tfm.vision.backbones.SpineNet
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Creates a SpineNet family model.

tfm.vision.backbones.SpineNet(
    input_specs: tf.keras.layers.InputSpec = tf.keras.layers.InputSpec(shape=[None, None, None, 3]),
    min_level: int = 3,
    max_level: int = 7,
    block_specs: Optional[List[BlockSpec]] = None,
    endpoints_num_filters: int = 256,
    resample_alpha: float = 0.5,
    block_repeats: int = 1,
    filter_size_scale: float = 1.0,
    init_stochastic_depth_rate: float = 0.0,
    kernel_initializer: str = 'VarianceScaling',
    kernel_regularizer: Optional[tf.keras.regularizers.Regularizer] = None,
    bias_regularizer: Optional[tf.keras.regularizers.Regularizer] = None,
    activation: str = 'relu',
    use_sync_bn: bool = False,
    norm_momentum: float = 0.99,
    norm_epsilon: float = 0.001,
    **kwargs
)

This implements
Xianzhi Du, Tsung-Yi Lin, Pengchong Jin, Golnaz Ghiasi, Mingxing Tan, Yin Cui, Quoc V. Le, Xiaodan Song.
`SpineNet`	Learning Scale-Permuted Backbone for Recognition and Localization. (https://arxiv.org/abs/1912.05027)

Args
`input_specs`	A `tf.keras.layers.InputSpec` of the input tensor.
`min_level`	An `int` of min level for output mutiscale features.
`max_level`	An `int` of max level for output mutiscale features.
`block_specs`	A list of block specifications for the SpineNet model discovered by NAS.
`endpoints_num_filters`	An `int` of feature dimension for the output endpoints.
`resample_alpha`	A `float` of resampling factor in cross-scale connections.
`block_repeats`	An `int` of number of blocks contained in the layer.
`filter_size_scale`	A `float` of multiplier for the filters (number of channels) for all convolution ops. The value must be greater than zero. Typical usage will be to set this value in (0, 1) to reduce the number of parameters or computation cost of the model.
`init_stochastic_depth_rate`	A `float` of initial stochastic depth rate.
`kernel_initializer`	A str for kernel initializer of convolutional layers.
`kernel_regularizer`	A `tf.keras.regularizers.Regularizer` object for Conv2D. Default to None.
`bias_regularizer`	A `tf.keras.regularizers.Regularizer` object for Conv2D. Default to None.
`activation`	A `str` name of the activation function.
`use_sync_bn`	If True, use synchronized batch normalization.
`norm_momentum`	A `float` of normalization momentum for the moving average.
`norm_epsilon`	A small `float` added to variance to avoid dividing by zero.
`**kwargs`	Additional keyword arguments to be passed.

Attributes
`activity_regularizer`	Optional regularizer function for the output of this layer.
`autotune_steps_per_execution`	Settable property to enable tuning for steps_per_execution
`compute_dtype`	The dtype of the layer's computations. This is equivalent to `Layer.dtype_policy.compute_dtype`. Unless mixed precision is used, this is the same as `Layer.dtype`, the dtype of the weights. Layers automatically cast their inputs to the compute dtype, which causes computations and the output to be in the compute dtype as well. This is done by the base Layer class in `Layer.call`, so you do not have to insert these casts if implementing your own layer. Layers often perform certain internal computations in higher precision when `compute_dtype` is float16 or bfloat16 for numeric stability. The output will still typically be float16 or bfloat16 in such cases.
`distribute_reduction_method`	The method employed to reduce per-replica values during training. Unless specified, the value "auto" will be assumed, indicating that the reduction strategy should be chosen based on the current running environment. See `reduce_per_replica` function for more details.
`distribute_strategy`	The `tf.distribute.Strategy` this model was created under.
`dtype`	The dtype of the layer weights. This is equivalent to `Layer.dtype_policy.variable_dtype`. Unless mixed precision is used, this is the same as `Layer.compute_dtype`, the dtype of the layer's computations.
`dtype_policy`	The dtype policy associated with this layer. This is an instance of a `tf.keras.mixed_precision.Policy`.
`dynamic`	Whether the layer is dynamic (eager-only); set in the constructor.
`input`	Retrieves the input tensor(s) of a layer. Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer.
`input_spec`	`InputSpec` instance(s) describing the input format for this layer. When you create a layer subclass, you can set `self.input_spec` to enable the layer to run input compatibility checks when it is called. Consider a `Conv2D` layer: it can only be called on a single input tensor of rank 4. As such, you can set, in `__init__()`: `self.input_spec = tf.keras.layers.InputSpec(ndim=4)` Now, if you try to call the layer on an input that isn't rank 4 (for instance, an input of shape `(2,)`, it will raise a nicely-formatted error: `ValueError: Input 0 of layer conv2d is incompatible with the layer: expected ndim=4, found ndim=1. Full shape received: [2]` Input checks that can be specified via `input_spec` include: Structure (e.g. a single input, a list of 2 inputs, etc) Shape Rank (ndim) Dtype For more information, see `tf.keras.layers.InputSpec`.
`jit_compile`	Specify whether to compile the model with XLA. XLA is an optimizing compiler for machine learning. `jit_compile` is not enabled by default. Note that `jit_compile=True` may not necessarily work for all models. For more information on supported operations please refer to the XLA documentation. Also refer to known XLA issues for more details.
`layers`
`losses`	List of losses added using the `add_loss()` API. Variable regularization tensors are created when this property is accessed, so it is eager safe: accessing `losses` under a `tf.GradientTape` will propagate gradients back to the corresponding variables. `class MyLayer(tf.keras.layers.Layer):` `def call(self, inputs):` `self.add_loss(tf.abs(tf.reduce_mean(inputs)))` `return inputs` `l = MyLayer()` `l(np.ones((10, 1)))` `l.losses` `[1.0]` `inputs = tf.keras.Input(shape=(10,))` `x = tf.keras.layers.Dense(10)(inputs)` `outputs = tf.keras.layers.Dense(1)(x)` `model = tf.keras.Model(inputs, outputs)` `# Activity regularization.` `len(model.losses)` `0` `model.add_loss(tf.abs(tf.reduce_mean(x)))` `len(model.losses)` `1` `inputs = tf.keras.Input(shape=(10,))` `d = tf.keras.layers.Dense(10, kernel_initializer='ones')` `x = d(inputs)` `outputs = tf.keras.layers.Dense(1)(x)` `model = tf.keras.Model(inputs, outputs)` `# Weight regularization.` `model.add_loss(lambda: tf.reduce_mean(d.kernel))` `model.losses` `[<tf.Tensor: shape=(), dtype=float32, numpy=1.0>]`
`metrics`	Return metrics added using `compile()` or `add_metric()`. Note: Metrics passed to `compile()` are available only after a `keras.Model` has been trained/evaluated on actual data. `inputs = tf.keras.layers.Input(shape=(3,))` `outputs = tf.keras.layers.Dense(2)(inputs)` `model = tf.keras.models.Model(inputs=inputs, outputs=outputs)` `model.compile(optimizer="Adam", loss="mse", metrics=["mae"])` `[m.name for m in model.metrics]` `[]` `x = np.random.random((2, 3))` `y = np.random.randint(0, 2, (2, 2))` `model.fit(x, y)` `[m.name for m in model.metrics]` `['loss', 'mae']` `inputs = tf.keras.layers.Input(shape=(3,))` `d = tf.keras.layers.Dense(2, name='out')` `output_1 = d(inputs)` `output_2 = d(inputs)` `model = tf.keras.models.Model(` `inputs=inputs, outputs=[output_1, output_2])` `model.add_metric(` `tf.reduce_sum(output_2), name='mean', aggregation='mean')` `model.compile(optimizer="Adam", loss="mse", metrics=["mae", "acc"])` `model.fit(x, (y, y))` `[m.name for m in model.metrics]` `['loss', 'out_loss', 'out_1_loss', 'out_mae', 'out_acc', 'out_1_mae',` `'out_1_acc', 'mean']`
`metrics_names`	Returns the model's display labels for all outputs. Note: `metrics_names` are available only after a `keras.Model` has been trained/evaluated on actual data. `inputs = tf.keras.layers.Input(shape=(3,))` `outputs = tf.keras.layers.Dense(2)(inputs)` `model = tf.keras.models.Model(inputs=inputs, outputs=outputs)` `model.compile(optimizer="Adam", loss="mse", metrics=["mae"])` `model.metrics_names` `[]` `x = np.random.random((2, 3))` `y = np.random.randint(0, 2, (2, 2))` `model.fit(x, y)` `model.metrics_names` `['loss', 'mae']` `inputs = tf.keras.layers.Input(shape=(3,))` `d = tf.keras.layers.Dense(2, name='out')` `output_1 = d(inputs)` `output_2 = d(inputs)` `model = tf.keras.models.Model(` `inputs=inputs, outputs=[output_1, output_2])` `model.compile(optimizer="Adam", loss="mse", metrics=["mae", "acc"])` `model.fit(x, (y, y))` `model.metrics_names` `['loss', 'out_loss', 'out_1_loss', 'out_mae', 'out_acc', 'out_1_mae',` `'out_1_acc']`
`non_trainable_weights`	List of all non-trainable weights tracked by this layer. Non-trainable weights are not updated during training. They are expected to be updated manually in `call()`.
`output`	Retrieves the output tensor(s) of a layer. Only applicable if the layer has exactly one output, i.e. if it is connected to one incoming layer.
`output_specs`	A dict of {level: TensorShape} pairs for the model output.
`run_eagerly`	Settable attribute indicating whether the model should run eagerly. Running eagerly means that your model will be run step by step, like Python code. Your model might run slower, but it should become easier for you to debug it by stepping into individual layer calls. By default, we will attempt to compile your model to a static graph to deliver the best execution performance.
`steps_per_execution`	Settable `steps_per_execution variable. Requires a compiled model. </td> </tr><tr> <td>`supports_masking`<a id="supports_masking"></a> </td> <td> Whether this layer supports computing a mask using`compute_mask`. </td> </tr><tr> <td>`trainable`
`trainable_weights`	List of all trainable weights tracked by this layer. Trainable weights are updated via gradient descent during training.
`variable_dtype`	Alias of `Layer.dtype`, the dtype of the weights.
`weights`	Returns the list of all layer variables/weights. Note: This will not track the weights of nested `tf.Modules` that are not themselves Keras layers.

