"""Training-related part of the Keras engine.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import warnings
import copy
import numpy as np
from .network import Network
from .base_layer import Layer
from .training_utils import collect_metrics
from .training_utils import check_array_length_consistency
from .training_utils import check_loss_and_target_compatibility
from .training_utils import standardize_class_weights
from .training_utils import standardize_input_data
from .training_utils import standardize_sample_weights
from .training_utils import standardize_weights
from .training_utils import weighted_masked_objective
from . import training_arrays
from . import training_generator
from .. import backend as K
from .. import optimizers
from .. import losses
from .. import metrics as metrics_module
from ..utils.generic_utils import slice_arrays
from ..utils.generic_utils import to_list
from ..utils.generic_utils import unpack_singleton
from ..legacy import interfaces
class Model(Network):
"""The `Model` class adds training & evaluation routines to a `Network`.
"""
def compile(self, optimizer,
loss=None,
metrics=None,
loss_weights=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 instance.
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'}`.
loss_weights: Optional list or dictionary specifying scalar
coefficients (Python floats) to weight the loss contributions
of different model outputs.
The loss value that will be minimized by the model
will then be the *weighted sum* of all individual losses,
weighted by the `loss_weights` coefficients.
If a list, it is expected to have a 1:1 mapping
to the model's outputs. If a tensor, it is expected to map
output names (strings) to scalar coefficients.
sample_weight_mode: If you need to do timestep-wise
sample weighting (2D weights), set this to `"temporal"`.
`None` defaults to sample-wise weights (1D).
If the model has multiple outputs, you can use a different
`sample_weight_mode` on each output by passing a
dictionary or a list of modes.
weighted_metrics: List of metrics to be evaluated and weighted
by sample_weight or class_weight during training and testing.
target_tensors: By default, Keras will create placeholders for the
model's target, which will be fed with the target data during
training. If instead you would like to use your own
target tensors (in turn, Keras will not expect external
Numpy data for these targets at training time), you
can specify them via the `target_tensors` argument. It can be
a single tensor (for a single-output model), a list of tensors,
or a dict mapping output names to target tensors.
**kwargs: When using the Theano/CNTK backends, these arguments
are passed into `K.function`.
When using the TensorFlow backend,
these arguments are passed into `tf.Session.run`.
# Raises
ValueError: In case of invalid arguments for
`optimizer`, `loss`, `metrics` or `sample_weight_mode`.
"""
self.optimizer = optimizers.get(optimizer)
self.loss = loss or []
self.metrics = metrics or []
self.loss_weights = loss_weights
self.sample_weight_mode = sample_weight_mode
self.weighted_metrics = weighted_metrics
if not self.built:
# Model is not compilable because
# it does not know its number of inputs
# and outputs, nor their shapes and names.
# We will compile after the first
# time the model gets called on training data.
return
self._is_compiled = True
# Prepare loss functions.
if isinstance(loss, dict):
for name in loss:
if name not in self.output_names:
raise ValueError('Unknown entry in loss '
'dictionary: "' + name + '". '
'Only expected the following keys: ' +
str(self.output_names))
loss_functions = []
for name in self.output_names:
if name not in loss:
warnings.warn('Output "' + name +
'" missing from loss dictionary. '
'We assume this was done on purpose, '
'and we will not be expecting '
'any data to be passed to "' + name +
'" during training.', stacklevel=2)
loss_functions.append(losses.get(loss.get(name)))
elif isinstance(loss, list):
if len(loss) != len(self.outputs):
raise ValueError('When passing a list as loss, '
'it should have one entry per model outputs. '
'The model has ' + str(len(self.outputs)) +
' outputs, but you passed loss=' +
str(loss))
loss_functions = [losses.get(l) for l in loss]
else:
loss_function = losses.get(loss)
loss_functions = [loss_function for _ in range(len(self.outputs))]
self.loss_functions = loss_functions
weighted_losses = [
weighted_masked_objective(fn) for fn in loss_functions]
skip_target_indices = []
skip_target_weighing_indices = []
self._feed_outputs = []
self._feed_output_names = []
self._feed_output_shapes = []
self._feed_loss_fns = []
for i in range(len(weighted_losses)):
if weighted_losses[i] is None:
skip_target_indices.append(i)
skip_target_weighing_indices.append(i)
# Prepare output masks.
masks = self.compute_mask(self.inputs, mask=None)
if masks is None:
masks = [None for _ in self.outputs]
masks = to_list(masks)
# Prepare loss weights.
if loss_weights is None:
loss_weights_list = [1. for _ in range(len(self.outputs))]
elif isinstance(loss_weights, dict):
for name in loss_weights:
if name not in self.output_names:
raise ValueError('Unknown entry in loss_weights '
'dictionary: "' + name + '". '
'Only expected the following keys: ' +
str(self.output_names))
loss_weights_list = []
for name in self.output_names:
loss_weights_list.append(loss_weights.get(name, 1.))
elif isinstance(loss_weights, list):
if len(loss_weights) != len(self.outputs):
raise ValueError('When passing a list as loss_weights, '
'it should have one entry per model output. '
'The model has ' + str(len(self.outputs)) +
' outputs, but you passed loss_weights=' +
str(loss_weights))
loss_weights_list = loss_weights
else:
raise TypeError('Could not interpret loss_weights argument: ' +
str(loss_weights) +
' - expected a list of dicts.')
