tf.keras.Sequential

TensorFlow 2 version

View source on GitHub

Linear stack of layers.

Inherits From: Model

View aliases

Main aliases

tf.keras.models.Sequential

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.Sequential, tf.compat.v1.keras.models.Sequential, `tf.compat.v2.keras.Sequential`, `tf.compat.v2.keras.models.Sequential`

tf.keras.Sequential(
    layers=None, name=None
)

Arguments
`layers`	list of layers to add to the model.

Example:

# Optionally, the first layer can receive an `input_shape` argument:
model = Sequential()
model.add(Dense(32, input_shape=(500,)))
# Afterwards, we do automatic shape inference:
model.add(Dense(32))

# This is identical to the following:
model = Sequential()
model.add(Dense(32, input_dim=500))

# And to the following:
model = Sequential()
model.add(Dense(32, batch_input_shape=(None, 500)))

# Note that you can also omit the `input_shape` argument:
# In that case the model gets built the first time you call `fit` (or other
# training and evaluation methods).
model = Sequential()
model.add(Dense(32))
model.add(Dense(32))
model.compile(optimizer=optimizer, loss=loss)
# This builds the model for the first time:
model.fit(x, y, batch_size=32, epochs=10)

# Note that when using this delayed-build pattern (no input shape specified),
# the model doesn't have any weights until the first call
# to a training/evaluation method (since it isn't yet built):
model = Sequential()
model.add(Dense(32))
model.add(Dense(32))
model.weights  # returns []

# Whereas if you specify the input shape, the model gets built continuously
# as you are adding layers:
model = Sequential()
model.add(Dense(32, input_shape=(500,)))
model.add(Dense(32))
model.weights  # returns list of length 4

# When using the delayed-build pattern (no input shape specified), you can
# choose to manually build your model by calling `build(batch_input_shape)`:
model = Sequential()
model.add(Dense(32))
model.add(Dense(32))
model.build((None, 500))
model.weights  # returns list of length 4

Attributes
`layers`
`metrics_names`	Returns the model's display labels for all outputs.
`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.
`sample_weights`
`state_updates`	Returns the `updates` from all layers that are stateful. This is useful for separating training updates and state updates, e.g. when we need to update a layer's internal state during prediction.
`stateful`

Methods

`add`

View source

add(
    layer
)

Adds a layer instance on top of the layer stack.

Arguments
`layer`	layer instance.

Raises
`TypeError`	If `layer` is not a layer instance.
`ValueError`	In case the `layer` argument does not know its input shape.
`ValueError`	In case the `layer` argument has multiple output tensors, or is already connected somewhere else (forbidden in `Sequential` models).

`compile`

View source

compile(
    optimizer='rmsprop', loss=None, metrics=None, loss_weights=None,
    sample_weight_mode=None, weighted_metrics=None, target_tensors=None,
    distribute=None, **kwargs
)

Configures the model for training.

Arguments
`optimizer`	String (name of optimizer) or optimizer instance. See `tf.keras.optimizers`.
`loss`	String (name of objective function), objective function or `tf.losses.Loss` instance. See `tf.losses`. 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.
`metrics`	List of metrics to be evaluated by the model during training and testing. Typically you will use `metrics=['accuracy']`. 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 (len = len(outputs)) of lists of metrics such as `metrics=[['accuracy'], ['accuracy', 'mse']]` or `metrics=['accuracy', ['accuracy', 'mse']]`.
`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 tensor, it is expected to map output names (strings) to scalar coefficients.
`sample_weight_mode`	If you need to do timestep-wise sample weighting (2D weights), set this to `"temporal"`. `None` defaults to sample-wise weights (1D). If the model has multiple outputs, you can use a different `sample_weight_mode` on each output by passing a dictionary or a list of modes.
`weighted_metrics`	List of metrics to be evaluated and weighted by sample_weight or class_weight during training and testing.
`target_tensors`	By default, Keras will create placeholders for the model's target, which will be fed with the target data during training. If instead you would like to use your own target tensors (in turn, Keras will not expect external Numpy data for these targets at training time), you can specify them via the `target_tensors` argument. It can be a single tensor (for a single-output model), a list of tensors, or a dict mapping output names to target tensors.
`distribute`	NOT SUPPORTED IN TF 2.0, please create and compile the model under distribution strategy scope instead of passing it to compile.
`**kwargs`	Any additional arguments.

Raises
`ValueError`	In case of invalid arguments for `optimizer`, `loss`, `metrics` or `sample_weight_mode`.

`evaluate`

View source

evaluate(
    x=None, y=None, batch_size=None, verbose=1, sample_weight=None, steps=None,
    callbacks=None, max_queue_size=10, workers=1, use_multiprocessing=False
)

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

Computation is done in batches.

Arguments
`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. A generator or `keras.utils.Sequence` instance.
`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 gradient update. If unspecified, `batch_size` will default to 32. Do not specify the `batch_size` is your data is in the form of symbolic tensors, dataset, generators, or `keras.utils.Sequence` instances (since they generate batches).
`verbose`	0 or 1. Verbosity mode. 0 = silent, 1 = progress bar.
`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. In this case you should make sure to specify `sample_weight_mode="temporal"` in `compile()`. 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. If 0, will execute the generator on the main thread.
`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-picklable arguments to the generator as they can't be passed easily to children processes.

