def build(self, input_shape=None):
if not self.inputs or not self.outputs:
raise TypeError('Sequential model cannot be built: model is empty.'
' Add some layers first.')
# actually create the model
self.model = Model(self.inputs,
self.outputs[0],
name=self.name + '_model')
self.model.trainable = self.trainable
# mirror model attributes
self.supports_masking = self.model.supports_masking
self._output_mask_cache = self.model._output_mask_cache
self._output_tensor_cache = self.model._output_tensor_cache
self._output_shape_cache = self.model._output_shape_cache
self._input_layers = self.model._input_layers
self._output_layers = self.model._output_layers
self._input_coordinates = self.model._input_coordinates
self._output_coordinates = self.model._output_coordinates
self._nodes_by_depth = self.model._nodes_by_depth
self._network_nodes = self.model._network_nodes
self.output_names = self.model.output_names
self.input_names = self.model.input_names
self._feed_input_names = self.model._feed_input_names
self._feed_inputs = self.model._feed_inputs
# Make sure child model callbacks
# will call the parent Sequential model.
self.model.callback_model = self
self.built = True
def build(self, input_shape=None):
if not self.inputs or not self.outputs:
raise TypeError('Sequential model cannot be built: model is empty.'
' Add some layers first.')
# actually create the model
self.model = Model(self.inputs, self.outputs[0], name=self.name + '_model')
self.model.trainable = self.trainable
# mirror model attributes
self.supports_masking = self.model.supports_masking
self._output_mask_cache = self.model._output_mask_cache
self._output_tensor_cache = self.model._output_tensor_cache
self._output_shape_cache = self.model._output_shape_cache
self._input_layers = self.model._input_layers
self._output_layers = self.model._output_layers
self._input_coordinates = self.model._input_coordinates
self._output_coordinates = self.model._output_coordinates
self._nodes_by_depth = self.model._nodes_by_depth
self._network_nodes = self.model._network_nodes
self.output_names = self.model.output_names
self.input_names = self.model.input_names
self._feed_input_names = self.model._feed_input_names
self._feed_inputs = self.model._feed_inputs
# Make sure child model callbacks
# will call the parent Sequential model.
self.model.callback_model = self
self.built = True
class Sequential(Model):
"""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))
```
"""
def __init__(self, layers=None, name=None):
self._is_graph_network = True
self._is_compiled = False
self._layers = [] # Stack of layers.
self.model = None # Internal Model instance.
self.inputs = [] # List of input tensors
self.outputs = [] # List of length 1: the output tensor (unique).
self._trainable = True
self._initial_weights = None
self._input_layers = []
# Model attributes.
self._inbound_nodes = []
self._outbound_nodes = []
self.built = False
# Set model name.
if not name:
prefix = 'sequential_'
name = prefix + str(K.get_uid(prefix))
self._name = name
# Used by Layer base class.
self._dtype = None
self._activity_regularizer = None
# The following properties are not actually used by Keras;
# they exist for compatibility with TF's variable scoping mechanism.
self._updates = []
self._losses = []
self._scope = None
self._reuse = None
self._base_name = name
self._graph = ops.get_default_graph()
# Add to the model any layers passed to the constructor.
if layers:
for layer in layers:
self.add(layer)
def add(self, 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).
"""
if not isinstance(layer, (base_layer.Layer, base_layer.TFBaseLayer)):
raise TypeError('The added layer must be '
'an instance of class Layer. '
'Found: ' + str(layer))
if not self.outputs:
# First layer in model: check that it is an input layer.
if not isinstance(layer, InputLayer):
# Create an input layer.
# First, we need to infer its expected input shape and dtype.
if isinstance(layer, (Model, Sequential)):
# We were passed a model as first layer.
# This requires a specific way to figure out the
# input shape and dtype.
if not layer.layers:
raise ValueError('Cannot add an empty model '
'to a `Sequential` model.')
# In case of nested models: recover the first layer
# of the deepest model to infer input shape and dtype.
first_layer = layer.layers[0]
while isinstance(first_layer, (Model, Sequential)):
first_layer = first_layer.layers[0]
batch_shape = first_layer._batch_input_shape
dtype = first_layer.dtype
else:
# We were passed a regular layer, and it should
# know about its input shape. Otherwise, that's an error.
if not hasattr(layer, '_batch_input_shape'):
raise ValueError('The first layer in a '
'Sequential model must '
'get an `input_shape` argument.')
batch_shape = layer._batch_input_shape
dtype = layer.dtype
# Instantiate the input layer.
x = Input(batch_shape=batch_shape,
dtype=dtype,
name=layer.name + '_input')
# This will build the current layer
# and create the node connecting the current layer
# to the input layer we just created.
layer(x)
if len(layer._inbound_nodes[-1].output_tensors) != 1:
raise ValueError('All layers in a Sequential model '
'should have a single output tensor. '
'For multi-output layers, '
'use the functional API.')
self.outputs = [layer._inbound_nodes[-1].output_tensors[0]]
self.inputs = network.get_source_inputs(self.outputs[0])
# We create an input node, which we will keep updated
# as we add more layers
base_layer.Node(outbound_layer=self,
inbound_layers=[],
node_indices=[],
tensor_indices=[],
input_tensors=self.inputs,
output_tensors=self.outputs)
else:
output_tensor = layer(self.outputs[0])
if isinstance(output_tensor, list):
raise TypeError('All layers in a Sequential model '
'should have a single output tensor. '
'For multi-output layers, '
'use the functional API.')
self.outputs = [output_tensor]
# update self._inbound_nodes
self._inbound_nodes[0].output_tensors = self.outputs
self._inbound_nodes[0].output_shapes = [
K.int_shape(self.outputs[0])
]
self._layers.append(layer)
self.built = False
def pop(self):
"""Removes the last layer in the model.