Methods

`add_loss`

add_loss(
    losses, **kwargs
)

Add loss tensor(s), potentially dependent on layer inputs.

Some losses (for instance, activity regularization losses) may be dependent on the inputs passed when calling a layer. Hence, when reusing the same layer on different inputs a and b, some entries in layer.losses may be dependent on a and some on b. This method automatically keeps track of dependencies.

This method can be used inside a subclassed layer or model's call function, in which case losses should be a Tensor or list of Tensors.

Example:

class MyLayer(tf.keras.layers.Layer):
  def call(self, inputs):
    self.add_loss(tf.abs(tf.reduce_mean(inputs)))
    return inputs

The same code works in distributed training: the input to add_loss() is treated like a regularization loss and averaged across replicas by the training loop (both built-in Model.fit() and compliant custom training loops).

The add_loss method can also be called directly on a Functional Model during construction. In this case, any loss Tensors passed to this Model must be symbolic and be able to be traced back to the model's Inputs. These losses become part of the model's topology and are tracked in get_config.

Example:

inputs = tf.keras.Input(shape=(10,))
x = tf.keras.layers.Dense(10)(inputs)
outputs = tf.keras.layers.Dense(1)(x)
model = tf.keras.Model(inputs, outputs)
# Activity regularization.
model.add_loss(tf.abs(tf.reduce_mean(x)))

If this is not the case for your loss (if, for example, your loss references a Variable of one of the model's layers), you can wrap your loss in a zero-argument lambda. These losses are not tracked as part of the model's topology since they can't be serialized.

Example:

inputs = tf.keras.Input(shape=(10,))
d = tf.keras.layers.Dense(10)
x = d(inputs)
outputs = tf.keras.layers.Dense(1)(x)
model = tf.keras.Model(inputs, outputs)
# Weight regularization.
model.add_loss(lambda: tf.reduce_mean(d.kernel))

Args
`losses`	Loss tensor, or list/tuple of tensors. Rather than tensors, losses may also be zero-argument callables which create a loss tensor.
`**kwargs`	Used for backwards compatibility only.

`build`

build(
    input_shape
)

Builds the model based on input shapes received.

This is to be used for subclassed models, which do not know at instantiation time what their inputs look like.

This method only exists for users who want to call model.build() in a standalone way (as a substitute for calling the model on real data to build it). It will never be called by the framework (and thus it will never throw unexpected errors in an unrelated workflow).

Args
`input_shape`	Single tuple, `TensorShape` instance, or list/dict of shapes, where shapes are tuples, integers, or `TensorShape` instances.

Raises

Raises
`ValueError`	In case of invalid user-provided data (not of type tuple, list, `TensorShape`, or dict). If the model requires call arguments that are agnostic to the input shapes (positional or keyword arg in call signature). If not all layers were properly built. If float type inputs are not supported within the layers. In each of these cases, the user should build their model by calling it on real tensor data.

ValueError

In case of invalid user-provided data (not of type tuple, list, TensorShape, or dict).
If the model requires call arguments that are agnostic to the input shapes (positional or keyword arg in call signature).
If not all layers were properly built.
If float type inputs are not supported within the layers.

In each of these cases, the user should build their model by calling it on real tensor data.

`build_from_config`

build_from_config(
    config
)

Builds the layer's states with the supplied config dict.

By default, this method calls the build(config["input_shape"]) method, which creates weights based on the layer's input shape in the supplied config. If your config contains other information needed to load the layer's state, you should override this method.

Args
`config`	Dict containing the input shape associated with this layer.

`call`

call(
    inputs, training=None, mask=None
)

Calls the model on new inputs and returns the outputs as tensors.

In this case call() just reapplies all ops in the graph to the new inputs (e.g. build a new computational graph from the provided inputs).

Args
`inputs`	Input tensor, or dict/list/tuple of input tensors.
`training`	Boolean or boolean scalar tensor, indicating whether to run the `Network` in training mode or inference mode.
`mask`	A mask or list of masks. A mask can be either a boolean tensor or None (no mask). For more details, check the guide here.

Returns
A tensor if there is a single output, or a list of tensors if there are more than one outputs.

`compile`

compile(
    optimizer='rmsprop',
    loss=None,
    metrics=None,
    loss_weights=None,
    weighted_metrics=None,
    run_eagerly=None,
    steps_per_execution=None,
    jit_compile=None,
    pss_evaluation_shards=0,
    **kwargs
)

Configures the model for training.

Example:

model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=1e-3),
              loss=tf.keras.losses.BinaryCrossentropy(),
              metrics=[tf.keras.metrics.BinaryAccuracy(),
                       tf.keras.metrics.FalseNegatives()])