# Prepare targets of model.
self.targets = []
self._feed_targets = []
if target_tensors is not None:
if isinstance(target_tensors, list):
if len(target_tensors) != len(self.outputs):
raise ValueError(
'When passing a list as `target_tensors`, '
'it should have one entry per model output. '
'The model has ' + str(len(self.outputs)) +
' outputs, but you passed target_tensors=' +
str(target_tensors))
elif isinstance(target_tensors, dict):
for name in target_tensors:
if name not in self.output_names:
raise ValueError('Unknown entry in `target_tensors` '
'dictionary: "' + name + '". '
'Only expected the following keys: ' +
str(self.output_names))
tmp_target_tensors = []
for name in self.output_names:
tmp_target_tensors.append(target_tensors.get(name, None))
target_tensors = tmp_target_tensors
else:
raise TypeError('Expected `target_tensors` to be '
'a list or dict, but got:', target_tensors)
for i in range(len(self.outputs)):
if i in skip_target_indices:
self.targets.append(None)
else:
shape = K.int_shape(self.outputs[i])
name = self.output_names[i]
if target_tensors is not None:
target = target_tensors[i]
else:
target = None
if target is None or K.is_placeholder(target):
if target is None:
target = K.placeholder(
ndim=len(shape),
name=name + '_target',
sparse=K.is_sparse(self.outputs[i]),
dtype=K.dtype(self.outputs[i]))
self._feed_targets.append(target)
self._feed_outputs.append(self.outputs[i])
self._feed_output_names.append(name)
self._feed_output_shapes.append(shape)
self._feed_loss_fns.append(self.loss_functions[i])
else:
skip_target_weighing_indices.append(i)
self.targets.append(target)
# Prepare sample weights.
sample_weights = []
sample_weight_modes = []
if isinstance(sample_weight_mode, dict):
for name in sample_weight_mode:
if name not in self.output_names:
raise ValueError('Unknown entry in '
'sample_weight_mode dictionary: "' +
name + '". '
'Only expected the following keys: ' +
str(self.output_names))
for i, name in enumerate(self.output_names):
if i in skip_target_weighing_indices:
weight = None
sample_weight_modes.append(None)
else:
if name not in sample_weight_mode:
raise ValueError('Output "' + name +
'" missing from sample_weight_modes '
'dictionary')
if sample_weight_mode.get(name) == 'temporal':
weight = K.placeholder(ndim=2,
name=name + '_sample_weights')
sample_weight_modes.append('temporal')
else:
weight = K.placeholder(ndim=1,
name=name + '_sample_weights')
sample_weight_modes.append(None)
sample_weights.append(weight)
elif isinstance(sample_weight_mode, list):
if len(sample_weight_mode) != len(self.outputs):
raise ValueError('When passing a list as sample_weight_mode, '
'it should have one entry per model output. '
'The model has ' + str(len(self.outputs)) +
' outputs, but you passed '
'sample_weight_mode=' +
str(sample_weight_mode))
for i in range(len(self.output_names)):
if i in skip_target_weighing_indices:
weight = None
sample_weight_modes.append(None)
else:
mode = sample_weight_mode[i]
name = self.output_names[i]
if mode == 'temporal':
weight = K.placeholder(ndim=2,
name=name + '_sample_weights')
sample_weight_modes.append('temporal')
else:
weight = K.placeholder(ndim=1,
name=name + '_sample_weights')
sample_weight_modes.append(None)
sample_weights.append(weight)
else:
for i, name in enumerate(self.output_names):
if i in skip_target_weighing_indices:
sample_weight_modes.append(None)
sample_weights.append(None)
else:
if sample_weight_mode == 'temporal':
sample_weights.append(
K.placeholder(ndim=2,
name=name + '_sample_weights'))
sample_weight_modes.append('temporal')
else:
sample_weights.append(
K.placeholder(ndim=1,
name=name + '_sample_weights'))
sample_weight_modes.append(None)
self.sample_weight_modes = sample_weight_modes
self._feed_sample_weight_modes = []
for i in range(len(self.outputs)):
if i not in skip_target_weighing_indices:
self._feed_sample_weight_modes.append(
self.sample_weight_modes[i])
# Prepare metrics.
self.metrics_names = ['loss']
self.metrics_tensors = []
# Compute total loss.
total_loss = None
with K.name_scope('loss'):
for i in range(len(self.outputs)):
if i in skip_target_indices:
continue
y_true = self.targets[i]
y_pred = self.outputs[i]
weighted_loss = weighted_losses[i]
sample_weight = sample_weights[i]
mask = masks[i]
loss_weight = loss_weights_list[i]
with K.name_scope(self.output_names[i] + '_loss'):
output_loss = weighted_loss(y_true, y_pred,
sample_weight, mask)
if len(self.outputs) > 1:
self.metrics_tensors.append(output_loss)
self.metrics_names.append(self.output_names[i] + '_loss')
if total_loss is None:
total_loss = loss_weight * output_loss
else:
total_loss += loss_weight * output_loss
if total_loss is None:
if not self.losses:
raise ValueError('The model cannot be compiled '
'because it has no loss to optimize.')
else:
total_loss = 0.