Raises:
TypeError: if there are no layers in the model.
"""
if not self.layers:
raise TypeError('There are no layers in the model.')
self.layers.pop()
if not self.layers:
self.outputs = []
self._inbound_nodes = []
self._outbound_nodes = []
else:
self.layers[-1]._outbound_nodes = []
self.outputs = [self.layers[-1].output]
# update self._inbound_nodes
self._inbound_nodes[0].output_tensors = self.outputs
self._inbound_nodes[0].output_shapes = [
K.int_shape(self.outputs[0])
]
self.built = False
def get_layer(self, 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.
"""
if not self.built:
self.build()
return self.model.get_layer(name, index)
def call(self, inputs, **kwargs):
if not self.built:
self.build()
return self.model.call(inputs, **kwargs)
def build(self, input_shape=None):
if not self.inputs or not self.outputs:
raise TypeError('Sequential model cannot be built: model is empty.'
' Add some layers first.')
# actually create the model
self.model = Model(self.inputs,
self.outputs[0],
name=self.name + '_model')
self.model.trainable = self.trainable
# mirror model attributes
self.supports_masking = self.model.supports_masking
self._output_mask_cache = self.model._output_mask_cache
self._output_tensor_cache = self.model._output_tensor_cache
self._output_shape_cache = self.model._output_shape_cache
self._input_layers = self.model._input_layers
self._output_layers = self.model._output_layers
self._input_coordinates = self.model._input_coordinates
self._output_coordinates = self.model._output_coordinates
self._nodes_by_depth = self.model._nodes_by_depth
self._network_nodes = self.model._network_nodes
self.output_names = self.model.output_names
self.input_names = self.model.input_names
self._feed_input_names = self.model._feed_input_names
self._feed_inputs = self.model._feed_inputs
# Make sure child model callbacks
# will call the parent Sequential model.
self.model.callback_model = self
self.built = True
@property
def uses_learning_phase(self):
if not self.built:
self.build()
return self.model.uses_learning_phase
def _gather_list_attr(self, attr):
all_attrs = []
for layer in self.layers:
all_attrs += getattr(layer, attr, [])
return all_attrs
def _make_train_function(self):
self.model._make_train_function()
def _make_test_function(self):
self.model._make_test_function()
def _make_predict_function(self):
self.model._make_predict_function()
@property
def trainable(self):
return self._trainable
@trainable.setter
def trainable(self, value):
if self.model:
self.model.trainable = value
self._trainable = value
@property
def trainable_weights(self):
if not self.trainable:
return []
return self._gather_list_attr('trainable_weights')
@property
def non_trainable_weights(self):
weights = self._gather_list_attr('non_trainable_weights')
if not self.trainable:
trainable_weights = self._gather_list_attr('trainable_weights')
return trainable_weights + weights
return weights
@property
def regularizers(self):
if not self.built:
self.build()
return self.model.regularizers
def get_weights(self):
"""Retrieves the weights of the model.
Returns:
A flat list of Numpy arrays
(one array per model weight).
"""
if not self.built:
self.build()
return self.model.get_weights()
def set_weights(self, 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()`.
"""
if not self.built:
self.build()
self.model.set_weights(weights)
def load_weights(self, filepath, by_name=False):
if h5py is None:
raise ImportError('`load_weights` requires h5py.')
f = h5py.File(filepath, mode='r')
if 'layer_names' not in f.attrs and 'model_weights' in f:
f = f['model_weights']
layers = self.layers
if by_name:
saving.load_weights_from_hdf5_group_by_name(f, layers)
else:
saving.load_weights_from_hdf5_group(f, layers)
if hasattr(f, 'close'):
f.close()
def save_weights(self, filepath, overwrite=True):
if h5py is None:
raise ImportError('`save_weights` requires h5py.')
# If file exists and should not be overwritten:
if not overwrite and os.path.isfile(filepath):
proceed = ask_to_proceed_with_overwrite(filepath)
if not proceed:
return
layers = self.layers
f = h5py.File(filepath, 'w')
saving.save_weights_to_hdf5_group(f, layers)
f.flush()
f.close()
def compile(self,
optimizer,
loss,
metrics=None,
sample_weight_mode=None,
weighted_metrics=None,
target_tensors=None,
**kwargs):
"""Configures the model for training.
Arguments:
optimizer: String (name of optimizer) or optimizer object.
See [optimizers](/optimizers).
loss: String (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.
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'}`.
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 a placeholder 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 tensor (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 should be a single tensor
(for a single-output `Sequential` model).
