contrib.keras.models.Model

tf.contrib.keras.models.Model

class tf.contrib.keras.models.Model

Defined in tensorflow/contrib/keras/python/keras/engine/training.py.

The Model class adds training & evaluation routines to a Container.

Properties

constraints

graph

input

Retrieves the input tensor(s) of a layer.

Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.

Returns:

Input tensor or list of input tensors.

Raises:

AttributeError: if the layer is connected to
more than one incoming layers.

input_mask

Retrieves the input mask tensor(s) of a layer.

Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.

Returns:

Input mask tensor (potentially None) or list of input
mask tensors.

Raises:

AttributeError: if the layer is connected to
more than one incoming layers.

input_shape

Retrieves the input shape(s) of a layer.

Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.

Returns:

Input shape, as `TensorShape`
(or list of `TensorShape`, one tuple per input tensor).

Raises:

AttributeError: if the layer is connected to
more than one incoming layers.

input_spec

Gets the model's input specs.

Returns:

A list of `InputSpec` instances (one per input to the model)
    or a single instance if the model has only one input.

losses

Retrieve the model's losses.

Will only include losses that are either inconditional, or conditional on inputs to this model (e.g. will not include losses that depend on tensors that aren't inputs to this model).

Returns:

A list of loss tensors.

non_trainable_variables

non_trainable_weights

output

Retrieves the output tensor(s) of a layer.

Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.

Returns:

Output tensor or list of output tensors.

Raises:

AttributeError: if the layer is connected to
more than one incoming layers.

output_mask

Retrieves the output mask tensor(s) of a layer.

Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.

Returns:

Output mask tensor (potentially None) or list of output
mask tensors.

Raises:

AttributeError: if the layer is connected to
more than one incoming layers.

output_shape

Retrieves the output shape(s) of a layer.

Only applicable if the layer has one inbound node, or if all inbound nodes have the same output shape.

Returns:

Output shape, as `TensorShape`
(or list of `TensorShape`, one tuple per output tensor).

Raises:

AttributeError: if the layer is connected to
more than one incoming layers.

scope_name

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.

Returns:

A list of update ops.

stateful

trainable_variables

trainable_weights

updates

Retrieve the model's updates.

Will only include updates that are either inconditional, or conditional on inputs to this model (e.g. will not include updates that depend on tensors that aren't inputs to this model).

Returns:

A list of update ops.

uses_learning_phase

variables

Returns the list of all layer variables/weights.

Returns:

A list of variables.

weights

Returns the list of all layer variables/weights.

Returns:

A list of variables.

Methods

__init__

__init__(
    inputs,
    outputs,
    name=None
)

__call__

__call__(
    inputs,
    **kwargs
)

Wrapper around self.call(), for handling internal references.

If a Keras tensor is passed: - We call self._add_inbound_node(). - If necessary, we build the layer to match the shape of the input(s). - We update the _keras_history of the output tensor(s) with the current layer. This is done as part of _add_inbound_node().

Arguments:

inputs: Can be a tensor or list/tuple of tensors.
**kwargs: Additional keyword arguments to be passed to `call()`.

Returns:

Output of the layer's `call` method.

Raises:

ValueError: in case the layer is missing shape information
    for its `build` call.

__deepcopy__

__deepcopy__(memo)

add_loss

add_loss(
    losses,
    inputs=None
)

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 a 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.

The get_losses_for method allows to retrieve the losses relevant to a specific set of inputs.

Arguments:

• losses: Loss tensor, or list/tuple of tensors.
• inputs: Optional input tensor(s) that the loss(es) depend on. Must match the inputs argument passed to the __call__ method at the time the losses are created. If None is passed, the losses are assumed to be unconditional, and will apply across all dataflows of the layer (e.g. weight regularization losses).

add_update

add_update(
    updates,
    inputs=None
)

Add update op(s), potentially dependent on layer inputs.

Weight updates (for instance, the updates of the moving mean and variance in a BatchNormalization layer) may be dependent on the inputs passed when calling a layer. Hence, when reusing a same layer on different inputs a and b, some entries in layer.updates may be dependent on a and some on b. This method automatically keeps track of dependencies.

The get_updates_for method allows to retrieve the updates relevant to a specific set of inputs.

Arguments:

• updates: Update op, or list/tuple of update ops.
• inputs: Optional input tensor(s) that the update(s) depend on. Must match the inputs argument passed to the __call__ method at the time the updates are created. If None is passed, the updates are assumed to be unconditional, and will apply across all dataflows of the layer.

add_variable

add_variable(
    name,
    shape,
    dtype=None,
    initializer=None,
    regularizer=None,
    trainable=True
)

Adds a new variable to the layer, or gets an existing one; returns it.