# Add regularization penalties
# and other layer-specific losses.
for loss_tensor in self.losses:
total_loss += loss_tensor
# List of same size as output_names.
# contains tuples (metrics for output, names of metrics).
nested_metrics = collect_metrics(metrics, self.output_names)
nested_weighted_metrics = collect_metrics(weighted_metrics,
self.output_names)
self.metrics_updates = []
self.stateful_metric_names = []
self.stateful_metric_functions = []
def handle_metrics(metrics, weights=None):
metric_name_prefix = 'weighted_' if weights is not None else ''
for metric in metrics:
if metric in ('accuracy', 'acc', 'crossentropy', 'ce'):
# custom handling of accuracy/crossentropy
# (because of class mode duality)
output_shape = K.int_shape(self.outputs[i])
if (output_shape[-1] == 1 or
self.loss_functions[i] == losses.binary_crossentropy):
# case: binary accuracy/crossentropy
if metric in ('accuracy', 'acc'):
metric_fn = metrics_module.binary_accuracy
elif metric in ('crossentropy', 'ce'):
metric_fn = metrics_module.binary_crossentropy
elif self.loss_functions[i] == losses.sparse_categorical_crossentropy:
# case: categorical accuracy/crossentropy
# with sparse targets
if metric in ('accuracy', 'acc'):
metric_fn = metrics_module.sparse_categorical_accuracy
elif metric in ('crossentropy', 'ce'):
metric_fn = metrics_module.sparse_categorical_crossentropy
else:
# case: categorical accuracy/crossentropy
if metric in ('accuracy', 'acc'):
metric_fn = metrics_module.categorical_accuracy
elif metric in ('crossentropy', 'ce'):
metric_fn = metrics_module.categorical_crossentropy
if metric in ('accuracy', 'acc'):
suffix = 'acc'
elif metric in ('crossentropy', 'ce'):
suffix = 'ce'
weighted_metric_fn = weighted_masked_objective(metric_fn)
metric_name = metric_name_prefix + suffix
else:
metric_fn = metrics_module.get(metric)
weighted_metric_fn = weighted_masked_objective(metric_fn)
# Get metric name as string
if hasattr(metric_fn, 'name'):
metric_name = metric_fn.name
else:
metric_name = metric_fn.__name__
metric_name = metric_name_prefix + metric_name
with K.name_scope(metric_name):
metric_result = weighted_metric_fn(y_true, y_pred,
weights=weights,
mask=masks[i])
# Append to self.metrics_names, self.metric_tensors,
# self.stateful_metric_names
if len(self.output_names) > 1:
metric_name = self.output_names[i] + '_' + metric_name
# Dedupe name
j = 1
base_metric_name = metric_name
while metric_name in self.metrics_names:
metric_name = base_metric_name + '_' + str(j)
j += 1
self.metrics_names.append(metric_name)
self.metrics_tensors.append(metric_result)
# Keep track of state updates created by
# stateful metrics (i.e. metrics layers).
if isinstance(metric_fn, Layer) and metric_fn.stateful:
self.stateful_metric_names.append(metric_name)
self.stateful_metric_functions.append(metric_fn)
self.metrics_updates += metric_fn.updates
with K.name_scope('metrics'):
for i in range(len(self.outputs)):
if i in skip_target_indices:
continue
y_true = self.targets[i]
y_pred = self.outputs[i]
weights = sample_weights[i]
output_metrics = nested_metrics[i]
output_weighted_metrics = nested_weighted_metrics[i]
handle_metrics(output_metrics)
handle_metrics(output_weighted_metrics, weights=weights)
# Prepare gradient updates and state updates.
self.total_loss = total_loss
self.sample_weights = sample_weights
self._feed_sample_weights = []
for i in range(len(self.sample_weights)):
if i not in skip_target_weighing_indices:
self._feed_sample_weights.append(sample_weights[i])
# Functions for train, test and predict will
# be compiled lazily when required.
# This saves time when the user is not using all functions.
self._function_kwargs = kwargs
self.train_function = None
self.test_function = None
self.predict_function = None
# Collected trainable weights, sorted in topological order.
trainable_weights = self.trainable_weights
self._collected_trainable_weights = trainable_weights
def _check_trainable_weights_consistency(self):
"""Check trainable weights count consistency.
This will raise a warning if `trainable_weights` and
`_collected_trainable_weights` are inconsistent (i.e. have different
number of parameters).
Inconsistency will typically arise when one modifies `model.trainable`
without calling `model.compile` again.
