def call(self, inputs, training=None):
if training is None:
training = K.learning_phase()
output = super(Dropout, self).call(inputs, training=training)
if training is K.learning_phase():
output._uses_learning_phase = True # pylint: disable=protected-access
return output
def call(self, inputs, training=None):
if training is None:
training = K.learning_phase()
output = super(BatchNormalization, self).call(inputs, training=training)
if context.in_graph_mode() and training is K.learning_phase():
output._uses_learning_phase = True # pylint: disable=protected-access
return output
def call(self, inputs, training=None):
if training is None:
training = K.learning_phase()
output = super(BatchNormalization, self).call(inputs, training=training)
if context.in_graph_mode() and training is K.learning_phase():
output._uses_learning_phase = True # pylint: disable=protected-access
return output
def call(self, inputs, training=None):
if training is None:
training = K.learning_phase()
output = super(Dropout, self).call(inputs, training=training)
# EagerTensor object has no attribute _uses_learning_phase
if not context.in_eager_mode() and training is K.learning_phase():
output._uses_learning_phase = True # pylint: disable=protected-access
return output
def call(self, inputs, training=None):
if training is None:
training = K.learning_phase()
output = super(Dropout, self).call(inputs, training=training)
# EagerTensor object has no attribute _uses_learning_phase
if not context.in_eager_mode() and training is K.learning_phase():
output._uses_learning_phase = True # pylint: disable=protected-access
return output
def call(self, inputs, training=None):
if training is None:
training = K.learning_phase()
def dropped_inputs():
return nn.dropout(inputs,
1 - self.rate,
noise_shape=self._get_noise_shape(inputs),
seed=self.seed)
output = tf_utils.smart_cond(training, dropped_inputs,
lambda: array_ops.identity(inputs))
# EagerTensor object has no attribute _uses_learning_phase
if not context.executing_eagerly() and training is K.learning_phase():
output._uses_learning_phase = True # pylint: disable=protected-access
return output
def on_epoch_end(self, epoch, logs=None):
logs = logs or {}
if not self.validation_data and self.histogram_freq:
raise ValueError('If printing histograms, validation_data must be '
'provided, and cannot be a generator.')
if self.validation_data and self.histogram_freq:
if epoch % self.histogram_freq == 0:
val_data = self.validation_data
tensors = (
self.model.inputs + self.model.targets + self.model.sample_weights)
if self.model.uses_learning_phase:
tensors += [K.learning_phase()]
assert len(val_data) == len(tensors)
val_size = val_data[0].shape[0]
i = 0
while i < val_size:
step = min(self.batch_size, val_size - i)
batch_val = []
batch_val.append(val_data[0][i:i + step]
if val_data[0] is not None else None)
batch_val.append(val_data[1][i:i + step]
if val_data[1] is not None else None)
batch_val.append(val_data[2][i:i + step]
if val_data[2] is not None else None)
if self.model.uses_learning_phase:
# do not slice the learning phase
batch_val = [x[i:i + step] if x is not None else None
for x in val_data[:-1]]
batch_val.append(val_data[-1])
else:
batch_val = [x[i:i + step] if x is not None else None
for x in val_data]
feed_dict = {}
for key, val in zip(tensors, batch_val):
if val is not None:
feed_dict[key] = val
result = self.sess.run([self.merged], feed_dict=feed_dict)
summary_str = result[0]
self.writer.add_summary(summary_str, epoch)
i += self.batch_size
for name, value in logs.items():
if name in ['batch', 'size']:
continue
summary = tf_summary.Summary()
summary_value = summary.value.add()
summary_value.simple_value = value.item()
summary_value.tag = name
self.writer.add_summary(summary, epoch)
self.writer.flush()
def on_epoch_end(self, epoch, logs=None):
logs = logs or {}
if not self.validation_data and self.histogram_freq:
raise ValueError('If printing histograms, validation_data must be '
'provided, and cannot be a generator.')
