weights, partition_strategy='mod', name=None, combiner=None)Computes embeddings for the given ids and weights.

Neural Network > Recurrent Neural Networks

Members
tf.nn.dynamic_rnn(cell, inputs, sequence_length=None, initial_state=None, dtype=None, parallel_iterations=None, swap_memory=False, time_major=False, scope=None)Creates a recurrent neural network specified by RNNCell cell.
tf.nn.rnn(cell, inputs, initial_state=None, dtype=None, sequence_length=None, scope=None)Creates a recurrent neural network specified by RNNCell cell.
tf.nn.state_saving_rnn(cell, inputs, state_saver, state_name, sequence_length=None, scope=None)RNN that accepts a state saver for time-truncated RNN calculation.
tf.nn.bidirectional_dynamic_rnn(cell_fw, cell_bw, inputs, sequence_length=None, initial_state_fw=None, initial_state_bw=None, dtype=None, parallel_iterations=None, swap_memory=False, time_major=False, scope=None)Creates a dynamic version of bidirectional recurrent neural network.
tf.nn.bidirectional_rnn(cell_fw, cell_bw, inputs, initial_state_fw=None, initial_state_bw=None, dtype=None, sequence_length=None, scope=None)Creates a bidirectional recurrent neural network.
tf.nn.raw_rnn(cell, loop_fn, parallel_iterations=None, swap_memory=False, scope=None)Creates an RNN specified by RNNCell cell and loop function loop_fn.

Neural Network > Conectionist Temporal Classification (CTC)

Members
tf.nn.ctc_loss(inputs, labels, sequence_length, preprocess_collapse_repeated=False, ctc_merge_repeated=True, time_major=True)Computes the CTC (Connectionist Temporal Classification) Loss.
tf.nn.ctc_greedy_decoder(inputs, sequence_length, merge_repeated=True)Performs greedy decoding on the logits given in input (best path).
tf.nn.ctc_beam_search_decoder(inputs, sequence_length, beam_width=100, top_paths=1, merge_repeated=True)Performs beam search decoding on the logits given in input.

Neural Network > Evaluation

Members
tf.nn.top_k(input, k=1, sorted=True, name=None)Finds values and indices of the k largest entries for the last dimension.
tf.nn.in_top_k(predictions, targets, k, name=None)Says whether the targets are in the top K predictions.

Neural Network > Candidate Sampling

Members
tf.nn.nce_loss(weights, biases, inputs, labels, num_sampled, num_classes, num_true=1, sampled_values=None, remove_accidental_hits=False, partition_strategy='mod', name='nce_loss')Computes and returns the noise-contrastive estimation training loss.
tf.nn.sampled_softmax_loss(weights, biases, inputs, labels, num_sampled, num_classes, num_true=1, sampled_values=None, remove_accidental_hits=True, partition_strategy='mod', name='sampled_softmax_loss')Computes and returns the sampled softmax training loss.
tf.nn.uniform_candidate_sampler(true_classes, num_true, num_sampled, unique, range_max, seed=None, name=None)Samples a set of classes using a uniform base distribution.
tf.nn.log_uniform_candidate_sampler(true_classes, num_true, num_sampled, unique, range_max, seed=None, name=None)Samples a set of classes using a log-uniform (Zipfian) base distribution.
tf.nn.learned_unigram_candidate_sampler(true_classes, num_true, num_sampled, unique, range_max, seed=None, name=None)Samples a set of classes from a distribution learned during training.
tf.nn.fixed_unigram_candidate_sampler(true_classes, num_true, num_sampled, unique, range_max, vocab_file='', distortion=1.0, num_reserved_ids=0, num_shards=1, shard=0, unigrams=(), seed=None, name=None)Samples a set of classes using the provided (fixed) base distribution.
tf.nn.compute_accidental_hits(true_classes, sampled_candidates, num_true, seed=None, name=None)Compute the position ids in sampled_candidates matching true_classes.

Neural Network > Other Functions and Classes

Members
tf.nn.batch_normalization(x, mean, variance, offset, scale, variance_epsilon, name=None)Batch normalization.
tf.nn.depthwise_conv2d_native(input, filter, strides, padding, name=None)Computes a 2-D depthwise convolution given 4-D input and filter tensors.

Neural Network RNN Cells > Base interface for all RNN Cells

Members
class tf.nn.rnn_cell.RNNCellAbstract object representing an RNN cell.
tf.nn.rnn_cell.RNNCell.__call__(inputs, state, scope=None)Run this RNN cell on inputs, starting from the given state.
tf.nn.rnn_cell.RNNCell.output_sizeInteger or TensorShape: size of outputs produced by this cell.
tf.nn.rnn_cell.RNNCell.state_sizesize(s) of state(s) used by this cell.
tf.nn.rnn_cell.RNNCell.zero_state(batch_size, dtype)Return zero-filled state tensor(s).

Neural Network RNN Cells > RNN Cells for use with TensorFlow's core RNN methods

Members
class tf.nn.rnn_cell.BasicRNNCellThe most basic RNN cell.
tf.nn.rnn_cell.BasicRNNCell.__call__(inputs, state, scope=None)Most basic RNN: output = new_state = activation(W * input + U * state + B).
tf.nn.rnn_cell.BasicRNNCell.__init__(num_units, input_size=None, activation=tanh)
tf.nn.rnn_cell.BasicRNNCell.output_size
tf.nn.rnn_cell.BasicRNNCell.state_size
tf.nn.rnn_cell.BasicRNNCell.zero_state(batch_size, dtype)Return zero-filled state tensor(s).
class tf.nn.rnn_cell.BasicLSTMCellBasic LSTM recurrent network cell.
tf.nn.rnn_cell.BasicLSTMCell.__call__(inputs, state, scope=None)Long short-term memory cell (LSTM).
tf.nn.rnn_cell.BasicLSTMCell.__init__(num_units, forget_bias=1.0, input_size=None, state_is_tuple=True, activation=tanh)Initialize the basic LSTM cell.
tf.nn.rnn_cell.BasicLSTMCell.output_size
tf.nn.rnn_cell.BasicLSTMCell.state_size
tf.nn.rnn_cell.BasicLSTMCell.zero_state(batch_size, dtype)Return zero-filled state tensor(s).
class tf.nn.rnn_cell.GRUCellGated Recurrent Unit cell (cf. http://arxiv.org/abs/1406.1078).
tf.nn.rnn_cell.GRUCell.__call__(inputs, state, scope=None)Gated recurrent unit (GRU) with nunits cells.
tf.nn.rnn_cell.GRUCell.__init__(num_units, input_size=None, activation=tanh)
tf.nn.rnn_cell.GRUCell.output_size
tf.nn.rnn_cell.GRUCell.state_size
tf.nn.rnn_cell.GRUCell.zero_state(batch_size, dtype)Return zero-filled state tensor(s).
class tf.nn.rnn_cell.LSTMCellLong short-term memory unit (LSTM) recurrent network cell.
tf.nn.rnn_cell.LSTMCell.__call__(inputs, state, scope=None)Run one step of LSTM.
tf.nn.rnn_cell.LSTMCell.__init__(num_units, input_size=None, use_peepholes=False, cell_clip=None, initializer=None, num_proj=None, proj_clip=None, num_unit_shards=1, num_proj_shards=1, forget_bias=1.0, state_is_tuple=True, activation=tanh)Initialize the parameters for an LSTM cell.
tf.nn.rnn_cell.LSTMCell.output_size
tf.nn.rnn_cell.LSTMCell.state_size
tf.nn.rnn_cell.LSTMCell.zero_state(batch_size, dtype)Return zero-filled state tensor(s).

Neural Network RNN Cells > Classes storing split RNNCell state

Members
class tf.nn.rnn_cell.LSTMStateTupleTuple used by LSTM Cells for state_size, zero_state, and output state.
tf.nn.rnn_cell.LSTMStateTuple.__getnewargs__()Return self as a plain tuple. Used by copy and pickle.
tf.nn.rnn_cell.LSTMStateTuple.__getstate__()Exclude the OrderedDict from pickling
tf.nn.rnn_cell.LSTMStateTuple.__new__(_cls, c, h)Create new instance of LSTMStateTuple(c, h)
tf.nn.rnn_cell.LSTMStateTuple.__repr__()Return a nicely formatted representation string
tf.nn.rnn_cell.LSTMStateTuple.cAlias for field number 0
tf.nn.rnn_cell.LSTMStateTuple.dtype
tf.nn.rnn_cell.LSTMStateTuple.hAlias for field number 1

Neural Network RNN Cells > RNN Cell wrappers (RNNCells that wrap other RNNCells)

Members
class tf.nn.rnn_cell.MultiRNNCellRNN cell composed sequentially of multiple simple cells.
tf.nn.rnn_cell.MultiRNNCell.__call__(inputs, state, scope=None)Run this multi-layer cell on inputs, starting from state.
tf.nn.rnn_cell.MultiRNNCell.__init__(cells, state_is_tuple=True)Create a RNN cell composed sequentially of a number of RNNCells.
tf.nn.rnn_cell.MultiRNNCell.output_size
tf.nn.rnn_cell.MultiRNNCell.state_size
tf.nn.rnn_cell.MultiRNNCell.zero_state(batch_size, dtype)Return zero-filled state tensor(s).
class tf.nn.rnn_cell.DropoutWrapperOperator adding dropout to inputs and outputs of the given cell.
tf.nn.rnn_cell.DropoutWrapper.__call__(inputs, state, scope=None)Run the cell with the declared dropouts.
tf.nn.rnn_cell.DropoutWrapper.__init__(cell, input_keep_prob=1.0, output_keep_prob=1.0, seed=None)Create a cell with added input and/or output dropout.
tf.nn.rnn_cell.DropoutWrapper.output_size
tf.nn.rnn_cell.DropoutWrapper.state_size
tf.nn.rnn_cell.DropoutWrapper.zero_state(batch_size, dtype)Return zero-filled state tensor(s).
class tf.nn.rnn_cell.EmbeddingWrapperOperator adding input embedding to the given cell.
tf.nn.rnn_cell.EmbeddingWrapper.__call__(inputs, state, scope=None)Run the cell on embedded inputs.
tf.nn.rnn_cell.EmbeddingWrapper.__init__(cell, embedding_classes, embedding_size, initializer=None)Create a cell with an added input embedding.
tf.nn.rnn_cell.EmbeddingWrapper.output_size
tf.nn.rnn_cell.EmbeddingWrapper.state_size
tf.nn.rnn_cell.EmbeddingWrapper.zero_state(batch_size, dtype)Return zero-filled state tensor(s).
class tf.nn.rnn_cell.InputProjectionWrapperOperator adding an input projection to the given cell.
tf.nn.rnn_cell.InputProjectionWrapper.__call__(inputs, state, scope=None)Run the input projection and then the cell.
tf.nn.rnn_cell.InputProjectionWrapper.__init__(cell, num_proj, input_size=None)Create a cell with input projection.
tf.nn.rnn_cell.InputProjectionWrapper.output_size
tf.nn.rnn_cell.InputProjectionWrapper.state_size
tf.nn.rnn_cell.InputProjectionWrapper.zero_state(batch_size, dtype)Return zero-filled state tensor(s).
class tf.nn.rnn_cell.OutputProjectionWrapperOperator adding an output projection to the given cell.
tf.nn.rnn_cell.OutputProjectionWrapper.__call__(inputs, state, scope=None)Run the cell and output projection on inputs, starting from state.
tf.nn.rnn_cell.OutputProjectionWrapper.__init__(cell, output_size)Create a cell with output projection.
tf.nn.rnn_cell.OutputProjectionWrapper.output_size
tf.nn.rnn_cell.OutputProjectionWrapper.state_size
tf.nn.rnn_cell.OutputProjectionWrapper.zero_state(batch_size, dtype)Return zero-filled state tensor(s).

Running Graphs > Session management

Members
class tf.SessionA class for running TensorFlow operations.
tf.Session.__init__(target='', graph=None, config=None)Creates a new TensorFlow session.
tf.Session.run(fetches, feed_dict=None, options=None, run_metadata=None)Runs operations and evaluates tensors in fetches.
tf.Session.close()Closes this session.
tf.Session.graphThe graph that was launched in this session.
tf.Session.as_default()Returns a context manager that makes this object the default session.
tf.Session.reset(target, containers=None, config=None)Resets resource containers on target, and close all connected sessions.
tf.Session.__enter__()
tf.Session.__exit__(exec_type, exec_value, exec_tb)
class tf.InteractiveSessionA TensorFlow Session for use in interactive contexts, such as a shell.
tf.InteractiveSession.__init__(target='', graph=None, config=None)Creates a new interactive TensorFlow session.
tf.InteractiveSession.close()Closes an InteractiveSession.
tf.get_default_session()Returns the default session for the current thread.

Running Graphs > Error classes

Members
class tf.OpErrorA generic error that is raised when TensorFlow execution fails.
tf.OpError.opThe operation that failed, if known.
tf.OpError.node_defThe NodeDef proto representing the op that failed.
tf.OpError.__init__(node_def, op, message, error_code)Creates a new OpError indicating that a particular op failed.
tf.OpError.__str__()
tf.OpError.error_codeThe integer error code that describes the error.
tf.OpError.messageThe error message that describes the error.
class tf.errors.CancelledErrorRaised when an operation or step is cancelled.
tf.errors.CancelledError.__init__(node_def, op, message)Creates a CancelledError.
class tf.errors.UnknownErrorUnknown error.
tf.errors.UnknownError.__init__(node_def, op, message, error_code=2)Creates an UnknownError.
class tf.errors.InvalidArgumentErrorRaised when an operation receives an invalid argument.
tf.errors.InvalidArgumentError.__init__(node_def, op, message)Creates an InvalidArgumentError.
class tf.errors.DeadlineExceededErrorRaised when a deadline expires before an operation could complete.
tf.errors.DeadlineExceededError.__init__(node_def, op, message)Creates a DeadlineExceededError.
class tf.errors.NotFoundErrorRaised when a requested entity (e.g., a file or directory) was not found.
tf.errors.NotFoundError.__init__(node_def, op, message)Creates a NotFoundError.
class tf.errors.AlreadyExistsErrorRaised when an entity that we attempted to create already exists.
tf.errors.AlreadyExistsError.__init__(node_def, op, message)Creates an AlreadyExistsError.
class tf.errors.PermissionDeniedErrorRaised when the caller does not have permission to run an operation.
tf.errors.PermissionDeniedError.__init__(node_def, op, message)Creates a PermissionDeniedError.
class tf.errors.UnauthenticatedErrorThe request does not have valid authentication credentials.
tf.errors.UnauthenticatedError.__init__(node_def, op, message)Creates an UnauthenticatedError.
class tf.errors.ResourceExhaustedErrorSome resource has been exhausted.
tf.errors.ResourceExhaustedError.__init__(node_def, op, message)Creates a ResourceExhaustedError.
class tf.errors.FailedPreconditionErrorOperation was rejected because the system is not in a state to execute it.
tf.errors.FailedPreconditionError.__init__(node_def, op, message)Creates a FailedPreconditionError.
class tf.errors.AbortedErrorThe operation was aborted, typically due to a concurrent action.
tf.errors.AbortedError.__init__(node_def, op, message)Creates an AbortedError.
class tf.errors.OutOfRangeErrorRaised when an operation iterates past the valid input range.
tf.errors.OutOfRangeError.__init__(node_def, op, message)Creates an OutOfRangeError.
class tf.errors.UnimplementedErrorRaised when an operation has not been implemented.
tf.errors.UnimplementedError.__init__(node_def, op, message)Creates an UnimplementedError.
class tf.errors.InternalErrorRaised when the system experiences an internal error.
tf.errors.InternalError.__init__(node_def, op, message)Creates an InternalError.
class tf.errors.UnavailableErrorRaised when the runtime is currently unavailable.
tf.errors.UnavailableError.__init__(node_def, op, message)Creates an UnavailableError.
class tf.errors.DataLossErrorRaised when unrecoverable data loss or corruption is encountered.
tf.errors.DataLossError.__init__(node_def, op, message)Creates a DataLossError.

Training > Optimizers

Members
class tf.train.OptimizerBase class for optimizers.
tf.train.Optimizer.__init__(use_locking, name)Create a new Optimizer.
tf.train.Optimizer.minimize(loss, global_step=None, var_list=None, gate_gradients=1, aggregation_method=None, colocate_gradients_with_ops=False, name=None, grad_loss=None)Add operations to minimize loss by updating var_list.
tf.train.Optimizer.compute_gradients(loss, var_list=None, gate_gradients=1, aggregation_method=None, colocate_gradients_with_ops=False, grad_loss=None)Compute gradients of loss for the variables in var_list.
tf.train.Optimizer.apply_gradients(grads_and_vars, global_step=None, name=None)Apply gradients to variables.
tf.train.Optimizer.get_slot_names()Return a list of the names of slots created by the Optimizer.
tf.train.Optimizer.get_slot(var, name)Return a slot named name created for var by the Optimizer.
tf.train.Optimizer.get_name()
class tf.train.GradientDescentOptimizerOptimizer that implements the gradient descent algorithm.
tf.train.GradientDescentOptimizer.__init__(learning_rate, use_locking=False, name='GradientDescent')Construct a new gradient descent optimizer.
class tf.train.AdadeltaOptimizerOptimizer that implements the Adadelta algorithm.
tf.train.AdadeltaOptimizer.__init__(learning_rate=0.001, rho=0.95, epsilon=1e-08, use_locking=False, name='Adadelta')Construct a new Adadelta optimizer.
class tf.train.AdagradOptimizerOptimizer that implements the Adagrad algorithm.
tf.train.AdagradOptimizer.__init__(learning_rate, initial_accumulator_value=0.1, use_locking=False, name='Adagrad')Construct a new Adagrad optimizer.
class tf.train.AdagradDAOptimizerAdagrad Dual Averaging algorithm for sparse linear models.
tf.train.AdagradDAOptimizer.__init__(learning_rate, global_step, initial_gradient_squared_accumulator_value=0.1, l1_regularization_strength=0.0, l2_regularization_strength=0.0, use_locking=False, name='AdagradDA')Construct a new AdagradDA optimizer.
class tf.train.MomentumOptimizerOptimizer that implements the Momentum algorithm.
tf.train.MomentumOptimizer.__init__(learning_rate, momentum, use_locking=False, name='Momentum', use_nesterov=False)Construct a new Momentum optimizer.
class tf.train.AdamOptimizerOptimizer that implements the Adam algorithm.
tf.train.AdamOptimizer.__init__(learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-08, use_locking=False, name='Adam')Construct a new Adam optimizer.
class tf.train.FtrlOptimizerOptimizer that implements the FTRL algorithm.
tf.train.FtrlOptimizer.__init__(learning_rate, learning_rate_power=-0.5, initial_accumulator_value=0.1, l1_regularization_strength=0.0, l2_regularization_strength=0.0, use_locking=False, name='Ftrl')Construct a new FTRL optimizer.
class tf.train.RMSPropOptimizerOptimizer that implements the RMSProp algorithm.
tf.train.RMSPropOptimizer.__init__(learning_rate, decay=0.9, momentum=0.0, epsilon=1e-10, use_locking=False, name='RMSProp')Construct a new RMSProp optimizer.

Training > Gradient Computation

Members
tf.gradients(ys, xs, grad_ys=None, name='gradients', colocate_gradients_with_ops=False, gate_gradients=False, aggregation_method=None)Constructs symbolic partial derivatives of sum of ys w.r.t. x in xs.
class tf.AggregationMethodA class listing aggregation methods used to combine gradients.
tf.stop_gradient(input, name=None)Stops gradient computation.

Training > Gradient Clipping

Members
tf.clip_by_value(t, clip_value_min, clip_value_max, name=None)Clips tensor values to a specified min and max.
tf.clip_by_norm(t, clip_norm, axes=None, name=None)Clips tensor values to a maximum L2-norm.
tf.clip_by_average_norm(t, clip_norm, name=None)Clips tensor values to a maximum average L2-norm.
tf.clip_by_global_norm(t_list, clip_norm, use_norm=None, name=None)Clips values of multiple tensors by the ratio of the sum of their norms.
tf.global_norm(t_list, name=None)Computes the global norm of multiple tensors.

Training > Decaying the learning rate

Members
tf.train.exponential_decay(learning_rate, global_step, decay_steps, decay_rate, staircase=False, name=None)Applies exponential decay to the learning rate.

Training > Moving Averages

Members
class tf.train.ExponentialMovingAverageMaintains moving averages of variables by employing an exponential decay.
tf.train.ExponentialMovingAverage.__init__(decay, num_updates=None, name='ExponentialMovingAverage')Creates a new ExponentialMovingAverage object.
tf.train.ExponentialMovingAverage.apply(var_list=None)Maintains moving averages of variables.
tf.train.ExponentialMovingAverage.average_name(var)Returns the name of the Variable holding the average for var.
tf.train.ExponentialMovingAverage.average(var)Returns the Variable holding the average of var.
tf.train.ExponentialMovingAverage.variables_to_restore(moving_avg_variables=None)Returns a map of names to Variables to restore.

Training > Coordinator and QueueRunner

Members
class tf.train.CoordinatorA coordinator for threads.
tf.train.Coordinator.__init__(clean_stop_exception_types=None)Create a new Coordinator.
tf.train.Coordinator.clear_stop()Clears the stop flag.
tf.train.Coordinator.join(threads=None, stop_grace_period_secs=120)Wait for threads to terminate.
tf.train.Coordinator.joined
tf.train.Coordinator.register_thread(thread)Register a thread to join.
tf.train.Coordinator.request_stop(ex=None)Request that the threads stop.
tf.train.Coordinator.should_stop()Check if stop was requested.
tf.train.Coordinator.stop_on_exception()Context manager to request stop when an Exception is raised.
tf.train.Coordinator.wait_for_stop(timeout=None)Wait till the Coordinator is told to stop.
class tf.train.QueueRunnerHolds a list of enqueue operations for a queue, each to be run in a thread.
tf.train.QueueRunner.__init__(queue=None, enqueue_ops=None, close_op=None, cancel_op=None, queue_closed_exception_types=None, queue_runner_def=None)Create a QueueRunner.
tf.train.QueueRunner.cancel_op
tf.train.QueueRunner.close_op
tf.train.QueueRunner.create_threads(sess, coord=None, daemon=False, start=False)Create threads to run the enqueue ops.
tf.train.QueueRunner.enqueue_ops
tf.train.QueueRunner.exceptions_raisedExceptions raised but not handled by the QueueRunner threads.
tf.train.QueueRunner.from_proto(queue_runner_def)Returns a QueueRunner object created from queue_runner_def.
tf.train.QueueRunner.nameThe string name of the underlying Queue.
tf.train.QueueRunner.queue
tf.train.QueueRunner.queue_closed_exception_types
tf.train.QueueRunner.to_proto()Converts this QueueRunner to a QueueRunnerDef protocol buffer.
tf.train.add_queue_runner(qr, collection='queue_runners')Adds a QueueRunner to a collection in the graph.
tf.train.start_queue_runners(sess=None, coord=None, daemon=True, start=True, collection='queue_runners')Starts all queue runners collected in the graph.