**kwargs: These arguments are passed into `tf.Session.run`.
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'])
```
"""
# create the underlying model
self.build()
# call compile method of Model class
self.model.compile(optimizer,
loss,
metrics=metrics,
sample_weight_mode=sample_weight_mode,
weighted_metrics=weighted_metrics,
target_tensors=target_tensors,
**kwargs)
self.optimizer = self.model.optimizer
self.loss = self.model.loss
self.metrics = self.model.metrics
self.loss_weights = self.model.loss_weights
self.sample_weight_mode = self.model.sample_weight_mode
self.weighted_metrics = self.model.weighted_metrics
self.targets = self.model.targets
self.metrics_tensors = self.model.metrics_tensors
self.metrics_names = self.model.metrics_names
self.sample_weights = self.model.sample_weights
self.total_loss = self.model.total_loss
def fit(self,
x=None,
y=None,
batch_size=None,
epochs=1,
verbose=1,
callbacks=None,
validation_split=0.,
validation_data=None,
shuffle=True,
class_weight=None,
sample_weight=None,
initial_epoch=0,
steps_per_epoch=None,
validation_steps=None,
**kwargs):
"""Trains the model for a fixed number of epochs.
Arguments:
x: Numpy array of training data.
If the input layer in the model is named, you can also pass a
dictionary mapping the input name to a Numpy array.
`x` can be `None` (default) if feeding from
TensorFlow data tensors.
y: Numpy array of target (label) data.
If the output layer in the model is named, you can also pass a
dictionary mapping the output name to a Numpy array.
`y` can be `None` (default) if feeding from
TensorFlow data tensors.
batch_size: Integer or `None`.
Number of samples per gradient update.
If unspecified, it will default to 32.
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.
callbacks: List of `keras.callbacks.Callback` instances.
List of callbacks to apply 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.
The validation data is selected from the last samples
in the `x` and `y` data provided, before shuffling.
validation_data: tuple `(x_val, y_val)` or tuple
`(x_val, y_val, val_sample_weights)` 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 will override `validation_split`.
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()`.
initial_epoch: Epoch at which to start training
(useful for resuming a previous training run).
steps_per_epoch: 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 unique samples in your dataset divided by
the batch size, or 1 if that cannot be determined.
validation_steps: Only relevant if `steps_per_epoch`
is specified. Total number of steps (batches of samples)
to validate before stopping.
**kwargs: Used for backwards compatibility support.
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.
"""
if not self.built:
raise RuntimeError(
'The model needs to be compiled before being used.')
return self.model.fit(x,
y,
batch_size=batch_size,
epochs=epochs,
verbose=verbose,
callbacks=callbacks,
validation_split=validation_split,
validation_data=validation_data,
shuffle=shuffle,
class_weight=class_weight,
sample_weight=sample_weight,
initial_epoch=initial_epoch,
steps_per_epoch=steps_per_epoch,
validation_steps=validation_steps)
def evaluate(self, 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.
"""
if not self.built:
raise RuntimeError(
'The model needs to be compiled before being used.')
return self.model.evaluate(x,
y,
batch_size=batch_size,
verbose=verbose,
sample_weight=sample_weight)
def predict(self, 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.
"""
if not self.built:
self.build()
return self.model.predict(x, batch_size=batch_size, verbose=verbose)
def predict_on_batch(self, 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.
"""
if not self.built:
self.build()
return self.model.predict_on_batch(x)
def train_on_batch(self, 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.
"""
if not self.built:
raise RuntimeError(
'The model needs to be compiled before being used.')
return self.model.train_on_batch(x,
y,
sample_weight=sample_weight,
class_weight=class_weight)
def test_on_batch(self, 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.
"""
if not self.built:
raise RuntimeError(
'The model needs to be compiled before being used.')
return self.model.test_on_batch(x, y, sample_weight=sample_weight)
def predict_proba(self, 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.
"""
preds = self.predict(x, batch_size, verbose)
if preds.min() < 0. or preds.max() > 1.:
logging.warning('Network returning invalid probability values. '
'The last layer might not normalize predictions '
'into probabilities '
'(like softmax or sigmoid would).')
return preds
def predict_classes(self, 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.
"""
proba = self.predict(x, batch_size=batch_size, verbose=verbose)
if proba.shape[-1] > 1:
return proba.argmax(axis=-1)
else:
return (proba > 0.5).astype('int32')
def fit_generator(self,
generator,
steps_per_epoch=None,
epochs=1,
verbose=1,
callbacks=None,
validation_data=None,
validation_steps=None,
class_weight=None,
max_queue_size=10,
workers=1,
use_multiprocessing=False,
shuffle=True,
initial_epoch=0,
**kwargs):
"""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 `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.
Note that in conjunction with initial_epoch, the parameter
epochs is to be understood as "final epoch". The model is
not trained for n steps given by epochs, but until the
epoch epochs is reached.
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 samples of your
validation dataset divided by the batch size.
Optional for `Sequence`: if unspecified, will use
the `len(validation_data)` as a number of steps.
class_weight: Dictionary mapping class indices to a weight
for the class.
max_queue_size: Maximum size for the generator queue
workers: Maximum number of processes to spin up
use_multiprocessing: 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.
shuffle: Whether to shuffle the order of the batches at
the beginning of each epoch. Only used with instances
of `Sequence` (keras.utils.Sequence).
initial_epoch: Epoch at which to start training
(useful for resuming a previous training run)
**kwargs: support for legacy arguments.
Returns:
A `History` object.
Raises:
RuntimeError: if the model was never compiled.
ValueError: In case the generator yields
data in an invalid format.
