contrib.keras.models.Sequential

tf.contrib.keras.models.Sequential

class tf.contrib.keras.models.Sequential

Defined in tensorflow/contrib/keras/python/keras/models.py.

Linear stack of layers.

Arguments:

layers: list of layers to add to the model.

Note

The first layer passed to a Sequential model
should have a defined input shape. What that
means is that it should have received an `input_shape`
or `batch_input_shape` argument,
or for some type of layers (recurrent, Dense...)
an `input_dim` argument.

Example:

```python
    model = Sequential()
    # first layer must have a defined input shape
    model.add(Dense(32, input_dim=500))
    # afterwards, Keras does automatic shape inference
    model.add(Dense(32))

    # also possible (equivalent to the above):
    model = Sequential()
    model.add(Dense(32, input_shape=(500,)))
    model.add(Dense(32))

    # also possible (equivalent to the above):
    model = Sequential()
    # here the batch dimension is None,
    # which means any batch size will be accepted by the model.
    model.add(Dense(32, batch_input_shape=(None, 500)))
    model.add(Dense(32))
```

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

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.

regularizers

scope_name

state_updates

stateful

trainable

trainable_variables

trainable_weights

updates

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__(
    layers=None,
    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

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).

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=None)

call

call(
    inputs,
    mask=None
)

compile

compile(
    optimizer,
    loss,
    metrics=None,
    sample_weight_mode=None,
    **kwargs
)

Configures the learning process.

Arguments:

optimizer: str (name of optimizer) or optimizer object.
    See [optimizers](/optimizers).
loss: str (name of objective function) or objective function.
    See [objectives](/objectives).
metrics: list of metrics to be evaluated by the model
    during training and testing.
    Typically you will use `metrics=['accuracy']`.
    See [metrics](/metrics).
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).
**kwargs: for Theano backend, these are passed into K.function.
    Ignored for Tensorflow backend.

Example: python model = Sequential() model.add(Dense(32, input_shape=(500,))) model.add(Dense(10, activation='softmax')) model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])

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
)

Computes the loss on some input data, batch by batch.

Arguments:

x: input data, as a Numpy array or list of Numpy arrays
    (if the model has multiple inputs).
y: labels, as a Numpy array.
batch_size: integer. Number of samples per gradient update.
verbose: verbosity mode, 0 or 1.
sample_weight: sample weights, as a Numpy array.

Returns:

Scalar test loss (if the model has no metrics)
or list of scalars (if the model computes other metrics).
The attribute `model.metrics_names` will give you
the display labels for the scalar outputs.

Raises:

RuntimeError: if the model was never compiled.

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
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 no metrics)
or list of scalars (if the model computes other metrics).
The attribute `model.metrics_names` will give you
the display labels for the scalar outputs.

Raises:

RuntimeError: if the model was never compiled.

fit

fit(
    x,
    y,
    batch_size=32,
    epochs=10,
    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.

Arguments:

x: input data, as a Numpy array or list of Numpy arrays
    (if the model has multiple inputs).
y: labels, as a Numpy array.
batch_size: integer. Number of samples per gradient update.
epochs: integer, the number of epochs to train the model.
verbose: 0 for no logging to stdout,
    1 for progress bar logging, 2 for one log line per epoch.
callbacks: list of `keras.callbacks.Callback` instances.
    List of callbacks to apply during training.
    See [callbacks](/callbacks).
validation_split: float (0. < x < 1).
    Fraction of the data to use as held-out validation data.
validation_data: tuple (x_val, y_val) or tuple
    (x_val, y_val, val_sample_weights) to be used as held-out
    validation data. Will override validation_split.
shuffle: boolean or str (for 'batch').
    Whether to shuffle the samples at each epoch.
    'batch' is a special option for dealing with the
    limitations of HDF5 data; it shuffles in batch-sized chunks.
class_weight: dictionary mapping classes to a weight value,
    used for scaling the loss function (during training only).
sample_weight: Numpy array of weights for
    the training samples, used for scaling 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().
initial_epoch: epoch at which to start training
    (useful for resuming a previous training run)

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.

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 generated 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 `samples_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 of 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.
    Number of steps to yield from validation generator
    at the end of every epoch. It should typically
    be equal to the number of unique samples of your
    validation dataset divided by the batch size.
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
pickle_safe: Ff 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.

Raises:

RuntimeError: if the model was never compiled.

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
            x, y = process_line(line)
            yield (x, y)
            f.close()

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

from_config

from_config(
    cls,
    config,
    custom_objects=None
)

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
)

Retrieve a layer that is part of the model.

Returns a layer based on either its name (unique) or its index in the graph. 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.

get_losses_for

get_losses_for(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)

get_weights

get_weights()

Retrieves the weights of the model.

Returns:

A flat list of Numpy arrays
(one array per model weight).

load_weights

load_weights(
    filepath,
    by_name=False
)

pop

pop()

Removes the last layer in the model.

Raises:

TypeError: if there are no layers in the model.

predict

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

Generates output predictions for the input samples.

The input samples are processed batch by batch.

Arguments:

x: the input data, as a Numpy array.
batch_size: integer.
verbose: verbosity mode, 0 or 1.

Returns:

A Numpy array of predictions.

predict_classes

predict_classes(
    x,
    batch_size=32,
    verbose=1
)

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

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
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:

A Numpy array of predictions.

predict_on_batch

predict_on_batch(x)

Returns predictions for a single batch of samples.

Arguments:

x: input data, as a Numpy array or list of Numpy arrays
    (if the model has multiple inputs).

Returns:

A Numpy array of predictions.

predict_proba

predict_proba(
    x,
    batch_size=32,
    verbose=1
)

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_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
)

set_weights

set_weights(weights)

Sets the weights of the model.

Arguments:

weights: Should be 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
)

Evaluates the model over a single batch of samples.

Arguments:

x: input data, as a Numpy array or list of Numpy arrays
    (if the model has multiple inputs).
y: labels, as a Numpy array.
sample_weight: sample weights, as a Numpy array.

Returns:

Scalar test loss (if the model has no metrics)
or list of scalars (if the model computes other metrics).
The attribute `model.metrics_names` will give you
the display labels for the scalar outputs.

Raises:

RuntimeError: if the model was never compiled.

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,
    class_weight=None,
    sample_weight=None
)

Single gradient update over one batch of samples.

Arguments:

x: input data, as a Numpy array or list of Numpy arrays
    (if the model has multiple inputs).
y: labels, as a Numpy array.
class_weight: dictionary mapping classes to a weight value,
    used for scaling the loss function (during training only).
sample_weight: sample weights, as a Numpy array.

Returns:

Scalar training loss (if the model has no metrics)
or list of scalars (if the model computes other metrics).
The attribute `model.metrics_names` will give you
the display labels for the scalar outputs.

Raises:

RuntimeError: if the model was never compiled.