Training > Distributed execution

Members
class tf.train.ServerAn in-process TensorFlow server, for use in distributed training.
tf.train.Server.__init__(server_or_cluster_def, job_name=None, task_index=None, protocol=None, config=None, start=True)Creates a new server with the given definition.
tf.train.Server.create_local_server(config=None, start=True)Creates a new single-process cluster running on the local host.
tf.train.Server.targetReturns the target for a tf.Session to connect to this server.
tf.train.Server.server_defReturns the tf.train.ServerDef for this server.
tf.train.Server.start()Starts this server.
tf.train.Server.join()Blocks until the server has shut down.
class tf.train.SupervisorA training helper that checkpoints models and computes summaries.
tf.train.Supervisor.__init__(graph=None, ready_op=0, ready_for_local_init_op=0, is_chief=True, init_op=0, init_feed_dict=None, local_init_op=0, logdir=None, summary_op=0, saver=0, global_step=0, save_summaries_secs=120, save_model_secs=600, recovery_wait_secs=30, stop_grace_secs=120, checkpoint_basename='model.ckpt', session_manager=None, summary_writer=0, init_fn=None)Create a Supervisor.
tf.train.Supervisor.managed_session(master='', config=None, start_standard_services=True, close_summary_writer=True)Returns a context manager for a managed session.
tf.train.Supervisor.prepare_or_wait_for_session(master='', config=None, wait_for_checkpoint=False, max_wait_secs=7200, start_standard_services=True)Make sure the model is ready to be used.
tf.train.Supervisor.start_standard_services(sess)Start the standard services for 'sess'.
tf.train.Supervisor.start_queue_runners(sess, queue_runners=None)Start threads for QueueRunners.
tf.train.Supervisor.summary_computed(sess, summary, global_step=None)Indicate that a summary was computed.
tf.train.Supervisor.stop(threads=None, close_summary_writer=True)Stop the services and the coordinator.
tf.train.Supervisor.request_stop(ex=None)Request that the coordinator stop the threads.
tf.train.Supervisor.should_stop()Check if the coordinator was told to stop.
tf.train.Supervisor.stop_on_exception()Context handler to stop the supervisor when an exception is raised.
tf.train.Supervisor.wait_for_stop()Block waiting for the coordinator to stop.
tf.train.Supervisor.Loop(timer_interval_secs, target, args=None, kwargs=None)Start a LooperThread that calls a function periodically.
tf.train.Supervisor.PrepareSession(master='', config=None, wait_for_checkpoint=False, max_wait_secs=7200, start_standard_services=True)Make sure the model is ready to be used.
tf.train.Supervisor.RequestStop(ex=None)Request that the coordinator stop the threads.
tf.train.Supervisor.ShouldStop()Check if the coordinator was told to stop.
tf.train.Supervisor.StartQueueRunners(sess, queue_runners=None)Start threads for QueueRunners.
tf.train.Supervisor.StartStandardServices(sess)Start the standard services for 'sess'.
tf.train.Supervisor.Stop(threads=None, close_summary_writer=True)Stop the services and the coordinator.
tf.train.Supervisor.StopOnException()Context handler to stop the supervisor when an exception is raised.
tf.train.Supervisor.SummaryComputed(sess, summary, global_step=None)Indicate that a summary was computed.
tf.train.Supervisor.WaitForStop()Block waiting for the coordinator to stop.
tf.train.Supervisor.coordReturn the Coordinator used by the Supervisor.
tf.train.Supervisor.global_stepReturn the global_step Tensor used by the supervisor.
tf.train.Supervisor.init_feed_dictReturn the feed dictionary used when evaluating the init_op.
tf.train.Supervisor.init_opReturn the Init Op used by the supervisor.
tf.train.Supervisor.is_chiefReturn True if this is a chief supervisor.
tf.train.Supervisor.loop(timer_interval_secs, target, args=None, kwargs=None)Start a LooperThread that calls a function periodically.
tf.train.Supervisor.ready_for_local_init_op
tf.train.Supervisor.ready_opReturn the Ready Op used by the supervisor.
tf.train.Supervisor.save_model_secsReturn the delay between checkpoints.
tf.train.Supervisor.save_pathReturn the save path used by the supervisor.
tf.train.Supervisor.save_summaries_secsReturn the delay between summary computations.
tf.train.Supervisor.saverReturn the Saver used by the supervisor.
tf.train.Supervisor.session_managerReturn the SessionManager used by the Supervisor.
tf.train.Supervisor.summary_opReturn the Summary Tensor used by the chief supervisor.
tf.train.Supervisor.summary_writerReturn the SummaryWriter used by the chief supervisor.
class tf.train.SessionManagerTraining helper that restores from checkpoint and creates session.
tf.train.SessionManager.__init__(local_init_op=None, ready_op=None, ready_for_local_init_op=None, graph=None, recovery_wait_secs=30)Creates a SessionManager.
tf.train.SessionManager.prepare_session(master, init_op=None, saver=None, checkpoint_dir=None, wait_for_checkpoint=False, max_wait_secs=7200, config=None, init_feed_dict=None, init_fn=None)Creates a Session. Makes sure the model is ready to be used.
tf.train.SessionManager.recover_session(master, saver=None, checkpoint_dir=None, wait_for_checkpoint=False, max_wait_secs=7200, config=None)Creates a Session, recovering if possible.
tf.train.SessionManager.wait_for_session(master, config=None, max_wait_secs=inf)Creates a new Session and waits for model to be ready.
class tf.train.ClusterSpecRepresents a cluster as a set of "tasks", organized into "jobs".
tf.train.ClusterSpec.as_cluster_def()Returns a tf.train.ClusterDef protocol buffer based on this cluster.
tf.train.ClusterSpec.as_dict()Returns a dictionary from job names to their tasks.
tf.train.ClusterSpec.__bool__()
tf.train.ClusterSpec.__eq__(other)
tf.train.ClusterSpec.__init__(cluster)Creates a ClusterSpec.
tf.train.ClusterSpec.__ne__(other)
tf.train.ClusterSpec.__nonzero__()
tf.train.ClusterSpec.job_tasks(job_name)Returns a mapping from task ID to address in the given job.
tf.train.ClusterSpec.jobsReturns a list of job names in this cluster.
tf.train.ClusterSpec.num_tasks(job_name)Returns the number of tasks defined in the given job.
tf.train.ClusterSpec.task_address(job_name, task_index)Returns the address of the given task in the given job.
tf.train.ClusterSpec.task_indices(job_name)Returns a list of valid task indices in the given job.
tf.train.replica_device_setter(ps_tasks=0, ps_device='/job:ps', worker_device='/job:worker', merge_devices=True, cluster=None, ps_ops=None)Return a device function to use when building a Graph for replicas.

Training > Summary Operations

Members
tf.scalar_summary(tags, values, collections=None, name=None)Outputs a Summary protocol buffer with scalar values.
tf.image_summary(tag, tensor, max_images=3, collections=None, name=None)Outputs a Summary protocol buffer with images.
tf.audio_summary(tag, tensor, sample_rate, max_outputs=3, collections=None, name=None)Outputs a Summary protocol buffer with audio.
tf.histogram_summary(tag, values, collections=None, name=None)Outputs a Summary protocol buffer with a histogram.
tf.nn.zero_fraction(value, name=None)Returns the fraction of zeros in value.
tf.merge_summary(inputs, collections=None, name=None)Merges summaries.
tf.merge_all_summaries(key='summaries')Merges all summaries collected in the default graph.

Training > Adding Summaries to Event Files

Members
class tf.train.SummaryWriterWrites Summary protocol buffers to event files.
tf.train.SummaryWriter.__init__(logdir, graph=None, max_queue=10, flush_secs=120, graph_def=None)Creates a SummaryWriter and an event file.
tf.train.SummaryWriter.add_summary(summary, global_step=None)Adds a Summary protocol buffer to the event file.
tf.train.SummaryWriter.add_session_log(session_log, global_step=None)Adds a SessionLog protocol buffer to the event file.
tf.train.SummaryWriter.add_event(event)Adds an event to the event file.
tf.train.SummaryWriter.add_graph(graph, global_step=None, graph_def=None)Adds a Graph to the event file.
tf.train.SummaryWriter.add_run_metadata(run_metadata, tag, global_step=None)Adds a metadata information for a single session.run() call.
tf.train.SummaryWriter.get_logdir()Returns the directory where event file will be written.
tf.train.SummaryWriter.flush()Flushes the event file to disk.
tf.train.SummaryWriter.close()Flushes the event file to disk and close the file.
tf.train.SummaryWriter.reopen()Reopens the summary writer.
tf.train.summary_iterator(path)An iterator for reading Event protocol buffers from an event file.

Training > Training utilities

Members
tf.train.global_step(sess, global_step_tensor)Small helper to get the global step.
tf.train.write_graph(graph_def, logdir, name, as_text=True)Writes a graph proto to a file.

Training > Other Functions and Classes

Members
class tf.train.LooperThreadA thread that runs code repeatedly, optionally on a timer.
tf.train.LooperThread.__init__(coord, timer_interval_secs, target=None, args=None, kwargs=None)Create a LooperThread.
tf.train.LooperThread.__repr__()
tf.train.LooperThread.daemonA boolean value indicating whether this thread is a daemon thread (True) or not (False).
tf.train.LooperThread.getName()
tf.train.LooperThread.identThread identifier of this thread or None if it has not been started.
tf.train.LooperThread.isAlive()Return whether the thread is alive.
tf.train.LooperThread.isDaemon()
tf.train.LooperThread.is_alive()Return whether the thread is alive.
tf.train.LooperThread.join(timeout=None)Wait until the thread terminates.
tf.train.LooperThread.loop(coord, timer_interval_secs, target, args=None, kwargs=None)Start a LooperThread that calls a function periodically.
tf.train.LooperThread.nameA string used for identification purposes only.
tf.train.LooperThread.run()
tf.train.LooperThread.run_loop()Called at 'timer_interval_secs' boundaries.
tf.train.LooperThread.setDaemon(daemonic)
tf.train.LooperThread.setName(name)
tf.train.LooperThread.start()Start the thread's activity.
tf.train.LooperThread.start_loop()Called when the thread starts.
tf.train.LooperThread.stop_loop()Called when the thread stops.
tf.train.do_quantize_training_on_graphdef(input_graph, num_bits)
tf.train.generate_checkpoint_state_proto(save_dir, model_checkpoint_path, all_model_checkpoint_paths=None)Generates a checkpoint state proto.

Wraps python functions > Script Language Operators.

Members
tf.py_func(func, inp, Tout, stateful=True, name=None)Wraps a python function and uses it as a tensorflow op.

Summary Operations > Summary Ops

Members
tf.summary.tensor_summary(display_name, tensor, description='', labels=None, collections=None, name=None)Outputs a Summary protocol buffer with a serialized tensor.proto.
tf.summary.scalar(display_name, tensor, description='', labels=None, collections=None, name=None)Outputs a Summary protocol buffer containing a single scalar value.

Testing > Unit tests

Members
tf.test.main()Runs all unit tests.

Testing > Utilities

Members
tf.test.assert_equal_graph_def(actual, expected)Asserts that two GraphDefs are (mostly) the same.
tf.test.get_temp_dir()Returns a temporary directory for use during tests.
tf.test.is_built_with_cuda()Returns whether TensorFlow was built with CUDA (GPU) support.

Testing > Gradient checking

Members
tf.test.compute_gradient(x, x_shape, y, y_shape, x_init_value=None, delta=0.001, init_targets=None)Computes and returns the theoretical and numerical Jacobian.
tf.test.compute_gradient_error(x, x_shape, y, y_shape, x_init_value=None, delta=0.001, init_targets=None)Computes the gradient error.

BayesFlow Entropy (contrib) > Background

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BayesFlow Entropy (contrib) > Examples

Members

BayesFlow Entropy (contrib) > Ops

Members
tf.contrib.bayesflow.entropy.elbo_ratio(log_p, q, z=None, n=None, seed=None, form=None, name='elbo_ratio')Estimate of the ratio appearing in the ELBO and KL divergence.
tf.contrib.bayesflow.entropy.entropy_shannon(p, z=None, n=None, seed=None, form=None, name='entropy_shannon')Monte Carlo or deterministic computation of Shannon's entropy.
tf.contrib.bayesflow.entropy.renyi_ratio(log_p, q, alpha, z=None, n=None, seed=None, name='renyi_ratio')Monte Carlo estimate of the ratio appearing in Renyi divergence.
tf.contrib.bayesflow.entropy.renyi_alpha(step, decay_time, alpha_min, alpha_max=0.99999, name='renyi_alpha')Exponentially decaying Tensor appropriate for Renyi ratios.

BayesFlow Monte Carlo (contrib) > Background

Members

BayesFlow Monte Carlo (contrib) > Log-space evaluation and subtracting the maximum.

Members

BayesFlow Monte Carlo (contrib) > Ops

Members
tf.contrib.bayesflow.monte_carlo.expectation(f, p, z=None, n=None, seed=None, name='expectation')Monte Carlo estimate of an expectation: E_p[f(Z)] with sample mean.
tf.contrib.bayesflow.monte_carlo.expectation_importance_sampler(f, log_p, sampling_dist_q, z=None, n=None, seed=None, name='expectation_importance_sampler')Monte Carlo estimate of E_p[f(Z)] = E_q[f(Z) p(Z) / q(Z)].
tf.contrib.bayesflow.monte_carlo.expectation_importance_sampler_logspace(log_f, log_p, sampling_dist_q, z=None, n=None, seed=None, name='expectation_importance_sampler_logspace')Importance sampling with a positive function, in log-space.

BayesFlow Stochastic Graph (contrib) > Stochastic Computation Graph Helper Functions

Members
tf.contrib.bayesflow.stochastic_graph.surrogate_loss(sample_losses, stochastic_tensors=None, name='SurrogateLoss')Surrogate loss for stochastic graphs.

BayesFlow Stochastic Tensors (contrib) > Stochastic Tensor Classes

Members
class tf.contrib.bayesflow.stochastic_tensor.BaseStochasticTensorBase Class for Tensor-like objects that emit stochastic values.
tf.contrib.bayesflow.stochastic_tensor.BaseStochasticTensor.__init__()
tf.contrib.bayesflow.stochastic_tensor.BaseStochasticTensor.dtype
tf.contrib.bayesflow.stochastic_tensor.BaseStochasticTensor.graph
tf.contrib.bayesflow.stochastic_tensor.BaseStochasticTensor.input_dict
tf.contrib.bayesflow.stochastic_tensor.BaseStochasticTensor.loss(sample_loss)Returns the term to add to the surrogate loss.
tf.contrib.bayesflow.stochastic_tensor.BaseStochasticTensor.name
tf.contrib.bayesflow.stochastic_tensor.BaseStochasticTensor.value(name=None)
class tf.contrib.bayesflow.stochastic_tensor.StochasticTensorStochasticTensor is a BaseStochasticTensor backed by a distribution.
tf.contrib.bayesflow.stochastic_tensor.StochasticTensor.__init__(dist_cls, name=None, dist_value_type=None, loss_fn=score_function, **dist_args)Construct a StochasticTensor.
tf.contrib.bayesflow.stochastic_tensor.StochasticTensor.clone(name=None, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.StochasticTensor.distribution
tf.contrib.bayesflow.stochastic_tensor.StochasticTensor.dtype
tf.contrib.bayesflow.stochastic_tensor.StochasticTensor.entropy(name='entropy')
tf.contrib.bayesflow.stochastic_tensor.StochasticTensor.graph
tf.contrib.bayesflow.stochastic_tensor.StochasticTensor.input_dict
tf.contrib.bayesflow.stochastic_tensor.StochasticTensor.loss(final_loss, name='Loss')
tf.contrib.bayesflow.stochastic_tensor.StochasticTensor.mean(name='mean')
tf.contrib.bayesflow.stochastic_tensor.StochasticTensor.name
tf.contrib.bayesflow.stochastic_tensor.StochasticTensor.value(name='value')
tf.contrib.bayesflow.stochastic_tensor.StochasticTensor.value_type

BayesFlow Stochastic Tensors (contrib) > Stochastic Tensor Value Types

Members
class tf.contrib.bayesflow.stochastic_tensor.MeanValue
tf.contrib.bayesflow.stochastic_tensor.MeanValue.__init__(stop_gradient=False)
tf.contrib.bayesflow.stochastic_tensor.MeanValue.declare_inputs(unused_stochastic_tensor, unused_inputs_dict)
tf.contrib.bayesflow.stochastic_tensor.MeanValue.popped_above(unused_value_type)
tf.contrib.bayesflow.stochastic_tensor.MeanValue.pushed_above(unused_value_type)
tf.contrib.bayesflow.stochastic_tensor.MeanValue.stop_gradient
class tf.contrib.bayesflow.stochastic_tensor.SampleValueDraw n samples along a new outer dimension.
tf.contrib.bayesflow.stochastic_tensor.SampleValue.__init__(n=1, stop_gradient=False)Sample n times and concatenate along a new outer dimension.
tf.contrib.bayesflow.stochastic_tensor.SampleValue.declare_inputs(unused_stochastic_tensor, unused_inputs_dict)
tf.contrib.bayesflow.stochastic_tensor.SampleValue.n
tf.contrib.bayesflow.stochastic_tensor.SampleValue.popped_above(unused_value_type)
tf.contrib.bayesflow.stochastic_tensor.SampleValue.pushed_above(unused_value_type)
tf.contrib.bayesflow.stochastic_tensor.SampleValue.stop_gradient
class tf.contrib.bayesflow.stochastic_tensor.SampleAndReshapeValueAsk the StochasticTensor for n samples and reshape the result.
tf.contrib.bayesflow.stochastic_tensor.SampleAndReshapeValue.__init__(n=1, stop_gradient=False)Sample n times and reshape the outer 2 axes so rank does not change.
tf.contrib.bayesflow.stochastic_tensor.SampleAndReshapeValue.declare_inputs(unused_stochastic_tensor, unused_inputs_dict)
tf.contrib.bayesflow.stochastic_tensor.SampleAndReshapeValue.n
tf.contrib.bayesflow.stochastic_tensor.SampleAndReshapeValue.popped_above(unused_value_type)
tf.contrib.bayesflow.stochastic_tensor.SampleAndReshapeValue.pushed_above(unused_value_type)
tf.contrib.bayesflow.stochastic_tensor.SampleAndReshapeValue.stop_gradient
tf.contrib.bayesflow.stochastic_tensor.value_type(dist_value_type)Creates a value type context for any StochasticTensor created within.
tf.contrib.bayesflow.stochastic_tensor.get_current_value_type()

BayesFlow Stochastic Tensors (contrib) > Automatically Generated StochasticTensors