Args
`optimizer`	String (name of optimizer) or optimizer instance. See `tf.keras.optimizers`.
`loss`	Loss function. May be a string (name of loss function), or a `tf.keras.losses.Loss` instance. See `tf.keras.losses`. A loss function is any callable with the signature `loss = fn(y_true, y_pred)`, where `y_true` are the ground truth values, and `y_pred` are the model's predictions. `y_true` should have shape `(batch_size, d0, .. dN)` (except in the case of sparse loss functions such as sparse categorical crossentropy which expects integer arrays of shape `(batch_size, d0, .. dN-1)`). `y_pred` should have shape `(batch_size, d0, .. dN)`. The loss function should return a float tensor. If a custom `Loss` instance is used and reduction is set to `None`, return value has shape `(batch_size, d0, .. dN-1)` i.e. per-sample or per-timestep loss values; otherwise, it is a scalar. If the model has multiple outputs, you can use a different loss on each output by passing a dictionary or a list of losses. The loss value that will be minimized by the model will then be the sum of all individual losses, unless `loss_weights` is specified.
`metrics`	List of metrics to be evaluated by the model during training and testing. Each of this can be a string (name of a built-in function), function or a `tf.keras.metrics.Metric` instance. See `tf.keras.metrics`. Typically you will use `metrics=['accuracy']`. A function is any callable with the signature `result = fn(y_true, y_pred)`. To specify different metrics for different outputs of a multi-output model, you could also pass a dictionary, such as `metrics={'output_a':'accuracy', 'output_b':['accuracy', 'mse']}`. You can also pass a list to specify a metric or a list of metrics for each output, such as `metrics=[['accuracy'], ['accuracy', 'mse']]` or `metrics=['accuracy', ['accuracy', 'mse']]`. When you pass the strings 'accuracy' or 'acc', we convert this to one of `tf.keras.metrics.BinaryAccuracy`, `tf.keras.metrics.CategoricalAccuracy`, `tf.keras.metrics.SparseCategoricalAccuracy` based on the shapes of the targets and of the model output. We do a similar conversion for the strings 'crossentropy' and 'ce' as well. The metrics passed here are evaluated without sample weighting; if you would like sample weighting to apply, you can specify your metrics via the `weighted_metrics` argument instead.
`loss_weights`	Optional list or dictionary specifying scalar coefficients (Python floats) to weight the loss contributions of different model outputs. The loss value that will be minimized by the model will then be the weighted sum of all individual losses, weighted by the `loss_weights` coefficients. If a list, it is expected to have a 1:1 mapping to the model's outputs. If a dict, it is expected to map output names (strings) to scalar coefficients.
`weighted_metrics`	List of metrics to be evaluated and weighted by `sample_weight` or `class_weight` during training and testing.
`run_eagerly`	Bool. If `True`, this `Model`'s logic will not be wrapped in a `tf.function`. Recommended to leave this as `None` unless your `Model` cannot be run inside a `tf.function`. `run_eagerly=True` is not supported when using `tf.distribute.experimental.ParameterServerStrategy`. Defaults to `False`.
`steps_per_execution`	Int or `'auto'`. The number of batches to run during each `tf.function` call. If set to "auto", keras will automatically tune `steps_per_execution` during runtime. Running multiple batches inside a single `tf.function` call can greatly improve performance on TPUs, when used with distributed strategies such as `ParameterServerStrategy`, or with small models with a large Python overhead. At most, one full epoch will be run each execution. If a number larger than the size of the epoch is passed, the execution will be truncated to the size of the epoch. Note that if `steps_per_execution` is set to `N`, `Callback.on_batch_begin` and `Callback.on_batch_end` methods will only be called every `N` batches (i.e. before/after each `tf.function` execution). Defaults to `1`.
`jit_compile`	If `True`, compile the model training step with XLA. XLA is an optimizing compiler for machine learning. `jit_compile` is not enabled for by default. Note that `jit_compile=True` may not necessarily work for all models. For more information on supported operations please refer to the XLA documentation. Also refer to known XLA issues for more details.
`pss_evaluation_shards`	Integer or 'auto'. Used for `tf.distribute.ParameterServerStrategy` training only. This arg sets the number of shards to split the dataset into, to enable an exact visitation guarantee for evaluation, meaning the model will be applied to each dataset element exactly once, even if workers fail. The dataset must be sharded to ensure separate workers do not process the same data. The number of shards should be at least the number of workers for good performance. A value of 'auto' turns on exact evaluation and uses a heuristic for the number of shards based on the number of workers. 0, meaning no visitation guarantee is provided. NOTE: Custom implementations of `Model.test_step` will be ignored when doing exact evaluation. Defaults to `0`.
`**kwargs`	Arguments supported for backwards compatibility only.

`compile_from_config`

compile_from_config(
    config
)

Compiles the model with the information given in config.

This method uses the information in the config (optimizer, loss, metrics, etc.) to compile the model.

Args
`config`	Dict containing information for compiling the model.

`compute_loss`

compute_loss(
    x=None, y=None, y_pred=None, sample_weight=None
)

Compute the total loss, validate it, and return it.

Subclasses can optionally override this method to provide custom loss computation logic.

Example:

class MyModel(tf.keras.Model):

  def __init__(self, *args, **kwargs):
    super(MyModel, self).__init__(*args, **kwargs)
    self.loss_tracker = tf.keras.metrics.Mean(name='loss')

  def compute_loss(self, x, y, y_pred, sample_weight):
    loss = tf.reduce_mean(tf.math.squared_difference(y_pred, y))
    loss += tf.add_n(self.losses)
    self.loss_tracker.update_state(loss)
    return loss

  def reset_metrics(self):
    self.loss_tracker.reset_states()

  @property
  def metrics(self):
    return [self.loss_tracker]

tensors = tf.random.uniform((10, 10)), tf.random.uniform((10,))
dataset = tf.data.Dataset.from_tensor_slices(tensors).repeat().batch(1)

inputs = tf.keras.layers.Input(shape=(10,), name='my_input')
outputs = tf.keras.layers.Dense(10)(inputs)
model = MyModel(inputs, outputs)
model.add_loss(tf.reduce_sum(outputs))

optimizer = tf.keras.optimizers.SGD()
model.compile(optimizer, loss='mse', steps_per_execution=10)
model.fit(dataset, epochs=2, steps_per_epoch=10)
print('My custom loss: ', model.loss_tracker.result().numpy())

Args
`x`	Input data.
`y`	Target data.
`y_pred`	Predictions returned by the model (output of `model(x)`)
`sample_weight`	Sample weights for weighting the loss function.

Returns
The total loss as a `tf.Tensor`, or `None` if no loss results (which is the case when called by `Model.test_step`).

`compute_mask`

compute_mask(
    inputs, mask=None
)

Computes an output mask tensor.

Args
`inputs`	Tensor or list of tensors.
`mask`	Tensor or list of tensors.

Returns
None or a tensor (or list of tensors, one per output tensor of the layer).

`compute_metrics`

compute_metrics(
    x, y, y_pred, sample_weight
)

Update metric states and collect all metrics to be returned.

Subclasses can optionally override this method to provide custom metric updating and collection logic.

Example:

class MyModel(tf.keras.Sequential):

  def compute_metrics(self, x, y, y_pred, sample_weight):

    # This super call updates `self.compiled_metrics` and returns
    # results for all metrics listed in `self.metrics`.
    metric_results = super(MyModel, self).compute_metrics(
        x, y, y_pred, sample_weight)

    # Note that `self.custom_metric` is not listed in `self.metrics`.
    self.custom_metric.update_state(x, y, y_pred, sample_weight)
    metric_results['custom_metric_name'] = self.custom_metric.result()
    return metric_results

Args
`x`	Input data.
`y`	Target data.
`y_pred`	Predictions returned by the model (output of `model.call(x)`)
`sample_weight`	Sample weights for weighting the loss function.

Returns
A `dict` containing values that will be passed to `tf.keras.callbacks.CallbackList.on_train_batch_end()`. Typically, the values of the metrics listed in `self.metrics` are returned. Example: `{'loss': 0.2, 'accuracy': 0.7}`.

`compute_output_shape`

compute_output_shape(
    input_shape
)

Computes the output shape of the layer.

This method will cause the layer's state to be built, if that has not happened before. This requires that the layer will later be used with inputs that match the input shape provided here.

Args
`input_shape`	Shape tuple (tuple of integers) or `tf.TensorShape`, or structure of shape tuples / `tf.TensorShape` instances (one per output tensor of the layer). Shape tuples can include None for free dimensions, instead of an integer.

Returns
A `tf.TensorShape` instance or structure of `tf.TensorShape` instances.

`count_params`

count_params()

Count the total number of scalars composing the weights.

Returns
An integer count.

Raises
`ValueError`	if the layer isn't yet built (in which case its weights aren't yet defined).

`evaluate`

evaluate(
    x=None,
    y=None,
    batch_size=None,
    verbose='auto',
    sample_weight=None,
    steps=None,
    callbacks=None,
    max_queue_size=10,
    workers=1,
    use_multiprocessing=False,
    return_dict=False,
    **kwargs
)

Returns the loss value & metrics values for the model in test mode.

Computation is done in batches (see the batch_size arg.)