Example:
```python
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'),
steps_per_epoch=1000, epochs=10)
```
"""
# Legacy support
if 'max_q_size' in kwargs:
max_queue_size = kwargs.pop('max_q_size')
logging.warning(
'The argument `max_q_size` has been renamed '
'`max_queue_size`. Update your method calls accordingly.')
if 'pickle_safe' in kwargs:
use_multiprocessing = kwargs.pop('pickle_safe')
logging.warning('The argument `pickle_safe` has been renamed '
'`use_multiprocessing`. '
'Update your method calls accordingly.')
if kwargs:
raise ValueError('Unrecognized keyword arguments: ' + str(kwargs))
if not self.built:
raise RuntimeError(
'The model needs to be compiled before being used.')
return self.model.fit_generator(
generator,
steps_per_epoch,
epochs,
verbose=verbose,
callbacks=callbacks,
validation_data=validation_data,
validation_steps=validation_steps,
class_weight=class_weight,
max_queue_size=max_queue_size,
workers=workers,
use_multiprocessing=use_multiprocessing,
shuffle=shuffle,
initial_epoch=initial_epoch)
def evaluate_generator(self,
generator,
steps=None,
max_queue_size=10,
workers=1,
use_multiprocessing=False,
**kwargs):
"""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.
Optional for `Sequence`: if unspecified, will use
the `len(generator)` as a number of steps.
max_queue_size: maximum size for the generator queue
workers: maximum number of processes to spin up
use_multiprocessing: 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.
**kwargs: support for legacy arguments.
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.
ValueError: In case the generator yields
data in an invalid format.
"""
# Legacy support
if 'max_q_size' in kwargs:
max_queue_size = kwargs.pop('max_q_size')
logging.warning(
'The argument `max_q_size` has been renamed '
'`max_queue_size`. Update your method calls accordingly.')
if 'pickle_safe' in kwargs:
use_multiprocessing = kwargs.pop('pickle_safe')
logging.warning('The argument `pickle_safe` has been renamed '
'`use_multiprocessing`. '
'Update your method calls accordingly.')
if kwargs:
raise ValueError('Unrecognized keyword arguments: ' + str(kwargs))
if not self.built:
raise RuntimeError(
'The model needs to be compiled before being used.')
return self.model.evaluate_generator(
generator,
steps,
max_queue_size=max_queue_size,
workers=workers,
use_multiprocessing=use_multiprocessing)
def predict_generator(self,
generator,
steps=None,
max_queue_size=10,
workers=1,
use_multiprocessing=False,
verbose=0,
**kwargs):
"""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.
Optional for `Sequence`: if unspecified, will use
the `len(generator)` as a number of steps.
max_queue_size: maximum size for the generator queue
workers: maximum number of processes to spin up
use_multiprocessing: 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.
**kwargs: support for legacy arguments.
Returns:
A Numpy array of predictions.
Raises:
ValueError: In case the generator yields
data in an invalid format.
"""
# Legacy support
if 'max_q_size' in kwargs:
max_queue_size = kwargs.pop('max_q_size')
logging.warning(
'The argument `max_q_size` has been renamed '
'`max_queue_size`. Update your method calls accordingly.')
if 'pickle_safe' in kwargs:
use_multiprocessing = kwargs.pop('pickle_safe')
logging.warning('The argument `pickle_safe` has been renamed '
'`use_multiprocessing`. '
'Update your method calls accordingly.')
if kwargs:
raise ValueError('Unrecognized keyword arguments: ' + str(kwargs))
if not self.built:
self.build()
return self.model.predict_generator(
generator,
steps,
max_queue_size=max_queue_size,
workers=workers,
use_multiprocessing=use_multiprocessing,
verbose=verbose)
def get_config(self):
config = []
for layer in self.layers:
config.append({
'class_name': layer.__class__.__name__,
'config': layer.get_config()
})
return copy.deepcopy(config)
@classmethod
def from_config(cls, config, custom_objects=None):
model = cls()
for conf in config:
layer = layer_module.deserialize(conf,
custom_objects=custom_objects)
model.add(layer)
return model
def multi_gpu_model(model, gpus):
"""Replicates a model on different GPUs.
Specifically, this function implements single-machine
multi-GPU data parallelism. It works in the following way:
- Divide the model's input(s) into multiple sub-batches.
- Apply a model copy on each sub-batch. Every model copy
is executed on a dedicated GPU.
- Concatenate the results (on CPU) into one big batch.
E.g. if your `batch_size` is 64 and you use `gpus=2`,
then we will divide the input into 2 sub-batches of 32 samples,
process each sub-batch on one GPU, then return the full
batch of 64 processed samples.
This induces quasi-linear speedup on up to 8 GPUs.
This function is only available with the TensorFlow backend
for the time being.
Arguments:
model: A Keras model instance. To avoid OOM errors,
this model could have been built on CPU, for instance
(see usage example below).
gpus: Integer >= 2, number of on GPUs on which to create
model replicas.
Returns:
A Keras `Model` instance which can be used just like the initial
`model` argument, but which distributes its workload on multiple GPUs.