Members
class tf.contrib.bayesflow.stochastic_tensor.BernoulliTensorBernoulliTensor is a StochasticTensor backed by the distribution Bernoulli.
tf.contrib.bayesflow.stochastic_tensor.BernoulliTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.BernoulliTensor.clone(name=None, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.BernoulliTensor.distribution
tf.contrib.bayesflow.stochastic_tensor.BernoulliTensor.dtype
tf.contrib.bayesflow.stochastic_tensor.BernoulliTensor.entropy(name='entropy')
tf.contrib.bayesflow.stochastic_tensor.BernoulliTensor.graph
tf.contrib.bayesflow.stochastic_tensor.BernoulliTensor.input_dict
tf.contrib.bayesflow.stochastic_tensor.BernoulliTensor.loss(final_loss, name='Loss')
tf.contrib.bayesflow.stochastic_tensor.BernoulliTensor.mean(name='mean')
tf.contrib.bayesflow.stochastic_tensor.BernoulliTensor.name
tf.contrib.bayesflow.stochastic_tensor.BernoulliTensor.value(name='value')
tf.contrib.bayesflow.stochastic_tensor.BernoulliTensor.value_type
class tf.contrib.bayesflow.stochastic_tensor.BernoulliWithSigmoidPTensorBernoulliWithSigmoidPTensor is a StochasticTensor backed by the distribution BernoulliWithSigmoidP.
tf.contrib.bayesflow.stochastic_tensor.BernoulliWithSigmoidPTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.BernoulliWithSigmoidPTensor.clone(name=None, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.BernoulliWithSigmoidPTensor.distribution
tf.contrib.bayesflow.stochastic_tensor.BernoulliWithSigmoidPTensor.dtype
tf.contrib.bayesflow.stochastic_tensor.BernoulliWithSigmoidPTensor.entropy(name='entropy')
tf.contrib.bayesflow.stochastic_tensor.BernoulliWithSigmoidPTensor.graph
tf.contrib.bayesflow.stochastic_tensor.BernoulliWithSigmoidPTensor.input_dict
tf.contrib.bayesflow.stochastic_tensor.BernoulliWithSigmoidPTensor.loss(final_loss, name='Loss')
tf.contrib.bayesflow.stochastic_tensor.BernoulliWithSigmoidPTensor.mean(name='mean')
tf.contrib.bayesflow.stochastic_tensor.BernoulliWithSigmoidPTensor.name
tf.contrib.bayesflow.stochastic_tensor.BernoulliWithSigmoidPTensor.value(name='value')
tf.contrib.bayesflow.stochastic_tensor.BernoulliWithSigmoidPTensor.value_type
class tf.contrib.bayesflow.stochastic_tensor.BetaTensorBetaTensor is a StochasticTensor backed by the distribution Beta.
tf.contrib.bayesflow.stochastic_tensor.BetaTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.BetaTensor.clone(name=None, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.BetaTensor.distribution
tf.contrib.bayesflow.stochastic_tensor.BetaTensor.dtype
tf.contrib.bayesflow.stochastic_tensor.BetaTensor.entropy(name='entropy')
tf.contrib.bayesflow.stochastic_tensor.BetaTensor.graph
tf.contrib.bayesflow.stochastic_tensor.BetaTensor.input_dict
tf.contrib.bayesflow.stochastic_tensor.BetaTensor.loss(final_loss, name='Loss')
tf.contrib.bayesflow.stochastic_tensor.BetaTensor.mean(name='mean')
tf.contrib.bayesflow.stochastic_tensor.BetaTensor.name
tf.contrib.bayesflow.stochastic_tensor.BetaTensor.value(name='value')
tf.contrib.bayesflow.stochastic_tensor.BetaTensor.value_type
class tf.contrib.bayesflow.stochastic_tensor.BetaWithSoftplusABTensorBetaWithSoftplusABTensor is a StochasticTensor backed by the distribution BetaWithSoftplusAB.
tf.contrib.bayesflow.stochastic_tensor.BetaWithSoftplusABTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.BetaWithSoftplusABTensor.clone(name=None, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.BetaWithSoftplusABTensor.distribution
tf.contrib.bayesflow.stochastic_tensor.BetaWithSoftplusABTensor.dtype
tf.contrib.bayesflow.stochastic_tensor.BetaWithSoftplusABTensor.entropy(name='entropy')
tf.contrib.bayesflow.stochastic_tensor.BetaWithSoftplusABTensor.graph
tf.contrib.bayesflow.stochastic_tensor.BetaWithSoftplusABTensor.input_dict
tf.contrib.bayesflow.stochastic_tensor.BetaWithSoftplusABTensor.loss(final_loss, name='Loss')
tf.contrib.bayesflow.stochastic_tensor.BetaWithSoftplusABTensor.mean(name='mean')
tf.contrib.bayesflow.stochastic_tensor.BetaWithSoftplusABTensor.name
tf.contrib.bayesflow.stochastic_tensor.BetaWithSoftplusABTensor.value(name='value')
tf.contrib.bayesflow.stochastic_tensor.BetaWithSoftplusABTensor.value_type
class tf.contrib.bayesflow.stochastic_tensor.BinomialTensorBinomialTensor is a StochasticTensor backed by the distribution Binomial.
tf.contrib.bayesflow.stochastic_tensor.BinomialTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.BinomialTensor.clone(name=None, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.BinomialTensor.distribution
tf.contrib.bayesflow.stochastic_tensor.BinomialTensor.dtype
tf.contrib.bayesflow.stochastic_tensor.BinomialTensor.entropy(name='entropy')
tf.contrib.bayesflow.stochastic_tensor.BinomialTensor.graph
tf.contrib.bayesflow.stochastic_tensor.BinomialTensor.input_dict
tf.contrib.bayesflow.stochastic_tensor.BinomialTensor.loss(final_loss, name='Loss')
tf.contrib.bayesflow.stochastic_tensor.BinomialTensor.mean(name='mean')
tf.contrib.bayesflow.stochastic_tensor.BinomialTensor.name
tf.contrib.bayesflow.stochastic_tensor.BinomialTensor.value(name='value')
tf.contrib.bayesflow.stochastic_tensor.BinomialTensor.value_type
class tf.contrib.bayesflow.stochastic_tensor.CategoricalTensorCategoricalTensor is a StochasticTensor backed by the distribution Categorical.
tf.contrib.bayesflow.stochastic_tensor.CategoricalTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.CategoricalTensor.clone(name=None, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.CategoricalTensor.distribution
tf.contrib.bayesflow.stochastic_tensor.CategoricalTensor.dtype
tf.contrib.bayesflow.stochastic_tensor.CategoricalTensor.entropy(name='entropy')
tf.contrib.bayesflow.stochastic_tensor.CategoricalTensor.graph
tf.contrib.bayesflow.stochastic_tensor.CategoricalTensor.input_dict
tf.contrib.bayesflow.stochastic_tensor.CategoricalTensor.loss(final_loss, name='Loss')
tf.contrib.bayesflow.stochastic_tensor.CategoricalTensor.mean(name='mean')
tf.contrib.bayesflow.stochastic_tensor.CategoricalTensor.name
tf.contrib.bayesflow.stochastic_tensor.CategoricalTensor.value(name='value')
tf.contrib.bayesflow.stochastic_tensor.CategoricalTensor.value_type
class tf.contrib.bayesflow.stochastic_tensor.Chi2TensorChi2Tensor is a StochasticTensor backed by the distribution Chi2.
tf.contrib.bayesflow.stochastic_tensor.Chi2Tensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.Chi2Tensor.clone(name=None, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.Chi2Tensor.distribution
tf.contrib.bayesflow.stochastic_tensor.Chi2Tensor.dtype
tf.contrib.bayesflow.stochastic_tensor.Chi2Tensor.entropy(name='entropy')
tf.contrib.bayesflow.stochastic_tensor.Chi2Tensor.graph
tf.contrib.bayesflow.stochastic_tensor.Chi2Tensor.input_dict
tf.contrib.bayesflow.stochastic_tensor.Chi2Tensor.loss(final_loss, name='Loss')
tf.contrib.bayesflow.stochastic_tensor.Chi2Tensor.mean(name='mean')
tf.contrib.bayesflow.stochastic_tensor.Chi2Tensor.name
tf.contrib.bayesflow.stochastic_tensor.Chi2Tensor.value(name='value')
tf.contrib.bayesflow.stochastic_tensor.Chi2Tensor.value_type
class tf.contrib.bayesflow.stochastic_tensor.Chi2WithAbsDfTensorChi2WithAbsDfTensor is a StochasticTensor backed by the distribution Chi2WithAbsDf.
tf.contrib.bayesflow.stochastic_tensor.Chi2WithAbsDfTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.Chi2WithAbsDfTensor.clone(name=None, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.Chi2WithAbsDfTensor.distribution
tf.contrib.bayesflow.stochastic_tensor.Chi2WithAbsDfTensor.dtype
tf.contrib.bayesflow.stochastic_tensor.Chi2WithAbsDfTensor.entropy(name='entropy')
tf.contrib.bayesflow.stochastic_tensor.Chi2WithAbsDfTensor.graph
tf.contrib.bayesflow.stochastic_tensor.Chi2WithAbsDfTensor.input_dict
tf.contrib.bayesflow.stochastic_tensor.Chi2WithAbsDfTensor.loss(final_loss, name='Loss')
tf.contrib.bayesflow.stochastic_tensor.Chi2WithAbsDfTensor.mean(name='mean')
tf.contrib.bayesflow.stochastic_tensor.Chi2WithAbsDfTensor.name
tf.contrib.bayesflow.stochastic_tensor.Chi2WithAbsDfTensor.value(name='value')
tf.contrib.bayesflow.stochastic_tensor.Chi2WithAbsDfTensor.value_type
class tf.contrib.bayesflow.stochastic_tensor.DirichletTensorDirichletTensor is a StochasticTensor backed by the distribution Dirichlet.
tf.contrib.bayesflow.stochastic_tensor.DirichletTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.DirichletTensor.clone(name=None, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.DirichletTensor.distribution
tf.contrib.bayesflow.stochastic_tensor.DirichletTensor.dtype
tf.contrib.bayesflow.stochastic_tensor.DirichletTensor.entropy(name='entropy')
tf.contrib.bayesflow.stochastic_tensor.DirichletTensor.graph
tf.contrib.bayesflow.stochastic_tensor.DirichletTensor.input_dict
tf.contrib.bayesflow.stochastic_tensor.DirichletTensor.loss(final_loss, name='Loss')
tf.contrib.bayesflow.stochastic_tensor.DirichletTensor.mean(name='mean')
tf.contrib.bayesflow.stochastic_tensor.DirichletTensor.name
tf.contrib.bayesflow.stochastic_tensor.DirichletTensor.value(name='value')
tf.contrib.bayesflow.stochastic_tensor.DirichletTensor.value_type
class tf.contrib.bayesflow.stochastic_tensor.DirichletMultinomialTensorDirichletMultinomialTensor is a StochasticTensor backed by the distribution DirichletMultinomial.
tf.contrib.bayesflow.stochastic_tensor.DirichletMultinomialTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.DirichletMultinomialTensor.clone(name=None, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.DirichletMultinomialTensor.distribution
tf.contrib.bayesflow.stochastic_tensor.DirichletMultinomialTensor.dtype
tf.contrib.bayesflow.stochastic_tensor.DirichletMultinomialTensor.entropy(name='entropy')
tf.contrib.bayesflow.stochastic_tensor.DirichletMultinomialTensor.graph
tf.contrib.bayesflow.stochastic_tensor.DirichletMultinomialTensor.input_dict
tf.contrib.bayesflow.stochastic_tensor.DirichletMultinomialTensor.loss(final_loss, name='Loss')
tf.contrib.bayesflow.stochastic_tensor.DirichletMultinomialTensor.mean(name='mean')
tf.contrib.bayesflow.stochastic_tensor.DirichletMultinomialTensor.name
tf.contrib.bayesflow.stochastic_tensor.DirichletMultinomialTensor.value(name='value')
tf.contrib.bayesflow.stochastic_tensor.DirichletMultinomialTensor.value_type
class tf.contrib.bayesflow.stochastic_tensor.ExponentialTensorExponentialTensor is a StochasticTensor backed by the distribution Exponential.
tf.contrib.bayesflow.stochastic_tensor.ExponentialTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.ExponentialTensor.clone(name=None, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.ExponentialTensor.distribution
tf.contrib.bayesflow.stochastic_tensor.ExponentialTensor.dtype
tf.contrib.bayesflow.stochastic_tensor.ExponentialTensor.entropy(name='entropy')
tf.contrib.bayesflow.stochastic_tensor.ExponentialTensor.graph
tf.contrib.bayesflow.stochastic_tensor.ExponentialTensor.input_dict
tf.contrib.bayesflow.stochastic_tensor.ExponentialTensor.loss(final_loss, name='Loss')
tf.contrib.bayesflow.stochastic_tensor.ExponentialTensor.mean(name='mean')
tf.contrib.bayesflow.stochastic_tensor.ExponentialTensor.name
tf.contrib.bayesflow.stochastic_tensor.ExponentialTensor.value(name='value')
tf.contrib.bayesflow.stochastic_tensor.ExponentialTensor.value_type
class tf.contrib.bayesflow.stochastic_tensor.ExponentialWithSoftplusLamTensorExponentialWithSoftplusLamTensor is a StochasticTensor backed by the distribution ExponentialWithSoftplusLam.
tf.contrib.bayesflow.stochastic_tensor.ExponentialWithSoftplusLamTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.ExponentialWithSoftplusLamTensor.clone(name=None, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.ExponentialWithSoftplusLamTensor.distribution
tf.contrib.bayesflow.stochastic_tensor.ExponentialWithSoftplusLamTensor.dtype
tf.contrib.bayesflow.stochastic_tensor.ExponentialWithSoftplusLamTensor.entropy(name='entropy')
tf.contrib.bayesflow.stochastic_tensor.ExponentialWithSoftplusLamTensor.graph
tf.contrib.bayesflow.stochastic_tensor.ExponentialWithSoftplusLamTensor.input_dict
tf.contrib.bayesflow.stochastic_tensor.ExponentialWithSoftplusLamTensor.loss(final_loss, name='Loss')
tf.contrib.bayesflow.stochastic_tensor.ExponentialWithSoftplusLamTensor.mean(name='mean')
tf.contrib.bayesflow.stochastic_tensor.ExponentialWithSoftplusLamTensor.name
tf.contrib.bayesflow.stochastic_tensor.ExponentialWithSoftplusLamTensor.value(name='value')
tf.contrib.bayesflow.stochastic_tensor.ExponentialWithSoftplusLamTensor.value_type
class tf.contrib.bayesflow.stochastic_tensor.GammaTensorGammaTensor is a StochasticTensor backed by the distribution Gamma.
tf.contrib.bayesflow.stochastic_tensor.GammaTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.GammaTensor.clone(name=None, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.GammaTensor.distribution
tf.contrib.bayesflow.stochastic_tensor.GammaTensor.dtype
tf.contrib.bayesflow.stochastic_tensor.GammaTensor.entropy(name='entropy')
tf.contrib.bayesflow.stochastic_tensor.GammaTensor.graph
tf.contrib.bayesflow.stochastic_tensor.GammaTensor.input_dict
tf.contrib.bayesflow.stochastic_tensor.GammaTensor.loss(final_loss, name='Loss')
tf.contrib.bayesflow.stochastic_tensor.GammaTensor.mean(name='mean')
tf.contrib.bayesflow.stochastic_tensor.GammaTensor.name
tf.contrib.bayesflow.stochastic_tensor.GammaTensor.value(name='value')
tf.contrib.bayesflow.stochastic_tensor.GammaTensor.value_type
class tf.contrib.bayesflow.stochastic_tensor.GammaWithSoftplusAlphaBetaTensorGammaWithSoftplusAlphaBetaTensor is a StochasticTensor backed by the distribution GammaWithSoftplusAlphaBeta.
tf.contrib.bayesflow.stochastic_tensor.GammaWithSoftplusAlphaBetaTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.GammaWithSoftplusAlphaBetaTensor.clone(name=None, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.GammaWithSoftplusAlphaBetaTensor.distribution
tf.contrib.bayesflow.stochastic_tensor.GammaWithSoftplusAlphaBetaTensor.dtype
tf.contrib.bayesflow.stochastic_tensor.GammaWithSoftplusAlphaBetaTensor.entropy(name='entropy')
tf.contrib.bayesflow.stochastic_tensor.GammaWithSoftplusAlphaBetaTensor.graph
tf.contrib.bayesflow.stochastic_tensor.GammaWithSoftplusAlphaBetaTensor.input_dict
tf.contrib.bayesflow.stochastic_tensor.GammaWithSoftplusAlphaBetaTensor.loss(final_loss, name='Loss')
tf.contrib.bayesflow.stochastic_tensor.GammaWithSoftplusAlphaBetaTensor.mean(name='mean')
tf.contrib.bayesflow.stochastic_tensor.GammaWithSoftplusAlphaBetaTensor.name
tf.contrib.bayesflow.stochastic_tensor.GammaWithSoftplusAlphaBetaTensor.value(name='value')
tf.contrib.bayesflow.stochastic_tensor.GammaWithSoftplusAlphaBetaTensor.value_type
class tf.contrib.bayesflow.stochastic_tensor.InverseGammaTensorInverseGammaTensor is a StochasticTensor backed by the distribution InverseGamma.
tf.contrib.bayesflow.stochastic_tensor.InverseGammaTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.InverseGammaTensor.clone(name=None, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.InverseGammaTensor.distribution
tf.contrib.bayesflow.stochastic_tensor.InverseGammaTensor.dtype
tf.contrib.bayesflow.stochastic_tensor.InverseGammaTensor.entropy(name='entropy')
tf.contrib.bayesflow.stochastic_tensor.InverseGammaTensor.graph
tf.contrib.bayesflow.stochastic_tensor.InverseGammaTensor.input_dict
tf.contrib.bayesflow.stochastic_tensor.InverseGammaTensor.loss(final_loss, name='Loss')
tf.contrib.bayesflow.stochastic_tensor.InverseGammaTensor.mean(name='mean')
tf.contrib.bayesflow.stochastic_tensor.InverseGammaTensor.name
tf.contrib.bayesflow.stochastic_tensor.InverseGammaTensor.value(name='value')
tf.contrib.bayesflow.stochastic_tensor.InverseGammaTensor.value_type
class tf.contrib.bayesflow.stochastic_tensor.InverseGammaWithSoftplusAlphaBetaTensorInverseGammaWithSoftplusAlphaBetaTensor is a StochasticTensor backed by the distribution InverseGammaWithSoftplusAlphaBeta.
tf.contrib.bayesflow.stochastic_tensor.InverseGammaWithSoftplusAlphaBetaTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.InverseGammaWithSoftplusAlphaBetaTensor.clone(name=None, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.InverseGammaWithSoftplusAlphaBetaTensor.distribution
tf.contrib.bayesflow.stochastic_tensor.InverseGammaWithSoftplusAlphaBetaTensor.dtype
tf.contrib.bayesflow.stochastic_tensor.InverseGammaWithSoftplusAlphaBetaTensor.entropy(name='entropy')
tf.contrib.bayesflow.stochastic_tensor.InverseGammaWithSoftplusAlphaBetaTensor.graph
tf.contrib.bayesflow.stochastic_tensor.InverseGammaWithSoftplusAlphaBetaTensor.input_dict
tf.contrib.bayesflow.stochastic_tensor.InverseGammaWithSoftplusAlphaBetaTensor.loss(final_loss, name='Loss')
tf.contrib.bayesflow.stochastic_tensor.InverseGammaWithSoftplusAlphaBetaTensor.mean(name='mean')
tf.contrib.bayesflow.stochastic_tensor.InverseGammaWithSoftplusAlphaBetaTensor.name
tf.contrib.bayesflow.stochastic_tensor.InverseGammaWithSoftplusAlphaBetaTensor.value(name='value')
tf.contrib.bayesflow.stochastic_tensor.InverseGammaWithSoftplusAlphaBetaTensor.value_type
class tf.contrib.bayesflow.stochastic_tensor.LaplaceTensorLaplaceTensor is a StochasticTensor backed by the distribution Laplace.
tf.contrib.bayesflow.stochastic_tensor.LaplaceTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.LaplaceTensor.clone(name=None, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.LaplaceTensor.distribution
tf.contrib.bayesflow.stochastic_tensor.LaplaceTensor.dtype
tf.contrib.bayesflow.stochastic_tensor.LaplaceTensor.entropy(name='entropy')
tf.contrib.bayesflow.stochastic_tensor.LaplaceTensor.graph
tf.contrib.bayesflow.stochastic_tensor.LaplaceTensor.input_dict
tf.contrib.bayesflow.stochastic_tensor.LaplaceTensor.loss(final_loss, name='Loss')
tf.contrib.bayesflow.stochastic_tensor.LaplaceTensor.mean(name='mean')
tf.contrib.bayesflow.stochastic_tensor.LaplaceTensor.name
tf.contrib.bayesflow.stochastic_tensor.LaplaceTensor.value(name='value')
tf.contrib.bayesflow.stochastic_tensor.LaplaceTensor.value_type
class tf.contrib.bayesflow.stochastic_tensor.LaplaceWithSoftplusScaleTensorLaplaceWithSoftplusScaleTensor is a StochasticTensor backed by the distribution LaplaceWithSoftplusScale.
tf.contrib.bayesflow.stochastic_tensor.LaplaceWithSoftplusScaleTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.LaplaceWithSoftplusScaleTensor.clone(name=None, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.LaplaceWithSoftplusScaleTensor.distribution
tf.contrib.bayesflow.stochastic_tensor.LaplaceWithSoftplusScaleTensor.dtype
tf.contrib.bayesflow.stochastic_tensor.LaplaceWithSoftplusScaleTensor.entropy(name='entropy')
tf.contrib.bayesflow.stochastic_tensor.LaplaceWithSoftplusScaleTensor.graph
tf.contrib.bayesflow.stochastic_tensor.LaplaceWithSoftplusScaleTensor.input_dict
tf.contrib.bayesflow.stochastic_tensor.LaplaceWithSoftplusScaleTensor.loss(final_loss, name='Loss')
tf.contrib.bayesflow.stochastic_tensor.LaplaceWithSoftplusScaleTensor.mean(name='mean')
tf.contrib.bayesflow.stochastic_tensor.LaplaceWithSoftplusScaleTensor.name
tf.contrib.bayesflow.stochastic_tensor.LaplaceWithSoftplusScaleTensor.value(name='value')
tf.contrib.bayesflow.stochastic_tensor.LaplaceWithSoftplusScaleTensor.value_type
class tf.contrib.bayesflow.stochastic_tensor.MixtureTensorMixtureTensor is a StochasticTensor backed by the distribution Mixture.
tf.contrib.bayesflow.stochastic_tensor.MixtureTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.MixtureTensor.clone(name=None, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.MixtureTensor.distribution
tf.contrib.bayesflow.stochastic_tensor.MixtureTensor.dtype
tf.contrib.bayesflow.stochastic_tensor.MixtureTensor.entropy(name='entropy')
tf.contrib.bayesflow.stochastic_tensor.MixtureTensor.graph
tf.contrib.bayesflow.stochastic_tensor.MixtureTensor.input_dict
tf.contrib.bayesflow.stochastic_tensor.MixtureTensor.loss(final_loss, name='Loss')
tf.contrib.bayesflow.stochastic_tensor.MixtureTensor.mean(name='mean')
tf.contrib.bayesflow.stochastic_tensor.MixtureTensor.name
tf.contrib.bayesflow.stochastic_tensor.MixtureTensor.value(name='value')
tf.contrib.bayesflow.stochastic_tensor.MixtureTensor.value_type
class tf.contrib.bayesflow.stochastic_tensor.MultinomialTensorMultinomialTensor is a StochasticTensor backed by the distribution Multinomial.
tf.contrib.bayesflow.stochastic_tensor.MultinomialTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.MultinomialTensor.clone(name=None, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.MultinomialTensor.distribution
tf.contrib.bayesflow.stochastic_tensor.MultinomialTensor.dtype
tf.contrib.bayesflow.stochastic_tensor.MultinomialTensor.entropy(name='entropy')
tf.contrib.bayesflow.stochastic_tensor.MultinomialTensor.graph
tf.contrib.bayesflow.stochastic_tensor.MultinomialTensor.input_dict
tf.contrib.bayesflow.stochastic_tensor.MultinomialTensor.loss(final_loss, name='Loss')
tf.contrib.bayesflow.stochastic_tensor.MultinomialTensor.mean(name='mean')
tf.contrib.bayesflow.stochastic_tensor.MultinomialTensor.name
tf.contrib.bayesflow.stochastic_tensor.MultinomialTensor.value(name='value')
tf.contrib.bayesflow.stochastic_tensor.MultinomialTensor.value_type
class tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalCholeskyTensorMultivariateNormalCholeskyTensor is a StochasticTensor backed by the distribution MultivariateNormalCholesky.
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalCholeskyTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalCholeskyTensor.clone(name=None, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalCholeskyTensor.distribution
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalCholeskyTensor.dtype
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalCholeskyTensor.entropy(name='entropy')
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalCholeskyTensor.graph
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalCholeskyTensor.input_dict
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalCholeskyTensor.loss(final_loss, name='Loss')
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalCholeskyTensor.mean(name='mean')
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalCholeskyTensor.name
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalCholeskyTensor.value(name='value')
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalCholeskyTensor.value_type
class tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagTensorMultivariateNormalDiagTensor is a StochasticTensor backed by the distribution MultivariateNormalDiag.
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagTensor.clone(name=None, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagTensor.distribution
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagTensor.dtype
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagTensor.entropy(name='entropy')
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagTensor.graph
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagTensor.input_dict
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagTensor.loss(final_loss, name='Loss')
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagTensor.mean(name='mean')
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagTensor.name
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagTensor.value(name='value')
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagTensor.value_type
class tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagPlusVDVTTensorMultivariateNormalDiagPlusVDVTTensor is a StochasticTensor backed by the distribution MultivariateNormalDiagPlusVDVT.
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagPlusVDVTTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagPlusVDVTTensor.clone(name=None, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagPlusVDVTTensor.distribution
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagPlusVDVTTensor.dtype
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagPlusVDVTTensor.entropy(name='entropy')
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagPlusVDVTTensor.graph
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagPlusVDVTTensor.input_dict
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagPlusVDVTTensor.loss(final_loss, name='Loss')
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagPlusVDVTTensor.mean(name='mean')
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagPlusVDVTTensor.name
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagPlusVDVTTensor.value(name='value')
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagPlusVDVTTensor.value_type
class tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagWithSoftplusStDevTensorMultivariateNormalDiagWithSoftplusStDevTensor is a StochasticTensor backed by the distribution MultivariateNormalDiagWithSoftplusStDev.
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagWithSoftplusStDevTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagWithSoftplusStDevTensor.clone(name=None, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagWithSoftplusStDevTensor.distribution
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagWithSoftplusStDevTensor.dtype
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagWithSoftplusStDevTensor.entropy(name='entropy')
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagWithSoftplusStDevTensor.graph
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagWithSoftplusStDevTensor.input_dict
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagWithSoftplusStDevTensor.loss(final_loss, name='Loss')
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagWithSoftplusStDevTensor.mean(name='mean')
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagWithSoftplusStDevTensor.name
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagWithSoftplusStDevTensor.value(name='value')
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagWithSoftplusStDevTensor.value_type
class tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalFullTensorMultivariateNormalFullTensor is a StochasticTensor backed by the distribution MultivariateNormalFull.
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalFullTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalFullTensor.clone(name=None, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalFullTensor.distribution
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalFullTensor.dtype
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalFullTensor.entropy(name='entropy')
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalFullTensor.graph
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalFullTensor.input_dict
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalFullTensor.loss(final_loss, name='Loss')
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalFullTensor.mean(name='mean')
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalFullTensor.name
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalFullTensor.value(name='value')
tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalFullTensor.value_type
class tf.contrib.bayesflow.stochastic_tensor.NormalTensorNormalTensor is a StochasticTensor backed by the distribution Normal.
tf.contrib.bayesflow.stochastic_tensor.NormalTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.NormalTensor.clone(name=None, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.NormalTensor.distribution
tf.contrib.bayesflow.stochastic_tensor.NormalTensor.dtype
tf.contrib.bayesflow.stochastic_tensor.NormalTensor.entropy(name='entropy')
tf.contrib.bayesflow.stochastic_tensor.NormalTensor.graph
tf.contrib.bayesflow.stochastic_tensor.NormalTensor.input_dict
tf.contrib.bayesflow.stochastic_tensor.NormalTensor.loss(final_loss, name='Loss')
tf.contrib.bayesflow.stochastic_tensor.NormalTensor.mean(name='mean')
tf.contrib.bayesflow.stochastic_tensor.NormalTensor.name
tf.contrib.bayesflow.stochastic_tensor.NormalTensor.value(name='value')
tf.contrib.bayesflow.stochastic_tensor.NormalTensor.value_type
class tf.contrib.bayesflow.stochastic_tensor.NormalWithSoftplusSigmaTensorNormalWithSoftplusSigmaTensor is a StochasticTensor backed by the distribution NormalWithSoftplusSigma.
tf.contrib.bayesflow.stochastic_tensor.NormalWithSoftplusSigmaTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.NormalWithSoftplusSigmaTensor.clone(name=None, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.NormalWithSoftplusSigmaTensor.distribution
tf.contrib.bayesflow.stochastic_tensor.NormalWithSoftplusSigmaTensor.dtype
tf.contrib.bayesflow.stochastic_tensor.NormalWithSoftplusSigmaTensor.entropy(name='entropy')
tf.contrib.bayesflow.stochastic_tensor.NormalWithSoftplusSigmaTensor.graph
tf.contrib.bayesflow.stochastic_tensor.NormalWithSoftplusSigmaTensor.input_dict
tf.contrib.bayesflow.stochastic_tensor.NormalWithSoftplusSigmaTensor.loss(final_loss, name='Loss')
tf.contrib.bayesflow.stochastic_tensor.NormalWithSoftplusSigmaTensor.mean(name='mean')
tf.contrib.bayesflow.stochastic_tensor.NormalWithSoftplusSigmaTensor.name
tf.contrib.bayesflow.stochastic_tensor.NormalWithSoftplusSigmaTensor.value(name='value')
tf.contrib.bayesflow.stochastic_tensor.NormalWithSoftplusSigmaTensor.value_type
class tf.contrib.bayesflow.stochastic_tensor.PoissonTensorPoissonTensor is a StochasticTensor backed by the distribution Poisson.
tf.contrib.bayesflow.stochastic_tensor.PoissonTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.PoissonTensor.clone(name=None, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.PoissonTensor.distribution
tf.contrib.bayesflow.stochastic_tensor.PoissonTensor.dtype
tf.contrib.bayesflow.stochastic_tensor.PoissonTensor.entropy(name='entropy')
tf.contrib.bayesflow.stochastic_tensor.PoissonTensor.graph
tf.contrib.bayesflow.stochastic_tensor.PoissonTensor.input_dict
tf.contrib.bayesflow.stochastic_tensor.PoissonTensor.loss(final_loss, name='Loss')
tf.contrib.bayesflow.stochastic_tensor.PoissonTensor.mean(name='mean')
tf.contrib.bayesflow.stochastic_tensor.PoissonTensor.name
tf.contrib.bayesflow.stochastic_tensor.PoissonTensor.value(name='value')
tf.contrib.bayesflow.stochastic_tensor.PoissonTensor.value_type
class tf.contrib.bayesflow.stochastic_tensor.QuantizedDistributionTensorQuantizedDistributionTensor is a StochasticTensor backed by the distribution QuantizedDistribution.
tf.contrib.bayesflow.stochastic_tensor.QuantizedDistributionTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.QuantizedDistributionTensor.clone(name=None, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.QuantizedDistributionTensor.distribution
tf.contrib.bayesflow.stochastic_tensor.QuantizedDistributionTensor.dtype
tf.contrib.bayesflow.stochastic_tensor.QuantizedDistributionTensor.entropy(name='entropy')
tf.contrib.bayesflow.stochastic_tensor.QuantizedDistributionTensor.graph
tf.contrib.bayesflow.stochastic_tensor.QuantizedDistributionTensor.input_dict
tf.contrib.bayesflow.stochastic_tensor.QuantizedDistributionTensor.loss(final_loss, name='Loss')
tf.contrib.bayesflow.stochastic_tensor.QuantizedDistributionTensor.mean(name='mean')
tf.contrib.bayesflow.stochastic_tensor.QuantizedDistributionTensor.name
tf.contrib.bayesflow.stochastic_tensor.QuantizedDistributionTensor.value(name='value')
tf.contrib.bayesflow.stochastic_tensor.QuantizedDistributionTensor.value_type
class tf.contrib.bayesflow.stochastic_tensor.StudentTTensorStudentTTensor is a StochasticTensor backed by the distribution StudentT.
tf.contrib.bayesflow.stochastic_tensor.StudentTTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.StudentTTensor.clone(name=None, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.StudentTTensor.distribution
tf.contrib.bayesflow.stochastic_tensor.StudentTTensor.dtype
tf.contrib.bayesflow.stochastic_tensor.StudentTTensor.entropy(name='entropy')
tf.contrib.bayesflow.stochastic_tensor.StudentTTensor.graph
tf.contrib.bayesflow.stochastic_tensor.StudentTTensor.input_dict
tf.contrib.bayesflow.stochastic_tensor.StudentTTensor.loss(final_loss, name='Loss')
tf.contrib.bayesflow.stochastic_tensor.StudentTTensor.mean(name='mean')
tf.contrib.bayesflow.stochastic_tensor.StudentTTensor.name
tf.contrib.bayesflow.stochastic_tensor.StudentTTensor.value(name='value')
tf.contrib.bayesflow.stochastic_tensor.StudentTTensor.value_type
class tf.contrib.bayesflow.stochastic_tensor.StudentTWithAbsDfSoftplusSigmaTensorStudentTWithAbsDfSoftplusSigmaTensor is a StochasticTensor backed by the distribution StudentTWithAbsDfSoftplusSigma.
tf.contrib.bayesflow.stochastic_tensor.StudentTWithAbsDfSoftplusSigmaTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.StudentTWithAbsDfSoftplusSigmaTensor.clone(name=None, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.StudentTWithAbsDfSoftplusSigmaTensor.distribution
tf.contrib.bayesflow.stochastic_tensor.StudentTWithAbsDfSoftplusSigmaTensor.dtype
tf.contrib.bayesflow.stochastic_tensor.StudentTWithAbsDfSoftplusSigmaTensor.entropy(name='entropy')
tf.contrib.bayesflow.stochastic_tensor.StudentTWithAbsDfSoftplusSigmaTensor.graph
tf.contrib.bayesflow.stochastic_tensor.StudentTWithAbsDfSoftplusSigmaTensor.input_dict
tf.contrib.bayesflow.stochastic_tensor.StudentTWithAbsDfSoftplusSigmaTensor.loss(final_loss, name='Loss')
tf.contrib.bayesflow.stochastic_tensor.StudentTWithAbsDfSoftplusSigmaTensor.mean(name='mean')
tf.contrib.bayesflow.stochastic_tensor.StudentTWithAbsDfSoftplusSigmaTensor.name
tf.contrib.bayesflow.stochastic_tensor.StudentTWithAbsDfSoftplusSigmaTensor.value(name='value')
tf.contrib.bayesflow.stochastic_tensor.StudentTWithAbsDfSoftplusSigmaTensor.value_type
class tf.contrib.bayesflow.stochastic_tensor.TransformedDistributionTensorTransformedDistributionTensor is a StochasticTensor backed by the distribution TransformedDistribution.
tf.contrib.bayesflow.stochastic_tensor.TransformedDistributionTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.TransformedDistributionTensor.clone(name=None, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.TransformedDistributionTensor.distribution
tf.contrib.bayesflow.stochastic_tensor.TransformedDistributionTensor.dtype
tf.contrib.bayesflow.stochastic_tensor.TransformedDistributionTensor.entropy(name='entropy')
tf.contrib.bayesflow.stochastic_tensor.TransformedDistributionTensor.graph
tf.contrib.bayesflow.stochastic_tensor.TransformedDistributionTensor.input_dict
tf.contrib.bayesflow.stochastic_tensor.TransformedDistributionTensor.loss(final_loss, name='Loss')
tf.contrib.bayesflow.stochastic_tensor.TransformedDistributionTensor.mean(name='mean')
tf.contrib.bayesflow.stochastic_tensor.TransformedDistributionTensor.name
tf.contrib.bayesflow.stochastic_tensor.TransformedDistributionTensor.value(name='value')
tf.contrib.bayesflow.stochastic_tensor.TransformedDistributionTensor.value_type
class tf.contrib.bayesflow.stochastic_tensor.UniformTensorUniformTensor is a StochasticTensor backed by the distribution Uniform.
tf.contrib.bayesflow.stochastic_tensor.UniformTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.UniformTensor.clone(name=None, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.UniformTensor.distribution
tf.contrib.bayesflow.stochastic_tensor.UniformTensor.dtype
tf.contrib.bayesflow.stochastic_tensor.UniformTensor.entropy(name='entropy')
tf.contrib.bayesflow.stochastic_tensor.UniformTensor.graph
tf.contrib.bayesflow.stochastic_tensor.UniformTensor.input_dict
tf.contrib.bayesflow.stochastic_tensor.UniformTensor.loss(final_loss, name='Loss')
tf.contrib.bayesflow.stochastic_tensor.UniformTensor.mean(name='mean')
tf.contrib.bayesflow.stochastic_tensor.UniformTensor.name
tf.contrib.bayesflow.stochastic_tensor.UniformTensor.value(name='value')
tf.contrib.bayesflow.stochastic_tensor.UniformTensor.value_type
class tf.contrib.bayesflow.stochastic_tensor.WishartCholeskyTensorWishartCholeskyTensor is a StochasticTensor backed by the distribution WishartCholesky.
tf.contrib.bayesflow.stochastic_tensor.WishartCholeskyTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.WishartCholeskyTensor.clone(name=None, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.WishartCholeskyTensor.distribution
tf.contrib.bayesflow.stochastic_tensor.WishartCholeskyTensor.dtype
tf.contrib.bayesflow.stochastic_tensor.WishartCholeskyTensor.entropy(name='entropy')
tf.contrib.bayesflow.stochastic_tensor.WishartCholeskyTensor.graph
tf.contrib.bayesflow.stochastic_tensor.WishartCholeskyTensor.input_dict
tf.contrib.bayesflow.stochastic_tensor.WishartCholeskyTensor.loss(final_loss, name='Loss')
tf.contrib.bayesflow.stochastic_tensor.WishartCholeskyTensor.mean(name='mean')
tf.contrib.bayesflow.stochastic_tensor.WishartCholeskyTensor.name
tf.contrib.bayesflow.stochastic_tensor.WishartCholeskyTensor.value(name='value')
tf.contrib.bayesflow.stochastic_tensor.WishartCholeskyTensor.value_type
class tf.contrib.bayesflow.stochastic_tensor.WishartFullTensorWishartFullTensor is a StochasticTensor backed by the distribution WishartFull.
tf.contrib.bayesflow.stochastic_tensor.WishartFullTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.WishartFullTensor.clone(name=None, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.WishartFullTensor.distribution
tf.contrib.bayesflow.stochastic_tensor.WishartFullTensor.dtype
tf.contrib.bayesflow.stochastic_tensor.WishartFullTensor.entropy(name='entropy')
tf.contrib.bayesflow.stochastic_tensor.WishartFullTensor.graph
tf.contrib.bayesflow.stochastic_tensor.WishartFullTensor.input_dict
tf.contrib.bayesflow.stochastic_tensor.WishartFullTensor.loss(final_loss, name='Loss')
tf.contrib.bayesflow.stochastic_tensor.WishartFullTensor.mean(name='mean')
tf.contrib.bayesflow.stochastic_tensor.WishartFullTensor.name
tf.contrib.bayesflow.stochastic_tensor.WishartFullTensor.value(name='value')
tf.contrib.bayesflow.stochastic_tensor.WishartFullTensor.value_type