Args
`x`	Input data. It could be: A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs). A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs). A dict mapping input names to the corresponding array/tensors, if the model has named inputs. A `tf.data` dataset. Should return a tuple of either `(inputs, targets)` or `(inputs, targets, sample_weights)`. A generator or `keras.utils.Sequence` returning `(inputs, targets)` or `(inputs, targets, sample_weights)`. A more detailed description of unpacking behavior for iterator types (Dataset, generator, Sequence) is given in the `Unpacking behavior for iterator-like inputs` section of `Model.fit`.
`y`	Target data. Like the input data `x`, it could be either Numpy array(s) or TensorFlow tensor(s). It should be consistent with `x` (you cannot have Numpy inputs and tensor targets, or inversely). If `x` is a dataset, generator or `keras.utils.Sequence` instance, `y` should not be specified (since targets will be obtained from the iterator/dataset).
`batch_size`	Integer or `None`. Number of samples per batch of computation. If unspecified, `batch_size` will default to 32. Do not specify the `batch_size` if your data is in the form of a dataset, generators, or `keras.utils.Sequence` instances (since they generate batches).
`verbose`	`"auto"`, 0, 1, or 2. Verbosity mode. 0 = silent, 1 = progress bar, 2 = single line. `"auto"` becomes 1 for most cases, and to 2 when used with `ParameterServerStrategy`. Note that the progress bar is not particularly useful when logged to a file, so `verbose=2` is recommended when not running interactively (e.g. in a production environment). Defaults to 'auto'.
`sample_weight`	Optional Numpy array of weights for the test samples, used for weighting the loss function. You can either pass a flat (1D) Numpy array with the same length as the input samples (1:1 mapping between weights and samples), or in the case of temporal data, you can pass a 2D array with shape `(samples, sequence_length)`, to apply a different weight to every timestep of every sample. This argument is not supported when `x` is a dataset, instead pass sample weights as the third element of `x`.
`steps`	Integer or `None`. Total number of steps (batches of samples) before declaring the evaluation round finished. Ignored with the default value of `None`. If x is a `tf.data` dataset and `steps` is None, 'evaluate' will run until the dataset is exhausted. This argument is not supported with array inputs.
`callbacks`	List of `keras.callbacks.Callback` instances. List of callbacks to apply during evaluation. See callbacks.
`max_queue_size`	Integer. Used for generator or `keras.utils.Sequence` input only. Maximum size for the generator queue. If unspecified, `max_queue_size` will default to 10.
`workers`	Integer. Used for generator or `keras.utils.Sequence` input only. Maximum number of processes to spin up when using process-based threading. If unspecified, `workers` will default to 1.
`use_multiprocessing`	Boolean. Used for generator or `keras.utils.Sequence` input only. If `True`, use process-based threading. If unspecified, `use_multiprocessing` will default to `False`. Note that because this implementation relies on multiprocessing, you should not pass non-pickleable arguments to the generator as they can't be passed easily to children processes.
`return_dict`	If `True`, loss and metric results are returned as a dict, with each key being the name of the metric. If `False`, they are returned as a list.
`**kwargs`	Unused at this time.

See the discussion of Unpacking behavior for iterator-like inputs for Model.fit.

Returns
Scalar test loss (if the model has a single output and no metrics) or list of scalars (if the model has multiple outputs and/or metrics). The attribute `model.metrics_names` will give you the display labels for the scalar outputs.

Raises
`RuntimeError`	If `model.evaluate` is wrapped in a `tf.function`.

`export`

export(
    filepath
)

Create a SavedModel artifact for inference (e.g. via TF-Serving).

This method lets you export a model to a lightweight SavedModel artifact that contains the model's forward pass only (its call() method) and can be served via e.g. TF-Serving. The forward pass is registered under the name serve() (see example below).

The original code of the model (including any custom layers you may have used) is no longer necessary to reload the artifact -- it is entirely standalone.

Args
`filepath`	`str` or `pathlib.Path` object. Path where to save the artifact.

Example:

# Create the artifact
model.export("path/to/location")

# Later, in a different process / environment...
reloaded_artifact = tf.saved_model.load("path/to/location")
predictions = reloaded_artifact.serve(input_data)

If you would like to customize your serving endpoints, you can use the lower-level keras.export.ExportArchive class. The export() method relies on ExportArchive internally.

`fit`

fit(
    x=None,
    y=None,
    batch_size=None,
    epochs=1,
    verbose='auto',
    callbacks=None,
    validation_split=0.0,
    validation_data=None,
    shuffle=True,
    class_weight=None,
    sample_weight=None,
    initial_epoch=0,
    steps_per_epoch=None,
    validation_steps=None,
    validation_batch_size=None,
    validation_freq=1,
    max_queue_size=10,
    workers=1,
    use_multiprocessing=False
)

Trains the model for a fixed number of epochs (dataset iterations).

Args
`x`	Input data. It could be: A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs). A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs). A dict mapping input names to the corresponding array/tensors, if the model has named inputs. A `tf.data` dataset. Should return a tuple of either `(inputs, targets)` or `(inputs, targets, sample_weights)`. A generator or `keras.utils.Sequence` returning `(inputs, targets)` or `(inputs, targets, sample_weights)`. A `tf.keras.utils.experimental.DatasetCreator`, which wraps a callable that takes a single argument of type `tf.distribute.InputContext`, and returns a `tf.data.Dataset`. `DatasetCreator` should be used when users prefer to specify the per-replica batching and sharding logic for the `Dataset`. See `tf.keras.utils.experimental.DatasetCreator` doc for more information. A more detailed description of unpacking behavior for iterator types (Dataset, generator, Sequence) is given below. If these include `sample_weights` as a third component, note that sample weighting applies to the `weighted_metrics` argument but not the `metrics` argument in `compile()`. If using `tf.distribute.experimental.ParameterServerStrategy`, only `DatasetCreator` type is supported for `x`.
`y`	Target data. Like the input data `x`, it could be either Numpy array(s) or TensorFlow tensor(s). It should be consistent with `x` (you cannot have Numpy inputs and tensor targets, or inversely). If `x` is a dataset, generator, or `keras.utils.Sequence` instance, `y` should not be specified (since targets will be obtained from `x`).
`batch_size`	Integer or `None`. Number of samples per gradient update. If unspecified, `batch_size` will default to 32. Do not specify the `batch_size` if your data is in the form of datasets, generators, or `keras.utils.Sequence` instances (since they generate batches).
`epochs`	Integer. Number of epochs to train the model. An epoch is an iteration over the entire `x` and `y` data provided (unless the `steps_per_epoch` flag is set to something other than None). Note that in conjunction with `initial_epoch`, `epochs` is to be understood as "final epoch". The model is not trained for a number of iterations given by `epochs`, but merely until the epoch of index `epochs` is reached.
`verbose`	'auto', 0, 1, or 2. Verbosity mode. 0 = silent, 1 = progress bar, 2 = one line per epoch. 'auto' becomes 1 for most cases, but 2 when used with `ParameterServerStrategy`. Note that the progress bar is not particularly useful when logged to a file, so verbose=2 is recommended when not running interactively (eg, in a production environment). Defaults to 'auto'.
`callbacks`	List of `keras.callbacks.Callback` instances. List of callbacks to apply during training. See `tf.keras.callbacks`. Note `tf.keras.callbacks.ProgbarLogger` and `tf.keras.callbacks.History` callbacks are created automatically and need not be passed into `model.fit`. `tf.keras.callbacks.ProgbarLogger` is created or not based on `verbose` argument to `model.fit`. Callbacks with batch-level calls are currently unsupported with `tf.distribute.experimental.ParameterServerStrategy`, and users are advised to implement epoch-level calls instead with an appropriate `steps_per_epoch` value.
`validation_split`	Float between 0 and 1. Fraction of the training data to be used as validation data. The model will set apart this fraction of the training data, will not train on it, and will evaluate the loss and any model metrics on this data at the end of each epoch. The validation data is selected from the last samples in the `x` and `y` data provided, before shuffling. This argument is not supported when `x` is a dataset, generator or `keras.utils.Sequence` instance. If both `validation_data` and `validation_split` are provided, `validation_data` will override `validation_split`. `validation_split` is not yet supported with `tf.distribute.experimental.ParameterServerStrategy`.
`validation_data`	Data on which to evaluate the loss and any model metrics at the end of each epoch. The model will not be trained on this data. Thus, note the fact that the validation loss of data provided using `validation_split` or `validation_data` is not affected by regularization layers like noise and dropout. `validation_data` will override `validation_split`. `validation_data` could be: A tuple `(x_val, y_val)` of Numpy arrays or tensors. A tuple `(x_val, y_val, val_sample_weights)` of NumPy arrays. A `tf.data.Dataset`. A Python generator or `keras.utils.Sequence` returning `(inputs, targets)` or `(inputs, targets, sample_weights)`. `validation_data` is not yet supported with `tf.distribute.experimental.ParameterServerStrategy`.
`shuffle`	Boolean (whether to shuffle the training data before each epoch) or str (for 'batch'). This argument is ignored when `x` is a generator or an object of tf.data.Dataset. 'batch' is a special option for dealing with the limitations of HDF5 data; it shuffles in batch-sized chunks. Has no effect when `steps_per_epoch` is not `None`.
`class_weight`	Optional dictionary mapping class indices (integers) to a weight (float) value, used for weighting the loss function (during training only). This can be useful to tell the model to "pay more attention" to samples from an under-represented class. When `class_weight` is specified and targets have a rank of 2 or greater, either `y` must be one-hot encoded, or an explicit final dimension of `1` must be included for sparse class labels.
`sample_weight`	Optional Numpy array of weights for the training samples, used for weighting the loss function (during training only). You can either pass a flat (1D) Numpy array with the same length as the input samples (1:1 mapping between weights and samples), or in the case of temporal data, you can pass a 2D array with shape `(samples, sequence_length)`, to apply a different weight to every timestep of every sample. This argument is not supported when `x` is a dataset, generator, or `keras.utils.Sequence` instance, instead provide the sample_weights as the third element of `x`. Note that sample weighting does not apply to metrics specified via the `metrics` argument in `compile()`. To apply sample weighting to your metrics, you can specify them via the `weighted_metrics` in `compile()` instead.
`initial_epoch`	Integer. Epoch at which to start training (useful for resuming a previous training run).
`steps_per_epoch`	Integer or `None`. Total number of steps (batches of samples) before declaring one epoch finished and starting the next epoch. When training with input tensors such as TensorFlow data tensors, the default `None` is equal to the number of samples in your dataset divided by the batch size, or 1 if that cannot be determined. If x is a `tf.data` dataset, and 'steps_per_epoch' is None, the epoch will run until the input dataset is exhausted. When passing an infinitely repeating dataset, you must specify the `steps_per_epoch` argument. If `steps_per_epoch=-1` the training will run indefinitely with an infinitely repeating dataset. This argument is not supported with array inputs. When using `tf.distribute.experimental.ParameterServerStrategy`: `steps_per_epoch=None` is not supported.
`validation_steps`	Only relevant if `validation_data` is provided and is a `tf.data` dataset. Total number of steps (batches of samples) to draw before stopping when performing validation at the end of every epoch. If 'validation_steps' is None, validation will run until the `validation_data` dataset is exhausted. In the case of an infinitely repeated dataset, it will run into an infinite loop. If 'validation_steps' is specified and only part of the dataset will be consumed, the evaluation will start from the beginning of the dataset at each epoch. This ensures that the same validation samples are used every time.
`validation_batch_size`	Integer or `None`. Number of samples per validation batch. If unspecified, will default to `batch_size`. Do not specify the `validation_batch_size` if your data is in the form of datasets, generators, or `keras.utils.Sequence` instances (since they generate batches).
`validation_freq`	Only relevant if validation data is provided. Integer or `collections.abc.Container` instance (e.g. list, tuple, etc.). If an integer, specifies how many training epochs to run before a new validation run is performed, e.g. `validation_freq=2` runs validation every 2 epochs. If a Container, specifies the epochs on which to run validation, e.g. `validation_freq=[1, 2, 10]` runs validation at the end of the 1st, 2nd, and 10th epochs.
`max_queue_size`	Integer. Used for generator or `keras.utils.Sequence` input only. Maximum size for the generator queue. If unspecified, `max_queue_size` will default to 10.
`workers`	Integer. Used for generator or `keras.utils.Sequence` input only. Maximum number of processes to spin up when using process-based threading. If unspecified, `workers` will default to 1.
`use_multiprocessing`	Boolean. Used for generator or `keras.utils.Sequence` input only. If `True`, use process-based threading. If unspecified, `use_multiprocessing` will default to `False`. Note that because this implementation relies on multiprocessing, you should not pass non-pickleable arguments to the generator as they can't be passed easily to children processes.