if self.validation_data and self.histogram_freq:
if epoch % self.histogram_freq == 0:
val_data = self.validation_data
tensors = (
self.model.inputs + self.model.targets + self.model.sample_weights)
if self.model.uses_learning_phase:
tensors += [K.learning_phase()]
assert len(val_data) == len(tensors)
val_size = val_data[0].shape[0]
i = 0
while i < val_size:
step = min(self.batch_size, val_size - i)
batch_val = []
batch_val.append(val_data[0][i:i + step]
if val_data[0] is not None else None)
batch_val.append(val_data[1][i:i + step]
if val_data[1] is not None else None)
batch_val.append(val_data[2][i:i + step]
if val_data[2] is not None else None)
if self.model.uses_learning_phase:
# do not slice the learning phase
batch_val = [x[i:i + step] if x is not None else None
for x in val_data[:-1]]
batch_val.append(val_data[-1])
else:
batch_val = [x[i:i + step] if x is not None else None
for x in val_data]
feed_dict = {}
for key, val in zip(tensors, batch_val):
if val is not None:
feed_dict[key] = val
result = self.sess.run([self.merged], feed_dict=feed_dict)
summary_str = result[0]
self.writer.add_summary(summary_str, epoch)
i += self.batch_size
for name, value in logs.items():
if name in ['batch', 'size']:
continue
summary = tf_summary.Summary()
summary_value = summary.value.add()
summary_value.simple_value = value.item()
summary_value.tag = name
self.writer.add_summary(summary, epoch)
self.writer.flush()
def call(self, inputs, training=None):
original_training_value = training
if training is None:
training = K.learning_phase()
def dropped_inputs():
return nn.dropout(inputs, 1 - self.rate,
noise_shape=self._get_noise_shape(inputs),
seed=self.seed)
output = tf_utils.smart_cond(training,
dropped_inputs,
lambda: array_ops.identity(inputs))
# EagerTensor object has no attribute _uses_learning_phase
if not context.executing_eagerly() and original_training_value is None:
output._uses_learning_phase = True # pylint: disable=protected-access
return output
def call(self, inputs, training=None):
original_training_value = training
if training is None:
training = K.learning_phase()
in_eager_mode = context.executing_eagerly()
if self.virtual_batch_size is not None:
# Virtual batches (aka ghost batches) can be simulated by reshaping the
# Tensor and reusing the existing batch norm implementation
original_shape = [-1] + inputs.shape.as_list()[1:]
expanded_shape = [self.virtual_batch_size, -1] + original_shape[1:]
# Will cause errors if virtual_batch_size does not divide the batch size
inputs = array_ops.reshape(inputs, expanded_shape)
def undo_virtual_batching(outputs):
outputs = array_ops.reshape(outputs, original_shape)
return outputs
if self.fused:
outputs = self._fused_batch_norm(inputs, training=training)
if self.virtual_batch_size is not None:
# Currently never reaches here since fused_batch_norm does not support
# virtual batching
outputs = undo_virtual_batching(outputs)
if not context.executing_eagerly(
) and original_training_value is None:
outputs._uses_learning_phase = True # pylint: disable=protected-access
return outputs
# Compute the axes along which to reduce the mean / variance
input_shape = inputs.get_shape()
ndims = len(input_shape)
reduction_axes = [i for i in range(ndims) if i not in self.axis]
if self.virtual_batch_size is not None:
del reduction_axes[1] # Do not reduce along virtual batch dim
# Broadcasting only necessary for single-axis batch norm where the axis is
# not the last dimension
broadcast_shape = [1] * ndims
broadcast_shape[self.axis[0]] = input_shape[self.axis[0]].value
def _broadcast(v):
if (v is not None and len(v.get_shape()) != ndims
and reduction_axes != list(range(ndims - 1))):
return array_ops.reshape(v, broadcast_shape)
return v
scale, offset = _broadcast(self.gamma), _broadcast(self.beta)
def _compose_transforms(scale, offset, then_scale, then_offset):
if then_scale is not None:
scale *= then_scale
offset *= then_scale
if then_offset is not None:
offset += then_offset
return (scale, offset)
# Determine a boolean value for `training`: could be True, False, or None.
training_value = tf_utils.constant_value(training)
if training_value is not False:
if self.adjustment:
adj_scale, adj_bias = self.adjustment(array_ops.shape(inputs))
# Adjust only during training.
adj_scale = tf_utils.smart_cond(
training, lambda: adj_scale,
lambda: array_ops.ones_like(adj_scale))
adj_bias = tf_utils.smart_cond(
training, lambda: adj_bias,
lambda: array_ops.zeros_like(adj_bias))
scale, offset = _compose_transforms(adj_scale, adj_bias, scale,
offset)
# Some of the computations here are not necessary when training==False
# but not a constant. However, this makes the code simpler.
keep_dims = self.virtual_batch_size is not None or len(
self.axis) > 1
mean, variance = nn.moments(inputs,
reduction_axes,
keep_dims=keep_dims)
moving_mean = self.moving_mean
moving_variance = self.moving_variance
mean = tf_utils.smart_cond(training, lambda: mean,
lambda: moving_mean)
variance = tf_utils.smart_cond(training, lambda: variance,
lambda: moving_variance)
if self.renorm:
r, d, new_mean, new_variance = self._renorm_correction_and_moments(
mean, variance, training)