BayesFlow Stochastic Tensors (contrib) > Other Functions and Classes

Members
class tf.contrib.bayesflow.stochastic_tensor.ObservedStochasticTensorA StochasticTensor with an observed value.
tf.contrib.bayesflow.stochastic_tensor.ObservedStochasticTensor.__init__(dist_cls, value, name=None, **dist_args)Construct an ObservedStochasticTensor.
tf.contrib.bayesflow.stochastic_tensor.ObservedStochasticTensor.clone(name=None, **dist_args)
tf.contrib.bayesflow.stochastic_tensor.ObservedStochasticTensor.distribution
tf.contrib.bayesflow.stochastic_tensor.ObservedStochasticTensor.dtype
tf.contrib.bayesflow.stochastic_tensor.ObservedStochasticTensor.entropy(name='entropy')
tf.contrib.bayesflow.stochastic_tensor.ObservedStochasticTensor.graph
tf.contrib.bayesflow.stochastic_tensor.ObservedStochasticTensor.input_dict
tf.contrib.bayesflow.stochastic_tensor.ObservedStochasticTensor.loss(final_loss, name=None)
tf.contrib.bayesflow.stochastic_tensor.ObservedStochasticTensor.mean(name='mean')
tf.contrib.bayesflow.stochastic_tensor.ObservedStochasticTensor.name
tf.contrib.bayesflow.stochastic_tensor.ObservedStochasticTensor.value(name='value')
tf.contrib.bayesflow.stochastic_tensor.ObservedStochasticTensor.value_type
tf.contrib.bayesflow.variational_inference.elbo(log_likelihood, variational_with_prior=None, keep_batch_dim=True, form=None, name='ELBO')Evidence Lower BOund. log p(x) >= ELBO.
tf.contrib.bayesflow.variational_inference.elbo_with_log_joint(log_joint, variational=None, keep_batch_dim=True, form=None, name='ELBO')Evidence Lower BOund. log p(x) >= ELBO.
class tf.contrib.bayesflow.variational_inference.ELBOFormsConstants to control the elbo calculation.
tf.contrib.bayesflow.variational_inference.ELBOForms.check_form(form)

BayesFlow Variational Inference (contrib) > Other Functions and Classes

Members
tf.contrib.bayesflow.variational_inference.register_prior(variational, prior)Associate a variational DistributionTensor with a Distribution prior.

CRF (contrib) > This package provides functions for building a linear-chain CRF layer.

Members
tf.contrib.crf.crf_sequence_score(inputs, tag_indices, sequence_lengths, transition_params)Computes the unnormalized score for a tag sequence.
tf.contrib.crf.crf_log_norm(inputs, sequence_lengths, transition_params)Computes the normalization for a CRF.
tf.contrib.crf.crf_log_likelihood(inputs, tag_indices, sequence_lengths, transition_params=None)Computes the log-likehood of tag sequences in a CRF.
tf.contrib.crf.crf_unary_score(tag_indices, sequence_lengths, inputs)Computes the unary scores of tag sequences.
tf.contrib.crf.crf_binary_score(tag_indices, sequence_lengths, transition_params)Computes the binary scores of tag sequences.
class tf.contrib.crf.CrfForwardRnnCellComputes the alpha values in a linear-chain CRF.
tf.contrib.crf.CrfForwardRnnCell.__call__(inputs, state, scope=None)Build the CrfForwardRnnCell.
tf.contrib.crf.CrfForwardRnnCell.__init__(transition_params)Initialize the CrfForwardRnnCell.
tf.contrib.crf.CrfForwardRnnCell.output_size
tf.contrib.crf.CrfForwardRnnCell.state_size
tf.contrib.crf.CrfForwardRnnCell.zero_state(batch_size, dtype)Return zero-filled state tensor(s).
tf.contrib.crf.viterbi_decode(score, transition_params)Decode the highest scoring sequence of tags outside of TensorFlow.

Statistical distributions (contrib) > Classes for statistical distributions.