Unpacking behavior for iterator-like inputs: A common pattern is to pass a tf.data.Dataset, generator, or tf.keras.utils.Sequence to the x argument of fit, which will in fact yield not only features (x) but optionally targets (y) and sample weights. Keras requires that the output of such iterator-likes be unambiguous. The iterator should return a tuple of length 1, 2, or 3, where the optional second and third elements will be used for y and sample_weight respectively. Any other type provided will be wrapped in a length one tuple, effectively treating everything as 'x'. When yielding dicts, they should still adhere to the top-level tuple structure. e.g. ({"x0": x0, "x1": x1}, y). Keras will not attempt to separate features, targets, and weights from the keys of a single dict. A notable unsupported data type is the namedtuple. The reason is that it behaves like both an ordered datatype (tuple) and a mapping datatype (dict). So given a namedtuple of the form: namedtuple("example_tuple", ["y", "x"]) it is ambiguous whether to reverse the order of the elements when interpreting the value. Even worse is a tuple of the form: namedtuple("other_tuple", ["x", "y", "z"]) where it is unclear if the tuple was intended to be unpacked into x, y, and sample_weight or passed through as a single element to x. As a result the data processing code will simply raise a ValueError if it encounters a namedtuple. (Along with instructions to remedy the issue.)

Returns
A `History` object. Its `History.history` attribute is a record of training loss values and metrics values at successive epochs, as well as validation loss values and validation metrics values (if applicable).

Raises

Raises
`RuntimeError`	If the model was never compiled or, If `model.fit` is wrapped in `tf.function`.
`ValueError`	In case of mismatch between the provided input data and what the model expects or when the input data is empty.

RuntimeError

If the model was never compiled or,
If model.fit is wrapped in tf.function.

ValueError In case of mismatch between the provided input data and what the model expects or when the input data is empty.

`from_config`

View source

@classmethod
from_config(
    config, custom_objects=None
)

Creates a layer from its config.

This method is the reverse of get_config, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Network), nor weights (handled by set_weights).

Args
`config`	A Python dictionary, typically the output of get_config.

Returns
A layer instance.

`get_build_config`

get_build_config()

Returns a dictionary with the layer's input shape.

This method returns a config dict that can be used by build_from_config(config) to create all states (e.g. Variables and Lookup tables) needed by the layer.

By default, the config only contains the input shape that the layer was built with. If you're writing a custom layer that creates state in an unusual way, you should override this method to make sure this state is already created when Keras attempts to load its value upon model loading.

Returns
A dict containing the input shape associated with the layer.

`get_compile_config`

get_compile_config()

Returns a serialized config with information for compiling the model.

This method returns a config dictionary containing all the information (optimizer, loss, metrics, etc.) with which the model was compiled.

Returns
A dict containing information for compiling the model.

`get_config`

View source

get_config()

Returns the config of the Model.

Config is a Python dictionary (serializable) containing the configuration of an object, which in this case is a Model. This allows the Model to be be reinstantiated later (without its trained weights) from this configuration.

Note that get_config() does not guarantee to return a fresh copy of dict every time it is called. The callers should make a copy of the returned dict if they want to modify it.

Developers of subclassed Model are advised to override this method, and continue to update the dict from super(MyModel, self).get_config() to provide the proper configuration of this Model. The default config will return config dict for init parameters if they are basic types. Raises NotImplementedError when in cases where a custom get_config() implementation is required for the subclassed model.

Returns
Python dictionary containing the configuration of this `Model`.

`get_layer`

get_layer(
    name=None, index=None
)

Retrieves a layer based on either its name (unique) or index.

If name and index are both provided, index will take precedence. Indices are based on order of horizontal graph traversal (bottom-up).

Args
`name`	String, name of layer.
`index`	Integer, index of layer.

Returns
A layer instance.

`get_metrics_result`

get_metrics_result()

Returns the model's metrics values as a dict.

If any of the metric result is a dict (containing multiple metrics), each of them gets added to the top level returned dict of this method.

Returns
A `dict` containing values of the metrics listed in `self.metrics`.
`Example`	`{'loss': 0.2, 'accuracy': 0.7}`.

`get_weight_paths`

get_weight_paths()

Retrieve all the variables and their paths for the model.

The variable path (string) is a stable key to identify a tf.Variable instance owned by the model. It can be used to specify variable-specific configurations (e.g. DTensor, quantization) from a global view.

This method returns a dict with weight object paths as keys and the corresponding tf.Variable instances as values.

Note that if the model is a subclassed model and the weights haven't been initialized, an empty dict will be returned.

Returns
A dict where keys are variable paths and values are `tf.Variable` instances.