Arguments:

• name: variable name.
• shape: variable shape.
• dtype: The type of the variable. Defaults to self.dtype.
• initializer: initializer instance (callable).
• regularizer: regularizer instance (callable).
• trainable: whether the variable should be part of the layer's "trainable_variables" (e.g. variables, biases) or "non_trainable_variables" (e.g. BatchNorm mean, stddev).

Returns:

The created variable.

add_weight

add_weight(
    name,
    shape,
    dtype=None,
    initializer=None,
    regularizer=None,
    trainable=True,
    constraint=None
)

Adds a weight variable to the layer.

Arguments:

name: String, the name for the weight variable.
shape: The shape tuple of the weight.
dtype: The dtype of the weight.
initializer: An Initializer instance (callable).
regularizer: An optional Regularizer instance.
trainable: A boolean, whether the weight should
    be trained via backprop or not (assuming
    that the layer itself is also trainable).
constraint: An optional Constraint instance.

Returns:

The created weight variable.

apply

apply(
    inputs,
    *args,
    **kwargs
)

Apply the layer on a input.

This simply wraps self.__call__.

Arguments:

• inputs: Input tensor(s). args: additional positional arguments to be passed to self.call.
  *kwargs: additional keyword arguments to be passed to self.call.

Returns:

Output tensor(s).

build

build(input_shape)

Creates the layer weights.

Must be implemented on all layers that have weights.

Arguments:

input_shape: Keras tensor (future input to layer)
    or list/tuple of Keras tensors to reference
    for weight shape computations.

call

call(
    inputs,
    mask=None
)

Call the model on new inputs.

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).

A model is callable on non-Keras tensors.

Arguments:

inputs: A tensor or list of tensors.
mask: A mask or list of masks. A mask can be
    either a tensor or None (no mask).

Returns:

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

compile

compile(
    optimizer,
    loss,
    metrics=None,
    loss_weights=None,
    sample_weight_mode=None
)

Configures the model for training.

Arguments:

optimizer: str (name of optimizer) or optimizer object.
    See [optimizers](/optimizers).
loss: str (name of objective function) or objective function.
    See [losses](/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.
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'}`.
loss_weights: Optional list or dictionary specifying scalar
    coefficients (Python floats) to weight the loss contributions
    of different model outputs.
    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.

Raises:

ValueError: In case of invalid arguments for
    `optimizer`, `loss`, `metrics` or `sample_weight_mode`.
RuntimeError: If the model has no loss to optimize.

compute_mask

compute_mask(
    inputs,
    mask
)

count_params

count_params()

Count the total number of scalars composing the weights.

Returns:

An integer count.

Raises:

RuntimeError: if the layer isn't yet built
    (in which case its weights aren't yet defined).

evaluate

evaluate(
    x,
    y,
    batch_size=32,
    verbose=1,
    sample_weight=None
)

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

Computation is done in batches.

Arguments:

x: Numpy array of test data,
    or list of Numpy arrays if the model has multiple inputs.
    If all inputs in the model are named,
    you can also pass a dictionary
    mapping input names to Numpy arrays.
y: Numpy array of target data,
    or list of Numpy arrays if the model has multiple outputs.
    If all outputs in the model are named,
    you can also pass a dictionary
    mapping output names to Numpy arrays.
batch_size: integer. Number of samples per gradient update.
verbose: verbosity mode, 0 or 1.
sample_weight: Array of weights to weight the contribution
    of different samples to the loss and metrics.

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.

evaluate_generator

evaluate_generator(
    generator,
    steps,
    max_q_size=10,
    workers=1,
    pickle_safe=False
)

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)
steps: Total number of steps (batches of samples)
    to yield from `generator` before stopping.
max_q_size: maximum size for the generator queue
workers: maximum number of processes to spin up
    when using process based threading
pickle_safe: if True, use process based threading.
    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 the generator yields
    data in an invalid format.

fit

fit(
    x=None,
    y=None,
    batch_size=32,
    epochs=1,
    verbose=1,
    callbacks=None,
    validation_split=0.0,
    validation_data=None,
    shuffle=True,
    class_weight=None,
    sample_weight=None,
    initial_epoch=0
)