# When training, the normalized values (say, x) will be transformed as
# x * gamma + beta without renorm, and (x * r + d) * gamma + beta
# = x * (r * gamma) + (d * gamma + beta) with renorm.
r = _broadcast(array_ops.stop_gradient(r, name='renorm_r'))
d = _broadcast(array_ops.stop_gradient(d, name='renorm_d'))
scale, offset = _compose_transforms(r, d, scale, offset)
else:
new_mean, new_variance = mean, variance
if self.virtual_batch_size is not None:
# This isn't strictly correct since in ghost batch norm, you are
# supposed to sequentially update the moving_mean and moving_variance
# with each sub-batch. However, since the moving statistics are only
# used during evaluation, it is more efficient to just update in one
# step and should not make a significant difference in the result.
new_mean = math_ops.reduce_mean(new_mean,
axis=1,
keepdims=True)
new_variance = math_ops.reduce_mean(new_variance,
axis=1,
keepdims=True)
def _do_update(var, value):
if in_eager_mode and not self.trainable:
return
return self._assign_moving_average(var, value, self.momentum)
mean_update = tf_utils.smart_cond(
training, lambda: _do_update(self.moving_mean, new_mean),
lambda: self.moving_mean)
variance_update = tf_utils.smart_cond(
training,
lambda: _do_update(self.moving_variance, new_variance),
lambda: self.moving_variance)
if not context.executing_eagerly():
self.add_update(mean_update, inputs=True)
self.add_update(variance_update, inputs=True)
else:
mean, variance = self.moving_mean, self.moving_variance
outputs = nn.batch_normalization(inputs, _broadcast(mean),
_broadcast(variance), offset, scale,
self.epsilon)
# If some components of the shape got lost due to adjustments, fix that.
outputs.set_shape(input_shape)
if self.virtual_batch_size is not None:
outputs = undo_virtual_batching(outputs)
if not context.executing_eagerly() and original_training_value is None:
outputs._uses_learning_phase = True # pylint: disable=protected-access
return outputs
def test_loop(model,
inputs,
targets,
sample_weights=None,
batch_size=None,
verbose=0,
steps=None):
"""Abstract method to loop over some data in batches.
Arguments:
model: Keras Model instance.
inputs: List of input arrays.
targets: List of target arrays.
sample_weights: Optional list of sample weight arrays.
batch_size: integer batch size or `None`.
verbose: verbosity mode.
steps: Total number of steps (batches of samples)
before declaring predictions finished.
Ignored with the default value of `None`.
Returns:
Scalar 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.
"""
model._make_test_function()
f = model.test_function
sample_weights = sample_weights or []
if model.uses_learning_phase and not isinstance(K.learning_phase(), int):
ins = inputs + targets + sample_weights + [0]
else:
ins = inputs + targets + sample_weights
if hasattr(model, 'metrics'):
for m in model.metrics:
if isinstance(m, Layer):
m.reset_states()
stateful_metric_indices = [
i for i, name in enumerate(model.metrics_names)
if str(name) in model.stateful_metric_names
]
else:
stateful_metric_indices = []
num_samples = training_utils.check_num_samples(ins, batch_size, steps,
'steps')
outs = []
if verbose == 1:
if steps is not None:
progbar = Progbar(target=steps)
else:
progbar = Progbar(target=num_samples)
# To prevent a slowdown, we find beforehand the arrays that need conversion.
feed = model._feed_inputs + model._feed_targets + model._feed_sample_weights
indices_for_conversion_to_dense = []
for i in range(len(feed)):
if issparse is not None and issparse(
ins[i]) and not K.is_sparse(feed[i]):
indices_for_conversion_to_dense.append(i)
if steps is not None:
for step in range(steps):
batch_outs = f(ins)
if isinstance(batch_outs, list):
if step == 0:
for _ in enumerate(batch_outs):
outs.append(0.)
for i, batch_out in enumerate(batch_outs):
if i in stateful_metric_indices:
outs[i] = batch_out
else:
outs[i] += batch_out
else:
if step == 0:
outs.append(0.)
outs[0] += batch_outs
if verbose == 1:
progbar.update(step + 1)
for i in range(len(outs)):
if i not in stateful_metric_indices:
outs[i] /= steps
else:
batches = make_batches(num_samples, batch_size)
index_array = np.arange(num_samples)
for batch_index, (batch_start, batch_end) in enumerate(batches):
batch_ids = index_array[batch_start:batch_end]
if isinstance(ins[-1], int):
# Do not slice the training phase flag.
ins_batch = slice_arrays(ins[:-1], batch_ids) + [ins[-1]]
else:
ins_batch = slice_arrays(ins, batch_ids)
for i in indices_for_conversion_to_dense:
ins_batch[i] = ins_batch[i].toarray()
batch_outs = f(ins_batch)
if isinstance(batch_outs, list):
if batch_index == 0:
for batch_out in enumerate(batch_outs):
outs.append(0.)