Example:
```python
import tensorflow as tf
from keras.applications import Xception
from keras.utils import multi_gpu_model
num_samples = 1000
height = 224
width = 224
num_classes = 1000
# Instantiate the base model (or "template" model).
# We recommend doing this with under a CPU device scope,
# so that the model's weights are hosted on CPU memory.
# Otherwise they may end up hosted on a GPU, which would
# complicate weight sharing.
with tf.device('/cpu:0'):
model = Xception(weights=None,
input_shape=(height, width, 3),
classes=num_classes)
# Replicates the model on 8 GPUs.
# This assumes that your machine has 8 available GPUs.
parallel_model = multi_gpu_model(model, gpus=8)
parallel_model.compile(loss='categorical_crossentropy',
optimizer='rmsprop')
# Generate dummy data.
x = np.random.random((num_samples, height, width, 3))
y = np.random.random((num_samples, num_classes))
# This `fit` call will be distributed on 8 GPUs.
# Since the batch size is 256, each GPU will process 32 samples.
parallel_model.fit(x, y, epochs=20, batch_size=256)
# Save model via the template model (which shares the same weights):
model.save('my_model.h5')
```
Raises:
ValueError: if the `gpus` argument does not match available devices.
"""
# pylint: disable=g-import-not-at-top
from tensorflow.python.keras._impl.keras.layers.core import Lambda
from tensorflow.python.keras._impl.keras.layers.merge import concatenate
if isinstance(gpus, (list, tuple)):
if len(gpus) <= 1:
raise ValueError('For multi-gpu usage to be effective, '
'call `multi_gpu_model` with `len(gpus) >= 2`. '
'Received: `gpus=%s`' % gpus)
num_gpus = len(gpus)
target_gpu_ids = gpus
else:
if gpus <= 1:
raise ValueError('For multi-gpu usage to be effective, '
'call `multi_gpu_model` with `gpus >= 2`. '
'Received: `gpus=%d`' % gpus)
num_gpus = gpus
target_gpu_ids = range(num_gpus)
target_devices = ['/cpu:0'] + ['/gpu:%d' % i for i in target_gpu_ids]
available_devices = _get_available_devices()
available_devices = [
_normalize_device_name(name) for name in available_devices
]
for device in target_devices:
if device not in available_devices:
raise ValueError(
'To call `multi_gpu_model` with `gpus=%d`, '
'we expect the following devices to be available: %s. '
'However this machine only has: %s. '
'Try reducing `gpus`.' %
(gpus, target_devices, available_devices))
def get_slice(data, i, parts):
"""Slice an array into `parts` slices and return slice `i`.
Arguments:
data: array to slice.
i: index of slice to return.
parts: number of slices to make.
Returns:
Slice `i` of `data`.
"""
shape = array_ops.shape(data)
batch_size = shape[:1]
input_shape = shape[1:]
step = batch_size // parts
if i == num_gpus - 1:
size = batch_size - step * i
else:
size = step
size = array_ops.concat([size, input_shape], axis=0)
stride = array_ops.concat([step, input_shape * 0], axis=0)
start = stride * i
return array_ops.slice(data, start, size)
all_outputs = []
for i in range(len(model.outputs)):
all_outputs.append([])
# Place a copy of the model on each GPU,
# each getting a slice of the inputs.
for i, gpu_id in enumerate(target_gpu_ids):
with ops.device('/gpu:%d' % gpu_id):
with ops.name_scope('replica_%d' % gpu_id):
inputs = []
# Retrieve a slice of the input.
for x in model.inputs:
input_shape = tuple(x.get_shape().as_list())[1:]
slice_i = Lambda(get_slice,
output_shape=input_shape,
arguments={
'i': i,
'parts': num_gpus
})(x)
inputs.append(slice_i)
# Apply model on slice
# (creating a model replica on the target device).
outputs = model(inputs)
if not isinstance(outputs, list):
outputs = [outputs]
# Save the outputs for merging back together later.
for o in range(len(outputs)):
all_outputs[o].append(outputs[o])
# Merge outputs on CPU.
with ops.device('/cpu:0'):
merged = []
for name, outputs in zip(model.output_names, all_outputs):
merged.append(concatenate(outputs, axis=0, name=name))
return Model(model.inputs, merged)
class Sequential(Model):
"""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))
```
"""
def __init__(self, layers=None, name=None):
self._is_graph_network = True
self._is_compiled = False
self._layers = [] # Stack of layers.
self.model = None # Internal Model instance.
self.inputs = [] # List of input tensors
self.outputs = [] # List of length 1: the output tensor (unique).
self._trainable = True
self._initial_weights = None
self._input_layers = []
# Model attributes.
self._inbound_nodes = []
self._outbound_nodes = []
self.built = False
# Set model name.
if not name:
prefix = 'sequential_'
name = prefix + str(K.get_uid(prefix))
self._name = name
# Used by Layer base class.
self._dtype = None
self._activity_regularizer = None
# The following properties are not actually used by Keras;
# they exist for compatibility with TF's variable scoping mechanism.
self._updates = []
self._losses = []
self._scope = None
self._reuse = None
self._base_name = name
self._graph = ops.get_default_graph()
# Add to the model any layers passed to the constructor.
if layers:
for layer in layers:
self.add(layer)
def add(self, 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).
"""
if not isinstance(layer, (base_layer.Layer, base_layer.TFBaseLayer)):
raise TypeError('The added layer must be '
'an instance of class Layer. '
'Found: ' + str(layer))
if not self.outputs:
# First layer in model: check that it is an input layer.
if not isinstance(layer, InputLayer):
# Create an input layer.
# First, we need to infer its expected input shape and dtype.
if isinstance(layer, (Model, Sequential)):
# We were passed a model as first layer.
# This requires a specific way to figure out the
# input shape and dtype.
if not layer.layers:
raise ValueError('Cannot add an empty model '
'to a `Sequential` model.')