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

Arguments:

x: Numpy array of training data,
    or list of Numpy arrays if the model has multiple inputs.
    If all inputs in the model are named,
    you can also pass a dictionary
    mapping input names to Numpy arrays.
y: Numpy array of target data,
    or list of Numpy arrays if the model has multiple outputs.
    If all outputs in the model are named,
    you can also pass a dictionary
    mapping output names to Numpy arrays.
batch_size: integer. Number of samples per gradient update.
epochs: integer, the number of times to iterate
    over the training data arrays.
    verbose: 0, 1, or 2. Verbosity mode.
    0 = silent, 1 = verbose, 2 = one log line per epoch.
callbacks: list of callbacks to be called during training.
    See [callbacks](/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.
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.
    This could be a tuple (x_val, y_val)
    or a tuple (x_val, y_val, val_sample_weights).
shuffle: boolean, whether to shuffle the training data
    before each epoch.
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.
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.
    In this case you should make sure to specify
    sample_weight_mode="temporal" in compile().
initial_epoch: epoch at which to start training
    (useful for resuming a previous training run)

Returns:

A `History` instance. Its `history` attribute contains
all information collected during training.

Raises:

ValueError: In case of mismatch between the provided input data
    and what the model expects.

fit_generator

fit_generator(
    generator,
    steps_per_epoch,
    epochs=1,
    verbose=1,
    callbacks=None,
    validation_data=None,
    validation_steps=None,
    class_weight=None,
    max_q_size=10,
    workers=1,
    pickle_safe=False,
    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.

Arguments:

generator: a generator.
    The output of the generator must be either
    - a tuple (inputs, targets)
    - a tuple (inputs, targets, sample_weights).
    All arrays should contain the same number of samples.
    The generator is expected to loop over its data
    indefinitely. An epoch finishes when `steps_per_epoch`
    samples 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 unique samples if your dataset
    divided by the batch size.
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.
class_weight: dictionary mapping class indices to a weight
    for the class.
max_q_size: maximum size for the generator queue
workers: maximum number of processes to spin up
    when using process based threading
pickle_safe: if True, use process based threading.
    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.
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.

from_config

from_config(
    cls,
    config,
    custom_objects=None
)

Instantiates a Model from its config (output of get_config()).

Arguments:

config: Model config dictionary.
custom_objects: Optional dictionary mapping names
    (strings) to custom classes or functions to be
    considered during deserialization.

Returns:

A model instance.

Raises:

ValueError: In case of improperly formatted config dict.

get_config

get_config()

get_input_at

get_input_at(node_index)

Retrieves the input tensor(s) of a layer at a given node.

Arguments:

node_index: Integer, index of the node
    from which to retrieve the attribute.
    E.g. `node_index=0` will correspond to the
    first time the layer was called.

Returns:

A tensor (or list of tensors if the layer has multiple inputs).

get_input_mask_at

get_input_mask_at(node_index)

Retrieves the input mask tensor(s) of a layer at a given node.

Arguments:

node_index: Integer, index of the node
    from which to retrieve the attribute.
    E.g. `node_index=0` will correspond to the
    first time the layer was called.

Returns:

A mask tensor
(or list of tensors if the layer has multiple inputs).

get_input_shape_at

get_input_shape_at(node_index)

Retrieves the input shape(s) of a layer at a given node.

Arguments:

node_index: Integer, index of the node
    from which to retrieve the attribute.
    E.g. `node_index=0` will correspond to the
    first time the layer was called.

Returns:

A shape tuple
(or list of shape tuples if the layer has multiple inputs).

get_layer

get_layer(
    name=None,
    index=None
)

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

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.

get_losses_for

get_losses_for(inputs)

Retrieves losses relevant to a specific set of inputs.

Arguments:

• inputs: Input tensor or list/tuple of input tensors. Must match the inputs argument passed to the __call__ method at the time the losses were created. If you pass inputs=None, unconditional losses are returned, such as weight regularization losses.

Returns:

List of loss tensors of the layer that depend on inputs.

get_output_at

get_output_at(node_index)

Retrieves the output tensor(s) of a layer at a given node.

Arguments:

node_index: Integer, index of the node
    from which to retrieve the attribute.
    E.g. `node_index=0` will correspond to the
    first time the layer was called.

Returns:

A tensor (or list of tensors if the layer has multiple outputs).

get_output_mask_at

get_output_mask_at(node_index)

Retrieves the output mask tensor(s) of a layer at a given node.

Arguments:

node_index: Integer, index of the node
    from which to retrieve the attribute.
    E.g. `node_index=0` will correspond to the
    first time the layer was called.

Returns:

A mask tensor
(or list of tensors if the layer has multiple outputs).

get_output_shape_at

get_output_shape_at(node_index)

Retrieves the output shape(s) of a layer at a given node.

Arguments:

node_index: Integer, index of the node
    from which to retrieve the attribute.
    E.g. `node_index=0` will correspond to the
    first time the layer was called.

Returns:

A shape tuple
(or list of shape tuples if the layer has multiple outputs).

get_updates_for

get_updates_for(inputs)

Retrieves updates relevant to a specific set of inputs.