for i, batch_out in enumerate(batch_outs):
if i in stateful_metric_indices:
outs[i] = batch_out
else:
outs[i] += batch_out * len(batch_ids)
else:
if batch_index == 0:
outs.append(0.)
outs[0] += batch_outs * len(batch_ids)
if verbose == 1:
progbar.update(batch_end)
for i in range(len(outs)):
if i not in stateful_metric_indices:
outs[i] /= num_samples
if len(outs) == 1:
return outs[0]
return outs
def fit_loop(model,
inputs,
targets,
sample_weights=None,
batch_size=None,
epochs=100,
verbose=1,
callbacks=None,
val_inputs=None,
val_targets=None,
val_sample_weights=None,
shuffle=True,
callback_metrics=None,
initial_epoch=0,
steps_per_epoch=None,
validation_steps=None):
"""Abstract fit function for arrays of data.
Arguments:
model: Keras Model instance.
inputs: List of input arrays.
targets: List of target arrays.
sample_weights: Optional list of sample weight arrays.
batch_size: Integer batch size or None if unknown.
epochs: Number of times to iterate over the data
verbose: Verbosity mode, 0, 1 or 2
callbacks: List of callbacks to be called during training
val_inputs: List of input arrays.
val_targets: List of target arrays.
val_sample_weights: Optional list of sample weight arrays.
shuffle: Whether to shuffle the data at the beginning of each epoch
callback_metrics: List of strings, the display names of the metrics
passed to the callbacks. They should be the
concatenation of list the display names of the outputs of
`f` and the list of display names of the outputs of `f_val`.
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. Ignored with the default value of `None`.
validation_steps: Number of steps to run validation for
(only if doing validation from data tensors).
Ignored with the default value of `None`.
Returns:
`History` object.
Raises:
ValueError: in case of invalid arguments.
"""
model._make_train_function()
f = model.train_function
sample_weights = sample_weights or []
val_sample_weights = val_sample_weights or []
if model.uses_learning_phase and not isinstance(K.learning_phase(), int):
ins = inputs + targets + sample_weights + [1]
if val_inputs:
val_ins = val_inputs + val_targets + val_sample_weights + [1]
else:
ins = inputs + targets + sample_weights
if val_inputs:
val_ins = val_inputs + val_targets + val_sample_weights
if not val_inputs:
val_ins = []
do_validation = False
if val_inputs:
do_validation = True
if verbose and inputs and hasattr(inputs[0], 'shape') and hasattr(
val_inputs[0], 'shape'):
print('Train on %d samples, validate on %d samples' %
(inputs[0].shape[0], val_inputs[0].shape[0]))
if validation_steps:
do_validation = True
if steps_per_epoch is None:
raise ValueError('Can only use `validation_steps` '
'when doing step-wise '
'training, i.e. `steps_per_epoch` '
'must be set.')
out_labels = model.metrics_names
if do_validation:
callback_metrics = copy.copy(out_labels) + [
'val_' + n for n in out_labels
]
else:
callback_metrics = copy.copy(out_labels)
num_train_samples = training_utils.check_num_samples(
ins, batch_size, steps_per_epoch, 'steps_per_epoch')
if num_train_samples is not None:
index_array = np.arange(num_train_samples)
model.history = cbks.History()
all_callbacks = [cbks.BaseLogger(
stateful_metrics=model.stateful_metric_names)]
if verbose:
if steps_per_epoch is not None:
count_mode = 'steps'
else:
count_mode = 'samples'
all_callbacks.append(
cbks.ProgbarLogger(
count_mode, stateful_metrics=model.stateful_metric_names))
all_callbacks += (callbacks or []) + [model.history]
callbacks = cbks.CallbackList(all_callbacks)
out_labels = out_labels or []
# it's possible to callback a different model than self
# (used by Sequential models)
if hasattr(model, 'callback_model') and model.callback_model:
callback_model = model.callback_model
else:
callback_model = model
callbacks.set_model(callback_model)
callbacks.set_params({
'batch_size': batch_size,
'epochs': epochs,
'steps': steps_per_epoch,
'samples': num_train_samples,
'verbose': verbose,
'do_validation': do_validation,
'metrics': callback_metrics or [],
})
callbacks.on_train_begin()
callback_model.stop_training = False
for cbk in callbacks:
cbk.validation_data = val_ins
# To prevent a slowdown, we find beforehand the arrays that need conversion.
feed = model._feed_inputs + model._feed_targets + model._feed_sample_weights
indices_for_conversion_to_dense = []
for i in range(len(feed)):
if issparse is not None and issparse(ins[i]) and not K.is_sparse(feed[i]):
indices_for_conversion_to_dense.append(i)
for epoch in range(initial_epoch, epochs):
# Reset stateful metrics
for m in model.metrics:
if isinstance(m, Layer):
m.reset_states()
# Update callbacks
callbacks.on_epoch_begin(epoch)
epoch_logs = {}
if steps_per_epoch is not None:
for step_index in range(steps_per_epoch):
batch_logs = {}
batch_logs['batch'] = step_index
batch_logs['size'] = 1
callbacks.on_batch_begin(step_index, batch_logs)
outs = f(ins)
if not isinstance(outs, list):
outs = [outs]
for l, o in zip(out_labels, outs):
batch_logs[l] = o
callbacks.on_batch_end(step_index, batch_logs)
if callback_model.stop_training:
break
if do_validation:
val_outs = test_loop(
model,
val_inputs,
val_targets,
sample_weights=val_sample_weights,
batch_size=batch_size,
steps=validation_steps,
verbose=0)
if not isinstance(val_outs, list):
val_outs = [val_outs]
# Same labels assumed.
for l, o in zip(out_labels, val_outs):
epoch_logs['val_' + l] = o
else:
if shuffle == 'batch':
index_array = training_utils.batch_shuffle(index_array, batch_size)
elif shuffle:
np.random.shuffle(index_array)
batches = make_batches(num_train_samples, batch_size)
for batch_index, (batch_start, batch_end) in enumerate(batches):
batch_ids = index_array[batch_start:batch_end]
try:
if isinstance(ins[-1], int):
# Do not slice the training phase flag.
ins_batch = slice_arrays(ins[:-1], batch_ids) + [ins[-1]]
else:
ins_batch = slice_arrays(ins, batch_ids)
except TypeError:
raise TypeError('TypeError while preparing batch. '
'If using HDF5 input data, '
'pass shuffle="batch".')
batch_logs = {}
batch_logs['batch'] = batch_index
batch_logs['size'] = len(batch_ids)
callbacks.on_batch_begin(batch_index, batch_logs)
for i in indices_for_conversion_to_dense:
ins_batch[i] = ins_batch[i].toarray()
outs = f(ins_batch)
if not isinstance(outs, list):
outs = [outs]
for l, o in zip(out_labels, outs):
batch_logs[l] = o
callbacks.on_batch_end(batch_index, batch_logs)
if callback_model.stop_training:
break
if batch_index == len(batches) - 1: # Last batch.
if do_validation:
val_outs = test_loop(
model,
val_inputs,
val_targets,
sample_weights=val_sample_weights,
batch_size=batch_size,
verbose=0)
if not isinstance(val_outs, list):
val_outs = [val_outs]
# Same labels assumed.
for l, o in zip(out_labels, val_outs):
epoch_logs['val_' + l] = o
callbacks.on_epoch_end(epoch, epoch_logs)
if callback_model.stop_training:
break
callbacks.on_train_end()
return model.history
def test_loop(model, inputs, targets,
sample_weights=None,
batch_size=None,
verbose=0,
steps=None):
"""Abstract method to loop over some data in batches.
Arguments:
model: Keras Model instance.
inputs: List of input arrays.
targets: List of target arrays.
sample_weights: Optional list of sample weight arrays.
batch_size: integer batch size or `None`.
verbose: verbosity mode.
steps: Total number of steps (batches of samples)
before declaring predictions finished.
Ignored with the default value of `None`.
Returns:
Scalar 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.
"""
model._make_test_function()
f = model.test_function
sample_weights = sample_weights or []
if model.uses_learning_phase and not isinstance(K.learning_phase(), int):
ins = inputs + targets + sample_weights + [0]
else:
ins = inputs + targets + sample_weights
if hasattr(model, 'metrics'):
for m in model.metrics:
if isinstance(m, Layer):
m.reset_states()
stateful_metric_indices = [
i for i, name in enumerate(model.metrics_names)
if str(name) in model.stateful_metric_names
]
else:
stateful_metric_indices = []
num_samples = training_utils.check_num_samples(
ins, batch_size, steps, 'steps')
outs = []
if verbose == 1:
if steps is not None:
progbar = Progbar(target=steps)
else:
progbar = Progbar(target=num_samples)
# To prevent a slowdown, we find beforehand the arrays that need conversion.
feed = model._feed_inputs + model._feed_targets + model._feed_sample_weights
indices_for_conversion_to_dense = []
for i in range(len(feed)):
if issparse is not None and issparse(ins[i]) and not K.is_sparse(feed[i]):
indices_for_conversion_to_dense.append(i)
if steps is not None:
for step in range(steps):
batch_outs = f(ins)
if isinstance(batch_outs, list):
if step == 0:
for _ in enumerate(batch_outs):
outs.append(0.)