Members
class tf.contrib.distributions.DistributionA generic probability distribution base class.
tf.contrib.distributions.Distribution.__init__(dtype, parameters, is_continuous, is_reparameterized, validate_args, allow_nan_stats, name=None)Constructs the Distribution.
tf.contrib.distributions.Distribution.allow_nan_statsPython boolean describing behavior when a stat is undefined.
tf.contrib.distributions.Distribution.batch_shape(name='batch_shape')Shape of a single sample from a single event index as a 1-D Tensor.
tf.contrib.distributions.Distribution.cdf(value, name='cdf')Cumulative distribution function.
tf.contrib.distributions.Distribution.dtypeThe DType of Tensors handled by this Distribution.
tf.contrib.distributions.Distribution.entropy(name='entropy')Shanon entropy in nats.
tf.contrib.distributions.Distribution.event_shape(name='event_shape')Shape of a single sample from a single batch as a 1-D int32 Tensor.
tf.contrib.distributions.Distribution.get_batch_shape()Shape of a single sample from a single event index as a TensorShape.
tf.contrib.distributions.Distribution.get_event_shape()Shape of a single sample from a single batch as a TensorShape.
tf.contrib.distributions.Distribution.is_continuous
tf.contrib.distributions.Distribution.is_reparameterized
tf.contrib.distributions.Distribution.log_cdf(value, name='log_cdf')Log cumulative distribution function.
tf.contrib.distributions.Distribution.log_pdf(value, name='log_pdf')Log probability density function.
tf.contrib.distributions.Distribution.log_pmf(value, name='log_pmf')Log probability mass function.
tf.contrib.distributions.Distribution.log_prob(value, name='log_prob')Log probability density/mass function (depending on is_continuous).
tf.contrib.distributions.Distribution.log_survival_function(value, name='log_survival_function')Log survival function.
tf.contrib.distributions.Distribution.mean(name='mean')Mean.
tf.contrib.distributions.Distribution.mode(name='mode')Mode.
tf.contrib.distributions.Distribution.nameName prepended to all ops created by this Distribution.
tf.contrib.distributions.Distribution.param_shapes(cls, sample_shape, name='DistributionParamShapes')Shapes of parameters given the desired shape of a call to sample().
tf.contrib.distributions.Distribution.param_static_shapes(cls, sample_shape)param_shapes with static (i.e. TensorShape) shapes.
tf.contrib.distributions.Distribution.parametersDictionary of parameters used by this Distribution.
tf.contrib.distributions.Distribution.pdf(value, name='pdf')Probability density function.
tf.contrib.distributions.Distribution.pmf(value, name='pmf')Probability mass function.
tf.contrib.distributions.Distribution.prob(value, name='prob')Probability density/mass function (depending on is_continuous).
tf.contrib.distributions.Distribution.sample(sample_shape=(), seed=None, name='sample')Generate samples of the specified shape.
tf.contrib.distributions.Distribution.sample_n(n, seed=None, name='sample_n')Generate n samples.
tf.contrib.distributions.Distribution.std(name='std')Standard deviation.
tf.contrib.distributions.Distribution.survival_function(value, name='survival_function')Survival function.
tf.contrib.distributions.Distribution.validate_argsPython boolean indicated possibly expensive checks are enabled.
tf.contrib.distributions.Distribution.variance(name='variance')Variance.
class tf.contrib.distributions.BinomialBinomial distribution.
tf.contrib.distributions.Binomial.__init__(n, logits=None, p=None, validate_args=False, allow_nan_stats=True, name='Binomial')Initialize a batch of Binomial distributions.
tf.contrib.distributions.Binomial.allow_nan_statsPython boolean describing behavior when a stat is undefined.
tf.contrib.distributions.Binomial.batch_shape(name='batch_shape')Shape of a single sample from a single event index as a 1-D Tensor.
tf.contrib.distributions.Binomial.cdf(value, name='cdf')Cumulative distribution function.
tf.contrib.distributions.Binomial.dtypeThe DType of Tensors handled by this Distribution.
tf.contrib.distributions.Binomial.entropy(name='entropy')Shanon entropy in nats.
tf.contrib.distributions.Binomial.event_shape(name='event_shape')Shape of a single sample from a single batch as a 1-D int32 Tensor.
tf.contrib.distributions.Binomial.get_batch_shape()Shape of a single sample from a single event index as a TensorShape.
tf.contrib.distributions.Binomial.get_event_shape()Shape of a single sample from a single batch as a TensorShape.
tf.contrib.distributions.Binomial.is_continuous
tf.contrib.distributions.Binomial.is_reparameterized
tf.contrib.distributions.Binomial.log_cdf(value, name='log_cdf')Log cumulative distribution function.
tf.contrib.distributions.Binomial.log_pdf(value, name='log_pdf')Log probability density function.
tf.contrib.distributions.Binomial.log_pmf(value, name='log_pmf')Log probability mass function.
tf.contrib.distributions.Binomial.log_prob(value, name='log_prob')Log probability density/mass function (depending on is_continuous).
tf.contrib.distributions.Binomial.log_survival_function(value, name='log_survival_function')Log survival function.
tf.contrib.distributions.Binomial.logitsLog-odds.
tf.contrib.distributions.Binomial.mean(name='mean')Mean.
tf.contrib.distributions.Binomial.mode(name='mode')Mode.
tf.contrib.distributions.Binomial.nNumber of trials.
tf.contrib.distributions.Binomial.nameName prepended to all ops created by this Distribution.
tf.contrib.distributions.Binomial.pProbability of success.
tf.contrib.distributions.Binomial.param_shapes(cls, sample_shape, name='DistributionParamShapes')Shapes of parameters given the desired shape of a call to sample().
tf.contrib.distributions.Binomial.param_static_shapes(cls, sample_shape)param_shapes with static (i.e. TensorShape) shapes.
tf.contrib.distributions.Binomial.parametersDictionary of parameters used by this Distribution.
tf.contrib.distributions.Binomial.pdf(value, name='pdf')Probability density function.
tf.contrib.distributions.Binomial.pmf(value, name='pmf')Probability mass function.
tf.contrib.distributions.Binomial.prob(value, name='prob')Probability density/mass function (depending on is_continuous).
tf.contrib.distributions.Binomial.sample(sample_shape=(), seed=None, name='sample')Generate samples of the specified shape.
tf.contrib.distributions.Binomial.sample_n(n, seed=None, name='sample_n')Generate n samples.
tf.contrib.distributions.Binomial.std(name='std')Standard deviation.
tf.contrib.distributions.Binomial.survival_function(value, name='survival_function')Survival function.
tf.contrib.distributions.Binomial.validate_argsPython boolean indicated possibly expensive checks are enabled.
tf.contrib.distributions.Binomial.variance(name='variance')Variance.
class tf.contrib.distributions.BernoulliBernoulli distribution.
tf.contrib.distributions.Bernoulli.__init__(logits=None, p=None, dtype=tf.int32, validate_args=False, allow_nan_stats=True, name='Bernoulli')Construct Bernoulli distributions.
tf.contrib.distributions.Bernoulli.allow_nan_statsPython boolean describing behavior when a stat is undefined.
tf.contrib.distributions.Bernoulli.batch_shape(name='batch_shape')Shape of a single sample from a single event index as a 1-D Tensor.
tf.contrib.distributions.Bernoulli.cdf(value, name='cdf')Cumulative distribution function.
tf.contrib.distributions.Bernoulli.dtypeThe DType of Tensors handled by this Distribution.
tf.contrib.distributions.Bernoulli.entropy(name='entropy')Shanon entropy in nats.
tf.contrib.distributions.Bernoulli.event_shape(name='event_shape')Shape of a single sample from a single batch as a 1-D int32 Tensor.
tf.contrib.distributions.Bernoulli.get_batch_shape()Shape of a single sample from a single event index as a TensorShape.
tf.contrib.distributions.Bernoulli.get_event_shape()Shape of a single sample from a single batch as a TensorShape.
tf.contrib.distributions.Bernoulli.is_continuous
tf.contrib.distributions.Bernoulli.is_reparameterized
tf.contrib.distributions.Bernoulli.log_cdf(value, name='log_cdf')Log cumulative distribution function.
tf.contrib.distributions.Bernoulli.log_pdf(value, name='log_pdf')Log probability density function.
tf.contrib.distributions.Bernoulli.log_pmf(value, name='log_pmf')Log probability mass function.
tf.contrib.distributions.Bernoulli.log_prob(value, name='log_prob')Log probability density/mass function (depending on is_continuous).
tf.contrib.distributions.Bernoulli.log_survival_function(value, name='log_survival_function')Log survival function.
tf.contrib.distributions.Bernoulli.logits
tf.contrib.distributions.Bernoulli.mean(name='mean')Mean.
tf.contrib.distributions.Bernoulli.mode(name='mode')Mode.
tf.contrib.distributions.Bernoulli.nameName prepended to all ops created by this Distribution.
tf.contrib.distributions.Bernoulli.p
tf.contrib.distributions.Bernoulli.param_shapes(cls, sample_shape, name='DistributionParamShapes')Shapes of parameters given the desired shape of a call to sample().
tf.contrib.distributions.Bernoulli.param_static_shapes(cls, sample_shape)param_shapes with static (i.e. TensorShape) shapes.
tf.contrib.distributions.Bernoulli.parametersDictionary of parameters used by this Distribution.
tf.contrib.distributions.Bernoulli.pdf(value, name='pdf')Probability density function.
tf.contrib.distributions.Bernoulli.pmf(value, name='pmf')Probability mass function.
tf.contrib.distributions.Bernoulli.prob(value, name='prob')Probability density/mass function (depending on is_continuous).
tf.contrib.distributions.Bernoulli.q1-p.
tf.contrib.distributions.Bernoulli.sample(sample_shape=(), seed=None, name='sample')Generate samples of the specified shape.
tf.contrib.distributions.Bernoulli.sample_n(n, seed=None, name='sample_n')Generate n samples.
tf.contrib.distributions.Bernoulli.std(name='std')Standard deviation.
tf.contrib.distributions.Bernoulli.survival_function(value, name='survival_function')Survival function.
tf.contrib.distributions.Bernoulli.validate_argsPython boolean indicated possibly expensive checks are enabled.
tf.contrib.distributions.Bernoulli.variance(name='variance')Variance.
class tf.contrib.distributions.BetaBeta distribution.
tf.contrib.distributions.Beta.__init__(a, b, validate_args=False, allow_nan_stats=True, name='Beta')Initialize a batch of Beta distributions.
tf.contrib.distributions.Beta.aShape parameter.
tf.contrib.distributions.Beta.a_b_sumSum of parameters.
tf.contrib.distributions.Beta.allow_nan_statsPython boolean describing behavior when a stat is undefined.
tf.contrib.distributions.Beta.bShape parameter.
tf.contrib.distributions.Beta.batch_shape(name='batch_shape')Shape of a single sample from a single event index as a 1-D Tensor.
tf.contrib.distributions.Beta.cdf(value, name='cdf')Cumulative distribution function.
tf.contrib.distributions.Beta.dtypeThe DType of Tensors handled by this Distribution.
tf.contrib.distributions.Beta.entropy(name='entropy')Shanon entropy in nats.
tf.contrib.distributions.Beta.event_shape(name='event_shape')Shape of a single sample from a single batch as a 1-D int32 Tensor.
tf.contrib.distributions.Beta.get_batch_shape()Shape of a single sample from a single event index as a TensorShape.
tf.contrib.distributions.Beta.get_event_shape()Shape of a single sample from a single batch as a TensorShape.
tf.contrib.distributions.Beta.is_continuous
tf.contrib.distributions.Beta.is_reparameterized
tf.contrib.distributions.Beta.log_cdf(value, name='log_cdf')Log cumulative distribution function.
tf.contrib.distributions.Beta.log_pdf(value, name='log_pdf')Log probability density function.
tf.contrib.distributions.Beta.log_pmf(value, name='log_pmf')Log probability mass function.
tf.contrib.distributions.Beta.log_prob(value, name='log_prob')Log probability density/mass function (depending on is_continuous).
tf.contrib.distributions.Beta.log_survival_function(value, name='log_survival_function')Log survival function.
tf.contrib.distributions.Beta.mean(name='mean')Mean.
tf.contrib.distributions.Beta.mode(name='mode')Mode.
tf.contrib.distributions.Beta.nameName prepended to all ops created by this Distribution.
tf.contrib.distributions.Beta.param_shapes(cls, sample_shape, name='DistributionParamShapes')Shapes of parameters given the desired shape of a call to sample().
tf.contrib.distributions.Beta.param_static_shapes(cls, sample_shape)param_shapes with static (i.e. TensorShape) shapes.
tf.contrib.distributions.Beta.parametersDictionary of parameters used by this Distribution.
tf.contrib.distributions.Beta.pdf(value, name='pdf')Probability density function.
tf.contrib.distributions.Beta.pmf(value, name='pmf')Probability mass function.
tf.contrib.distributions.Beta.prob(value, name='prob')Probability density/mass function (depending on is_continuous).
tf.contrib.distributions.Beta.sample(sample_shape=(), seed=None, name='sample')Generate samples of the specified shape.
tf.contrib.distributions.Beta.sample_n(n, seed=None, name='sample_n')Generate n samples.
tf.contrib.distributions.Beta.std(name='std')Standard deviation.
tf.contrib.distributions.Beta.survival_function(value, name='survival_function')Survival function.
tf.contrib.distributions.Beta.validate_argsPython boolean indicated possibly expensive checks are enabled.
tf.contrib.distributions.Beta.variance(name='variance')Variance.
class tf.contrib.distributions.CategoricalCategorical distribution.
tf.contrib.distributions.Categorical.__init__(logits, dtype=tf.int32, validate_args=False, allow_nan_stats=True, name='Categorical')Initialize Categorical distributions using class log-probabilities.
tf.contrib.distributions.Categorical.allow_nan_statsPython boolean describing behavior when a stat is undefined.
tf.contrib.distributions.Categorical.batch_shape(name='batch_shape')Shape of a single sample from a single event index as a 1-D Tensor.
tf.contrib.distributions.Categorical.cdf(value, name='cdf')Cumulative distribution function.
tf.contrib.distributions.Categorical.dtypeThe DType of Tensors handled by this Distribution.
tf.contrib.distributions.Categorical.entropy(name='entropy')Shanon entropy in nats.
tf.contrib.distributions.Categorical.event_shape(name='event_shape')Shape of a single sample from a single batch as a 1-D int32 Tensor.
tf.contrib.distributions.Categorical.get_batch_shape()Shape of a single sample from a single event index as a TensorShape.
tf.contrib.distributions.Categorical.get_event_shape()Shape of a single sample from a single batch as a TensorShape.
tf.contrib.distributions.Categorical.is_continuous
tf.contrib.distributions.Categorical.is_reparameterized
tf.contrib.distributions.Categorical.log_cdf(value, name='log_cdf')Log cumulative distribution function.
tf.contrib.distributions.Categorical.log_pdf(value, name='log_pdf')Log probability density function.
tf.contrib.distributions.Categorical.log_pmf(value, name='log_pmf')Log probability mass function.
tf.contrib.distributions.Categorical.log_prob(value, name='log_prob')Log probability density/mass function (depending on is_continuous).
tf.contrib.distributions.Categorical.log_survival_function(value, name='log_survival_function')Log survival function.
tf.contrib.distributions.Categorical.logits
tf.contrib.distributions.Categorical.mean(name='mean')Mean.
tf.contrib.distributions.Categorical.mode(name='mode')Mode.
tf.contrib.distributions.Categorical.nameName prepended to all ops created by this Distribution.
tf.contrib.distributions.Categorical.num_classesScalar int32 tensor: the number of classes.
tf.contrib.distributions.Categorical.param_shapes(cls, sample_shape, name='DistributionParamShapes')Shapes of parameters given the desired shape of a call to sample().
tf.contrib.distributions.Categorical.param_static_shapes(cls, sample_shape)param_shapes with static (i.e. TensorShape) shapes.
tf.contrib.distributions.Categorical.parametersDictionary of parameters used by this Distribution.
tf.contrib.distributions.Categorical.pdf(value, name='pdf')Probability density function.
tf.contrib.distributions.Categorical.pmf(value, name='pmf')Probability mass function.
tf.contrib.distributions.Categorical.prob(value, name='prob')Probability density/mass function (depending on is_continuous).
tf.contrib.distributions.Categorical.sample(sample_shape=(), seed=None, name='sample')Generate samples of the specified shape.
tf.contrib.distributions.Categorical.sample_n(n, seed=None, name='sample_n')Generate n samples.
tf.contrib.distributions.Categorical.std(name='std')Standard deviation.
tf.contrib.distributions.Categorical.survival_function(value, name='survival_function')Survival function.
tf.contrib.distributions.Categorical.validate_argsPython boolean indicated possibly expensive checks are enabled.
tf.contrib.distributions.Categorical.variance(name='variance')Variance.
class tf.contrib.distributions.Chi2The Chi2 distribution with degrees of freedom df.
tf.contrib.distributions.Chi2.__init__(df, validate_args=False, allow_nan_stats=True, name='Chi2')Construct Chi2 distributions with parameter df.
tf.contrib.distributions.Chi2.allow_nan_statsPython boolean describing behavior when a stat is undefined.
tf.contrib.distributions.Chi2.alphaShape parameter.
tf.contrib.distributions.Chi2.batch_shape(name='batch_shape')Shape of a single sample from a single event index as a 1-D Tensor.
tf.contrib.distributions.Chi2.betaInverse scale parameter.
tf.contrib.distributions.Chi2.cdf(value, name='cdf')Cumulative distribution function.
tf.contrib.distributions.Chi2.df
tf.contrib.distributions.Chi2.dtypeThe DType of Tensors handled by this Distribution.
tf.contrib.distributions.Chi2.entropy(name='entropy')Shanon entropy in nats.
tf.contrib.distributions.Chi2.event_shape(name='event_shape')Shape of a single sample from a single batch as a 1-D int32 Tensor.
tf.contrib.distributions.Chi2.get_batch_shape()Shape of a single sample from a single event index as a TensorShape.
tf.contrib.distributions.Chi2.get_event_shape()Shape of a single sample from a single batch as a TensorShape.
tf.contrib.distributions.Chi2.is_continuous
tf.contrib.distributions.Chi2.is_reparameterized
tf.contrib.distributions.Chi2.log_cdf(value, name='log_cdf')Log cumulative distribution function.
tf.contrib.distributions.Chi2.log_pdf(value, name='log_pdf')Log probability density function.
tf.contrib.distributions.Chi2.log_pmf(value, name='log_pmf')Log probability mass function.
tf.contrib.distributions.Chi2.log_prob(value, name='log_prob')Log probability density/mass function (depending on is_continuous).
tf.contrib.distributions.Chi2.log_survival_function(value, name='log_survival_function')Log survival function.
tf.contrib.distributions.Chi2.mean(name='mean')Mean.
tf.contrib.distributions.Chi2.mode(name='mode')Mode.
tf.contrib.distributions.Chi2.nameName prepended to all ops created by this Distribution.
tf.contrib.distributions.Chi2.param_shapes(cls, sample_shape, name='DistributionParamShapes')Shapes of parameters given the desired shape of a call to sample().
tf.contrib.distributions.Chi2.param_static_shapes(cls, sample_shape)param_shapes with static (i.e. TensorShape) shapes.
tf.contrib.distributions.Chi2.parametersDictionary of parameters used by this Distribution.
tf.contrib.distributions.Chi2.pdf(value, name='pdf')Probability density function.
tf.contrib.distributions.Chi2.pmf(value, name='pmf')Probability mass function.
tf.contrib.distributions.Chi2.prob(value, name='prob')Probability density/mass function (depending on is_continuous).
tf.contrib.distributions.Chi2.sample(sample_shape=(), seed=None, name='sample')Generate samples of the specified shape.
tf.contrib.distributions.Chi2.sample_n(n, seed=None, name='sample_n')Generate n samples.
tf.contrib.distributions.Chi2.std(name='std')Standard deviation.
tf.contrib.distributions.Chi2.survival_function(value, name='survival_function')Survival function.
tf.contrib.distributions.Chi2.validate_argsPython boolean indicated possibly expensive checks are enabled.
tf.contrib.distributions.Chi2.variance(name='variance')Variance.
class tf.contrib.distributions.ExponentialThe Exponential distribution with rate parameter lam.
tf.contrib.distributions.Exponential.__init__(lam, validate_args=False, allow_nan_stats=True, name='Exponential')Construct Exponential distribution with parameter lam.
tf.contrib.distributions.Exponential.allow_nan_statsPython boolean describing behavior when a stat is undefined.
tf.contrib.distributions.Exponential.alphaShape parameter.
tf.contrib.distributions.Exponential.batch_shape(name='batch_shape')Shape of a single sample from a single event index as a 1-D Tensor.
tf.contrib.distributions.Exponential.betaInverse scale parameter.
tf.contrib.distributions.Exponential.cdf(value, name='cdf')Cumulative distribution function.
tf.contrib.distributions.Exponential.dtypeThe DType of Tensors handled by this Distribution.
tf.contrib.distributions.Exponential.entropy(name='entropy')Shanon entropy in nats.
tf.contrib.distributions.Exponential.event_shape(name='event_shape')Shape of a single sample from a single batch as a 1-D int32 Tensor.
tf.contrib.distributions.Exponential.get_batch_shape()Shape of a single sample from a single event index as a TensorShape.
tf.contrib.distributions.Exponential.get_event_shape()Shape of a single sample from a single batch as a TensorShape.
tf.contrib.distributions.Exponential.is_continuous
tf.contrib.distributions.Exponential.is_reparameterized
tf.contrib.distributions.Exponential.lam
tf.contrib.distributions.Exponential.log_cdf(value, name='log_cdf')Log cumulative distribution function.
tf.contrib.distributions.Exponential.log_pdf(value, name='log_pdf')Log probability density function.
tf.contrib.distributions.Exponential.log_pmf(value, name='log_pmf')Log probability mass function.
tf.contrib.distributions.Exponential.log_prob(value, name='log_prob')Log probability density/mass function (depending on is_continuous).
tf.contrib.distributions.Exponential.log_survival_function(value, name='log_survival_function')Log survival function.
tf.contrib.distributions.Exponential.mean(name='mean')Mean.
tf.contrib.distributions.Exponential.mode(name='mode')Mode.
tf.contrib.distributions.Exponential.nameName prepended to all ops created by this Distribution.
tf.contrib.distributions.Exponential.param_shapes(cls, sample_shape, name='DistributionParamShapes')Shapes of parameters given the desired shape of a call to sample().
tf.contrib.distributions.Exponential.param_static_shapes(cls, sample_shape)param_shapes with static (i.e. TensorShape) shapes.
tf.contrib.distributions.Exponential.parametersDictionary of parameters used by this Distribution.
tf.contrib.distributions.Exponential.pdf(value, name='pdf')Probability density function.
tf.contrib.distributions.Exponential.pmf(value, name='pmf')Probability mass function.
tf.contrib.distributions.Exponential.prob(value, name='prob')Probability density/mass function (depending on is_continuous).
tf.contrib.distributions.Exponential.sample(sample_shape=(), seed=None, name='sample')Generate samples of the specified shape.
tf.contrib.distributions.Exponential.sample_n(n, seed=None, name='sample_n')Generate n samples.
tf.contrib.distributions.Exponential.std(name='std')Standard deviation.
tf.contrib.distributions.Exponential.survival_function(value, name='survival_function')Survival function.
tf.contrib.distributions.Exponential.validate_argsPython boolean indicated possibly expensive checks are enabled.
tf.contrib.distributions.Exponential.variance(name='variance')Variance.
class tf.contrib.distributions.GammaThe Gamma distribution with parameter alpha and beta.
tf.contrib.distributions.Gamma.__init__(alpha, beta, validate_args=False, allow_nan_stats=True, name='Gamma')Construct Gamma distributions with parameters alpha and beta.
tf.contrib.distributions.Gamma.allow_nan_statsPython boolean describing behavior when a stat is undefined.
tf.contrib.distributions.Gamma.alphaShape parameter.
tf.contrib.distributions.Gamma.batch_shape(name='batch_shape')Shape of a single sample from a single event index as a 1-D Tensor.
tf.contrib.distributions.Gamma.betaInverse scale parameter.
tf.contrib.distributions.Gamma.cdf(value, name='cdf')Cumulative distribution function.
tf.contrib.distributions.Gamma.dtypeThe DType of Tensors handled by this Distribution.
tf.contrib.distributions.Gamma.entropy(name='entropy')Shanon entropy in nats.
tf.contrib.distributions.Gamma.event_shape(name='event_shape')Shape of a single sample from a single batch as a 1-D int32 Tensor.
tf.contrib.distributions.Gamma.get_batch_shape()Shape of a single sample from a single event index as a TensorShape.
tf.contrib.distributions.Gamma.get_event_shape()Shape of a single sample from a single batch as a TensorShape.
tf.contrib.distributions.Gamma.is_continuous
tf.contrib.distributions.Gamma.is_reparameterized
tf.contrib.distributions.Gamma.log_cdf(value, name='log_cdf')Log cumulative distribution function.
tf.contrib.distributions.Gamma.log_pdf(value, name='log_pdf')Log probability density function.
tf.contrib.distributions.Gamma.log_pmf(value, name='log_pmf')Log probability mass function.
tf.contrib.distributions.Gamma.log_prob(value, name='log_prob')Log probability density/mass function (depending on is_continuous).
tf.contrib.distributions.Gamma.log_survival_function(value, name='log_survival_function')Log survival function.
tf.contrib.distributions.Gamma.mean(name='mean')Mean.
tf.contrib.distributions.Gamma.mode(name='mode')Mode.
tf.contrib.distributions.Gamma.nameName prepended to all ops created by this Distribution.
tf.contrib.distributions.Gamma.param_shapes(cls, sample_shape, name='DistributionParamShapes')Shapes of parameters given the desired shape of a call to sample().
tf.contrib.distributions.Gamma.param_static_shapes(cls, sample_shape)param_shapes with static (i.e. TensorShape) shapes.
tf.contrib.distributions.Gamma.parametersDictionary of parameters used by this Distribution.
tf.contrib.distributions.Gamma.pdf(value, name='pdf')Probability density function.
tf.contrib.distributions.Gamma.pmf(value, name='pmf')Probability mass function.
tf.contrib.distributions.Gamma.prob(value, name='prob')Probability density/mass function (depending on is_continuous).
tf.contrib.distributions.Gamma.sample(sample_shape=(), seed=None, name='sample')Generate samples of the specified shape.
tf.contrib.distributions.Gamma.sample_n(n, seed=None, name='sample_n')Generate n samples.
tf.contrib.distributions.Gamma.std(name='std')Standard deviation.
tf.contrib.distributions.Gamma.survival_function(value, name='survival_function')Survival function.
tf.contrib.distributions.Gamma.validate_argsPython boolean indicated possibly expensive checks are enabled.
tf.contrib.distributions.Gamma.variance(name='variance')Variance.
class tf.contrib.distributions.InverseGammaThe InverseGamma distribution with parameter alpha and beta.
tf.contrib.distributions.InverseGamma.__init__(alpha, beta, validate_args=False, allow_nan_stats=True, name='InverseGamma')Construct InverseGamma distributions with parameters alpha and beta.
tf.contrib.distributions.InverseGamma.allow_nan_statsPython boolean describing behavior when a stat is undefined.
tf.contrib.distributions.InverseGamma.alphaShape parameter.
tf.contrib.distributions.InverseGamma.batch_shape(name='batch_shape')Shape of a single sample from a single event index as a 1-D Tensor.
tf.contrib.distributions.InverseGamma.betaScale parameter.
tf.contrib.distributions.InverseGamma.cdf(value, name='cdf')Cumulative distribution function.
tf.contrib.distributions.InverseGamma.dtypeThe DType of Tensors handled by this Distribution.
tf.contrib.distributions.InverseGamma.entropy(name='entropy')Shanon entropy in nats.
tf.contrib.distributions.InverseGamma.event_shape(name='event_shape')Shape of a single sample from a single batch as a 1-D int32 Tensor.
tf.contrib.distributions.InverseGamma.get_batch_shape()Shape of a single sample from a single event index as a TensorShape.
tf.contrib.distributions.InverseGamma.get_event_shape()Shape of a single sample from a single batch as a TensorShape.
tf.contrib.distributions.InverseGamma.is_continuous
tf.contrib.distributions.InverseGamma.is_reparameterized
tf.contrib.distributions.InverseGamma.log_cdf(value, name='log_cdf')Log cumulative distribution function.
tf.contrib.distributions.InverseGamma.log_pdf(value, name='log_pdf')Log probability density function.
tf.contrib.distributions.InverseGamma.log_pmf(value, name='log_pmf')Log probability mass function.
tf.contrib.distributions.InverseGamma.log_prob(value, name='log_prob')Log probability density/mass function (depending on is_continuous).
tf.contrib.distributions.InverseGamma.log_survival_function(value, name='log_survival_function')Log survival function.
tf.contrib.distributions.InverseGamma.mean(name='mean')Mean.
tf.contrib.distributions.InverseGamma.mode(name='mode')Mode.
tf.contrib.distributions.InverseGamma.nameName prepended to all ops created by this Distribution.
tf.contrib.distributions.InverseGamma.param_shapes(cls, sample_shape, name='DistributionParamShapes')Shapes of parameters given the desired shape of a call to sample().
tf.contrib.distributions.InverseGamma.param_static_shapes(cls, sample_shape)param_shapes with static (i.e. TensorShape) shapes.
tf.contrib.distributions.InverseGamma.parametersDictionary of parameters used by this Distribution.
tf.contrib.distributions.InverseGamma.pdf(value, name='pdf')Probability density function.
tf.contrib.distributions.InverseGamma.pmf(value, name='pmf')Probability mass function.
tf.contrib.distributions.InverseGamma.prob(value, name='prob')Probability density/mass function (depending on is_continuous).
tf.contrib.distributions.InverseGamma.sample(sample_shape=(), seed=None, name='sample')Generate samples of the specified shape.
tf.contrib.distributions.InverseGamma.sample_n(n, seed=None, name='sample_n')Generate n samples.
tf.contrib.distributions.InverseGamma.std(name='std')Standard deviation.