Example:

class SubclassModel(tf.keras.Model):

  def __init__(self, name=None):
    super().__init__(name=name)
    self.d1 = tf.keras.layers.Dense(10)
    self.d2 = tf.keras.layers.Dense(20)

  def call(self, inputs):
    x = self.d1(inputs)
    return self.d2(x)

model = SubclassModel()
model(tf.zeros((10, 10)))
weight_paths = model.get_weight_paths()
# weight_paths:
# {
#    'd1.kernel': model.d1.kernel,
#    'd1.bias': model.d1.bias,
#    'd2.kernel': model.d2.kernel,
#    'd2.bias': model.d2.bias,
# }

# Functional model
inputs = tf.keras.Input((10,), batch_size=10)
x = tf.keras.layers.Dense(20, name='d1')(inputs)
output = tf.keras.layers.Dense(30, name='d2')(x)
model = tf.keras.Model(inputs, output)
d1 = model.layers[1]
d2 = model.layers[2]
weight_paths = model.get_weight_paths()
# weight_paths:
# {
#    'd1.kernel': d1.kernel,
#    'd1.bias': d1.bias,
#    'd2.kernel': d2.kernel,
#    'd2.bias': d2.bias,
# }

`get_weights`

get_weights()

Retrieves the weights of the model.

Returns
A flat list of Numpy arrays.

`load_own_variables`

load_own_variables(
    store
)

Loads the state of the layer.

You can override this method to take full control of how the state of the layer is loaded upon calling keras.models.load_model().

Args
`store`	Dict from which the state of the model will be loaded.

`load_weights`

load_weights(
    filepath, skip_mismatch=False, by_name=False, options=None
)

Loads all layer weights from a saved files.

The saved file could be a SavedModel file, a .keras file (v3 saving format), or a file created via model.save_weights().

By default, weights are loaded based on the network's topology. This means the architecture should be the same as when the weights were saved. Note that layers that don't have weights are not taken into account in the topological ordering, so adding or removing layers is fine as long as they don't have weights.

Partial weight loading

If you have modified your model, for instance by adding a new layer (with weights) or by changing the shape of the weights of a layer, you can choose to ignore errors and continue loading by setting skip_mismatch=True. In this case any layer with mismatching weights will be skipped. A warning will be displayed for each skipped layer.

Weight loading by name

If your weights are saved as a .h5 file created via model.save_weights(), you can use the argument by_name=True.

In this case, weights are loaded into layers only if they share the same name. This is useful for fine-tuning or transfer-learning models where some of the layers have changed.

Note that only topological loading (by_name=False) is supported when loading weights from the .keras v3 format or from the TensorFlow SavedModel format.

Args
`filepath`	String, path to the weights file to load. For weight files in TensorFlow format, this is the file prefix (the same as was passed to `save_weights()`). This can also be a path to a SavedModel or a `.keras` file (v3 saving format) saved via `model.save()`.
`skip_mismatch`	Boolean, whether to skip loading of layers where there is a mismatch in the number of weights, or a mismatch in the shape of the weights.
`by_name`	Boolean, whether to load weights by name or by topological order. Only topological loading is supported for weight files in the `.keras` v3 format or in the TensorFlow SavedModel format.
`options`	Optional `tf.train.CheckpointOptions` object that specifies options for loading weights (only valid for a SavedModel file).

`make_predict_function`

make_predict_function(
    force=False
)

Creates a function that executes one step of inference.

This method can be overridden to support custom inference logic. This method is called by Model.predict and Model.predict_on_batch.

Typically, this method directly controls tf.function and tf.distribute.Strategy settings, and delegates the actual evaluation logic to Model.predict_step.

This function is cached the first time Model.predict or Model.predict_on_batch is called. The cache is cleared whenever Model.compile is called. You can skip the cache and generate again the function with force=True.

Args
`force`	Whether to regenerate the predict function and skip the cached function if available.

Returns
Function. The function created by this method should accept a `tf.data.Iterator`, and return the outputs of the `Model`.

`make_test_function`

make_test_function(
    force=False
)

Creates a function that executes one step of evaluation.

This method can be overridden to support custom evaluation logic. This method is called by Model.evaluate and Model.test_on_batch.

Typically, this method directly controls tf.function and tf.distribute.Strategy settings, and delegates the actual evaluation logic to Model.test_step.

This function is cached the first time Model.evaluate or Model.test_on_batch is called. The cache is cleared whenever Model.compile is called. You can skip the cache and generate again the function with force=True.

Args
`force`	Whether to regenerate the test function and skip the cached function if available.

Returns
Function. The function created by this method should accept a `tf.data.Iterator`, and return a `dict` containing values that will be passed to `tf.keras.Callbacks.on_test_batch_end`.

`make_train_function`

make_train_function(
    force=False
)

Creates a function that executes one step of training.

This method can be overridden to support custom training logic. This method is called by Model.fit and Model.train_on_batch.

Typically, this method directly controls tf.function and tf.distribute.Strategy settings, and delegates the actual training logic to Model.train_step.

This function is cached the first time Model.fit or Model.train_on_batch is called. The cache is cleared whenever Model.compile is called. You can skip the cache and generate again the function with force=True.

Args
`force`	Whether to regenerate the train function and skip the cached function if available.

Returns
Function. The function created by this method should accept a `tf.data.Iterator`, and return a `dict` containing values that will be passed to `tf.keras.Callbacks.on_train_batch_end`, such as `{'loss': 0.2, 'accuracy': 0.7}`.

`predict`

predict(
    x,
    batch_size=None,
    verbose='auto',
    steps=None,
    callbacks=None,
    max_queue_size=10,
    workers=1,
    use_multiprocessing=False
)

Generates output predictions for the input samples.

Computation is done in batches. This method is designed for batch processing of large numbers of inputs. It is not intended for use inside of loops that iterate over your data and process small numbers of inputs at a time.

For small numbers of inputs that fit in one batch, directly use __call__() for faster execution, e.g., model(x), or model(x, training=False) if you have layers such as tf.keras.layers.BatchNormalization that behave differently during inference. You may pair the individual model call with a tf.function for additional performance inside your inner loop. If you need access to numpy array values instead of tensors after your model call, you can use tensor.numpy() to get the numpy array value of an eager tensor.

Also, note the fact that test loss is not affected by regularization layers like noise and dropout.

Args
`x`	Input samples. It could be: A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs). A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs). A `tf.data` dataset. A generator or `keras.utils.Sequence` instance. A more detailed description of unpacking behavior for iterator types (Dataset, generator, Sequence) is given in the `Unpacking behavior for iterator-like inputs` section of `Model.fit`.
`batch_size`	Integer or `None`. Number of samples per batch. If unspecified, `batch_size` will default to 32. Do not specify the `batch_size` if your data is in the form of dataset, generators, or `keras.utils.Sequence` instances (since they generate batches).
`verbose`	`"auto"`, 0, 1, or 2. Verbosity mode. 0 = silent, 1 = progress bar, 2 = single line. `"auto"` becomes 1 for most cases, and to 2 when used with `ParameterServerStrategy`. Note that the progress bar is not particularly useful when logged to a file, so `verbose=2` is recommended when not running interactively (e.g. in a production environment). Defaults to 'auto'.
`steps`	Total number of steps (batches of samples) before declaring the prediction round finished. Ignored with the default value of `None`. If x is a `tf.data` dataset and `steps` is None, `predict()` will run until the input dataset is exhausted.
`callbacks`	List of `keras.callbacks.Callback` instances. List of callbacks to apply during prediction. See callbacks.
`max_queue_size`	Integer. Used for generator or `keras.utils.Sequence` input only. Maximum size for the generator queue. If unspecified, `max_queue_size` will default to 10.
`workers`	Integer. Used for generator or `keras.utils.Sequence` input only. Maximum number of processes to spin up when using process-based threading. If unspecified, `workers` will default to 1.
`use_multiprocessing`	Boolean. Used for generator or `keras.utils.Sequence` input only. If `True`, use process-based threading. If unspecified, `use_multiprocessing` will default to `False`. Note that because this implementation relies on multiprocessing, you should not pass non-pickleable arguments to the generator as they can't be passed easily to children processes.

See the discussion of Unpacking behavior for iterator-like inputs for Model.fit. Note that Model.predict uses the same interpretation rules as Model.fit and Model.evaluate, so inputs must be unambiguous for all three methods.

Returns
Numpy array(s) of predictions.

Raises
`RuntimeError`	If `model.predict` is wrapped in a `tf.function`.
`ValueError`	In case of mismatch between the provided input data and the model's expectations, or in case a stateful model receives a number of samples that is not a multiple of the batch size.