for i, batch_out in enumerate(batch_outs):
if i in stateful_metric_indices:
outs[i] = batch_out
else:
outs[i] += batch_out
else:
if step == 0:
outs.append(0.)
outs[0] += batch_outs
if verbose == 1:
progbar.update(step + 1)
for i in range(len(outs)):
if i not in stateful_metric_indices:
outs[i] /= steps
else:
batches = make_batches(num_samples, batch_size)
index_array = np.arange(num_samples)
for batch_index, (batch_start, batch_end) in enumerate(batches):
batch_ids = index_array[batch_start:batch_end]
if isinstance(ins[-1], int):
# Do not slice the training phase flag.
ins_batch = slice_arrays(ins[:-1], batch_ids) + [ins[-1]]
else:
ins_batch = slice_arrays(ins, batch_ids)
for i in indices_for_conversion_to_dense:
ins_batch[i] = ins_batch[i].toarray()
batch_outs = f(ins_batch)
if isinstance(batch_outs, list):
if batch_index == 0:
for batch_out in enumerate(batch_outs):
outs.append(0.)
for i, batch_out in enumerate(batch_outs):
if i in stateful_metric_indices:
outs[i] = batch_out
else:
outs[i] += batch_out * len(batch_ids)
else:
if batch_index == 0:
outs.append(0.)
outs[0] += batch_outs * len(batch_ids)
if verbose == 1:
progbar.update(batch_end)
for i in range(len(outs)):
if i not in stateful_metric_indices:
outs[i] /= num_samples
if len(outs) == 1:
return outs[0]
return outs
def predict_loop(model, inputs, batch_size=32, verbose=0, steps=None):
"""Abstract method to loop over some data in batches.
Arguments:
model: Keras Model instance.
inputs: list of tensors to be fed to `f`.
batch_size: integer batch size.
verbose: verbosity mode.
steps: Total number of steps (batches of samples)
before declaring `_predict_loop` finished.
Ignored with the default value of `None`.
Returns:
Array of predictions (if the model has a single output)
or list of arrays of predictions
(if the model has multiple outputs).
"""
model._make_predict_function()
f = model.predict_function
if model.uses_learning_phase and not isinstance(K.learning_phase(), int):
ins = inputs + [0]
else:
ins = inputs
num_samples = training_utils.check_num_samples(
inputs, batch_size, steps, 'steps')
if verbose == 1:
if steps is not None:
progbar = Progbar(target=steps)
else:
progbar = Progbar(target=num_samples)
indices_for_conversion_to_dense = []
for i in range(len(model._feed_inputs)):
if (issparse is not None and issparse(inputs[i]) and
not K.is_sparse(model._feed_inputs[i])):
indices_for_conversion_to_dense.append(i)
if steps is not None:
# Step-based predictions.
# Since we do not know how many samples
# we will see, we cannot pre-allocate
# the returned Numpy arrays.
# Instead, we store one array per batch seen
# and concatenate them upon returning.
unconcatenated_outs = []
for step in range(steps):
batch_outs = f(ins)
if not isinstance(batch_outs, list):
batch_outs = [batch_outs]
if step == 0:
for batch_out in batch_outs:
unconcatenated_outs.append([])
for i, batch_out in enumerate(batch_outs):
unconcatenated_outs[i].append(batch_out)
if verbose == 1:
progbar.update(step + 1)
if len(unconcatenated_outs) == 1:
return np.concatenate(unconcatenated_outs[0], axis=0)
return [
np.concatenate(unconcatenated_outs[i], axis=0)
for i in range(len(unconcatenated_outs))
]
else:
# Sample-based predictions.
outs = []
batches = make_batches(num_samples, batch_size)
index_array = np.arange(num_samples)
for batch_index, (batch_start, batch_end) in enumerate(batches):
batch_ids = index_array[batch_start:batch_end]
if ins and isinstance(ins[-1], int):
# Do not slice the training phase flag.
ins_batch = slice_arrays(ins[:-1], batch_ids) + [ins[-1]]
else:
ins_batch = slice_arrays(ins, batch_ids)
for i in indices_for_conversion_to_dense:
ins_batch[i] = ins_batch[i].toarray()
batch_outs = f(ins_batch)
if not isinstance(batch_outs, list):
batch_outs = [batch_outs]
if batch_index == 0:
# Pre-allocate the results arrays.
for batch_out in batch_outs:
shape = (num_samples,) + batch_out.shape[1:]
outs.append(np.zeros(shape, dtype=batch_out.dtype))
for i, batch_out in enumerate(batch_outs):
outs[i][batch_start:batch_end] = batch_out
if verbose == 1:
progbar.update(batch_end)
if len(outs) == 1:
return outs[0]
return outs
# load mudules
import tensorflow as tf
import numpy as np
import matplotlib
# matplotlib.use('Agg')
import matplotlib.pyplot as plt
import urllib, cStringIO
import os
# load the model
model = tf.keras.applications.ResNet50()
# load compute_margin class
from compute_margin import margin
from tensorflow.python.keras._impl.keras import backend as K
m = margin(tf.log(model.output), model.input, K.get_session(), [K.learning_phase()])
# open the file
i = 0
#f = open('/mnt/nfs/nfsshare/user_homes/zybill/imagenet_data/fall11_urls.txt')
f = open('./imagenet_data/fall11_urls.txt')
# make the write directory
#write_dir = '/mnt/nfs/nfsshare/user_homes/zybill/results_imagenet/'
write_dir = './results/imagenet_adversarials/'
if not os.path.exists(write_dir):
os.makedirs(write_dir)
# run compute margin
input_shape = (224, 224)
for i in xrange(100):
def predict_loop(model, inputs, batch_size=32, verbose=0, steps=None):
"""Abstract method to loop over some data in batches.
Arguments:
model: Keras Model instance.
inputs: list of tensors to be fed to `f`.
batch_size: integer batch size.
verbose: verbosity mode.
steps: Total number of steps (batches of samples)
before declaring `_predict_loop` finished.
Ignored with the default value of `None`.
Returns:
Array of predictions (if the model has a single output)
or list of arrays of predictions
(if the model has multiple outputs).
"""
model._make_predict_function()
f = model.predict_function
if model.uses_learning_phase and not isinstance(K.learning_phase(), int):
ins = inputs + [0]
else:
ins = inputs
num_samples = training_utils.check_num_samples(inputs, batch_size, steps,
'steps')
if verbose == 1:
if steps is not None:
progbar = Progbar(target=steps)
else:
progbar = Progbar(target=num_samples)
indices_for_conversion_to_dense = []
for i in range(len(model._feed_inputs)):
if (issparse is not None and issparse(inputs[i])
and not K.is_sparse(model._feed_inputs[i])):
indices_for_conversion_to_dense.append(i)
if steps is not None:
# Step-based predictions.
# Since we do not know how many samples
# we will see, we cannot pre-allocate
# the returned Numpy arrays.
# Instead, we store one array per batch seen
# and concatenate them upon returning.
unconcatenated_outs = []
for step in range(steps):
batch_outs = f(ins)
if not isinstance(batch_outs, list):
batch_outs = [batch_outs]
if step == 0:
for batch_out in batch_outs:
unconcatenated_outs.append([])
for i, batch_out in enumerate(batch_outs):
unconcatenated_outs[i].append(batch_out)
if verbose == 1:
progbar.update(step + 1)
if len(unconcatenated_outs) == 1:
return np.concatenate(unconcatenated_outs[0], axis=0)
return [
np.concatenate(unconcatenated_outs[i], axis=0)
for i in range(len(unconcatenated_outs))
]
else:
# Sample-based predictions.
outs = []
batches = make_batches(num_samples, batch_size)
index_array = np.arange(num_samples)
for batch_index, (batch_start, batch_end) in enumerate(batches):
batch_ids = index_array[batch_start:batch_end]
if ins and isinstance(ins[-1], int):
# Do not slice the training phase flag.
ins_batch = slice_arrays(ins[:-1], batch_ids) + [ins[-1]]
else:
ins_batch = slice_arrays(ins, batch_ids)
for i in indices_for_conversion_to_dense:
ins_batch[i] = ins_batch[i].toarray()
batch_outs = f(ins_batch)
if not isinstance(batch_outs, list):
batch_outs = [batch_outs]
if batch_index == 0:
# Pre-allocate the results arrays.
for batch_out in batch_outs:
shape = (num_samples, ) + batch_out.shape[1:]
outs.append(np.zeros(shape, dtype=batch_out.dtype))
for i, batch_out in enumerate(batch_outs):
outs[i][batch_start:batch_end] = batch_out
if verbose == 1:
progbar.update(batch_end)
if len(outs) == 1:
return outs[0]
return outs
def fit_generator(model,
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):
"""See docstring for `Model.fit_generator`."""
wait_time = 0.01 # in seconds
epoch = initial_epoch
do_validation = bool(validation_data)
model._make_train_function()
if do_validation:
model._make_test_function()
is_sequence = isinstance(generator, Sequence)
if not is_sequence and use_multiprocessing and workers > 1:
logging.warning(
UserWarning('Using a generator with `use_multiprocessing=True`'
' and multiple workers may duplicate your data.'