"""
if not hasattr(self, '_collected_trainable_weights'):
return
if (len(self.trainable_weights) !=
len(self._collected_trainable_weights)):
warnings.warn(UserWarning(
'Discrepancy between trainable weights and collected trainable'
' weights, did you set `model.trainable` without calling'
' `model.compile` after ?'))
def _make_train_function(self):
if not hasattr(self, 'train_function'):
raise RuntimeError('You must compile your model before using it.')
self._check_trainable_weights_consistency()
if self.train_function is None:
inputs = (self._feed_inputs +
self._feed_targets +
self._feed_sample_weights)
if self._uses_dynamic_learning_phase():
inputs += [K.learning_phase()]
with K.name_scope('training'):
with K.name_scope(self.optimizer.__class__.__name__):
training_updates = self.optimizer.get_updates(
params=self._collected_trainable_weights,
loss=self.total_loss)
updates = (self.updates +
training_updates +
self.metrics_updates)
# Gets loss and metrics. Updates weights at each call.
self.train_function = K.function(
inputs,
[self.total_loss] + self.metrics_tensors,
updates=updates,
name='train_function',
**self._function_kwargs)
def _make_test_function(self):
if not hasattr(self, 'test_function'):
raise RuntimeError('You must compile your model before using it.')
if self.test_function is None:
inputs = (self._feed_inputs +
self._feed_targets +
self._feed_sample_weights)
if self._uses_dynamic_learning_phase():
inputs += [K.learning_phase()]
# Return loss and metrics, no gradient updates.
# Does update the network states.
self.test_function = K.function(
inputs,
[self.total_loss] + self.metrics_tensors,
updates=self.state_updates + self.metrics_updates,
name='test_function',
**self._function_kwargs)
def _make_predict_function(self):
if not hasattr(self, 'predict_function'):
self.predict_function = None
if self.predict_function is None:
if self._uses_dynamic_learning_phase():
inputs = self._feed_inputs + [K.learning_phase()]
else:
inputs = self._feed_inputs
# Gets network outputs. Does not update weights.
# Does update the network states.
kwargs = getattr(self, '_function_kwargs', {})
self.predict_function = K.function(inputs,
self.outputs,
updates=self.state_updates,
name='predict_function',
**kwargs)
def _uses_dynamic_learning_phase(self):
return (self.uses_learning_phase and
not isinstance(K.learning_phase(), int))
def _set_inputs(self, inputs, outputs=None, training=None):
"""Set model's input and output specs based on the input data received.
This is to be used for Model subclasses, which do not know at instantiation
time what their inputs look like.
# Arguments
inputs: Single array, or list of arrays. The arrays could be placeholders,
Numpy arrays, or data tensors.
- if placeholders: the model is built on top of these placeholders,
and we expect Numpy data to be fed for them when calling `fit`/etc.
- if Numpy data: we create placeholders matching the shape of the Numpy
arrays. We expect Numpy data to be fed for these placeholders
when calling `fit`/etc.
- if data tensors: the model is built on top of these tensors.
We do not expect any Numpy data to be provided when calling `fit`/etc.
outputs: Optional output tensors (if already computed by running the model).
training: Boolean or None. Only relevant in symbolic mode. Specifies
whether to build the model's graph in inference mode (False), training
mode (True), or using the Keras learning phase (None).
"""
if self.__class__.__name__ == 'Sequential':
# Note: we can't test whether the model
# is `Sequential` via `isinstance`
# since `Sequential` depends on `Model`.
if isinstance(inputs, list):
assert len(inputs) == 1
inputs = inputs[0]
self.build(input_shape=(None,) + inputs.shape[1:])
return
if self.inputs:
raise ValueError('Model inputs are already set.')
# On-the-fly setting of symbolic model inputs
# (either by using the tensor provided,
# or by creating a placeholder if Numpy data was provided).
self.inputs = []
self.input_names = []
self._feed_inputs = []
self._feed_input_names = []
self._feed_input_shapes = []
if isinstance(inputs, (list, tuple)):
inputs = list(inputs)
else:
inputs = [inputs]
for i, v in enumerate(inputs):
name = 'input_%d' % (i + 1)
self.input_names.append(name)
if isinstance(v, list):
v = np.asarray(v)
if v.ndim == 1:
v = np.expand_dims(v, 1)
if isinstance(v, (np.ndarray)):
# We fix the placeholder shape except the batch size.
# This is suboptimal, but it is the best we can do with the info
# we have. The user should call `model._set_inputs(placeholders)`
# to specify custom placeholders if the need arises.
shape = (None,) + v.shape[1:]
placeholder = K.placeholder(shape=shape, name=name)
self.inputs.append(placeholder)
self._feed_inputs.append(placeholder)
self._feed_input_names.append(name)
self._feed_input_shapes.append(shape)
else:
# Assumed tensor - TODO(fchollet) additional type check?
self.inputs.append(v)
if K.is_placeholder(v):
self._feed_inputs.append(v)
self._feed_input_names.append(name)
self._feed_input_shapes.append(K.int_shape(v))
if outputs is None:
# Obtain symbolic outputs by calling the model.
if self._expects_training_arg:
outputs = self.call(unpack_singleton(self.inputs), training=training)
else:
outputs = self.call(unpack_singleton(self.inputs))
if isinstance(outputs, (list, tuple)):
outputs = list(outputs)
else:
outputs = [outputs]
self.outputs = outputs
self.output_names = [
'output_%d' % (i + 1) for i in range(len(self.outputs))]
self.built = True
def _standardize_user_data(self, x,
y=None,
sample_weight=None,
class_weight=None,
check_array_lengths=True,
batch_size=None):
all_inputs = []
if not self.built:
# We need to use `x` to set the model inputs.
# We type-check that `x` and `y` are either single arrays
# or lists of arrays.
if isinstance(x, (list, tuple)):
if not all(isinstance(v, np.ndarray) or
K.is_tensor(v) for v in x):
raise ValueError('Please provide as model inputs '
'either a single '
'array or a list of arrays. '
'You passed: x=' + str(x))
all_inputs += list(x)
elif isinstance(x, dict):
raise ValueError('Please do not pass a dictionary '
'as model inputs.')