`predict_on_batch`

predict_on_batch(
    x
)

Returns predictions for a single batch of samples.

Args
`x`	Input data. It could be: A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs). A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs).

Returns
Numpy array(s) of predictions.

Raises
`RuntimeError`	If `model.predict_on_batch` is wrapped in a `tf.function`.

`predict_step`

predict_step(
    data
)

The logic for one inference step.

This method can be overridden to support custom inference logic. This method is called by Model.make_predict_function.

This method should contain the mathematical logic for one step of inference. This typically includes the forward pass.

Configuration details for how this logic is run (e.g. tf.function and tf.distribute.Strategy settings), should be left to Model.make_predict_function, which can also be overridden.

Args
`data`	A nested structure of `Tensor`s.

Returns
The result of one inference step, typically the output of calling the `Model` on data.

`reset_metrics`

reset_metrics()

Resets the state of all the metrics in the model.

Examples:

inputs = tf.keras.layers.Input(shape=(3,))
outputs = tf.keras.layers.Dense(2)(inputs)
model = tf.keras.models.Model(inputs=inputs, outputs=outputs)
model.compile(optimizer="Adam", loss="mse", metrics=["mae"])

x = np.random.random((2, 3))
y = np.random.randint(0, 2, (2, 2))
_ = model.fit(x, y, verbose=0)
assert all(float(m.result()) for m in model.metrics)

model.reset_metrics()
assert all(float(m.result()) == 0 for m in model.metrics)

`reset_states`

reset_states()

`save`

save(
    filepath, overwrite=True, save_format=None, **kwargs
)

Saves a model as a TensorFlow SavedModel or HDF5 file.

See the Serialization and Saving guide for details.

Args
`model`	Keras model instance to be saved.
`filepath`	`str` or `pathlib.Path` object. Path where to save the model.
`overwrite`	Whether we should overwrite any existing model at the target location, or instead ask the user via an interactive prompt.
`save_format`	Either `"keras"`, `"tf"`, `"h5"`, indicating whether to save the model in the native Keras format (`.keras`), in the TensorFlow SavedModel format (referred to as "SavedModel" below), or in the legacy HDF5 format (`.h5`). Defaults to `"tf"` in TF 2.X, and `"h5"` in TF 1.X.

SavedModel format arguments: include_optimizer: Only applied to SavedModel and legacy HDF5 formats. If False, do not save the optimizer state. Defaults to True. signatures: Only applies to SavedModel format. Signatures to save with the SavedModel. See the signatures argument in tf.saved_model.save for details. options: Only applies to SavedModel format. tf.saved_model.SaveOptions object that specifies SavedModel saving options. save_traces: Only applies to SavedModel format. When enabled, the SavedModel will store the function traces for each layer. This can be disabled, so that only the configs of each layer are stored. Defaults to True. Disabling this will decrease serialization time and reduce file size, but it requires that all custom layers/models implement a get_config() method.

Example:

model = tf.keras.Sequential([
    tf.keras.layers.Dense(5, input_shape=(3,)),
    tf.keras.layers.Softmax()])
model.save("model.keras")
loaded_model = tf.keras.models.load_model("model.keras")
x = tf.random.uniform((10, 3))
assert np.allclose(model.predict(x), loaded_model.predict(x))

Note that model.save() is an alias for tf.keras.models.save_model().

`save_own_variables`

save_own_variables(
    store
)

Saves the state of the layer.

You can override this method to take full control of how the state of the layer is saved upon calling model.save().

Args
`store`	Dict where the state of the model will be saved.

`save_spec`

save_spec(
    dynamic_batch=True
)

Returns the tf.TensorSpec of call args as a tuple (args, kwargs).

This value is automatically defined after calling the model for the first time. Afterwards, you can use it when exporting the model for serving:

model = tf.keras.Model(...)

@tf.function
def serve(*args, **kwargs):
  outputs = model(*args, **kwargs)
  # Apply postprocessing steps, or add additional outputs.
  ...
  return outputs

# arg_specs is `[tf.TensorSpec(...), ...]`. kwarg_specs, in this
# example, is an empty dict since functional models do not use keyword
# arguments.
arg_specs, kwarg_specs = model.save_spec()

model.save(path, signatures={
  'serving_default': serve.get_concrete_function(*arg_specs,
                                                 **kwarg_specs)
})

Args
`dynamic_batch`	Whether to set the batch sizes of all the returned `tf.TensorSpec` to `None`. (Note that when defining functional or Sequential models with `tf.keras.Input([...], batch_size=X)`, the batch size will always be preserved). Defaults to `True`.

Returns
If the model inputs are defined, returns a tuple `(args, kwargs)`. All elements in `args` and `kwargs` are `tf.TensorSpec`. If the model inputs are not defined, returns `None`. The model inputs are automatically set when calling the model, `model.fit`, `model.evaluate` or `model.predict`.

`save_weights`

save_weights(
    filepath, overwrite=True, save_format=None, options=None
)

Saves all layer weights.

Either saves in HDF5 or in TensorFlow format based on the save_format argument.

When saving in HDF5 format, the weight file has:

layer_names (attribute), a list of strings (ordered names of model layers).
For every layer, a group named layer.name
- For every such layer group, a group attribute weight_names, a list of strings (ordered names of weights tensor of the layer).
- For every weight in the layer, a dataset storing the weight value, named after the weight tensor.

When saving in TensorFlow format, all objects referenced by the network are saved in the same format as tf.train.Checkpoint, including any Layer instances or Optimizer instances assigned to object attributes. For networks constructed from inputs and outputs using tf.keras.Model(inputs, outputs), Layer instances used by the network are tracked/saved automatically. For user-defined classes which inherit from tf.keras.Model, Layer instances must be assigned to object attributes, typically in the constructor. See the documentation of tf.train.Checkpoint and tf.keras.Model for details.

While the formats are the same, do not mix save_weights and tf.train.Checkpoint. Checkpoints saved by Model.save_weights should be loaded using Model.load_weights. Checkpoints saved using tf.train.Checkpoint.save should be restored using the corresponding tf.train.Checkpoint.restore. Prefer tf.train.Checkpoint over save_weights for training checkpoints.

The TensorFlow format matches objects and variables by starting at a root object, self for save_weights, and greedily matching attribute names. For Model.save this is the Model, and for Checkpoint.save this is the Checkpoint even if the Checkpoint has a model attached. This means saving a tf.keras.Model using save_weights and loading into a tf.train.Checkpoint with a Model attached (or vice versa) will not match the Model's variables. See the guide to training checkpoints for details on the TensorFlow format.

Args
`filepath`	String or PathLike, path to the file to save the weights to. When saving in TensorFlow format, this is the prefix used for checkpoint files (multiple files are generated). Note that the '.h5' suffix causes weights to be saved in HDF5 format.
`overwrite`	Whether to silently overwrite any existing file at the target location, or provide the user with a manual prompt.
`save_format`	Either 'tf' or 'h5'. A `filepath` ending in '.h5' or '.keras' will default to HDF5 if `save_format` is `None`. Otherwise, `None` becomes 'tf'. Defaults to `None`.
`options`	Optional `tf.train.CheckpointOptions` object that specifies options for saving weights.

Raises
`ImportError`	If `h5py` is not available when attempting to save in HDF5 format.

`set_weights`

set_weights(
    weights
)

Sets the weights of the layer, from NumPy arrays.

The weights of a layer represent the state of the layer. This function sets the weight values from numpy arrays. The weight values should be passed in the order they are created by the layer. Note that the layer's weights must be instantiated before calling this function, by calling the layer.

For example, a Dense layer returns a list of two values: the kernel matrix and the bias vector. These can be used to set the weights of another Dense layer:

layer_a = tf.keras.layers.Dense(1,
  kernel_initializer=tf.constant_initializer(1.))
a_out = layer_a(tf.convert_to_tensor([[1., 2., 3.]]))
layer_a.get_weights()
[array([[1.],
       [1.],
       [1.]], dtype=float32), array([0.], dtype=float32)]
layer_b = tf.keras.layers.Dense(1,
  kernel_initializer=tf.constant_initializer(2.))
b_out = layer_b(tf.convert_to_tensor([[10., 20., 30.]]))
layer_b.get_weights()
[array([[2.],
       [2.],
       [2.]], dtype=float32), array([0.], dtype=float32)]
layer_b.set_weights(layer_a.get_weights())
layer_b.get_weights()
[array([[1.],
       [1.],
       [1.]], dtype=float32), array([0.], dtype=float32)]

Args
`weights`	a list of NumPy arrays. The number of arrays and their shape must match number of the dimensions of the weights of the layer (i.e. it should match the output of `get_weights`).