# In case of nested models: recover the first layer
# of the deepest model to infer input shape and dtype.
first_layer = layer.layers[0]
while isinstance(first_layer, (Model, Sequential)):
first_layer = first_layer.layers[0]
batch_shape = first_layer._batch_input_shape
dtype = first_layer.dtype
else:
# We were passed a regular layer, and it should
# know about its input shape. Otherwise, that's an error.
if not hasattr(layer, '_batch_input_shape'):
raise ValueError('The first layer in a '
'Sequential model must '
'get an `input_shape` argument.')
batch_shape = layer._batch_input_shape
dtype = layer.dtype
# Instantiate the input layer.
x = Input(
batch_shape=batch_shape, dtype=dtype, name=layer.name + '_input')
# This will build the current layer
# and create the node connecting the current layer
# to the input layer we just created.
layer(x)
if len(layer._inbound_nodes[-1].output_tensors) != 1:
raise ValueError('All layers in a Sequential model '
'should have a single output tensor. '
'For multi-output layers, '
'use the functional API.')
self.outputs = [layer._inbound_nodes[-1].output_tensors[0]]
self.inputs = network.get_source_inputs(self.outputs[0])
# We create an input node, which we will keep updated
# as we add more layers
base_layer.Node(
outbound_layer=self,
inbound_layers=[],
node_indices=[],
tensor_indices=[],
input_tensors=self.inputs,
output_tensors=self.outputs)
else:
output_tensor = layer(self.outputs[0])
if isinstance(output_tensor, list):
raise TypeError('All layers in a Sequential model '
'should have a single output tensor. '
'For multi-output layers, '
'use the functional API.')
self.outputs = [output_tensor]
# update self._inbound_nodes
self._inbound_nodes[0].output_tensors = self.outputs
self._inbound_nodes[0].output_shapes = [K.int_shape(self.outputs[0])]
self._layers.append(layer)
self.built = False
def pop(self):
"""Removes the last layer in the model.
Raises:
TypeError: if there are no layers in the model.
"""
if not self.layers:
raise TypeError('There are no layers in the model.')
self.layers.pop()
if not self.layers:
self.outputs = []
self._inbound_nodes = []
self._outbound_nodes = []
else:
self.layers[-1]._outbound_nodes = []
self.outputs = [self.layers[-1].output]
# update self._inbound_nodes
self._inbound_nodes[0].output_tensors = self.outputs
self._inbound_nodes[0].output_shapes = [K.int_shape(self.outputs[0])]
self.built = False
def get_layer(self, 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.
"""
if not self.built:
self.build()
return self.model.get_layer(name, index)
def call(self, inputs, **kwargs):
if not self.built:
self.build()
return self.model.call(inputs, **kwargs)
def build(self, input_shape=None):
if not self.inputs or not self.outputs:
raise TypeError('Sequential model cannot be built: model is empty.'
' Add some layers first.')
# actually create the model
self.model = Model(self.inputs, self.outputs[0], name=self.name + '_model')
self.model.trainable = self.trainable
# mirror model attributes
self.supports_masking = self.model.supports_masking
self._output_mask_cache = self.model._output_mask_cache
self._output_tensor_cache = self.model._output_tensor_cache
self._output_shape_cache = self.model._output_shape_cache
self._input_layers = self.model._input_layers
self._output_layers = self.model._output_layers
self._input_coordinates = self.model._input_coordinates
self._output_coordinates = self.model._output_coordinates
self._nodes_by_depth = self.model._nodes_by_depth
self._network_nodes = self.model._network_nodes
self.output_names = self.model.output_names
self.input_names = self.model.input_names
self._feed_input_names = self.model._feed_input_names
self._feed_inputs = self.model._feed_inputs
# Make sure child model callbacks
# will call the parent Sequential model.
self.model.callback_model = self
self.built = True
@property
def uses_learning_phase(self):
if not self.built:
self.build()
return self.model.uses_learning_phase
def _gather_list_attr(self, attr):
all_attrs = []
for layer in self.layers:
all_attrs += getattr(layer, attr, [])
return all_attrs
def _make_train_function(self):
self.model._make_train_function()
def _make_test_function(self):
self.model._make_test_function()
def _make_predict_function(self):
self.model._make_predict_function()
@property
def trainable(self):
return self._trainable
@trainable.setter
def trainable(self, value):
if self.model:
self.model.trainable = value
self._trainable = value
@property
def trainable_weights(self):
if not self.trainable:
return []
return self._gather_list_attr('trainable_weights')
@property
def non_trainable_weights(self):
weights = self._gather_list_attr('non_trainable_weights')
if not self.trainable:
trainable_weights = self._gather_list_attr('trainable_weights')
return trainable_weights + weights
return weights
@property
def regularizers(self):
if not self.built:
self.build()
return self.model.regularizers
def get_weights(self):
"""Retrieves the weights of the model.
Returns:
A flat list of Numpy arrays
(one array per model weight).
"""
if not self.built:
self.build()
return self.model.get_weights()
def set_weights(self, 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()`.