else:
if not isinstance(x, np.ndarray) and not K.is_tensor(x):
raise ValueError('Please provide as model inputs '
'either a single '
'array or a list of arrays. '
'You passed: x=' + str(x))
all_inputs.append(x)
# Build the model using the retrieved inputs (value or symbolic).
# If values, then in symbolic-mode placeholders will be created
# to match the value shapes.
if not self.inputs:
self._set_inputs(x)
if y is not None:
if not self.optimizer:
raise RuntimeError('You must compile a model before '
'training/testing. '
'Use `model.compile(optimizer, loss)`.')
if not self._is_compiled:
# On-the-fly compilation of the model.
# We need to use `y` to set the model targets.
if isinstance(y, (list, tuple)):
if not all(isinstance(v, np.ndarray) or
K.is_tensor(v) for v in y):
raise ValueError('Please provide as model targets '
'either a single '
'array or a list of arrays. '
'You passed: y=' + str(y))
elif isinstance(y, dict):
raise ValueError('Please do not pass a dictionary '
'as model targets.')
else:
if not isinstance(y, np.ndarray) and not K.is_tensor(y):
raise ValueError('Please provide as model targets '
'either a single '
'array or a list of arrays. '
'You passed: y=' + str(y))
# Typecheck that all inputs are *either* value *or* symbolic.
if y is not None:
if isinstance(y, (list, tuple)):
all_inputs += list(y)
else:
all_inputs.append(y)
if any(K.is_tensor(v) for v in all_inputs):
if not all(K.is_tensor(v) for v in all_inputs):
raise ValueError('Do not pass inputs that mix Numpy '
'arrays and symbolic tensors. '
'You passed: x=' + str(x) +
'; y=' + str(y))
# Handle target tensors if any passed.
if not isinstance(y, (list, tuple)):
y = [y]
target_tensors = [v for v in y if K.is_tensor(v)]
if not target_tensors:
target_tensors = None
self.compile(optimizer=self.optimizer,
loss=self.loss,
metrics=self.metrics,
loss_weights=self.loss_weights,
target_tensors=target_tensors)
# If `x` and `y` were all symbolic,
# then the model should not be fed any inputs and targets.
# Note: in this case, `any` and `all` are equivalent since we disallow
# mixed symbolic/value inputs.
if any(K.is_tensor(v) for v in all_inputs):
return [], [], []
# What follows is input validation and standardization to list format,
# in the case where all inputs are value arrays.
if not self._is_graph_network:
# Case: symbolic-mode subclassed network.
# Do not do shape validation.
feed_input_names = self._feed_input_names
feed_input_shapes = None
else:
# Case: symbolic-mode graph network.
# In this case, we run extensive shape validation checks.
feed_input_names = self._feed_input_names
feed_input_shapes = self._feed_input_shapes
# Standardize the inputs.
x = standardize_input_data(
x,
feed_input_names,
feed_input_shapes,
check_batch_axis=False, # Don't enforce the batch size.
exception_prefix='input')
if y is not None:
if not self._is_graph_network:
feed_output_names = self._feed_output_names
feed_output_shapes = None
# Sample weighting not supported in this case.
# TODO: consider supporting it.
feed_sample_weight_modes = [None for _ in self.outputs]
else:
feed_output_names = self._feed_output_names
feed_sample_weight_modes = self._feed_sample_weight_modes
feed_output_shapes = []
for output_shape, loss_fn in zip(self._feed_output_shapes,
self._feed_loss_fns):
if loss_fn is losses.sparse_categorical_crossentropy:
if K.image_data_format() == 'channels_first' and len(
output_shape) in [4, 5]:
feed_output_shapes.append(
(output_shape[0], 1) + output_shape[2:])
else:
feed_output_shapes.append(output_shape[:-1] + (1,))
elif (not hasattr(loss_fn, '__name__') or
getattr(losses, loss_fn.__name__, None) is None):
# If `loss_fn` is not a function (e.g. callable class)
# or if it not in the `losses` module, then
# it is a user-defined loss and we make no assumptions
# about it.
feed_output_shapes.append(None)
else:
feed_output_shapes.append(output_shape)
# Standardize the outputs.
y = standardize_input_data(
y,
feed_output_names,
feed_output_shapes,
check_batch_axis=False, # Don't enforce the batch size.