Arguments:

• inputs: Input tensor or list/tuple of input tensors. Must match the inputs argument passed to the __call__ method at the time the updates were created. If you pass inputs=None, unconditional updates are returned.

Returns:

List of update ops of the layer that depend on inputs.

get_weights

get_weights()

Retrieves the weights of the model.

Returns:

A flat list of Numpy arrays.

load_weights

load_weights(
    filepath,
    by_name=False
)

Loads all layer weights from a HDF5 save file.

If by_name is False (default) weights are loaded based on the network's topology, meaning 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.

If by_name is True, 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.

Arguments:

filepath: String, path to the weights file to load.
by_name: Boolean, whether to load weights by name
    or by topological order.

Raises:

ImportError: If h5py is not available.

predict

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

Generates output predictions for the input samples.

Computation is done in batches.

Arguments:

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

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_generator

predict_generator(
    generator,
    steps,
    max_q_size=10,
    workers=1,
    pickle_safe=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.
steps: Total number of steps (batches of samples)
    to yield from `generator` before stopping.
max_q_size: Maximum size for the generator queue.
workers: Maximum number of processes to spin up
    when using process based threading
pickle_safe: If `True`, use process based threading.
    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

predict_on_batch(x)

Returns predictions for a single batch of samples.

Arguments:

x: Input samples, as a Numpy array.

Returns:

Numpy array(s) of predictions.

reset_states

reset_states()

run_internal_graph

run_internal_graph(
    inputs,
    masks=None
)

Computes output tensors for new inputs.

Note:

- Expects `inputs` to be a list (potentially with 1 element).
- Can be run on non-Keras tensors.

Arguments:

inputs: List of tensors
masks: List of masks (tensors or None).

Returns:

Three lists: output_tensors, output_masks, output_shapes

save

save(
    filepath,
    overwrite=True,
    include_optimizer=True
)

Save the model to a single HDF5 file.

The savefile includes: - The model architecture, allowing to re-instantiate the model. - The model weights. - The state of the optimizer, allowing to resume training exactly where you left off.

This allows you to save the entirety of the state of a model in a single file.

Saved models can be reinstantiated via keras.models.load_model. The model returned by load_model is a compiled model ready to be used (unless the saved model was never compiled in the first place).

Arguments:

filepath: String, path to the file to save the weights to.
overwrite: Whether to silently overwrite any existing file at the
    target location, or provide the user with a manual prompt.
include_optimizer: If True, save optimizer's state together.

Example:

from keras.models import load_model

model.save('my_model.h5')  # creates a HDF5 file 'my_model.h5'
del model  # deletes the existing model

# returns a compiled model
# identical to the previous one
model = load_model('my_model.h5')

save_weights

save_weights(
    filepath,
    overwrite=True
)

Dumps all layer weights to a HDF5 file.

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.

Arguments:

filepath: String, path to the file to save the weights to.
overwrite: Whether to silently overwrite any existing file at the
    target location, or provide the user with a manual prompt.

Raises:

ImportError: If h5py is not available.

set_weights

set_weights(weights)

Sets the weights of the model.

Arguments:

weights: A list of Numpy arrays with shapes and types matching
    the output of `model.get_weights()`.

summary

summary(
    line_length=None,
    positions=None
)

test_on_batch

test_on_batch(
    x,
    y,
    sample_weight=None
)

Test the model on a single batch of samples.

Arguments:

x: Numpy array of test data,
    or list of Numpy arrays if the model has multiple inputs.
    If all inputs in the model are named,
    you can also pass a dictionary
    mapping input names to Numpy arrays.
y: Numpy array of target data,
    or list of Numpy arrays if the model has multiple outputs.
    If all outputs in the model are named,
    you can also pass a dictionary
    mapping output names to Numpy arrays.
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.
    In this case you should make sure to specify
    sample_weight_mode="temporal" in compile().

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.

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={}).

Arguments:

**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.

Arguments:

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

Returns:

A YAML string.

Raises:

ImportError: if yaml module is not found.

train_on_batch

train_on_batch(
    x,
    y,
    sample_weight=None,
    class_weight=None
)

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

Arguments:

x: Numpy array of training data,
    or list of Numpy arrays if the model has multiple inputs.
    If all inputs in the model are named,
    you can also pass a dictionary
    mapping input names to Numpy arrays.
y: Numpy array of target data,
    or list of Numpy arrays if the model has multiple outputs.
    If all outputs in the model are named,
    you can also pass a dictionary
    mapping output names to Numpy arrays.
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.
    In this case you should make sure to specify
    sample_weight_mode="temporal" in compile().
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.

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.