tf.contrib.distributions.InverseGamma.survival_function(value, name='survival_function')Survival function.
tf.contrib.distributions.InverseGamma.validate_argsPython boolean indicated possibly expensive checks are enabled.
tf.contrib.distributions.InverseGamma.variance(name='variance')Variance.
class tf.contrib.distributions.LaplaceThe Laplace distribution with location and scale > 0 parameters.
tf.contrib.distributions.Laplace.__init__(loc, scale, validate_args=False, allow_nan_stats=True, name='Laplace')Construct Laplace distribution with parameters loc and scale.
tf.contrib.distributions.Laplace.allow_nan_statsPython boolean describing behavior when a stat is undefined.
tf.contrib.distributions.Laplace.batch_shape(name='batch_shape')Shape of a single sample from a single event index as a 1-D Tensor.
tf.contrib.distributions.Laplace.cdf(value, name='cdf')Cumulative distribution function.
tf.contrib.distributions.Laplace.dtypeThe DType of Tensors handled by this Distribution.
tf.contrib.distributions.Laplace.entropy(name='entropy')Shanon entropy in nats.
tf.contrib.distributions.Laplace.event_shape(name='event_shape')Shape of a single sample from a single batch as a 1-D int32 Tensor.
tf.contrib.distributions.Laplace.get_batch_shape()Shape of a single sample from a single event index as a TensorShape.
tf.contrib.distributions.Laplace.get_event_shape()Shape of a single sample from a single batch as a TensorShape.
tf.contrib.distributions.Laplace.is_continuous
tf.contrib.distributions.Laplace.is_reparameterized
tf.contrib.distributions.Laplace.locDistribution parameter for the location.
tf.contrib.distributions.Laplace.log_cdf(value, name='log_cdf')Log cumulative distribution function.
tf.contrib.distributions.Laplace.log_pdf(value, name='log_pdf')Log probability density function.
tf.contrib.distributions.Laplace.log_pmf(value, name='log_pmf')Log probability mass function.
tf.contrib.distributions.Laplace.log_prob(value, name='log_prob')Log probability density/mass function (depending on is_continuous).
tf.contrib.distributions.Laplace.log_survival_function(value, name='log_survival_function')Log survival function.
tf.contrib.distributions.Laplace.mean(name='mean')Mean.
tf.contrib.distributions.Laplace.mode(name='mode')Mode.
tf.contrib.distributions.Laplace.nameName prepended to all ops created by this Distribution.
tf.contrib.distributions.Laplace.param_shapes(cls, sample_shape, name='DistributionParamShapes')Shapes of parameters given the desired shape of a call to sample().
tf.contrib.distributions.Laplace.param_static_shapes(cls, sample_shape)param_shapes with static (i.e. TensorShape) shapes.
tf.contrib.distributions.Laplace.parametersDictionary of parameters used by this Distribution.
tf.contrib.distributions.Laplace.pdf(value, name='pdf')Probability density function.
tf.contrib.distributions.Laplace.pmf(value, name='pmf')Probability mass function.
tf.contrib.distributions.Laplace.prob(value, name='prob')Probability density/mass function (depending on is_continuous).
tf.contrib.distributions.Laplace.sample(sample_shape=(), seed=None, name='sample')Generate samples of the specified shape.
tf.contrib.distributions.Laplace.sample_n(n, seed=None, name='sample_n')Generate n samples.
tf.contrib.distributions.Laplace.scaleDistribution parameter for scale.
tf.contrib.distributions.Laplace.std(name='std')Standard deviation.
tf.contrib.distributions.Laplace.survival_function(value, name='survival_function')Survival function.
tf.contrib.distributions.Laplace.validate_argsPython boolean indicated possibly expensive checks are enabled.
tf.contrib.distributions.Laplace.variance(name='variance')Variance.
class tf.contrib.distributions.NormalThe scalar Normal distribution with mean and stddev parameters mu, sigma.
tf.contrib.distributions.Normal.__init__(mu, sigma, validate_args=False, allow_nan_stats=True, name='Normal')Construct Normal distributions with mean and stddev mu and sigma.
tf.contrib.distributions.Normal.allow_nan_statsPython boolean describing behavior when a stat is undefined.
tf.contrib.distributions.Normal.batch_shape(name='batch_shape')Shape of a single sample from a single event index as a 1-D Tensor.
tf.contrib.distributions.Normal.cdf(value, name='cdf')Cumulative distribution function.
tf.contrib.distributions.Normal.dtypeThe DType of Tensors handled by this Distribution.
tf.contrib.distributions.Normal.entropy(name='entropy')Shanon entropy in nats.
tf.contrib.distributions.Normal.event_shape(name='event_shape')Shape of a single sample from a single batch as a 1-D int32 Tensor.
tf.contrib.distributions.Normal.get_batch_shape()Shape of a single sample from a single event index as a TensorShape.
tf.contrib.distributions.Normal.get_event_shape()Shape of a single sample from a single batch as a TensorShape.
tf.contrib.distributions.Normal.is_continuous
tf.contrib.distributions.Normal.is_reparameterized
tf.contrib.distributions.Normal.log_cdf(value, name='log_cdf')Log cumulative distribution function.
tf.contrib.distributions.Normal.log_pdf(value, name='log_pdf')Log probability density function.
tf.contrib.distributions.Normal.log_pmf(value, name='log_pmf')Log probability mass function.
tf.contrib.distributions.Normal.log_prob(value, name='log_prob')Log probability density/mass function (depending on is_continuous).
tf.contrib.distributions.Normal.log_survival_function(value, name='log_survival_function')Log survival function.
tf.contrib.distributions.Normal.mean(name='mean')Mean.
tf.contrib.distributions.Normal.mode(name='mode')Mode.
tf.contrib.distributions.Normal.muDistribution parameter for the mean.
tf.contrib.distributions.Normal.nameName prepended to all ops created by this Distribution.
tf.contrib.distributions.Normal.param_shapes(cls, sample_shape, name='DistributionParamShapes')Shapes of parameters given the desired shape of a call to sample().
tf.contrib.distributions.Normal.param_static_shapes(cls, sample_shape)param_shapes with static (i.e. TensorShape) shapes.
tf.contrib.distributions.Normal.parametersDictionary of parameters used by this Distribution.
tf.contrib.distributions.Normal.pdf(value, name='pdf')Probability density function.
tf.contrib.distributions.Normal.pmf(value, name='pmf')Probability mass function.
tf.contrib.distributions.Normal.prob(value, name='prob')Probability density/mass function (depending on is_continuous).
tf.contrib.distributions.Normal.sample(sample_shape=(), seed=None, name='sample')Generate samples of the specified shape.
tf.contrib.distributions.Normal.sample_n(n, seed=None, name='sample_n')Generate n samples.
tf.contrib.distributions.Normal.sigmaDistribution parameter for standard deviation.
tf.contrib.distributions.Normal.std(name='std')Standard deviation.
tf.contrib.distributions.Normal.survival_function(value, name='survival_function')Survival function.
tf.contrib.distributions.Normal.validate_argsPython boolean indicated possibly expensive checks are enabled.
tf.contrib.distributions.Normal.variance(name='variance')Variance.
class tf.contrib.distributions.PoissonPoisson distribution.
tf.contrib.distributions.Poisson.__init__(lam, validate_args=False, allow_nan_stats=True, name='Poisson')Construct Poisson distributions.
tf.contrib.distributions.Poisson.allow_nan_statsPython boolean describing behavior when a stat is undefined.
tf.contrib.distributions.Poisson.batch_shape(name='batch_shape')Shape of a single sample from a single event index as a 1-D Tensor.
tf.contrib.distributions.Poisson.cdf(value, name='cdf')Cumulative distribution function.
tf.contrib.distributions.Poisson.dtypeThe DType of Tensors handled by this Distribution.
tf.contrib.distributions.Poisson.entropy(name='entropy')Shanon entropy in nats.
tf.contrib.distributions.Poisson.event_shape(name='event_shape')Shape of a single sample from a single batch as a 1-D int32 Tensor.
tf.contrib.distributions.Poisson.get_batch_shape()Shape of a single sample from a single event index as a TensorShape.
tf.contrib.distributions.Poisson.get_event_shape()Shape of a single sample from a single batch as a TensorShape.
tf.contrib.distributions.Poisson.is_continuous
tf.contrib.distributions.Poisson.is_reparameterized
tf.contrib.distributions.Poisson.lamRate parameter.
tf.contrib.distributions.Poisson.log_cdf(value, name='log_cdf')Log cumulative distribution function.
tf.contrib.distributions.Poisson.log_pdf(value, name='log_pdf')Log probability density function.
tf.contrib.distributions.Poisson.log_pmf(value, name='log_pmf')Log probability mass function.
tf.contrib.distributions.Poisson.log_prob(value, name='log_prob')Log probability density/mass function (depending on is_continuous).
tf.contrib.distributions.Poisson.log_survival_function(value, name='log_survival_function')Log survival function.
tf.contrib.distributions.Poisson.mean(name='mean')Mean.
tf.contrib.distributions.Poisson.mode(name='mode')Mode.
tf.contrib.distributions.Poisson.nameName prepended to all ops created by this Distribution.
tf.contrib.distributions.Poisson.param_shapes(cls, sample_shape, name='DistributionParamShapes')Shapes of parameters given the desired shape of a call to sample().
tf.contrib.distributions.Poisson.param_static_shapes(cls, sample_shape)param_shapes with static (i.e. TensorShape) shapes.
tf.contrib.distributions.Poisson.parametersDictionary of parameters used by this Distribution.
tf.contrib.distributions.Poisson.pdf(value, name='pdf')Probability density function.
tf.contrib.distributions.Poisson.pmf(value, name='pmf')Probability mass function.
tf.contrib.distributions.Poisson.prob(value, name='prob')Probability density/mass function (depending on is_continuous).
tf.contrib.distributions.Poisson.sample(sample_shape=(), seed=None, name='sample')Generate samples of the specified shape.
tf.contrib.distributions.Poisson.sample_n(n, seed=None, name='sample_n')Generate n samples.
tf.contrib.distributions.Poisson.std(name='std')Standard deviation.
tf.contrib.distributions.Poisson.survival_function(value, name='survival_function')Survival function.
tf.contrib.distributions.Poisson.validate_argsPython boolean indicated possibly expensive checks are enabled.
tf.contrib.distributions.Poisson.variance(name='variance')Variance.
class tf.contrib.distributions.StudentTStudent's t distribution with degree-of-freedom parameter df.
tf.contrib.distributions.StudentT.__init__(df, mu, sigma, validate_args=False, allow_nan_stats=True, name='StudentT')Construct Student's t distributions.
tf.contrib.distributions.StudentT.allow_nan_statsPython boolean describing behavior when a stat is undefined.
tf.contrib.distributions.StudentT.batch_shape(name='batch_shape')Shape of a single sample from a single event index as a 1-D Tensor.
tf.contrib.distributions.StudentT.cdf(value, name='cdf')Cumulative distribution function.
tf.contrib.distributions.StudentT.dfDegrees of freedom in these Student's t distribution(s).
tf.contrib.distributions.StudentT.dtypeThe DType of Tensors handled by this Distribution.
tf.contrib.distributions.StudentT.entropy(name='entropy')Shanon entropy in nats.
tf.contrib.distributions.StudentT.event_shape(name='event_shape')Shape of a single sample from a single batch as a 1-D int32 Tensor.
tf.contrib.distributions.StudentT.get_batch_shape()Shape of a single sample from a single event index as a TensorShape.
tf.contrib.distributions.StudentT.get_event_shape()Shape of a single sample from a single batch as a TensorShape.
tf.contrib.distributions.StudentT.is_continuous
tf.contrib.distributions.StudentT.is_reparameterized
tf.contrib.distributions.StudentT.log_cdf(value, name='log_cdf')Log cumulative distribution function.
tf.contrib.distributions.StudentT.log_pdf(value, name='log_pdf')Log probability density function.
tf.contrib.distributions.StudentT.log_pmf(value, name='log_pmf')Log probability mass function.
tf.contrib.distributions.StudentT.log_prob(value, name='log_prob')Log probability density/mass function (depending on is_continuous).
tf.contrib.distributions.StudentT.log_survival_function(value, name='log_survival_function')Log survival function.
tf.contrib.distributions.StudentT.mean(name='mean')Mean.
tf.contrib.distributions.StudentT.mode(name='mode')Mode.
tf.contrib.distributions.StudentT.muLocations of these Student's t distribution(s).
tf.contrib.distributions.StudentT.nameName prepended to all ops created by this Distribution.
tf.contrib.distributions.StudentT.param_shapes(cls, sample_shape, name='DistributionParamShapes')Shapes of parameters given the desired shape of a call to sample().
tf.contrib.distributions.StudentT.param_static_shapes(cls, sample_shape)param_shapes with static (i.e. TensorShape) shapes.
tf.contrib.distributions.StudentT.parametersDictionary of parameters used by this Distribution.
tf.contrib.distributions.StudentT.pdf(value, name='pdf')Probability density function.
tf.contrib.distributions.StudentT.pmf(value, name='pmf')Probability mass function.
tf.contrib.distributions.StudentT.prob(value, name='prob')Probability density/mass function (depending on is_continuous).
tf.contrib.distributions.StudentT.sample(sample_shape=(), seed=None, name='sample')Generate samples of the specified shape.
tf.contrib.distributions.StudentT.sample_n(n, seed=None, name='sample_n')Generate n samples.
tf.contrib.distributions.StudentT.sigmaScaling factors of these Student's t distribution(s).
tf.contrib.distributions.StudentT.std(name='std')Standard deviation.
tf.contrib.distributions.StudentT.survival_function(value, name='survival_function')Survival function.
tf.contrib.distributions.StudentT.validate_argsPython boolean indicated possibly expensive checks are enabled.
tf.contrib.distributions.StudentT.variance(name='variance')Variance.
class tf.contrib.distributions.UniformUniform distribution with a and b parameters.
tf.contrib.distributions.Uniform.__init__(a=0.0, b=1.0, validate_args=False, allow_nan_stats=True, name='Uniform')Construct Uniform distributions with a and b.
tf.contrib.distributions.Uniform.a
tf.contrib.distributions.Uniform.allow_nan_statsPython boolean describing behavior when a stat is undefined.
tf.contrib.distributions.Uniform.b
tf.contrib.distributions.Uniform.batch_shape(name='batch_shape')Shape of a single sample from a single event index as a 1-D Tensor.
tf.contrib.distributions.Uniform.cdf(value, name='cdf')Cumulative distribution function.
tf.contrib.distributions.Uniform.dtypeThe DType of Tensors handled by this Distribution.
tf.contrib.distributions.Uniform.entropy(name='entropy')Shanon entropy in nats.
tf.contrib.distributions.Uniform.event_shape(name='event_shape')Shape of a single sample from a single batch as a 1-D int32 Tensor.
tf.contrib.distributions.Uniform.get_batch_shape()Shape of a single sample from a single event index as a TensorShape.
tf.contrib.distributions.Uniform.get_event_shape()Shape of a single sample from a single batch as a TensorShape.
tf.contrib.distributions.Uniform.is_continuous
tf.contrib.distributions.Uniform.is_reparameterized
tf.contrib.distributions.Uniform.log_cdf(value, name='log_cdf')Log cumulative distribution function.
tf.contrib.distributions.Uniform.log_pdf(value, name='log_pdf')Log probability density function.
tf.contrib.distributions.Uniform.log_pmf(value, name='log_pmf')Log probability mass function.
tf.contrib.distributions.Uniform.log_prob(value, name='log_prob')Log probability density/mass function (depending on is_continuous).
tf.contrib.distributions.Uniform.log_survival_function(value, name='log_survival_function')Log survival function.
tf.contrib.distributions.Uniform.mean(name='mean')Mean.
tf.contrib.distributions.Uniform.mode(name='mode')Mode.
tf.contrib.distributions.Uniform.nameName prepended to all ops created by this Distribution.
tf.contrib.distributions.Uniform.param_shapes(cls, sample_shape, name='DistributionParamShapes')Shapes of parameters given the desired shape of a call to sample().
tf.contrib.distributions.Uniform.param_static_shapes(cls, sample_shape)param_shapes with static (i.e. TensorShape) shapes.
tf.contrib.distributions.Uniform.parametersDictionary of parameters used by this Distribution.
tf.contrib.distributions.Uniform.pdf(value, name='pdf')Probability density function.
tf.contrib.distributions.Uniform.pmf(value, name='pmf')Probability mass function.
tf.contrib.distributions.Uniform.prob(value, name='prob')Probability density/mass function (depending on is_continuous).
tf.contrib.distributions.Uniform.range(name='range')b - a.
tf.contrib.distributions.Uniform.sample(sample_shape=(), seed=None, name='sample')Generate samples of the specified shape.
tf.contrib.distributions.Uniform.sample_n(n, seed=None, name='sample_n')Generate n samples.
tf.contrib.distributions.Uniform.std(name='std')Standard deviation.
tf.contrib.distributions.Uniform.survival_function(value, name='survival_function')Survival function.
tf.contrib.distributions.Uniform.validate_argsPython boolean indicated possibly expensive checks are enabled.
tf.contrib.distributions.Uniform.variance(name='variance')Variance.
class tf.contrib.distributions.MultivariateNormalDiagThe multivariate normal distribution on R^k.
tf.contrib.distributions.MultivariateNormalDiag.__init__(mu, diag_stdev, validate_args=False, allow_nan_stats=True, name='MultivariateNormalDiag')Multivariate Normal distributions on R^k.
tf.contrib.distributions.MultivariateNormalDiag.allow_nan_statsPython boolean describing behavior when a stat is undefined.
tf.contrib.distributions.MultivariateNormalDiag.batch_shape(name='batch_shape')Shape of a single sample from a single event index as a 1-D Tensor.
tf.contrib.distributions.MultivariateNormalDiag.cdf(value, name='cdf')Cumulative distribution function.
tf.contrib.distributions.MultivariateNormalDiag.dtypeThe DType of Tensors handled by this Distribution.
tf.contrib.distributions.MultivariateNormalDiag.entropy(name='entropy')Shanon entropy in nats.
tf.contrib.distributions.MultivariateNormalDiag.event_shape(name='event_shape')Shape of a single sample from a single batch as a 1-D int32 Tensor.
tf.contrib.distributions.MultivariateNormalDiag.get_batch_shape()Shape of a single sample from a single event index as a TensorShape.
tf.contrib.distributions.MultivariateNormalDiag.get_event_shape()Shape of a single sample from a single batch as a TensorShape.
tf.contrib.distributions.MultivariateNormalDiag.is_continuous
tf.contrib.distributions.MultivariateNormalDiag.is_reparameterized
tf.contrib.distributions.MultivariateNormalDiag.log_cdf(value, name='log_cdf')Log cumulative distribution function.
tf.contrib.distributions.MultivariateNormalDiag.log_pdf(value, name='log_pdf')Log probability density function.
tf.contrib.distributions.MultivariateNormalDiag.log_pmf(value, name='log_pmf')Log probability mass function.
tf.contrib.distributions.MultivariateNormalDiag.log_prob(value, name='log_prob')Log probability density/mass function (depending on is_continuous).
tf.contrib.distributions.MultivariateNormalDiag.log_sigma_det(name='log_sigma_det')Log of determinant of covariance matrix.
tf.contrib.distributions.MultivariateNormalDiag.log_survival_function(value, name='log_survival_function')Log survival function.
tf.contrib.distributions.MultivariateNormalDiag.mean(name='mean')Mean.
tf.contrib.distributions.MultivariateNormalDiag.mode(name='mode')Mode.
tf.contrib.distributions.MultivariateNormalDiag.mu
tf.contrib.distributions.MultivariateNormalDiag.nameName prepended to all ops created by this Distribution.
tf.contrib.distributions.MultivariateNormalDiag.param_shapes(cls, sample_shape, name='DistributionParamShapes')Shapes of parameters given the desired shape of a call to sample().
tf.contrib.distributions.MultivariateNormalDiag.param_static_shapes(cls, sample_shape)param_shapes with static (i.e. TensorShape) shapes.
tf.contrib.distributions.MultivariateNormalDiag.parametersDictionary of parameters used by this Distribution.
tf.contrib.distributions.MultivariateNormalDiag.pdf(value, name='pdf')Probability density function.
tf.contrib.distributions.MultivariateNormalDiag.pmf(value, name='pmf')Probability mass function.
tf.contrib.distributions.MultivariateNormalDiag.prob(value, name='prob')Probability density/mass function (depending on is_continuous).
tf.contrib.distributions.MultivariateNormalDiag.sample(sample_shape=(), seed=None, name='sample')Generate samples of the specified shape.
tf.contrib.distributions.MultivariateNormalDiag.sample_n(n, seed=None, name='sample_n')Generate n samples.
tf.contrib.distributions.MultivariateNormalDiag.sigmaDense (batch) covariance matrix, if available.
tf.contrib.distributions.MultivariateNormalDiag.sigma_det(name='sigma_det')Determinant of covariance matrix.
tf.contrib.distributions.MultivariateNormalDiag.std(name='std')Standard deviation.
tf.contrib.distributions.MultivariateNormalDiag.survival_function(value, name='survival_function')Survival function.
tf.contrib.distributions.MultivariateNormalDiag.validate_argsPython boolean indicated possibly expensive checks are enabled.
tf.contrib.distributions.MultivariateNormalDiag.variance(name='variance')Variance.
class tf.contrib.distributions.MultivariateNormalFullThe multivariate normal distribution on R^k.
tf.contrib.distributions.MultivariateNormalFull.__init__(mu, sigma, validate_args=False, allow_nan_stats=True, name='MultivariateNormalFull')Multivariate Normal distributions on R^k.
tf.contrib.distributions.MultivariateNormalFull.allow_nan_statsPython boolean describing behavior when a stat is undefined.
tf.contrib.distributions.MultivariateNormalFull.batch_shape(name='batch_shape')Shape of a single sample from a single event index as a 1-D Tensor.
tf.contrib.distributions.MultivariateNormalFull.cdf(value, name='cdf')Cumulative distribution function.
tf.contrib.distributions.MultivariateNormalFull.dtypeThe DType of Tensors handled by this Distribution.
tf.contrib.distributions.MultivariateNormalFull.entropy(name='entropy')Shanon entropy in nats.
tf.contrib.distributions.MultivariateNormalFull.event_shape(name='event_shape')Shape of a single sample from a single batch as a 1-D int32 Tensor.
tf.contrib.distributions.MultivariateNormalFull.get_batch_shape()Shape of a single sample from a single event index as a TensorShape.
tf.contrib.distributions.MultivariateNormalFull.get_event_shape()Shape of a single sample from a single batch as a TensorShape.
tf.contrib.distributions.MultivariateNormalFull.is_continuous
tf.contrib.distributions.MultivariateNormalFull.is_reparameterized
tf.contrib.distributions.MultivariateNormalFull.log_cdf(value, name='log_cdf')Log cumulative distribution function.
tf.contrib.distributions.MultivariateNormalFull.log_pdf(value, name='log_pdf')Log probability density function.
tf.contrib.distributions.MultivariateNormalFull.log_pmf(value, name='log_pmf')Log probability mass function.
tf.contrib.distributions.MultivariateNormalFull.log_prob(value, name='log_prob')Log probability density/mass function (depending on is_continuous).
tf.contrib.distributions.MultivariateNormalFull.log_sigma_det(name='log_sigma_det')Log of determinant of covariance matrix.
tf.contrib.distributions.MultivariateNormalFull.log_survival_function(value, name='log_survival_function')Log survival function.
tf.contrib.distributions.MultivariateNormalFull.mean(name='mean')Mean.
tf.contrib.distributions.MultivariateNormalFull.mode(name='mode')Mode.
tf.contrib.distributions.MultivariateNormalFull.mu
tf.contrib.distributions.MultivariateNormalFull.nameName prepended to all ops created by this Distribution.
tf.contrib.distributions.MultivariateNormalFull.param_shapes(cls, sample_shape, name='DistributionParamShapes')Shapes of parameters given the desired shape of a call to sample().
tf.contrib.distributions.MultivariateNormalFull.param_static_shapes(cls, sample_shape)param_shapes with static (i.e. TensorShape) shapes.
tf.contrib.distributions.MultivariateNormalFull.parametersDictionary of parameters used by this Distribution.
tf.contrib.distributions.MultivariateNormalFull.pdf(value, name='pdf')Probability density function.
tf.contrib.distributions.MultivariateNormalFull.pmf(value, name='pmf')Probability mass function.
tf.contrib.distributions.MultivariateNormalFull.prob(value, name='prob')Probability density/mass function (depending on is_continuous).
tf.contrib.distributions.MultivariateNormalFull.sample(sample_shape=(), seed=None, name='sample')Generate samples of the specified shape.
tf.contrib.distributions.MultivariateNormalFull.sample_n(n, seed=None, name='sample_n')Generate n samples.
tf.contrib.distributions.MultivariateNormalFull.sigmaDense (batch) covariance matrix, if available.
tf.contrib.distributions.MultivariateNormalFull.sigma_det(name='sigma_det')Determinant of covariance matrix.
tf.contrib.distributions.MultivariateNormalFull.std(name='std')Standard deviation.
tf.contrib.distributions.MultivariateNormalFull.survival_function(value, name='survival_function')Survival function.
tf.contrib.distributions.MultivariateNormalFull.validate_argsPython boolean indicated possibly expensive checks are enabled.
tf.contrib.distributions.MultivariateNormalFull.variance(name='variance')Variance.
class tf.contrib.distributions.MultivariateNormalCholeskyThe multivariate normal distribution on R^k.
tf.contrib.distributions.MultivariateNormalCholesky.__init__(mu, chol, validate_args=False, allow_nan_stats=True, name='MultivariateNormalCholesky')Multivariate Normal distributions on R^k.
tf.contrib.distributions.MultivariateNormalCholesky.allow_nan_statsPython boolean describing behavior when a stat is undefined.
tf.contrib.distributions.MultivariateNormalCholesky.batch_shape(name='batch_shape')Shape of a single sample from a single event index as a 1-D Tensor.
tf.contrib.distributions.MultivariateNormalCholesky.cdf(value, name='cdf')Cumulative distribution function.
tf.contrib.distributions.MultivariateNormalCholesky.dtypeThe DType of Tensors handled by this Distribution.
tf.contrib.distributions.MultivariateNormalCholesky.entropy(name='entropy')Shanon entropy in nats.
tf.contrib.distributions.MultivariateNormalCholesky.event_shape(name='event_shape')Shape of a single sample from a single batch as a 1-D int32 Tensor.
tf.contrib.distributions.MultivariateNormalCholesky.get_batch_shape()Shape of a single sample from a single event index as a TensorShape.
tf.contrib.distributions.MultivariateNormalCholesky.get_event_shape()Shape of a single sample from a single batch as a TensorShape.
tf.contrib.distributions.MultivariateNormalCholesky.is_continuous
tf.contrib.distributions.MultivariateNormalCholesky.is_reparameterized
tf.contrib.distributions.MultivariateNormalCholesky.log_cdf(value, name='log_cdf')Log cumulative distribution function.
tf.contrib.distributions.MultivariateNormalCholesky.log_pdf(value, name='log_pdf')Log probability density function.
tf.contrib.distributions.MultivariateNormalCholesky.log_pmf(value, name='log_pmf')Log probability mass function.
tf.contrib.distributions.MultivariateNormalCholesky.log_prob(value, name='log_prob')Log probability density/mass function (depending on is_continuous).
tf.contrib.distributions.MultivariateNormalCholesky.log_sigma_det(name='log_sigma_det')Log of determinant of covariance matrix.
tf.contrib.distributions.MultivariateNormalCholesky.log_survival_function(value, name='log_survival_function')Log survival function.
tf.contrib.distributions.MultivariateNormalCholesky.mean(name='mean')Mean.
tf.contrib.distributions.MultivariateNormalCholesky.mode(name='mode')Mode.
tf.contrib.distributions.MultivariateNormalCholesky.mu
tf.contrib.distributions.MultivariateNormalCholesky.nameName prepended to all ops created by this Distribution.
tf.contrib.distributions.MultivariateNormalCholesky.param_shapes(cls, sample_shape, name='DistributionParamShapes')Shapes of parameters given the desired shape of a call to sample().
tf.contrib.distributions.MultivariateNormalCholesky.param_static_shapes(cls, sample_shape)param_shapes with static (i.e. TensorShape) shapes.
tf.contrib.distributions.MultivariateNormalCholesky.parametersDictionary of parameters used by this Distribution.
tf.contrib.distributions.MultivariateNormalCholesky.pdf(value, name='pdf')Probability density function.
tf.contrib.distributions.MultivariateNormalCholesky.pmf(value, name='pmf')Probability mass function.
tf.contrib.distributions.MultivariateNormalCholesky.prob(value, name='prob')Probability density/mass function (depending on is_continuous).
tf.contrib.distributions.MultivariateNormalCholesky.sample(sample_shape=(), seed=None, name='sample')Generate samples of the specified shape.
tf.contrib.distributions.MultivariateNormalCholesky.sample_n(n, seed=None, name='sample_n')Generate n samples.
tf.contrib.distributions.MultivariateNormalCholesky.sigmaDense (batch) covariance matrix, if available.
tf.contrib.distributions.MultivariateNormalCholesky.sigma_det(name='sigma_det')Determinant of covariance matrix.
tf.contrib.distributions.MultivariateNormalCholesky.std(name='std')Standard deviation.
tf.contrib.distributions.MultivariateNormalCholesky.survival_function(value, name='survival_function')Survival function.
tf.contrib.distributions.MultivariateNormalCholesky.validate_argsPython boolean indicated possibly expensive checks are enabled.
tf.contrib.distributions.MultivariateNormalCholesky.variance(name='variance')Variance.
tf.contrib.distributions.matrix_diag_transform(matrix, transform=None, name=None)Transform diagonal of [batch-]matrix, leave rest of matrix unchanged.
class tf.contrib.distributions.DirichletDirichlet distribution.
tf.contrib.distributions.Dirichlet.__init__(alpha, validate_args=False, allow_nan_stats=True, name='Dirichlet')Initialize a batch of Dirichlet distributions.
tf.contrib.distributions.Dirichlet.allow_nan_statsPython boolean describing behavior when a stat is undefined.
tf.contrib.distributions.Dirichlet.alphaShape parameter.
tf.contrib.distributions.Dirichlet.alpha_sumSum of shape parameter.