Raises
`ValueError`	If the provided weights list does not match the layer's specifications.

`summary`

summary(
    line_length=None,
    positions=None,
    print_fn=None,
    expand_nested=False,
    show_trainable=False,
    layer_range=None
)

Prints a string summary of the network.

Args
`line_length`	Total length of printed lines (e.g. set this to adapt the display to different terminal window sizes).
`positions`	Relative or absolute positions of log elements in each line. If not provided, becomes `[0.3, 0.6, 0.70, 1.]`. Defaults to `None`.
`print_fn`	Print function to use. By default, prints to `stdout`. If `stdout` doesn't work in your environment, change to `print`. It will be called on each line of the summary. You can set it to a custom function in order to capture the string summary.
`expand_nested`	Whether to expand the nested models. Defaults to `False`.
`show_trainable`	Whether to show if a layer is trainable. Defaults to `False`.
`layer_range`	a list or tuple of 2 strings, which is the starting layer name and ending layer name (both inclusive) indicating the range of layers to be printed in summary. It also accepts regex patterns instead of exact name. In such case, start predicate will be the first element it matches to `layer_range[0]` and the end predicate will be the last element it matches to `layer_range[1]`. By default `None` which considers all layers of model.

Raises
`ValueError`	if `summary()` is called before the model is built.

`test_on_batch`

test_on_batch(
    x, y=None, sample_weight=None, reset_metrics=True, return_dict=False
)

Test the model on a single batch of samples.

Args
`x`	Input data. It could be: A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs). A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs). A dict mapping input names to the corresponding array/tensors, if the model has named inputs.
`y`	Target data. Like the input data `x`, it could be either Numpy array(s) or TensorFlow tensor(s). It should be consistent with `x` (you cannot have Numpy inputs and tensor targets, or inversely).
`sample_weight`	Optional array of the same length as x, containing weights to apply to the model's loss for each sample. In the case of temporal data, you can pass a 2D array with shape (samples, sequence_length), to apply a different weight to every timestep of every sample.
`reset_metrics`	If `True`, the metrics returned will be only for this batch. If `False`, the metrics will be statefully accumulated across batches.
`return_dict`	If `True`, loss and metric results are returned as a dict, with each key being the name of the metric. If `False`, they are returned as a list.

Returns
Scalar test loss (if the model has a single output and no metrics) or list of scalars (if the model has multiple outputs and/or metrics). The attribute `model.metrics_names` will give you the display labels for the scalar outputs.

Raises
`RuntimeError`	If `model.test_on_batch` is wrapped in a `tf.function`.

`test_step`

test_step(
    data
)

The logic for one evaluation step.

This method can be overridden to support custom evaluation logic. This method is called by Model.make_test_function.

This function should contain the mathematical logic for one step of evaluation. This typically includes the forward pass, loss calculation, and metrics updates.

Configuration details for how this logic is run (e.g. tf.function and tf.distribute.Strategy settings), should be left to Model.make_test_function, which can also be overridden.

Args
`data`	A nested structure of `Tensor`s.

Returns
A `dict` containing values that will be passed to `tf.keras.callbacks.CallbackList.on_train_batch_end`. Typically, the values of the `Model`'s metrics are returned.

`to_json`

to_json(
    **kwargs
)

Returns a JSON string containing the network configuration.

To load a network from a JSON save file, use keras.models.model_from_json(json_string, custom_objects={}).

Args
`**kwargs`	Additional keyword arguments to be passed to *`json.dumps()`.

Returns
A JSON string.

`to_yaml`

to_yaml(
    **kwargs
)

Returns a yaml string containing the network configuration.

To load a network from a yaml save file, use keras.models.model_from_yaml(yaml_string, custom_objects={}).

custom_objects should be a dictionary mapping the names of custom losses / layers / etc to the corresponding functions / classes.

Args
`**kwargs`	Additional keyword arguments to be passed to `yaml.dump()`.

Returns
A YAML string.

Raises
`RuntimeError`	announces that the method poses a security risk

`train_on_batch`

train_on_batch(
    x,
    y=None,
    sample_weight=None,
    class_weight=None,
    reset_metrics=True,
    return_dict=False
)

Runs a single gradient update on a single batch of data.

Args
`x`	Input data. It could be: A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs). A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs). A dict mapping input names to the corresponding array/tensors, if the model has named inputs.
`y`	Target data. Like the input data `x`, it could be either Numpy array(s) or TensorFlow tensor(s).
`sample_weight`	Optional array of the same length as x, containing weights to apply to the model's loss for each sample. In the case of temporal data, you can pass a 2D array with shape (samples, sequence_length), to apply a different weight to every timestep of every sample.
`class_weight`	Optional dictionary mapping class indices (integers) to a weight (float) to apply to the model's loss for the samples from this class during training. This can be useful to tell the model to "pay more attention" to samples from an under-represented class. When `class_weight` is specified and targets have a rank of 2 or greater, either `y` must be one-hot encoded, or an explicit final dimension of `1` must be included for sparse class labels.
`reset_metrics`	If `True`, the metrics returned will be only for this batch. If `False`, the metrics will be statefully accumulated across batches.
`return_dict`	If `True`, loss and metric results are returned as a dict, with each key being the name of the metric. If `False`, they are returned as a list.

Returns
Scalar training loss (if the model has a single output and no metrics) or list of scalars (if the model has multiple outputs and/or metrics). The attribute `model.metrics_names` will give you the display labels for the scalar outputs.

Raises
`RuntimeError`	If `model.train_on_batch` is wrapped in a `tf.function`.

`train_step`

train_step(
    data
)

The logic for one training step.

This method can be overridden to support custom training logic. For concrete examples of how to override this method see Customizing what happens in fit. This method is called by Model.make_train_function.

This method should contain the mathematical logic for one step of training. This typically includes the forward pass, loss calculation, backpropagation, and metric updates.

Configuration details for how this logic is run (e.g. tf.function and tf.distribute.Strategy settings), should be left to Model.make_train_function, which can also be overridden.

Args
`data`	A nested structure of `Tensor`s.

Returns
A `dict` containing values that will be passed to `tf.keras.callbacks.CallbackList.on_train_batch_end`. Typically, the values of the `Model`'s metrics are returned. Example: `{'loss': 0.2, 'accuracy': 0.7}`.

`call`

__call__(
    *args, **kwargs
)

tfm.vision.backbones.SpineNet Stay organized with collections Save and categorize content based on your preferences.

This implements

Args

Attributes

Methods

add_loss

Example:

Example:

Example:

build

build_from_config

call

compile

Example:

compile_from_config

compute_loss

Example:

compute_mask

compute_metrics

Example:

compute_output_shape

count_params

evaluate

export

Example:

fit

from_config

get_build_config

get_compile_config

get_config

get_layer

get_metrics_result

get_weight_paths

Example:

get_weights

load_own_variables

load_weights

make_predict_function

make_test_function

make_train_function

predict

predict_on_batch

predict_step

reset_metrics

Examples:

reset_states

save

Example:

save_own_variables

save_spec

save_weights

set_weights

summary

test_on_batch

test_step

to_json

to_yaml

train_on_batch

train_step

__call__

tfm.vision.backbones.SpineNet
Stay organized with collections Save and categorize content based on your preferences.

`add_loss`

`build`

`build_from_config`

`call`

`compile`

`compile_from_config`

`compute_loss`

`compute_mask`

`compute_metrics`

`compute_output_shape`

`count_params`

`evaluate`

`export`

`fit`

`from_config`

`get_build_config`

`get_compile_config`

`get_config`

`get_layer`

`get_metrics_result`

`get_weight_paths`

`get_weights`

`load_own_variables`

`load_weights`

`make_predict_function`

`make_test_function`

`make_train_function`

`predict`

`predict_on_batch`

`predict_step`

`reset_metrics`

`reset_states`

`save`

`save_own_variables`

`save_spec`

`save_weights`

`set_weights`

`summary`

`test_on_batch`

`test_step`

`to_json`

`to_yaml`

`train_on_batch`

`train_step`

`call`