"""
if not self.built:
self.build()
self.model.set_weights(weights)
def load_weights(self, filepath, by_name=False):
if h5py is None:
raise ImportError('`load_weights` requires h5py.')
f = h5py.File(filepath, mode='r')
if 'layer_names' not in f.attrs and 'model_weights' in f:
f = f['model_weights']
layers = self.layers
if by_name:
saving.load_weights_from_hdf5_group_by_name(f, layers)
else:
saving.load_weights_from_hdf5_group(f, layers)
if hasattr(f, 'close'):
f.close()
def save_weights(self, filepath, overwrite=True):
if h5py is None:
raise ImportError('`save_weights` requires h5py.')
# If file exists and should not be overwritten:
if not overwrite and os.path.isfile(filepath):
proceed = ask_to_proceed_with_overwrite(filepath)
if not proceed:
return
layers = self.layers
f = h5py.File(filepath, 'w')
saving.save_weights_to_hdf5_group(f, layers)
f.flush()
f.close()
def compile(self,
optimizer,
loss,
metrics=None,
sample_weight_mode=None,
weighted_metrics=None,
target_tensors=None,
**kwargs):
"""Configures the model for training.
Arguments:
optimizer: String (name of optimizer) or optimizer object.
See [optimizers](/optimizers).
loss: String (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.
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'}`.
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 a placeholder 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 tensor (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 should be a single tensor
(for a single-output `Sequential` model).
**kwargs: These arguments are passed into `tf.Session.run`.
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'])
```
"""
# create the underlying model
self.build()
# call compile method of Model class
self.model.compile(
optimizer,
loss,
metrics=metrics,
sample_weight_mode=sample_weight_mode,
weighted_metrics=weighted_metrics,
target_tensors=target_tensors,
**kwargs)
self.optimizer = self.model.optimizer
self.loss = self.model.loss
self.metrics = self.model.metrics
self.loss_weights = self.model.loss_weights
self.sample_weight_mode = self.model.sample_weight_mode
self.weighted_metrics = self.model.weighted_metrics
self.targets = self.model.targets
self.metrics_tensors = self.model.metrics_tensors
self.metrics_names = self.model.metrics_names
self.sample_weights = self.model.sample_weights
self.total_loss = self.model.total_loss
def fit(self,
x=None,
y=None,
batch_size=None,
epochs=1,
verbose=1,
callbacks=None,
validation_split=0.,
validation_data=None,
shuffle=True,
class_weight=None,
sample_weight=None,
initial_epoch=0,
steps_per_epoch=None,
validation_steps=None,
**kwargs):
"""Trains the model for a fixed number of epochs.
Arguments:
x: Numpy array of training data.
If the input layer in the model is named, you can also pass a
dictionary mapping the input name to a Numpy array.
`x` can be `None` (default) if feeding from
TensorFlow data tensors.
y: Numpy array of target (label) data.
If the output layer in the model is named, you can also pass a
dictionary mapping the output name to a Numpy array.
`y` can be `None` (default) if feeding from
TensorFlow data tensors.
batch_size: Integer or `None`.
Number of samples per gradient update.
If unspecified, it will default to 32.
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.
callbacks: List of `keras.callbacks.Callback` instances.
List of callbacks to apply 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.
The validation data is selected from the last samples
in the `x` and `y` data provided, before shuffling.
validation_data: tuple `(x_val, y_val)` or tuple
`(x_val, y_val, val_sample_weights)` 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 will override `validation_split`.
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()`.
initial_epoch: Epoch at which to start training
(useful for resuming a previous training run).
steps_per_epoch: 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 unique samples in your dataset divided by
the batch size, or 1 if that cannot be determined.
validation_steps: Only relevant if `steps_per_epoch`
is specified. Total number of steps (batches of samples)
to validate before stopping.
**kwargs: Used for backwards compatibility support.
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.
"""
if not self.built:
raise RuntimeError('The model needs to be compiled before being used.')
return self.model.fit(
x,
y,
batch_size=batch_size,
epochs=epochs,
verbose=verbose,
callbacks=callbacks,
validation_split=validation_split,
validation_data=validation_data,
shuffle=shuffle,
class_weight=class_weight,
sample_weight=sample_weight,
initial_epoch=initial_epoch,
steps_per_epoch=steps_per_epoch,
validation_steps=validation_steps)
def evaluate(self, 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.
"""
if not self.built:
raise RuntimeError('The model needs to be compiled before being used.')
return self.model.evaluate(
x,
y,
batch_size=batch_size,
verbose=verbose,
sample_weight=sample_weight)
def predict(self, 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.
"""
if not self.built:
self.build()
return self.model.predict(x, batch_size=batch_size, verbose=verbose)
def predict_on_batch(self, 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.
"""
if not self.built:
self.build()
return self.model.predict_on_batch(x)
def train_on_batch(self, 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.
"""
if not self.built:
raise RuntimeError('The model needs to be compiled before being used.')
return self.model.train_on_batch(
x, y, sample_weight=sample_weight, class_weight=class_weight)
def test_on_batch(self, 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.
"""
if not self.built:
raise RuntimeError('The model needs to be compiled before being used.')
return self.model.test_on_batch(x, y, sample_weight=sample_weight)
def predict_proba(self, 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.
"""
preds = self.predict(x, batch_size, verbose)
if preds.min() < 0. or preds.max() > 1.:
logging.warning('Network returning invalid probability values. '
'The last layer might not normalize predictions '
'into probabilities '
'(like softmax or sigmoid would).')
return preds
def predict_classes(self, 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.