exception_prefix='target')
# Generate sample-wise weight values given the `sample_weight` and
# `class_weight` arguments.
sample_weights = standardize_sample_weights(
sample_weight, feed_output_names)
class_weights = standardize_class_weights(
class_weight, feed_output_names)
sample_weights = [
standardize_weights(ref, sw, cw, mode)
for (ref, sw, cw, mode) in
zip(y, sample_weights, class_weights,
feed_sample_weight_modes)
]
# Check that all arrays have the same length.
check_array_length_consistency(x, y, sample_weights)
if self._is_graph_network:
# Additional checks to avoid users mistakenly
# using improper loss fns.
check_loss_and_target_compatibility(
y, self._feed_loss_fns, feed_output_shapes)
else:
y = []
sample_weights = []
if self.stateful and batch_size:
# Check that for stateful networks, number of samples is a multiple
# of the static batch size.
if x[0].shape[0] % batch_size != 0:
raise ValueError('In a stateful network, '
'you should only pass inputs with '
'a number of samples that can be '
'divided by the batch size. Found: ' +
str(x[0].shape[0]) + ' samples')
return x, y, sample_weights
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 given number of epochs (iterations on a dataset).
# Arguments
x: Numpy array of training data (if the model has a single input),
or list of Numpy arrays (if the model has multiple inputs).
If input layers in the model are named, you can also pass a
dictionary mapping input names to Numpy arrays.
`x` can be `None` (default) if feeding from
framework-native tensors (e.g. TensorFlow data tensors).
y: Numpy array of target (label) data
(if the model has a single output),
or list of Numpy arrays (if the model has multiple outputs).
If output layers in the model are named, you can also pass a
dictionary mapping output names to Numpy arrays.
`y` can be `None` (default) if feeding from
framework-native tensors (e.g. TensorFlow data tensors).
batch_size: Integer or `None`.
Number of samples per gradient update.
If unspecified, `batch_size` 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: Integer. 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.
`validation_data` 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: Integer.
Epoch at which to start training
(useful for resuming a previous training run).
steps_per_epoch: Integer or `None`.
Total number of steps (batches of samples)
before declaring one epoch finished and starting the
next epoch. When training with input tensors such as
TensorFlow data tensors, the default `None` is equal to
the number of samples in your dataset divided by
the batch size, or 1 if that cannot be determined.
validation_steps: Only relevant if `steps_per_epoch`
is specified. Total number of steps (batches of samples)
to validate before stopping.
# 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.
"""
# Backwards compatibility
if batch_size is None and steps_per_epoch is None:
batch_size = 32
# Legacy support
if 'nb_epoch' in kwargs:
warnings.warn('The `nb_epoch` argument in `fit` '
'has been renamed `epochs`.', stacklevel=2)
epochs = kwargs.pop('nb_epoch')
if kwargs:
raise TypeError('Unrecognized keyword arguments: ' + str(kwargs))
if x is None and y is None and steps_per_epoch is None:
raise ValueError('If fitting from data tensors, '
'you should specify the `steps_per_epoch` '
'argument.')
# Validate user data.
x, y, sample_weights = self._standardize_user_data(
x, y,
sample_weight=sample_weight,
class_weight=class_weight,
batch_size=batch_size)
# Prepare validation data.
do_validation = False
if validation_data:
do_validation = True
if len(validation_data) == 2:
val_x, val_y = validation_data
val_sample_weight = None
elif len(validation_data) == 3:
val_x, val_y, val_sample_weight = validation_data
else:
raise ValueError('When passing validation_data, '
'it must contain 2 (x_val, y_val) '
'or 3 (x_val, y_val, val_sample_weights) '
'items, however it contains %d items' %
len(validation_data))
val_x, val_y, val_sample_weights = self._standardize_user_data(
val_x, val_y,
sample_weight=val_sample_weight,
batch_size=batch_size)
if self._uses_dynamic_learning_phase():
val_ins = val_x + val_y + val_sample_weights + [0.]
else:
val_ins = val_x + val_y + val_sample_weights
elif validation_split and 0. < validation_split < 1.:
if any(K.is_tensor(t) for t in x):
raise ValueError(
'If your data is in the form of symbolic tensors, '
'you cannot use `validation_split`.')
do_validation = True
if hasattr(x[0], 'shape'):
split_at = int(int(x[0].shape[0]) * (1. - validation_split))
else:
split_at = int(len(x[0]) * (1. - validation_split))
x, val_x = (slice_arrays(x, 0, split_at),
slice_arrays(x, split_at))
y, val_y = (slice_arrays(y, 0, split_at),
slice_arrays(y, split_at))
sample_weights, val_sample_weights = (
slice_arrays(sample_weights, 0, split_at),
slice_arrays(sample_weights, split_at))
if self._uses_dynamic_learning_phase():
val_ins = val_x + val_y + val_sample_weights + [0.]
else:
val_ins = val_x + val_y + val_sample_weights
elif validation_steps:
do_validation = True
if self._uses_dynamic_learning_phase():
val_ins = [0.]