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
`ValueError`	in case of invalid arguments.

`evaluate_generator`

View source

evaluate_generator(
    generator, steps=None, callbacks=None, max_queue_size=10, workers=1,
    use_multiprocessing=False, verbose=0
)

Evaluates the model on a data generator.

The generator should return the same kind of data as accepted by test_on_batch.

Arguments
`generator`	Generator yielding tuples (inputs, targets) or (inputs, targets, sample_weights) or an instance of `keras.utils.Sequence` object in order to avoid duplicate data when using multiprocessing.
`steps`	Total number of steps (batches of samples) to yield from `generator` before stopping. Optional for `Sequence`: if unspecified, will use the `len(generator)` as a number of steps.
`callbacks`	List of `keras.callbacks.Callback` instances. List of callbacks to apply during evaluation. See callbacks.
`max_queue_size`	maximum size for the generator queue
`workers`	Integer. Maximum number of processes to spin up when using process-based threading. If unspecified, `workers` will default to 1. If 0, will execute the generator on the main thread.
`use_multiprocessing`	Boolean. 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-picklable arguments to the generator as they can't be passed easily to children processes.
`verbose`	Verbosity mode, 0 or 1.

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
`ValueError`	in case of invalid arguments.

Raises
`ValueError`	In case the generator yields data in an invalid format.

`fit`

View source

fit(
    x=None, y=None, batch_size=None, epochs=1, verbose=1, 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_freq=1, max_queue_size=10, workers=1,
    use_multiprocessing=False, **kwargs
)

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

Arguments
`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)`.
`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 symbolic tensors, 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. 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`	0, 1, or 2. Verbosity mode. 0 = silent, 1 = progress bar, 2 = one line per epoch. 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).
`callbacks`	List of `keras.callbacks.Callback` instances. List of callbacks to apply during training. See `tf.keras.callbacks`.
`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.
`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. `validation_data` will override `validation_split`. `validation_data` could be: tuple `(x_val, y_val)` of Numpy arrays or tensors tuple `(x_val, y_val, val_sample_weights)` of Numpy arrays dataset For the first two cases, `batch_size` must be provided. For the last case, `validation_steps` must be provided.
`shuffle`	Boolean (whether to shuffle the training data before each epoch) or str (for 'batch'). '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.
`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. In this case you should make sure to specify `sample_weight_mode="temporal"` in `compile()`. 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`.
`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. This argument is not supported with array inputs.
`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_data is a `tf.data` dataset and 'validation_steps' is None, validation will run until the `validation_data` dataset is exhausted.
`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. If 0, will execute the generator on the main thread.
`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-picklable arguments to the generator as they can't be passed easily to children processes.
`**kwargs`	Used for backwards compatibility.

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
`RuntimeError`	If the model was never compiled.
`ValueError`	In case of mismatch between the provided input data and what the model expects.

`fit_generator`

View source

fit_generator(
    generator, steps_per_epoch=None, epochs=1, verbose=1, callbacks=None,
    validation_data=None, validation_steps=None, validation_freq=1,
    class_weight=None, max_queue_size=10, workers=1, use_multiprocessing=False,
    shuffle=True, initial_epoch=0
)

Fits the model on data yielded batch-by-batch by a Python generator.

The generator is run in parallel to the model, for efficiency. For instance, this allows you to do real-time data augmentation on images on CPU in parallel to training your model on GPU.

The use of keras.utils.Sequence guarantees the ordering and guarantees the single use of every input per epoch when using use_multiprocessing=True.

Arguments
`generator`	A generator or an instance of `Sequence` (`keras.utils.Sequence`) object in order to avoid duplicate data when using multiprocessing. The output of the generator must be either a tuple `(inputs, targets)` a tuple `(inputs, targets, sample_weights)`. This tuple (a single output of the generator) makes a single batch. Therefore, all arrays in this tuple must have the same length (equal to the size of this batch). Different batches may have different sizes. For example, the last batch of the epoch is commonly smaller than the others, if the size of the dataset is not divisible by the batch size. The generator is expected to loop over its data indefinitely. An epoch finishes when `steps_per_epoch` batches have been seen by the model.
`steps_per_epoch`	Total number of steps (batches of samples) to yield from `generator` before declaring one epoch finished and starting the next epoch. It should typically be equal to the number of samples of your dataset divided by the batch size. Optional for `Sequence`: if unspecified, will use the `len(generator)` as a number of steps.
`epochs`	Integer, total number of iterations on the data.
`verbose`	Verbosity mode, 0, 1, or 2.
`callbacks`	List of callbacks to be called during training.
`validation_data`	This can be either a generator for the validation data a tuple (inputs, targets) a tuple (inputs, targets, sample_weights).
`validation_steps`	Only relevant if `validation_data` is a generator. Total number of steps (batches of samples) to yield from `generator` before stopping. Optional for `Sequence`: if unspecified, will use the `len(validation_data)` as a number of steps.
`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.
`class_weight`	Dictionary mapping class indices to a weight for the class.
`max_queue_size`	Integer. Maximum size for the generator queue. If unspecified, `max_queue_size` will default to 10.
`workers`	Integer. Maximum number of processes to spin up when using process-based threading. If unspecified, `workers` will default to 1. If 0, will execute the generator on the main thread.
`use_multiprocessing`	Boolean. 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-picklable arguments to the generator as they can't be passed easily to children processes.
`shuffle`	Boolean. Whether to shuffle the order of the batches at the beginning of each epoch. Only used with instances of `Sequence` (`keras.utils.Sequence`). Has no effect when `steps_per_epoch` is not `None`.
`initial_epoch`	Epoch at which to start training (useful for resuming a previous training run)