tf.contrib.distributions.Dirichlet.batch_shape(name='batch_shape')Shape of a single sample from a single event index as a 1-D Tensor.
tf.contrib.distributions.Dirichlet.cdf(value, name='cdf')Cumulative distribution function.
tf.contrib.distributions.Dirichlet.dtypeThe DType of Tensors handled by this Distribution.
tf.contrib.distributions.Dirichlet.entropy(name='entropy')Shanon entropy in nats.
tf.contrib.distributions.Dirichlet.event_shape(name='event_shape')Shape of a single sample from a single batch as a 1-D int32 Tensor.
tf.contrib.distributions.Dirichlet.get_batch_shape()Shape of a single sample from a single event index as a TensorShape.
tf.contrib.distributions.Dirichlet.get_event_shape()Shape of a single sample from a single batch as a TensorShape.
tf.contrib.distributions.Dirichlet.is_continuous
tf.contrib.distributions.Dirichlet.is_reparameterized
tf.contrib.distributions.Dirichlet.log_cdf(value, name='log_cdf')Log cumulative distribution function.
tf.contrib.distributions.Dirichlet.log_pdf(value, name='log_pdf')Log probability density function.
tf.contrib.distributions.Dirichlet.log_pmf(value, name='log_pmf')Log probability mass function.
tf.contrib.distributions.Dirichlet.log_prob(value, name='log_prob')Log probability density/mass function (depending on is_continuous).
tf.contrib.distributions.Dirichlet.log_survival_function(value, name='log_survival_function')Log survival function.
tf.contrib.distributions.Dirichlet.mean(name='mean')Mean.
tf.contrib.distributions.Dirichlet.mode(name='mode')Mode.
tf.contrib.distributions.Dirichlet.nameName prepended to all ops created by this Distribution.
tf.contrib.distributions.Dirichlet.param_shapes(cls, sample_shape, name='DistributionParamShapes')Shapes of parameters given the desired shape of a call to sample().
tf.contrib.distributions.Dirichlet.param_static_shapes(cls, sample_shape)param_shapes with static (i.e. TensorShape) shapes.
tf.contrib.distributions.Dirichlet.parametersDictionary of parameters used by this Distribution.
tf.contrib.distributions.Dirichlet.pdf(value, name='pdf')Probability density function.
tf.contrib.distributions.Dirichlet.pmf(value, name='pmf')Probability mass function.
tf.contrib.distributions.Dirichlet.prob(value, name='prob')Probability density/mass function (depending on is_continuous).
tf.contrib.distributions.Dirichlet.sample(sample_shape=(), seed=None, name='sample')Generate samples of the specified shape.
tf.contrib.distributions.Dirichlet.sample_n(n, seed=None, name='sample_n')Generate n samples.
tf.contrib.distributions.Dirichlet.std(name='std')Standard deviation.
tf.contrib.distributions.Dirichlet.survival_function(value, name='survival_function')Survival function.
tf.contrib.distributions.Dirichlet.validate_argsPython boolean indicated possibly expensive checks are enabled.
tf.contrib.distributions.Dirichlet.variance(name='variance')Variance.
class tf.contrib.distributions.DirichletMultinomialDirichletMultinomial mixture distribution.
tf.contrib.distributions.DirichletMultinomial.__init__(n, alpha, validate_args=False, allow_nan_stats=True, name='DirichletMultinomial')Initialize a batch of DirichletMultinomial distributions.
tf.contrib.distributions.DirichletMultinomial.allow_nan_statsPython boolean describing behavior when a stat is undefined.
tf.contrib.distributions.DirichletMultinomial.alphaParameter defining this distribution.
tf.contrib.distributions.DirichletMultinomial.alpha_sumSummation of alpha parameter.
tf.contrib.distributions.DirichletMultinomial.batch_shape(name='batch_shape')Shape of a single sample from a single event index as a 1-D Tensor.
tf.contrib.distributions.DirichletMultinomial.cdf(value, name='cdf')Cumulative distribution function.
tf.contrib.distributions.DirichletMultinomial.dtypeThe DType of Tensors handled by this Distribution.
tf.contrib.distributions.DirichletMultinomial.entropy(name='entropy')Shanon entropy in nats.
tf.contrib.distributions.DirichletMultinomial.event_shape(name='event_shape')Shape of a single sample from a single batch as a 1-D int32 Tensor.
tf.contrib.distributions.DirichletMultinomial.get_batch_shape()Shape of a single sample from a single event index as a TensorShape.
tf.contrib.distributions.DirichletMultinomial.get_event_shape()Shape of a single sample from a single batch as a TensorShape.
tf.contrib.distributions.DirichletMultinomial.is_continuous
tf.contrib.distributions.DirichletMultinomial.is_reparameterized
tf.contrib.distributions.DirichletMultinomial.log_cdf(value, name='log_cdf')Log cumulative distribution function.
tf.contrib.distributions.DirichletMultinomial.log_pdf(value, name='log_pdf')Log probability density function.
tf.contrib.distributions.DirichletMultinomial.log_pmf(value, name='log_pmf')Log probability mass function.
tf.contrib.distributions.DirichletMultinomial.log_prob(value, name='log_prob')Log probability density/mass function (depending on is_continuous).
tf.contrib.distributions.DirichletMultinomial.log_survival_function(value, name='log_survival_function')Log survival function.
tf.contrib.distributions.DirichletMultinomial.mean(name='mean')Mean.
tf.contrib.distributions.DirichletMultinomial.mode(name='mode')Mode.
tf.contrib.distributions.DirichletMultinomial.nParameter defining this distribution.
tf.contrib.distributions.DirichletMultinomial.nameName prepended to all ops created by this Distribution.
tf.contrib.distributions.DirichletMultinomial.param_shapes(cls, sample_shape, name='DistributionParamShapes')Shapes of parameters given the desired shape of a call to sample().
tf.contrib.distributions.DirichletMultinomial.param_static_shapes(cls, sample_shape)param_shapes with static (i.e. TensorShape) shapes.
tf.contrib.distributions.DirichletMultinomial.parametersDictionary of parameters used by this Distribution.
tf.contrib.distributions.DirichletMultinomial.pdf(value, name='pdf')Probability density function.
tf.contrib.distributions.DirichletMultinomial.pmf(value, name='pmf')Probability mass function.
tf.contrib.distributions.DirichletMultinomial.prob(value, name='prob')Probability density/mass function (depending on is_continuous).
tf.contrib.distributions.DirichletMultinomial.sample(sample_shape=(), seed=None, name='sample')Generate samples of the specified shape.
tf.contrib.distributions.DirichletMultinomial.sample_n(n, seed=None, name='sample_n')Generate n samples.
tf.contrib.distributions.DirichletMultinomial.std(name='std')Standard deviation.
tf.contrib.distributions.DirichletMultinomial.survival_function(value, name='survival_function')Survival function.
tf.contrib.distributions.DirichletMultinomial.validate_argsPython boolean indicated possibly expensive checks are enabled.
tf.contrib.distributions.DirichletMultinomial.variance(name='variance')Variance.
class tf.contrib.distributions.MultinomialMultinomial distribution.
tf.contrib.distributions.Multinomial.__init__(n, logits=None, p=None, validate_args=False, allow_nan_stats=True, name='Multinomial')Initialize a batch of Multinomial distributions.
tf.contrib.distributions.Multinomial.allow_nan_statsPython boolean describing behavior when a stat is undefined.
tf.contrib.distributions.Multinomial.batch_shape(name='batch_shape')Shape of a single sample from a single event index as a 1-D Tensor.
tf.contrib.distributions.Multinomial.cdf(value, name='cdf')Cumulative distribution function.
tf.contrib.distributions.Multinomial.dtypeThe DType of Tensors handled by this Distribution.
tf.contrib.distributions.Multinomial.entropy(name='entropy')Shanon entropy in nats.
tf.contrib.distributions.Multinomial.event_shape(name='event_shape')Shape of a single sample from a single batch as a 1-D int32 Tensor.
tf.contrib.distributions.Multinomial.get_batch_shape()Shape of a single sample from a single event index as a TensorShape.
tf.contrib.distributions.Multinomial.get_event_shape()Shape of a single sample from a single batch as a TensorShape.
tf.contrib.distributions.Multinomial.is_continuous
tf.contrib.distributions.Multinomial.is_reparameterized
tf.contrib.distributions.Multinomial.log_cdf(value, name='log_cdf')Log cumulative distribution function.
tf.contrib.distributions.Multinomial.log_pdf(value, name='log_pdf')Log probability density function.
tf.contrib.distributions.Multinomial.log_pmf(value, name='log_pmf')Log probability mass function.
tf.contrib.distributions.Multinomial.log_prob(value, name='log_prob')Log probability density/mass function (depending on is_continuous).
tf.contrib.distributions.Multinomial.log_survival_function(value, name='log_survival_function')Log survival function.
tf.contrib.distributions.Multinomial.logitsLog-odds.
tf.contrib.distributions.Multinomial.mean(name='mean')Mean.
tf.contrib.distributions.Multinomial.mode(name='mode')Mode.
tf.contrib.distributions.Multinomial.nNumber of trials.
tf.contrib.distributions.Multinomial.nameName prepended to all ops created by this Distribution.
tf.contrib.distributions.Multinomial.pEvent probabilities.
tf.contrib.distributions.Multinomial.param_shapes(cls, sample_shape, name='DistributionParamShapes')Shapes of parameters given the desired shape of a call to sample().
tf.contrib.distributions.Multinomial.param_static_shapes(cls, sample_shape)param_shapes with static (i.e. TensorShape) shapes.
tf.contrib.distributions.Multinomial.parametersDictionary of parameters used by this Distribution.
tf.contrib.distributions.Multinomial.pdf(value, name='pdf')Probability density function.
tf.contrib.distributions.Multinomial.pmf(value, name='pmf')Probability mass function.
tf.contrib.distributions.Multinomial.prob(value, name='prob')Probability density/mass function (depending on is_continuous).
tf.contrib.distributions.Multinomial.sample(sample_shape=(), seed=None, name='sample')Generate samples of the specified shape.
tf.contrib.distributions.Multinomial.sample_n(n, seed=None, name='sample_n')Generate n samples.
tf.contrib.distributions.Multinomial.std(name='std')Standard deviation.
tf.contrib.distributions.Multinomial.survival_function(value, name='survival_function')Survival function.
tf.contrib.distributions.Multinomial.validate_argsPython boolean indicated possibly expensive checks are enabled.
tf.contrib.distributions.Multinomial.variance(name='variance')Variance.
class tf.contrib.distributions.WishartCholeskyThe matrix Wishart distribution on positive definite matrices.
tf.contrib.distributions.WishartCholesky.__init__(df, scale, cholesky_input_output_matrices=False, validate_args=False, allow_nan_stats=True, name='WishartCholesky')Construct Wishart distributions.
tf.contrib.distributions.WishartCholesky.allow_nan_statsPython boolean describing behavior when a stat is undefined.
tf.contrib.distributions.WishartCholesky.batch_shape(name='batch_shape')Shape of a single sample from a single event index as a 1-D Tensor.
tf.contrib.distributions.WishartCholesky.cdf(value, name='cdf')Cumulative distribution function.
tf.contrib.distributions.WishartCholesky.cholesky_input_output_matricesBoolean indicating if Tensor input/outputs are Cholesky factorized.
tf.contrib.distributions.WishartCholesky.dfWishart distribution degree(s) of freedom.
tf.contrib.distributions.WishartCholesky.dimensionDimension of underlying vector space. The p in R^(p*p).
tf.contrib.distributions.WishartCholesky.dtypeThe DType of Tensors handled by this Distribution.
tf.contrib.distributions.WishartCholesky.entropy(name='entropy')Shanon entropy in nats.
tf.contrib.distributions.WishartCholesky.event_shape(name='event_shape')Shape of a single sample from a single batch as a 1-D int32 Tensor.
tf.contrib.distributions.WishartCholesky.get_batch_shape()Shape of a single sample from a single event index as a TensorShape.
tf.contrib.distributions.WishartCholesky.get_event_shape()Shape of a single sample from a single batch as a TensorShape.
tf.contrib.distributions.WishartCholesky.is_continuous
tf.contrib.distributions.WishartCholesky.is_reparameterized
tf.contrib.distributions.WishartCholesky.log_cdf(value, name='log_cdf')Log cumulative distribution function.
tf.contrib.distributions.WishartCholesky.log_normalizing_constant(name='log_normalizing_constant')Computes the log normalizing constant, log(Z).
tf.contrib.distributions.WishartCholesky.log_pdf(value, name='log_pdf')Log probability density function.
tf.contrib.distributions.WishartCholesky.log_pmf(value, name='log_pmf')Log probability mass function.
tf.contrib.distributions.WishartCholesky.log_prob(value, name='log_prob')Log probability density/mass function (depending on is_continuous).
tf.contrib.distributions.WishartCholesky.log_survival_function(value, name='log_survival_function')Log survival function.
tf.contrib.distributions.WishartCholesky.mean(name='mean')Mean.
tf.contrib.distributions.WishartCholesky.mean_log_det(name='mean_log_det')Computes E[log(det(X))] under this Wishart distribution.
tf.contrib.distributions.WishartCholesky.mode(name='mode')Mode.
tf.contrib.distributions.WishartCholesky.nameName prepended to all ops created by this Distribution.
tf.contrib.distributions.WishartCholesky.param_shapes(cls, sample_shape, name='DistributionParamShapes')Shapes of parameters given the desired shape of a call to sample().
tf.contrib.distributions.WishartCholesky.param_static_shapes(cls, sample_shape)param_shapes with static (i.e. TensorShape) shapes.
tf.contrib.distributions.WishartCholesky.parametersDictionary of parameters used by this Distribution.
tf.contrib.distributions.WishartCholesky.pdf(value, name='pdf')Probability density function.
tf.contrib.distributions.WishartCholesky.pmf(value, name='pmf')Probability mass function.
tf.contrib.distributions.WishartCholesky.prob(value, name='prob')Probability density/mass function (depending on is_continuous).
tf.contrib.distributions.WishartCholesky.sample(sample_shape=(), seed=None, name='sample')Generate samples of the specified shape.
tf.contrib.distributions.WishartCholesky.sample_n(n, seed=None, name='sample_n')Generate n samples.
tf.contrib.distributions.WishartCholesky.scale()Wishart distribution scale matrix.
tf.contrib.distributions.WishartCholesky.scale_operator_pdWishart distribution scale matrix as an OperatorPD.
tf.contrib.distributions.WishartCholesky.std(name='std')Standard deviation.
tf.contrib.distributions.WishartCholesky.survival_function(value, name='survival_function')Survival function.
tf.contrib.distributions.WishartCholesky.validate_argsPython boolean indicated possibly expensive checks are enabled.
tf.contrib.distributions.WishartCholesky.variance(name='variance')Variance.
class tf.contrib.distributions.WishartFullThe matrix Wishart distribution on positive definite matrices.
tf.contrib.distributions.WishartFull.__init__(df, scale, cholesky_input_output_matrices=False, validate_args=False, allow_nan_stats=True, name='WishartFull')Construct Wishart distributions.
tf.contrib.distributions.WishartFull.allow_nan_statsPython boolean describing behavior when a stat is undefined.
tf.contrib.distributions.WishartFull.batch_shape(name='batch_shape')Shape of a single sample from a single event index as a 1-D Tensor.
tf.contrib.distributions.WishartFull.cdf(value, name='cdf')Cumulative distribution function.
tf.contrib.distributions.WishartFull.cholesky_input_output_matricesBoolean indicating if Tensor input/outputs are Cholesky factorized.
tf.contrib.distributions.WishartFull.dfWishart distribution degree(s) of freedom.
tf.contrib.distributions.WishartFull.dimensionDimension of underlying vector space. The p in R^(p*p).
tf.contrib.distributions.WishartFull.dtypeThe DType of Tensors handled by this Distribution.
tf.contrib.distributions.WishartFull.entropy(name='entropy')Shanon entropy in nats.
tf.contrib.distributions.WishartFull.event_shape(name='event_shape')Shape of a single sample from a single batch as a 1-D int32 Tensor.
tf.contrib.distributions.WishartFull.get_batch_shape()Shape of a single sample from a single event index as a TensorShape.
tf.contrib.distributions.WishartFull.get_event_shape()Shape of a single sample from a single batch as a TensorShape.
tf.contrib.distributions.WishartFull.is_continuous
tf.contrib.distributions.WishartFull.is_reparameterized
tf.contrib.distributions.WishartFull.log_cdf(value, name='log_cdf')Log cumulative distribution function.
tf.contrib.distributions.WishartFull.log_normalizing_constant(name='log_normalizing_constant')Computes the log normalizing constant, log(Z).
tf.contrib.distributions.WishartFull.log_pdf(value, name='log_pdf')Log probability density function.
tf.contrib.distributions.WishartFull.log_pmf(value, name='log_pmf')Log probability mass function.
tf.contrib.distributions.WishartFull.log_prob(value, name='log_prob')Log probability density/mass function (depending on is_continuous).
tf.contrib.distributions.WishartFull.log_survival_function(value, name='log_survival_function')Log survival function.
tf.contrib.distributions.WishartFull.mean(name='mean')Mean.
tf.contrib.distributions.WishartFull.mean_log_det(name='mean_log_det')Computes E[log(det(X))] under this Wishart distribution.
tf.contrib.distributions.WishartFull.mode(name='mode')Mode.
tf.contrib.distributions.WishartFull.nameName prepended to all ops created by this Distribution.
tf.contrib.distributions.WishartFull.param_shapes(cls, sample_shape, name='DistributionParamShapes')Shapes of parameters given the desired shape of a call to sample().
tf.contrib.distributions.WishartFull.param_static_shapes(cls, sample_shape)param_shapes with static (i.e. TensorShape) shapes.
tf.contrib.distributions.WishartFull.parametersDictionary of parameters used by this Distribution.
tf.contrib.distributions.WishartFull.pdf(value, name='pdf')Probability density function.
tf.contrib.distributions.WishartFull.pmf(value, name='pmf')Probability mass function.
tf.contrib.distributions.WishartFull.prob(value, name='prob')Probability density/mass function (depending on is_continuous).
tf.contrib.distributions.WishartFull.sample(sample_shape=(), seed=None, name='sample')Generate samples of the specified shape.
tf.contrib.distributions.WishartFull.sample_n(n, seed=None, name='sample_n')Generate n samples.
tf.contrib.distributions.WishartFull.scale()Wishart distribution scale matrix.
tf.contrib.distributions.WishartFull.scale_operator_pdWishart distribution scale matrix as an OperatorPD.
tf.contrib.distributions.WishartFull.std(name='std')Standard deviation.
tf.contrib.distributions.WishartFull.survival_function(value, name='survival_function')Survival function.
tf.contrib.distributions.WishartFull.validate_argsPython boolean indicated possibly expensive checks are enabled.
tf.contrib.distributions.WishartFull.variance(name='variance')Variance.
class tf.contrib.distributions.TransformedDistributionA Transformed Distribution.
tf.contrib.distributions.TransformedDistribution.__init__(base_dist_cls, transform, inverse, log_det_jacobian, name='TransformedDistribution', **base_dist_args)Construct a Transformed Distribution.
tf.contrib.distributions.TransformedDistribution.allow_nan_statsPython boolean describing behavior when a stat is undefined.
tf.contrib.distributions.TransformedDistribution.base_distributionBase distribution, p(x).
tf.contrib.distributions.TransformedDistribution.batch_shape(name='batch_shape')Shape of a single sample from a single event index as a 1-D Tensor.
tf.contrib.distributions.TransformedDistribution.cdf(value, name='cdf')Cumulative distribution function.
tf.contrib.distributions.TransformedDistribution.dtypeThe DType of Tensors handled by this Distribution.
tf.contrib.distributions.TransformedDistribution.entropy(name='entropy')Shanon entropy in nats.
tf.contrib.distributions.TransformedDistribution.event_shape(name='event_shape')Shape of a single sample from a single batch as a 1-D int32 Tensor.
tf.contrib.distributions.TransformedDistribution.get_batch_shape()Shape of a single sample from a single event index as a TensorShape.
tf.contrib.distributions.TransformedDistribution.get_event_shape()Shape of a single sample from a single batch as a TensorShape.
tf.contrib.distributions.TransformedDistribution.inverseInverse function of transform, y => x.
tf.contrib.distributions.TransformedDistribution.is_continuous
tf.contrib.distributions.TransformedDistribution.is_reparameterized
tf.contrib.distributions.TransformedDistribution.log_cdf(value, name='log_cdf')Log cumulative distribution function.
tf.contrib.distributions.TransformedDistribution.log_det_jacobianFunction computing the log determinant of the Jacobian of transform.
tf.contrib.distributions.TransformedDistribution.log_pdf(value, name='log_pdf')Log probability density function.
tf.contrib.distributions.TransformedDistribution.log_pmf(value, name='log_pmf')Log probability mass function.
tf.contrib.distributions.TransformedDistribution.log_prob(value, name='log_prob')Log probability density/mass function (depending on is_continuous).
tf.contrib.distributions.TransformedDistribution.log_survival_function(value, name='log_survival_function')Log survival function.
tf.contrib.distributions.TransformedDistribution.mean(name='mean')Mean.
tf.contrib.distributions.TransformedDistribution.mode(name='mode')Mode.
tf.contrib.distributions.TransformedDistribution.nameName prepended to all ops created by this Distribution.
tf.contrib.distributions.TransformedDistribution.param_shapes(cls, sample_shape, name='DistributionParamShapes')Shapes of parameters given the desired shape of a call to sample().
tf.contrib.distributions.TransformedDistribution.param_static_shapes(cls, sample_shape)param_shapes with static (i.e. TensorShape) shapes.
tf.contrib.distributions.TransformedDistribution.parametersDictionary of parameters used by this Distribution.
tf.contrib.distributions.TransformedDistribution.pdf(value, name='pdf')Probability density function.
tf.contrib.distributions.TransformedDistribution.pmf(value, name='pmf')Probability mass function.
tf.contrib.distributions.TransformedDistribution.prob(value, name='prob')Probability density/mass function (depending on is_continuous).
tf.contrib.distributions.TransformedDistribution.sample(sample_shape=(), seed=None, name='sample')Generate samples of the specified shape.
tf.contrib.distributions.TransformedDistribution.sample_n(n, seed=None, name='sample_n')Generate n samples.
tf.contrib.distributions.TransformedDistribution.std(name='std')Standard deviation.
tf.contrib.distributions.TransformedDistribution.survival_function(value, name='survival_function')Survival function.
tf.contrib.distributions.TransformedDistribution.transformFunction transforming x => y.
tf.contrib.distributions.TransformedDistribution.validate_argsPython boolean indicated possibly expensive checks are enabled.
tf.contrib.distributions.TransformedDistribution.variance(name='variance')Variance.
class tf.contrib.distributions.QuantizedDistributionDistribution representing the quantization Y = ceiling(X).
tf.contrib.distributions.QuantizedDistribution.__init__(base_dist_cls, lower_cutoff=None, upper_cutoff=None, name='QuantizedDistribution', **base_dist_args)Construct a Quantized Distribution.
tf.contrib.distributions.QuantizedDistribution.allow_nan_statsPython boolean describing behavior when a stat is undefined.
tf.contrib.distributions.QuantizedDistribution.base_distributionBase distribution, p(x).
tf.contrib.distributions.QuantizedDistribution.batch_shape(name='batch_shape')Shape of a single sample from a single event index as a 1-D Tensor.
tf.contrib.distributions.QuantizedDistribution.cdf(value, name='cdf')Cumulative distribution function.
tf.contrib.distributions.QuantizedDistribution.dtypeThe DType of Tensors handled by this Distribution.
tf.contrib.distributions.QuantizedDistribution.entropy(name='entropy')Shanon entropy in nats.
tf.contrib.distributions.QuantizedDistribution.event_shape(name='event_shape')Shape of a single sample from a single batch as a 1-D int32 Tensor.
tf.contrib.distributions.QuantizedDistribution.get_batch_shape()Shape of a single sample from a single event index as a TensorShape.
tf.contrib.distributions.QuantizedDistribution.get_event_shape()Shape of a single sample from a single batch as a TensorShape.
tf.contrib.distributions.QuantizedDistribution.is_continuous
tf.contrib.distributions.QuantizedDistribution.is_reparameterized
tf.contrib.distributions.QuantizedDistribution.log_cdf(value, name='log_cdf')Log cumulative distribution function.
tf.contrib.distributions.QuantizedDistribution.log_pdf(value, name='log_pdf')Log probability density function.
tf.contrib.distributions.QuantizedDistribution.log_pmf(value, name='log_pmf')Log probability mass function.
tf.contrib.distributions.QuantizedDistribution.log_prob(value, name='log_prob')Log probability density/mass function (depending on is_continuous).
tf.contrib.distributions.QuantizedDistribution.log_survival_function(value, name='log_survival_function')Log survival function.
tf.contrib.distributions.QuantizedDistribution.mean(name='mean')Mean.
tf.contrib.distributions.QuantizedDistribution.mode(name='mode')Mode.
tf.contrib.distributions.QuantizedDistribution.nameName prepended to all ops created by this Distribution.
tf.contrib.distributions.QuantizedDistribution.param_shapes(cls, sample_shape, name='DistributionParamShapes')Shapes of parameters given the desired shape of a call to sample().
tf.contrib.distributions.QuantizedDistribution.param_static_shapes(cls, sample_shape)param_shapes with static (i.e. TensorShape) shapes.
tf.contrib.distributions.QuantizedDistribution.parametersDictionary of parameters used by this Distribution.
tf.contrib.distributions.QuantizedDistribution.pdf(value, name='pdf')Probability density function.
tf.contrib.distributions.QuantizedDistribution.pmf(value, name='pmf')Probability mass function.
tf.contrib.distributions.QuantizedDistribution.prob(value, name='prob')Probability density/mass function (depending on is_continuous).
tf.contrib.distributions.QuantizedDistribution.sample(sample_shape=(), seed=None, name='sample')Generate samples of the specified shape.
tf.contrib.distributions.QuantizedDistribution.sample_n(n, seed=None, name='sample_n')Generate n samples.
tf.contrib.distributions.QuantizedDistribution.std(name='std')Standard deviation.
tf.contrib.distributions.QuantizedDistribution.survival_function(value, name='survival_function')Survival function.
tf.contrib.distributions.QuantizedDistribution.validate_argsPython boolean indicated possibly expensive checks are enabled.
tf.contrib.distributions.QuantizedDistribution.variance(name='variance')Variance.
class tf.contrib.distributions.MixtureMixture distribution.
tf.contrib.distributions.Mixture.__init__(cat, components, validate_args=False, allow_nan_stats=True, name='Mixture')Initialize a Mixture distribution.
tf.contrib.distributions.Mixture.allow_nan_statsPython boolean describing behavior when a stat is undefined.
tf.contrib.distributions.Mixture.batch_shape(name='batch_shape')Shape of a single sample from a single event index as a 1-D Tensor.
tf.contrib.distributions.Mixture.cat
tf.contrib.distributions.Mixture.cdf(value, name='cdf')Cumulative distribution function.
tf.contrib.distributions.Mixture.components
tf.contrib.distributions.Mixture.dtypeThe DType of Tensors handled by this Distribution.
tf.contrib.distributions.Mixture.entropy(name='entropy')Shanon entropy in nats.
tf.contrib.distributions.Mixture.entropy_lower_bound(name='entropy_lower_bound')A lower bound on the entropy of this mixture model.
tf.contrib.distributions.Mixture.event_shape(name='event_shape')Shape of a single sample from a single batch as a 1-D int32 Tensor.
tf.contrib.distributions.Mixture.get_batch_shape()Shape of a single sample from a single event index as a TensorShape.
tf.contrib.distributions.Mixture.get_event_shape()Shape of a single sample from a single batch as a TensorShape.
tf.contrib.distributions.Mixture.is_continuous
tf.contrib.distributions.Mixture.is_reparameterized
tf.contrib.distributions.Mixture.log_cdf(value, name='log_cdf')Log cumulative distribution function.
tf.contrib.distributions.Mixture.log_pdf(value, name='log_pdf')Log probability density function.
tf.contrib.distributions.Mixture.log_pmf(value, name='log_pmf')Log probability mass function.
tf.contrib.distributions.Mixture.log_prob(value, name='log_prob')Log probability density/mass function (depending on is_continuous).
tf.contrib.distributions.Mixture.log_survival_function(value, name='log_survival_function')Log survival function.
tf.contrib.distributions.Mixture.mean(name='mean')Mean.
tf.contrib.distributions.Mixture.mode(name='mode')Mode.
tf.contrib.distributions.Mixture.nameName prepended to all ops created by this Distribution.
tf.contrib.distributions.Mixture.num_components
tf.contrib.distributions.Mixture.param_shapes(cls, sample_shape, name='DistributionParamShapes')Shapes of parameters given the desired shape of a call to sample().
tf.contrib.distributions.Mixture.param_static_shapes(cls, sample_shape)param_shapes with static (i.e. TensorShape) shapes.
tf.contrib.distributions.Mixture.parametersDictionary of parameters used by this Distribution.
tf.contrib.distributions.Mixture.pdf(value, name='pdf')Probability density function.
tf.contrib.distributions.Mixture.pmf(value, name='pmf')Probability mass function.
tf.contrib.distributions.Mixture.prob(value, name='prob')Probability density/mass function (depending on is_continuous).
tf.contrib.distributions.Mixture.sample(sample_shape=(), seed=None, name='sample')Generate samples of the specified shape.
tf.contrib.distributions.Mixture.sample_n(n, seed=None, name='sample_n')Generate n samples.
tf.contrib.distributions.Mixture.std(name='std')Standard deviation.
tf.contrib.distributions.Mixture.survival_function(value, name='survival_function')Survival function.
tf.contrib.distributions.Mixture.validate_argsPython boolean indicated possibly expensive checks are enabled.
tf.contrib.distributions.Mixture.variance(name='variance')Variance.