# Prepare input arrays and training function.
if self._uses_dynamic_learning_phase():
ins = x + y + sample_weights + [1.]
else:
ins = x + y + sample_weights
self._make_train_function()
f = self.train_function
# Prepare display labels.
out_labels = self.metrics_names
if do_validation:
self._make_test_function()
val_f = self.test_function
callback_metrics = copy.copy(out_labels) + [
'val_' + n for n in out_labels]
else:
callback_metrics = copy.copy(out_labels)
val_f = None
val_ins = []
# Delegate logic to `fit_loop`.
return training_arrays.fit_loop(self, f, ins,
out_labels=out_labels,
batch_size=batch_size,
epochs=epochs,
verbose=verbose,
callbacks=callbacks,
val_f=val_f,
val_ins=val_ins,
shuffle=shuffle,
callback_metrics=callback_metrics,
initial_epoch=initial_epoch,
steps_per_epoch=steps_per_epoch,
validation_steps=validation_steps)
def evaluate(self, x=None, y=None,
batch_size=None,
verbose=1,
sample_weight=None,
steps=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 (if the model has a single input),
or list of Numpy arrays (if the model has multiple inputs).
If input layers in the model are named, you can also pass a
dictionary mapping input names to Numpy arrays.
`x` can be `None` (default) if feeding from
framework-native tensors (e.g. TensorFlow data tensors).
y: Numpy array of target (label) data
(if the model has a single output),
or list of Numpy arrays (if the model has multiple outputs).
If output layers in the model are named, you can also pass a
dictionary mapping output names to Numpy arrays.
`y` can be `None` (default) if feeding from
framework-native tensors (e.g. TensorFlow data tensors).
batch_size: Integer or `None`.
Number of samples per evaluation step.
If unspecified, `batch_size` will default to 32.
verbose: 0 or 1. Verbosity mode.
0 = silent, 1 = progress bar.
sample_weight: Optional Numpy array of weights for
the test samples, used for weighting the loss function.
You can either pass a flat (1D)
Numpy array with the same length as the input samples
(1:1 mapping between weights and samples),
or in the case of temporal data,
you can pass a 2D array with shape
`(samples, sequence_length)`,
to apply a different weight to every timestep of every sample.
In this case you should make sure to specify
`sample_weight_mode="temporal"` in `compile()`.
steps: Integer or `None`.
Total number of steps (batches of samples)
before declaring the evaluation round finished.
Ignored with the default value of `None`.
# 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.
"""
# Backwards compatibility.
if batch_size is None and steps is None:
batch_size = 32
if x is None and y is None and steps is None:
raise ValueError('If evaluating from data tensors, '
'you should specify the `steps` '
'argument.')
# Validate user data.
x, y, sample_weights = self._standardize_user_data(
x, y,
sample_weight=sample_weight,
batch_size=batch_size)
# Prepare inputs, delegate logic to `test_loop`.
if self._uses_dynamic_learning_phase():
ins = x + y + sample_weights + [0.]
else:
ins = x + y + sample_weights
self._make_test_function()
f = self.test_function
return training_arrays.test_loop(self, f, ins,
batch_size=batch_size,
verbose=verbose,
steps=steps)
def predict(self, x,
batch_size=None,
verbose=0,
steps=None):
"""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 inputs).
batch_size: Integer. If unspecified, it will default to 32.
verbose: Verbosity mode, 0 or 1.
steps: Total number of steps (batches of samples)
before declaring the prediction round finished.
Ignored with the default value of `None`.
# 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.
"""
# Backwards compatibility.
if batch_size is None and steps is None:
batch_size = 32
if x is None and steps is None:
raise ValueError('If predicting from data tensors, '
'you should specify the `steps` '
'argument.')
# Validate user data.
x, _, _ = self._standardize_user_data(x)
if self.stateful:
if x[0].shape[0] > batch_size and x[0].shape[0] % batch_size != 0:
raise ValueError('In a stateful network, '
'you should only pass inputs with '
'a number of samples that can be '
'divided by the batch size. Found: ' +
str(x[0].shape[0]) + ' samples. '
'Batch size: ' + str(batch_size) + '.')
# Prepare inputs, delegate logic to `predict_loop`.
if self._uses_dynamic_learning_phase():
ins = x + [0.]
else:
ins = x
self._make_predict_function()
f = self.predict_function
return training_arrays.predict_loop(self, f, ins,
batch_size=batch_size,
verbose=verbose,
steps=steps)
def train_on_batch(self, 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.
"""
x, y, sample_weights = self._standardize_user_data(
x, y,
sample_weight=sample_weight,
class_weight=class_weight)
if self._uses_dynamic_learning_phase():
ins = x + y + sample_weights + [1.]
else:
ins = x + y + sample_weights
self._make_train_function()
outputs = self.train_function(ins)
return unpack_singleton(outputs)
def test_on_batch(self, 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.
"""
x, y, sample_weights = self._standardize_user_data(
x, y,
sample_weight=sample_weight)
if self._uses_dynamic_learning_phase():
ins = x + y + sample_weights + [0.]
else:
ins = x + y + sample_weights
self._make_test_function()
outputs = self.test_function(ins)
return unpack_singleton(outputs)
def predict_on_batch(self, x):
"""Returns predictions for a single batch of samples.
# Arguments
x: Input samples, as a Numpy array.
# Returns
Numpy array(s) of predictions.