"""
proba = self.predict(x, batch_size=batch_size, verbose=verbose)
if proba.shape[-1] > 1:
return proba.argmax(axis=-1)
else:
return (proba > 0.5).astype('int32')
def fit_generator(self,
generator,
steps_per_epoch=None,
epochs=1,
verbose=1,
callbacks=None,
validation_data=None,
validation_steps=None,
class_weight=None,
max_queue_size=10,
workers=1,
use_multiprocessing=False,
shuffle=True,
initial_epoch=0,
**kwargs):
"""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 `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.
Note that in conjunction with initial_epoch, the parameter
epochs is to be understood as "final epoch". The model is
not trained for n steps given by epochs, but until the
epoch epochs is reached.
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 samples of your
validation dataset divided by the batch size.
Optional for `Sequence`: if unspecified, will use
the `len(validation_data)` as a number of steps.
class_weight: Dictionary mapping class indices to a weight
for the class.
max_queue_size: Maximum size for the generator queue
workers: Maximum number of processes to spin up
use_multiprocessing: 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.
shuffle: Whether to shuffle the order of the batches at
the beginning of each epoch. Only used with instances
of `Sequence` (keras.utils.Sequence).
initial_epoch: Epoch at which to start training
(useful for resuming a previous training run)
**kwargs: support for legacy arguments.
Returns:
A `History` object.
Raises:
RuntimeError: if the model was never compiled.
ValueError: In case the generator yields
data in an invalid format.
Example:
```python
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'),
steps_per_epoch=1000, epochs=10)
```
"""
# Legacy support
if 'max_q_size' in kwargs:
max_queue_size = kwargs.pop('max_q_size')
logging.warning('The argument `max_q_size` has been renamed '
'`max_queue_size`. Update your method calls accordingly.')
if 'pickle_safe' in kwargs:
use_multiprocessing = kwargs.pop('pickle_safe')
logging.warning('The argument `pickle_safe` has been renamed '
'`use_multiprocessing`. '
'Update your method calls accordingly.')
if kwargs:
raise ValueError('Unrecognized keyword arguments: ' + str(kwargs))
if not self.built:
raise RuntimeError('The model needs to be compiled before being used.')
return self.model.fit_generator(
generator,
steps_per_epoch,
epochs,
verbose=verbose,
callbacks=callbacks,
validation_data=validation_data,
validation_steps=validation_steps,
class_weight=class_weight,
max_queue_size=max_queue_size,
workers=workers,
use_multiprocessing=use_multiprocessing,
shuffle=shuffle,
initial_epoch=initial_epoch)
def evaluate_generator(self,
generator,
steps=None,
max_queue_size=10,
workers=1,
use_multiprocessing=False,
**kwargs):
"""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.
Optional for `Sequence`: if unspecified, will use
the `len(generator)` as a number of steps.
max_queue_size: maximum size for the generator queue
workers: maximum number of processes to spin up
use_multiprocessing: 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.
**kwargs: support for legacy arguments.
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.
ValueError: In case the generator yields
data in an invalid format.
"""
# Legacy support
if 'max_q_size' in kwargs:
max_queue_size = kwargs.pop('max_q_size')
logging.warning('The argument `max_q_size` has been renamed '
'`max_queue_size`. Update your method calls accordingly.')
if 'pickle_safe' in kwargs:
use_multiprocessing = kwargs.pop('pickle_safe')
logging.warning('The argument `pickle_safe` has been renamed '
'`use_multiprocessing`. '
'Update your method calls accordingly.')
if kwargs:
raise ValueError('Unrecognized keyword arguments: ' + str(kwargs))
if not self.built:
raise RuntimeError('The model needs to be compiled before being used.')
return self.model.evaluate_generator(
generator,
steps,
max_queue_size=max_queue_size,
workers=workers,
use_multiprocessing=use_multiprocessing)
def predict_generator(self,
generator,
steps=None,
max_queue_size=10,
workers=1,
use_multiprocessing=False,
verbose=0,
**kwargs):
"""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.
Optional for `Sequence`: if unspecified, will use
the `len(generator)` as a number of steps.
max_queue_size: maximum size for the generator queue
workers: maximum number of processes to spin up
use_multiprocessing: 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.
**kwargs: support for legacy arguments.
Returns:
A Numpy array of predictions.
Raises:
ValueError: In case the generator yields
data in an invalid format.
"""
# Legacy support
if 'max_q_size' in kwargs:
max_queue_size = kwargs.pop('max_q_size')
logging.warning('The argument `max_q_size` has been renamed '
'`max_queue_size`. Update your method calls accordingly.')
if 'pickle_safe' in kwargs:
use_multiprocessing = kwargs.pop('pickle_safe')
logging.warning('The argument `pickle_safe` has been renamed '
'`use_multiprocessing`. '
'Update your method calls accordingly.')
if kwargs:
raise ValueError('Unrecognized keyword arguments: ' + str(kwargs))
if not self.built:
self.build()
return self.model.predict_generator(
generator,
steps,
max_queue_size=max_queue_size,
workers=workers,
use_multiprocessing=use_multiprocessing,
verbose=verbose)
def get_config(self):
config = []
for layer in self.layers:
config.append({
'class_name': layer.__class__.__name__,
'config': layer.get_config()
})
return copy.deepcopy(config)
@classmethod
def from_config(cls, config, custom_objects=None):
model = cls()
for conf in config:
layer = layer_module.deserialize(conf, custom_objects=custom_objects)
model.add(layer)
return model