Returns
A `History` object.

Example:

    def generate_arrays_from_file(path):
        while 1:
            f = open(path)
            for line in f:
                # create numpy arrays of input data
                # and labels, from each line in the file
                x1, x2, y = process_line(line)
                yield ({'input_1': x1, 'input_2': x2}, {'output': y})
            f.close()

    model.fit_generator(generate_arrays_from_file('/my_file.txt'),
                        steps_per_epoch=10000, epochs=10)

Raises: ValueError: In case the generator yields data in an invalid format.

`get_layer`

View source

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).

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

Returns
A layer instance.

Raises
`ValueError`	In case of invalid layer name or index.

`load_weights`

View source

load_weights(
    filepath, by_name=False
)

Loads all layer weights, either from a TensorFlow or an HDF5 file.

`pop`

View source

pop()

Removes the last layer in the model.

Raises
`TypeError`	if there are no layers in the model.

`predict`

View source

predict(
    x, batch_size=None, verbose=0, 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.

Arguments
`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.
`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` is your data is in the form of symbolic tensors, dataset, generators, or `keras.utils.Sequence` instances (since they generate batches).
`verbose`	Verbosity mode, 0 or 1.
`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. If 0, will execute the generator on the main thread.
`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-picklable arguments to the generator as they can't be passed easily to children processes.

Returns
Numpy array(s) of predictions.

Raises
`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_classes`

View source

predict_classes(
    x, batch_size=32, verbose=0
)

Generate class predictions for the input samples.

The input samples are processed batch by batch.

Arguments
`x`	input data, as a Numpy array or list of Numpy arrays (if the model has multiple inputs).
`batch_size`	integer.
`verbose`	verbosity mode, 0 or 1.

Returns
A numpy array of class predictions.

`predict_generator`

View source

predict_generator(
    generator, steps=None, callbacks=None, max_queue_size=10, workers=1,
    use_multiprocessing=False, verbose=0
)

Generates predictions for the input samples from a data generator.

The generator should return the same kind of data as accepted by predict_on_batch.

Arguments
`generator`	Generator yielding batches of input samples or an instance of `keras.utils.Sequence` object in order to avoid duplicate data when using multiprocessing.
`steps`	Total number of steps (batches of samples) to yield from `generator` before stopping. Optional for `Sequence`: if unspecified, will use the `len(generator)` as a number of steps.
`callbacks`	List of `keras.callbacks.Callback` instances. List of callbacks to apply during prediction. See callbacks.
`max_queue_size`	Maximum size for the generator queue.
`workers`	Integer. Maximum number of processes to spin up when using process-based threading. If unspecified, `workers` will default to 1. If 0, will execute the generator on the main thread.
`use_multiprocessing`	Boolean. 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-picklable arguments to the generator as they can't be passed easily to children processes.
`verbose`	verbosity mode, 0 or 1.

Returns
Numpy array(s) of predictions.

Raises
`ValueError`	In case the generator yields data in an invalid format.

`predict_on_batch`

View source

predict_on_batch(
    x
)

Returns predictions for a single batch of samples.

Arguments
`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 `tf.data` dataset.

Returns
Numpy array(s) of predictions.

Raises
`ValueError`	In case of mismatch between given number of inputs and expectations of the model.

`predict_proba`

View source

predict_proba(
    x, batch_size=32, verbose=0
)

Generates class probability predictions for the input samples.

The input samples are processed batch by batch.

Arguments
`x`	input data, as a Numpy array or list of Numpy arrays (if the model has multiple inputs).
`batch_size`	integer.
`verbose`	verbosity mode, 0 or 1.

Returns
A Numpy array of probability predictions.

`reset_metrics`

View source

reset_metrics()

Resets the state of metrics.

`reset_states`

View source

reset_states()

`save`

View source

tf.keras.Sequential

View aliases

Arguments

Example:

Attributes

Methods

add

compile

evaluate

evaluate_generator

fit

fit_generator

Example:

get_layer

load_weights

pop

predict

predict_classes

predict_generator

predict_on_batch

predict_proba

reset_metrics

reset_states

save

`add`

`compile`

`evaluate`

`evaluate_generator`

`fit`

`fit_generator`

`get_layer`

`load_weights`

`pop`

`predict`

`predict_classes`

`predict_generator`

`predict_on_batch`

`predict_proba`

`reset_metrics`

`reset_states`

`save`