"""
x, _, _ = self._standardize_user_data(x)
if self._uses_dynamic_learning_phase():
ins = x + [0.]
else:
ins = x
self._make_predict_function()
outputs = self.predict_function(ins)
return unpack_singleton(outputs)
@interfaces.legacy_generator_methods_support
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):
"""Trains the model on data generated batch-by-batch by a Python generator (or an instance of `Sequence`).
The generator is run in parallel to the model, for efficiency.
For instance, this allows you to do real-time data augmentation
on images on CPU in parallel to training your model on GPU.
The use of `keras.utils.Sequence` guarantees the ordering
and guarantees the single use of every input per epoch when
using `use_multiprocessing=True`.
# Arguments
generator: A generator or an instance of `Sequence`
(`keras.utils.Sequence`) object in order to avoid
duplicate data when using multiprocessing.
The output of the generator must be either
- a tuple `(inputs, targets)`
- a tuple `(inputs, targets, sample_weights)`.
This tuple (a single output of the generator) makes a single
batch. Therefore, all arrays in this tuple must have the same
length (equal to the size of this batch). Different batches may
have different sizes. For example, the last batch of the epoch
is commonly smaller than the others, if the size of the dataset
is not divisible by the batch size.
The generator is expected to loop over its data
indefinitely. An epoch finishes when `steps_per_epoch`
batches have been seen by the model.
steps_per_epoch: Integer.
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. Number of epochs to train the model.
An epoch is an iteration over the entire data provided,
as defined by `steps_per_epoch`.
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: Integer. 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_data: This can be either
- a generator or a `Sequence` object for the validation data
- tuple `(x_val, y_val)`
- 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.
validation_steps: Only relevant if `validation_data`
is a generator. Total number of steps (batches of samples)
to yield from `validation_data` generator before stopping
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: 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.
max_queue_size: Integer. Maximum size for the generator queue.
If unspecified, `max_queue_size` will default to 10.
workers: Integer. Maximum number of processes to spin up
when using process-based threading.
If unspecified, `workers` will default to 1. If 0, will
execute the generator on the main thread.
use_multiprocessing: Boolean.
If `True`, use process-based threading.
If unspecified, `use_multiprocessing` will default to `False`.
Note that because this implementation
relies on multiprocessing,
you should not pass non-picklable arguments to the generator
as they can't be passed easily to children processes.
shuffle: Boolean. Whether to shuffle the order of the batches at
the beginning of each epoch. Only used with instances
of `Sequence` (`keras.utils.Sequence`).
Has no effect when `steps_per_epoch` is not `None`.
initial_epoch: Integer.
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
ValueError: In case the generator yields data in an invalid format.
# Example
```python
def generate_arrays_from_file(path):
while True:
with open(path) as f:
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})
model.fit_generator(generate_arrays_from_file('/my_file.txt'),
steps_per_epoch=10000, epochs=10)
```
"""
return training_generator.fit_generator(
self, generator,
steps_per_epoch=steps_per_epoch,
epochs=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)
@interfaces.legacy_generator_methods_support
def evaluate_generator(self, generator,
steps=None,
max_queue_size=10,
workers=1,
use_multiprocessing=False,
verbose=0):
"""Evaluates the model on a data generator.
The generator should return the same kind of data
as accepted by `test_on_batch`.
# Arguments
generator: Generator yielding tuples (inputs, targets)
or (inputs, targets, sample_weights)
or an instance of Sequence (keras.utils.Sequence)
object in order to avoid duplicate data
when using multiprocessing.
steps: Total number of steps (batches of samples)
to yield from `generator` before stopping.
Optional for `Sequence`: if unspecified, will use
the `len(generator)` as a number of steps.
max_queue_size: maximum size for the generator queue
workers: Integer. Maximum number of processes to spin up
when using process based threading.
If unspecified, `workers` will default to 1. If 0, will
execute the generator on the main thread.
use_multiprocessing: 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
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.
"""
return training_generator.evaluate_generator(
self, generator,
steps=steps,
max_queue_size=max_queue_size,
workers=workers,
use_multiprocessing=use_multiprocessing,
verbose=verbose)
@interfaces.legacy_generator_methods_support
def predict_generator(self, generator,
steps=None,
max_queue_size=10,
workers=1,
use_multiprocessing=False,
verbose=0):
"""Generates predictions for the input samples from a data generator.
The generator should return the same kind of data as accepted by
`predict_on_batch`.
# Arguments
generator: Generator yielding batches of input samples
or an instance of Sequence (keras.utils.Sequence)
object in order to avoid duplicate data
when using multiprocessing.
steps: Total number of steps (batches of samples)
to yield from `generator` before stopping.
Optional for `Sequence`: if unspecified, will use
the `len(generator)` as a number of steps.
max_queue_size: Maximum size for the generator queue.
workers: Integer. Maximum number of processes to spin up
when using process based threading.
If unspecified, `workers` will default to 1. If 0, will
execute the generator on the main thread.
use_multiprocessing: 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.
"""
return training_generator.predict_generator(
self, generator,
steps=steps,
max_queue_size=max_queue_size,
workers=workers,
use_multiprocessing=use_multiprocessing,
verbose=verbose)