Statistical distributions (contrib) > Posterior inference with conjugate priors.

Members
tf.contrib.distributions.normal_conjugates_known_sigma_posterior(prior, sigma, s, n)Posterior Normal distribution with conjugate prior on the mean.
tf.contrib.distributions.normal_congugates_known_sigma_predictive(prior, sigma, s, n)Posterior predictive Normal distribution w. conjugate prior on the mean.

Statistical distributions (contrib) > Kullback Leibler Divergence

Members
tf.contrib.distributions.kl(dist_a, dist_b, allow_nan=False, name=None)Get the KL-divergence KL(dist_a || dist_b).
class tf.contrib.distributions.RegisterKLDecorator to register a KL divergence implementation function.
tf.contrib.distributions.RegisterKL.__call__(kl_fn)Perform the KL registration.
tf.contrib.distributions.RegisterKL.__init__(dist_cls_a, dist_cls_b)Initialize the KL registrar.

Statistical distributions (contrib) > Other Functions and Classes

Members
class tf.contrib.distributions.BaseDistributionSimple abstract base class for probability distributions.
tf.contrib.distributions.BaseDistribution.log_prob(value, name='log_prob')Log probability density/mass function (depending on is_continuous).
tf.contrib.distributions.BaseDistribution.sample_n(n, seed=None, name='sample')Generate n samples.
class tf.contrib.distributions.BernoulliWithSigmoidPBernoulli with p = sigmoid(p).
tf.contrib.distributions.BernoulliWithSigmoidP.__init__(p=None, dtype=tf.int32, validate_args=False, allow_nan_stats=True, name='BernoulliWithSigmoidP')
tf.contrib.distributions.BernoulliWithSigmoidP.allow_nan_statsPython boolean describing behavior when a stat is undefined.
tf.contrib.distributions.BernoulliWithSigmoidP.batch_shape(name='batch_shape')Shape of a single sample from a single event index as a 1-D Tensor.
tf.contrib.distributions.BernoulliWithSigmoidP.cdf(value, name='cdf')Cumulative distribution function.
tf.contrib.distributions.BernoulliWithSigmoidP.dtypeThe DType of Tensors handled by this Distribution.
tf.contrib.distributions.BernoulliWithSigmoidP.entropy(name='entropy')Shanon entropy in nats.
tf.contrib.distributions.BernoulliWithSigmoidP.event_shape(name='event_shape')Shape of a single sample from a single batch as a 1-D int32 Tensor.
tf.contrib.distributions.BernoulliWithSigmoidP.get_batch_shape()Shape of a single sample from a single event index as a TensorShape.
tf.contrib.distributions.BernoulliWithSigmoidP.get_event_shape()Shape of a single sample from a single batch as a TensorShape.
tf.contrib.distributions.BernoulliWithSigmoidP.is_continuous
tf.contrib.distributions.BernoulliWithSigmoidP.is_reparameterized
tf.contrib.distributions.BernoulliWithSigmoidP.log_cdf(value, name='log_cdf')Log cumulative distribution function.
tf.contrib.distributions.BernoulliWithSigmoidP.log_pdf(value, name='log_pdf')Log probability density function.
tf.contrib.distributions.BernoulliWithSigmoidP.log_pmf(value, name='log_pmf')Log probability mass function.
tf.contrib.distributions.BernoulliWithSigmoidP.log_prob(value, name='log_prob')Log probability density/mass function (depending on is_continuous).
tf.contrib.distributions.BernoulliWithSigmoidP.log_survival_function(value, name='log_survival_function')Log survival function.
tf.contrib.distributions.BernoulliWithSigmoidP.logits
tf.contrib.distributions.BernoulliWithSigmoidP.mean(name='mean')Mean.
tf.contrib.distributions.BernoulliWithSigmoidP.mode(name='mode')Mode.
tf.contrib.distributions.BernoulliWithSigmoidP.nameName prepended to all ops created by this Distribution.
tf.contrib.distributions.BernoulliWithSigmoidP.p
tf.contrib.distributions.BernoulliWithSigmoidP.param_shapes(cls, sample_shape, name='DistributionParamShapes')Shapes of parameters given the desired shape of a call to sample().
tf.contrib.distributions.BernoulliWithSigmoidP.param_static_shapes(cls, sample_shape)param_shapes with static (i.e. TensorShape) shapes.
tf.contrib.distributions.BernoulliWithSigmoidP.parametersDictionary of parameters used by this Distribution.
tf.contrib.distributions.BernoulliWithSigmoidP.pdf(value, name='pdf')Probability density function.
tf.contrib.distributions.BernoulliWithSigmoidP.pmf(value, name='pmf')Probability mass function.
tf.contrib.distributions.BernoulliWithSigmoidP.prob(value, name='prob')Probability density/mass function (depending on is_continuous).
tf.contrib.distributions.BernoulliWithSigmoidP.q1-p.
tf.contrib.distributions.BernoulliWithSigmoidP.sample(sample_shape=(), seed=None, name='sample')Generate samples of the specified shape.
tf.contrib.distributions.BernoulliWithSigmoidP.sample_n(n, seed=None, name='sample_n')Generate n samples.
tf.contrib.distributions.BernoulliWithSigmoidP.std(name='std')Standard deviation.
tf.contrib.distributions.BernoulliWithSigmoidP.survival_function(value, name='survival_function')Survival function.
tf.contrib.distributions.BernoulliWithSigmoidP.validate_argsPython boolean indicated possibly expensive checks are enabled.
tf.contrib.distributions.BernoulliWithSigmoidP.variance(name='variance')Variance.
class tf.contrib.distributions.BetaWithSoftplusABBeta with softplus transform on a and b.
tf.contrib.distributions.BetaWithSoftplusAB.__init__(a, b, validate_args=False, allow_nan_stats=True, name='BetaWithSoftplusAB')
tf.contrib.distributions.BetaWithSoftplusAB.aShape parameter.
tf.contrib.distributions.BetaWithSoftplusAB.a_b_sumSum of parameters.
tf.contrib.distributions.BetaWithSoftplusAB.allow_nan_statsPython boolean describing behavior when a stat is undefined.
tf.contrib.distributions.BetaWithSoftplusAB.bShape parameter.
tf.contrib.distributions.BetaWithSoftplusAB.batch_shape(name='batch_shape')Shape of a single sample from a single event index as a 1-D Tensor.
tf.contrib.distributions.BetaWithSoftplusAB.cdf(value, name='cdf')Cumulative distribution function.
tf.contrib.distributions.BetaWithSoftplusAB.dtypeThe DType of Tensors handled by this Distribution.
tf.contrib.distributions.BetaWithSoftplusAB.entropy(name='entropy')Shanon entropy in nats.
tf.contrib.distributions.BetaWithSoftplusAB.event_shape(name='event_shape')Shape of a single sample from a single batch as a 1-D int32 Tensor.
tf.contrib.distributions.BetaWithSoftplusAB.get_batch_shape()Shape of a single sample from a single event index as a TensorShape.
tf.contrib.distributions.BetaWithSoftplusAB.get_event_shape()Shape of a single sample from a single batch as a TensorShape.
tf.contrib.distributions.BetaWithSoftplusAB.is_continuous
tf.contrib.distributions.BetaWithSoftplusAB.is_reparameterized
tf.contrib.distributions.BetaWithSoftplusAB.log_cdf(value, name='log_cdf')Log cumulative distribution function.
tf.contrib.distributions.BetaWithSoftplusAB.log_pdf(value, name='log_pdf')Log probability density function.
tf.contrib.distributions.BetaWithSoftplusAB.log_pmf(value, name='log_pmf')Log probability mass function.
tf.contrib.distributions.BetaWithSoftplusAB.log_prob(value, name='log_prob')Log probability density/mass function (depending on is_continuous).
tf.contrib.distributions.BetaWithSoftplusAB.log_survival_function(value, name='log_survival_function')Log survival function.
tf.contrib.distributions.BetaWithSoftplusAB.mean(name='mean')Mean.
tf.contrib.distributions.BetaWithSoftplusAB.mode(name='mode')Mode.
tf.contrib.distributions.BetaWithSoftplusAB.nameName prepended to all ops created by this Distribution.
tf.contrib.distributions.BetaWithSoftplusAB.param_shapes(cls, sample_shape, name='DistributionParamShapes')Shapes of parameters given the desired shape of a call to sample().
tf.contrib.distributions.BetaWithSoftplusAB.param_static_shapes(cls, sample_shape)param_shapes with static (i.e. TensorShape) shapes.
tf.contrib.distributions.BetaWithSoftplusAB.parametersDictionary of parameters used by this Distribution.
tf.contrib.distributions.BetaWithSoftplusAB.pdf(value, name='pdf')Probability density function.
tf.contrib.distributions.BetaWithSoftplusAB.pmf(value, name='pmf')Probability mass function.
tf.contrib.distributions.BetaWithSoftplusAB.prob(value, name='prob')Probability density/mass function (depending on is_continuous).
tf.contrib.distributions.BetaWithSoftplusAB.sample(sample_shape=(), seed=None, name='sample')Generate samples of the specified shape.
tf.contrib.distributions.BetaWithSoftplusAB.sample_n(n, seed=None, name='sample_n')Generate n samples.
tf.contrib.distributions.BetaWithSoftplusAB.std(name='std')Standard deviation.
tf.contrib.distributions.BetaWithSoftplusAB.survival_function(value, name='survival_function')Survival function.
tf.contrib.distributions.BetaWithSoftplusAB.validate_argsPython boolean indicated possibly expensive checks are enabled.
tf.contrib.distributions.BetaWithSoftplusAB.variance(name='variance')Variance.
class tf.contrib.distributions.Chi2WithAbsDfChi2 with parameter transform df = floor(abs(df)).
tf.contrib.distributions.Chi2WithAbsDf.__init__(df, validate_args=False, allow_nan_stats=True, name='Chi2WithAbsDf')
tf.contrib.distributions.Chi2WithAbsDf.allow_nan_statsPython boolean describing behavior when a stat is undefined.
tf.contrib.distributions.Chi2WithAbsDf.alphaShape parameter.
tf.contrib.distributions.Chi2WithAbsDf.batch_shape(name='batch_shape')Shape of a single sample from a single event index as a 1-D Tensor.
tf.contrib.distributions.Chi2WithAbsDf.betaInverse scale parameter.
tf.contrib.distributions.Chi2WithAbsDf.cdf(value, name='cdf')Cumulative distribution function.
tf.contrib.distributions.Chi2WithAbsDf.df
tf.contrib.distributions.Chi2WithAbsDf.dtypeThe DType of Tensors handled by this Distribution.
tf.contrib.distributions.Chi2WithAbsDf.entropy(name='entropy')Shanon entropy in nats.
tf.contrib.distributions.Chi2WithAbsDf.event_shape(name='event_shape')Shape of a single sample from a single batch as a 1-D int32 Tensor.
tf.contrib.distributions.Chi2WithAbsDf.get_batch_shape()Shape of a single sample from a single event index as a TensorShape.
tf.contrib.distributions.Chi2WithAbsDf.get_event_shape()Shape of a single sample from a single batch as a TensorShape.
tf.contrib.distributions.Chi2WithAbsDf.is_continuous
tf.contrib.distributions.Chi2WithAbsDf.is_reparameterized
tf.contrib.distributions.Chi2WithAbsDf.log_cdf(value, name='log_cdf')Log cumulative distribution function.
tf.contrib.distributions.Chi2WithAbsDf.log_pdf(value, name='log_pdf')Log probability density function.
tf.contrib.distributions.Chi2WithAbsDf.log_pmf(value, name='log_pmf')Log probability mass function.
tf.contrib.distributions.Chi2WithAbsDf.log_prob(value, name='log_prob')Log probability density/mass function (depending on is_continuous).
tf.contrib.distributions.Chi2WithAbsDf.log_survival_function(value, name='log_survival_function')Log survival function.
tf.contrib.distributions.Chi2WithAbsDf.mean(name='mean')Mean.
tf.contrib.distributions.Chi2WithAbsDf.mode(name='mode')Mode.
tf.contrib.distributions.Chi2WithAbsDf.nameName prepended to all ops created by this Distribution.
tf.contrib.distributions.Chi2WithAbsDf.param_shapes(cls, sample_shape, name='DistributionParamShapes')Shapes of parameters given the desired shape of a call to sample().
tf.contrib.distributions.Chi2WithAbsDf.param_static_shapes(cls, sample_shape)param_shapes with static (i.e. TensorShape) shapes.
tf.contrib.distributions.Chi2WithAbsDf.parametersDictionary of parameters used by this Distribution.
tf.contrib.distributions.Chi2WithAbsDf.pdf(value, name='pdf')Probability density function.
tf.contrib.distributions.Chi2WithAbsDf.pmf(value, name='pmf')Probability mass function.
tf.contrib.distributions.Chi2WithAbsDf.prob(value, name='prob')Probability density/mass function (depending on is_continuous).
tf.contrib.distributions.Chi2WithAbsDf.sample(sample_shape=(), seed=None, name='sample')Generate samples of the specified shape.
tf.contrib.distributions.Chi2WithAbsDf.sample_n(n, seed=None, name='sample_n')Generate n samples.
tf.contrib.distributions.Chi2WithAbsDf.std(name='std')Standard deviation.
tf.contrib.distributions.Chi2WithAbsDf.survival_function(value, name='survival_function')Survival function.
tf.contrib.distributions.Chi2WithAbsDf.validate_argsPython boolean indicated possibly expensive checks are enabled.
tf.contrib.distributions.Chi2WithAbsDf.variance(name='variance')Variance.
class tf.contrib.distributions.ExponentialWithSoftplusLamExponential with softplus transform on lam.
tf.contrib.distributions.ExponentialWithSoftplusLam.__init__(lam, validate_args=False, allow_nan_stats=True, name='ExponentialWithSoftplusLam')
tf.contrib.distributions.ExponentialWithSoftplusLam.allow_nan_statsPython boolean describing behavior when a stat is undefined.
tf.contrib.distributions.ExponentialWithSoftplusLam.alphaShape parameter.
tf.contrib.distributions.ExponentialWithSoftplusLam.batch_shape(name='batch_shape')Shape of a single sample from a single event index as a 1-D Tensor.
tf.contrib.distributions.ExponentialWithSoftplusLam.betaInverse scale parameter.
tf.contrib.distributions.ExponentialWithSoftplusLam.cdf(value, name='cdf')Cumulative distribution function.
tf.contrib.distributions.ExponentialWithSoftplusLam.dtypeThe DType of Tensors handled by this Distribution.
tf.contrib.distributions.ExponentialWithSoftplusLam.entropy(name='entropy')Shanon entropy in nats.
tf.contrib.distributions.ExponentialWithSoftplusLam.event_shape(name='event_shape')Shape of a single sample from a single batch as a 1-D int32 Tensor.
tf.contrib.distributions.ExponentialWithSoftplusLam.get_batch_shape()Shape of a single sample from a single event index as a TensorShape.
tf.contrib.distributions.ExponentialWithSoftplusLam.get_event_shape()Shape of a single sample from a single batch as a TensorShape.
tf.contrib.distributions.ExponentialWithSoftplusLam.is_continuous
tf.contrib.distributions.ExponentialWithSoftplusLam.is_reparameterized
tf.contrib.distributions.ExponentialWithSoftplusLam.lam
tf.contrib.distributions.ExponentialWithSoftplusLam.log_cdf(value, name='log_cdf')Log cumulative distribution function.
tf.contrib.distributions.ExponentialWithSoftplusLam.log_pdf(value, name='log_pdf')Log probability density function.
tf.contrib.distributions.ExponentialWithSoftplusLam.log_pmf(value, name='log_pmf')Log probability mass function.
tf.contrib.distributions.ExponentialWithSoftplusLam.log_prob(value, name='log_prob')Log probability density/mass function (depending on is_continuous).
tf.contrib.distributions.ExponentialWithSoftplusLam.log_survival_function(value, name='log_survival_function')Log survival function.
tf.contrib.distributions.ExponentialWithSoftplusLam.mean(name='mean')Mean.
tf.contrib.distributions.ExponentialWithSoftplusLam.mode(name='mode')Mode.
tf.contrib.distributions.ExponentialWithSoftplusLam.nameName prepended to all ops created by this Distribution.
tf.contrib.distributions.ExponentialWithSoftplusLam.param_shapes(cls, sample_shape, name='DistributionParamShapes')Shapes of parameters given the desired shape of a call to sample().
tf.contrib.distributions.ExponentialWithSoftplusLam.param_static_shapes(cls, sample_shape)param_shapes with static (i.e. TensorShape) shapes.
tf.contrib.distributions.ExponentialWithSoftplusLam.parametersDictionary of parameters used by this Distribution.
tf.contrib.distributions.ExponentialWithSoftplusLam.pdf(value, name='pdf')Probability density function.
tf.contrib.distributions.ExponentialWithSoftplusLam.pmf(value, name='pmf')Probability mass function.
tf.contrib.distributions.ExponentialWithSoftplusLam.prob(value, name='prob')Probability density/mass function (depending on is_continuous).
tf.contrib.distributions.ExponentialWithSoftplusLam.sample(sample_shape=(), seed=None, name='sample')Generate samples of the specified shape.
tf.contrib.distributions.ExponentialWithSoftplusLam.sample_n(n, seed=None, name='sample_n')Generate n samples.
tf.contrib.distributions.ExponentialWithSoftplusLam.std(name='std')Standard deviation.
tf.contrib.distributions.ExponentialWithSoftplusLam.survival_function(value, name='survival_function')Survival function.
tf.contrib.distributions.ExponentialWithSoftplusLam.validate_argsPython boolean indicated possibly expensive checks are enabled.
tf.contrib.distributions.ExponentialWithSoftplusLam.variance(name='variance')Variance.
class tf.contrib.distributions.GammaWithSoftplusAlphaBetaGamma with softplus transform on alpha and beta.
tf.contrib.distributions.GammaWithSoftplusAlphaBeta.__init__(alpha, beta, validate_args=False, allow_nan_stats=True, name='GammaWithSoftplusAlphaBeta')
tf.contrib.distributions.GammaWithSoftplusAlphaBeta.allow_nan_statsPython boolean describing behavior when a stat is undefined.
tf.contrib.distributions.GammaWithSoftplusAlphaBeta.alphaShape parameter.
tf.contrib.distributions.GammaWithSoftplusAlphaBeta.batch_shape(name='batch_shape')Shape of a single sample from a single event index as a 1-D Tensor.
tf.contrib.distributions.GammaWithSoftplusAlphaBeta.betaInverse scale parameter.
tf.contrib.distributions.GammaWithSoftplusAlphaBeta.cdf(value, name='cdf')Cumulative distribution function.
tf.contrib.distributions.GammaWithSoftplusAlphaBeta.dtypeThe DType of Tensors handled by this Distribution.
tf.contrib.distributions.GammaWithSoftplusAlphaBeta.entropy(name='entropy')Shanon entropy in nats.
tf.contrib.distributions.GammaWithSoftplusAlphaBeta.event_shape(name='event_shape')Shape of a single sample from a single batch as a 1-D int32 Tensor.
tf.contrib.distributions.GammaWithSoftplusAlphaBeta.get_batch_shape()Shape of a single sample from a single event index as a TensorShape.
tf.contrib.distributions.GammaWithSoftplusAlphaBeta.get_event_shape()Shape of a single sample from a single batch as a TensorShape.
tf.contrib.distributions.GammaWithSoftplusAlphaBeta.is_continuous
tf.contrib.distributions.GammaWithSoftplusAlphaBeta.is_reparameterized
tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_cdf(value, name='log_cdf')Log cumulative distribution function.
tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_pdf(value, name='log_pdf')Log probability density function.
tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_pmf(value, name='log_pmf')Log probability mass function.
tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_prob(value, name='log_prob')Log probability density/mass function (depending on is_continuous).
tf.contrib.distributions.GammaWithSoftplusAlphaBeta.log_survival_function(value, name='log_survival_function')Log survival function.
tf.contrib.distributions.GammaWithSoftplusAlphaBeta.mean(name='mean')Mean.
tf.contrib.distributions.GammaWithSoftplusAlphaBeta.mode(name='mode')Mode.
tf.contrib.distributions.GammaWithSoftplusAlphaBeta.nameName prepended to all ops created by this Distribution.
tf.contrib.distributions.GammaWithSoftplusAlphaBeta.param_shapes(cls, sample_shape, name='DistributionParamShapes')Shapes of parameters given the desired shape of a call to sample().
tf.contrib.distributions.GammaWithSoftplusAlphaBeta.param_static_shapes(cls, sample_shape)param_shapes with static (i.e. TensorShape) shapes.
tf.contrib.distributions.GammaWithSoftplusAlphaBeta.parametersDictionary of parameters used by this Distribution.
tf.contrib.distributions.GammaWithSoftplusAlphaBeta.pdf(value, name='pdf')Probability density function.
tf.contrib.distributions.GammaWithSoftplusAlphaBeta.pmf(value, name='pmf')Probability mass function.
tf.contrib.distributions.GammaWithSoftplusAlphaBeta.prob(value, name='prob')Probability density/mass function (depending on is_continuous).
tf.contrib.distributions.GammaWithSoftplusAlphaBeta.sample(sample_shape=(), seed=None, name='sample')Generate samples of the specified shape.
tf.contrib.distributions.GammaWithSoftplusAlphaBeta.sample_n(n, seed=None, name='sample_n')Generate n samples.
tf.contrib.distributions.GammaWithSoftplusAlphaBeta.std(name='std')Standard deviation.
tf.contrib.distributions.GammaWithSoftplusAlphaBeta.survival_function(value, name='survival_function')Survival function.
tf.contrib.distributions.GammaWithSoftplusAlphaBeta.validate_argsPython boolean indicated possibly expensive checks are enabled.
tf.contrib.distributions.GammaWithSoftplusAlphaBeta.variance(name='variance')Variance.
class tf.contrib.distributions.InverseGammaWithSoftplusAlphaBetaInverse Gamma with softplus applied to alpha and beta.
tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.__init__(alpha, beta, validate_args=False, allow_nan_stats=True, name='InverseGammaWithSoftplusAlphaBeta')
tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.allow_nan_statsPython boolean describing behavior when a stat is undefined.
tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.alphaShape parameter.
tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.batch_shape(name='batch_shape')Shape of a single sample from a single event index as a 1-D Tensor.
tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.betaScale parameter.
tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.cdf(value, name='cdf')Cumulative distribution function.
tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.dtypeThe DType of Tensors handled by this Distribution.
tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.entropy(name='entropy')Shanon entropy in nats.
tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.event_shape(name='event_shape')Shape of a single sample from a single batch as a 1-D int32 Tensor.
tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.get_batch_shape()Shape of a single sample from a single event index as a TensorShape.
tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.get_event_shape()Shape of a single sample from a single batch as a TensorShape.
tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.is_continuous
tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.is_reparameterized
tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_cdf(value, name='log_cdf')Log cumulative distribution function.
tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_pdf(value, name='log_pdf')Log probability density function.
tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_pmf(value, name='log_pmf')Log probability mass function.
tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_prob(value, name='log_prob')Log probability density/mass function (depending on is_continuous).
tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.log_survival_function(value, name='log_survival_function')Log survival function.
tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.mean(name='mean')Mean.
tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.mode(name='mode')Mode.
tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.nameName prepended to all ops created by this Distribution.
tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.param_shapes(cls, sample_shape, name='DistributionParamShapes')Shapes of parameters given the desired shape of a call to sample().
tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.param_static_shapes(cls, sample_shape)param_shapes with static (i.e. TensorShape) shapes.
tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.parametersDictionary of parameters used by this Distribution.
tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.pdf(value, name='pdf')Probability density function.
tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.pmf(value, name='pmf')Probability mass function.
tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.prob(value, name='prob')Probability density/mass function (depending on is_continuous).
tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.sample(sample_shape=(), seed=None, name='sample')Generate samples of the specified shape.
tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.sample_n(n, seed=None, name='sample_n')Generate n samples.
tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.std(name='std')Standard deviation.
tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.survival_function(value, name='survival_function')Survival function.
tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.validate_argsPython boolean indicated possibly expensive checks are enabled.
tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.variance(name='variance')Variance.
class tf.contrib.distributions.LaplaceWithSoftplusScaleLaplace with softplus applied to scale.
tf.contrib.distributions.LaplaceWithSoftplusScale.__init__(loc, scale, validate_args=False, allow_nan_stats=True, name='LaplaceWithSoftplusScale')
tf.contrib.distributions.LaplaceWithSoftplusScale.allow_nan_statsPython boolean describing behavior when a stat is undefined.
tf.contrib.distributions.LaplaceWithSoftplusScale.batch_shape(name='batch_shape')Shape of a single sample from a single event index as a 1-D Tensor.
tf.contrib.distributions.LaplaceWithSoftplusScale.cdf(value, name='cdf')Cumulative distribution function.
tf.contrib.distributions.LaplaceWithSoftplusScale.dtypeThe DType of Tensors handled by this Distribution.
tf.contrib.distributions.LaplaceWithSoftplusScale.entropy(name='entropy')Shanon entropy in nats.
tf.contrib.distributions.LaplaceWithSoftplusScale.event_shape(name='event_shape')Shape of a single sample from a single batch as a 1-D int32 Tensor.
tf.contrib.distributions.LaplaceWithSoftplusScale.get_batch_shape()Shape of a single sample from a single event index as a TensorShape.
tf.contrib.distributions.LaplaceWithSoftplusScale.get_event_shape()Shape of a single sample from a single batch as a TensorShape.
tf.contrib.distributions.LaplaceWithSoftplusScale.is_continuous
tf.contrib.distributions.LaplaceWithSoftplusScale.is_reparameterized
tf.contrib.distributions.LaplaceWithSoftplusScale.locDistribution parameter for the location.
tf.contrib.distributions.LaplaceWithSoftplusScale.log_cdf(value, name='log_cdf')Log cumulative distribution function.
tf.contrib.distributions.LaplaceWithSoftplusScale.log_pdf(value, name='log_pdf')Log probability density function.
tf.contrib.distributions.LaplaceWithSoftplusScale.log_pmf(value, name='log_pmf')Log probability mass function.
tf.contrib.distributions.LaplaceWithSoftplusScale.log_prob(value, name='log_prob')Log probability density/mass function (depending on is_continuous).
tf.contrib.distributions.LaplaceWithSoftplusScale.log_survival_function(value, name='log_survival_function')Log survival function.
tf.contrib.distributions.LaplaceWithSoftplusScale.mean(name='mean')Mean.
tf.contrib.distributions.LaplaceWithSoftplusScale.mode(name='mode')Mode.
tf.contrib.distributions.LaplaceWithSoftplusScale.nameName prepended to all ops created by this Distribution.
tf.contrib.distributions.LaplaceWithSoftplusScale.param_shapes(cls, sample_shape, name='DistributionParamShapes')Shapes of parameters given the desired shape of a call to sample().
tf.contrib.distributions.LaplaceWithSoftplusScale.param_static_shapes(cls, sample_shape)param_shapes with static (i.e. TensorShape) shapes.
tf.contrib.distributions.LaplaceWithSoftplusScale.parametersDictionary of parameters used by this Distribution.
tf.contrib.distributions.LaplaceWithSoftplusScale.pdf(value, name='pdf')Probability density function.
tf.contrib.distributions.LaplaceWithSoftplusScale.pmf(value, name='pmf')Probability mass function.
tf.contrib.distributions.LaplaceWithSoftplusScale.prob(value, name='prob')Probability density/mass function (depending on is_continuous).
tf.contrib.distributions.LaplaceWithSoftplusScale.sample(sample_shape=(), seed=None, name='sample')Generate samples of the specified shape.
tf.contrib.distributions.LaplaceWithSoftplusScale.sample_n(n, seed=None, name='sample_n')Generate n samples.
tf.contrib.distributions.LaplaceWithSoftplusScale.scaleDistribution parameter for scale.
tf.contrib.distributions.LaplaceWithSoftplusScale.std(name='std')Standard deviation.
tf.contrib.distributions.LaplaceWithSoftplusScale.survival_function(value, name='survival_function')Survival function.
tf.contrib.distributions.LaplaceWithSoftplusScale.validate_argsPython boolean indicated possibly expensive checks are enabled.
tf.contrib.distributions.LaplaceWithSoftplusScale.variance(name='variance')Variance.
class tf.contrib.distributions.MultivariateNormalDiagPlusVDVTThe multivariate normal distribution on R^k.
tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.__init__(mu, diag_large, v, diag_small=None, validate_args=False, allow_nan_stats=True, name='MultivariateNormalDiagPlusVDVT')Multivariate Normal distributions on R^k.
tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.allow_nan_statsPython boolean describing behavior when a stat is undefined.
tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.batch_shape(name='batch_shape')Shape of a single sample from a single event index as a 1-D Tensor.
tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.cdf(value, name='cdf')Cumulative distribution function.
tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.dtypeThe DType of Tensors handled by this Distribution.
tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.entropy(name='entropy')Shanon entropy in nats.
tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.event_shape(name='event_shape')Shape of a single sample from a single batch as a 1-D int32 Tensor.
tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.get_batch_shape()Shape of a single sample from a single event index as a TensorShape.
tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.get_event_shape()Shape of a single sample from a single batch as a TensorShape.
tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.is_continuous
tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.is_reparameterized
tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_cdf(value, name='log_cdf')Log cumulative distribution function.
tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_pdf(value, name='log_pdf')Log probability density function.
tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_pmf(value, name='log_pmf')Log probability mass function.
tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_prob(value, name='log_prob')Log probability density/mass function (depending on is_continuous).
tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_sigma_det(name='log_sigma_det')Log of determinant of covariance matrix.
tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_survival_function(value, name='log_survival_function')Log survival function.
tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.mean(name='mean')Mean.
tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.mode(name='mode')Mode.
tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.mu
tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.nameName prepended to all ops created by this Distribution.
tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.param_shapes(cls, sample_shape, name='DistributionParamShapes')Shapes of parameters given the desired shape of a call to sample().
tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.param_static_shapes(cls, sample_shape)param_shapes with static (i.e. TensorShape) shapes.
tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.parametersDictionary of parameters used by this Distribution.
tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.pdf(value, name='pdf')Probability density function.
tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.pmf(value, name='pmf')Probability mass function.
tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.prob(value, name='prob')Probability density/mass function (depending on is_continuous).
tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.sample(sample_shape=(), seed=None, name='sample')Generate samples of the specified shape.
tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.sample_n(n, seed=None, name='sample_n')Generate n samples.
tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.sigmaDense (batch) covariance matrix, if available.
tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.sigma_det(name='sigma_det')Determinant of covariance matrix.
tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.std(name='std')Standard deviation.
tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.survival_function(value, name='survival_function')Survival function.
tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.validate_argsPython boolean indicated possibly expensive checks are enabled.
tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.variance(name='variance')Variance.
class tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDevMultivariateNormalDiag with diag_stddev = softplus(diag_stddev).
tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.__init__(mu, diag_stdev, validate_args=False, allow_nan_stats=True, name='MultivariateNormalDiagWithSoftplusStdDev')
tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.allow_nan_statsPython boolean describing behavior when a stat is undefined.
tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.batch_shape(name='batch_shape')Shape of a single sample from a single event index as a 1-D Tensor.
tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.cdf(value, name='cdf')Cumulative distribution function.
tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.dtypeThe DType of Tensors handled by this Distribution.
tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.entropy(name='entropy')Shanon entropy in nats.
tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.event_shape(name='event_shape')Shape of a single sample from a single batch as a 1-D int32 Tensor.
tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.get_batch_shape()Shape of a single sample from a single event index as a TensorShape.
tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.get_event_shape()Shape of a single sample from a single batch as a TensorShape.
tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.is_continuous
tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.is_reparameterized
tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.log_cdf(value, name='log_cdf')Log cumulative distribution function.
tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.log_pdf(value, name='log_pdf')Log probability density function.
tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.log_pmf(value, name='log_pmf')Log probability mass function.
tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.log_prob(value, name='log_prob')Log probability density/mass function (depending on is_continuous).
tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.log_sigma_det(name='log_sigma_det')Log of determinant of covariance matrix.
tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.log_survival_function(value, name='log_survival_function')Log survival function.
tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.mean(name='mean')Mean.
tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.mode(name='mode')Mode.
tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.mu
tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.nameName prepended to all ops created by this Distribution.
tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.param_shapes(cls, sample_shape, name='DistributionParamShapes')Shapes of parameters given the desired shape of a call to sample().
tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.param_static_shapes(cls, sample_shape)param_shapes with static (i.e. TensorShape) shapes.
tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.parametersDictionary of parameters used by this Distribution.
tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.pdf(value, name='pdf')Probability density function.
tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.pmf(value, name='pmf')Probability mass function.
tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.prob(value, name='prob')Probability density/mass function (depending on is_continuous).
tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.sample(sample_shape=(), seed=None, name='sample')Generate samples of the specified shape.
tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.sample_n(n, seed=None, name='sample_n')Generate n samples.
tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.sigmaDense (batch) covariance matrix, if available.
tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.sigma_det(name='sigma_det')Determinant of covariance matrix.
tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.std(name='std')Standard deviation.
tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.survival_function(value, name='survival_function')Survival function.
tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.validate_argsPython boolean indicated possibly expensive checks are enabled.
tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.variance(name='variance')Variance.
class tf.contrib.distributions.NormalWithSoftplusSigmaNormal with softplus applied to sigma.