' Please consider using the`keras.utils.Sequence'
' class.'))
if steps_per_epoch is None:
if is_sequence:
steps_per_epoch = len(generator)
else:
raise ValueError('`steps_per_epoch=None` is only valid for a'
' generator based on the `keras.utils.Sequence`'
' class. Please specify `steps_per_epoch` or use'
' the `keras.utils.Sequence` class.')
# python 2 has 'next', 3 has '__next__'
# avoid any explicit version checks
val_gen = (
hasattr(validation_data, 'next') or
hasattr(validation_data, '__next__') or
isinstance(validation_data, Sequence))
if (val_gen and not isinstance(validation_data, Sequence) and
not validation_steps):
raise ValueError('`validation_steps=None` is only valid for a'
' generator based on the `keras.utils.Sequence`'
' class. Please specify `validation_steps` or use'
' the `keras.utils.Sequence` class.')
# Prepare display labels.
out_labels = model.metrics_names
callback_metrics = out_labels + ['val_%s' % n for n in out_labels]
# prepare callbacks
model.history = cbks.History()
callbacks = [cbks.BaseLogger()] + (callbacks or []) + [model.history]
if verbose:
callbacks += [cbks.ProgbarLogger(count_mode='steps')]
callbacks = cbks.CallbackList(callbacks)
# it's possible to callback a different model than self:
if hasattr(model, 'callback_model') and model.callback_model:
callback_model = model.callback_model
else:
callback_model = model
callbacks.set_model(callback_model)
callbacks.set_params({
'epochs': epochs,
'steps': steps_per_epoch,
'verbose': verbose,
'do_validation': do_validation,
'metrics': callback_metrics,
})
callbacks.on_train_begin()
enqueuer = None
val_enqueuer = None
try:
if do_validation:
if val_gen:
if workers > 0:
if isinstance(validation_data, Sequence):
val_enqueuer = OrderedEnqueuer(
validation_data, use_multiprocessing=use_multiprocessing)
if validation_steps is None:
validation_steps = len(validation_data)
else:
val_enqueuer = GeneratorEnqueuer(
validation_data,
use_multiprocessing=use_multiprocessing,
wait_time=wait_time)
val_enqueuer.start(workers=workers, max_queue_size=max_queue_size)
validation_generator = val_enqueuer.get()
else:
validation_generator = validation_data
else:
if len(validation_data) == 2:
val_x, val_y = validation_data # pylint: disable=unpacking-non-sequence
val_sample_weight = None
elif len(validation_data) == 3:
val_x, val_y, val_sample_weight = validation_data # pylint: disable=unpacking-non-sequence
else:
raise ValueError(
'`validation_data` should be a tuple '
'`(val_x, val_y, val_sample_weight)` '
'or `(val_x, val_y)`. Found: ' + str(validation_data))
val_x, val_y, val_sample_weights = model._standardize_user_data(
val_x, val_y, val_sample_weight)
val_data = val_x + val_y + val_sample_weights
if model.uses_learning_phase and not isinstance(
K.learning_phase(), int):
val_data += [0]
for cbk in callbacks:
cbk.validation_data = val_data
if workers > 0:
if is_sequence:
enqueuer = OrderedEnqueuer(
generator,
use_multiprocessing=use_multiprocessing,
shuffle=shuffle)
else:
enqueuer = GeneratorEnqueuer(
generator,
use_multiprocessing=use_multiprocessing,
wait_time=wait_time)
enqueuer.start(workers=workers, max_queue_size=max_queue_size)
output_generator = enqueuer.get()
else:
output_generator = generator
callback_model.stop_training = False
# Construct epoch logs.
epoch_logs = {}
while epoch < epochs:
callbacks.on_epoch_begin(epoch)
steps_done = 0
batch_index = 0
while steps_done < steps_per_epoch:
generator_output = next(output_generator)
if not hasattr(generator_output, '__len__'):
raise ValueError('Output of generator should be '
'a tuple `(x, y, sample_weight)` '
'or `(x, y)`. Found: ' + str(generator_output))
if len(generator_output) == 2:
x, y = generator_output
sample_weight = None
elif len(generator_output) == 3:
x, y, sample_weight = generator_output
else:
raise ValueError('Output of generator should be '
'a tuple `(x, y, sample_weight)` '
'or `(x, y)`. Found: ' + str(generator_output))
# build batch logs
batch_logs = {}
if isinstance(x, list):
batch_size = x[0].shape[0]
elif isinstance(x, dict):
batch_size = list(x.values())[0].shape[0]
else:
batch_size = x.shape[0]
batch_logs['batch'] = batch_index
batch_logs['size'] = batch_size
callbacks.on_batch_begin(batch_index, batch_logs)
outs = model.train_on_batch(
x, y, sample_weight=sample_weight, class_weight=class_weight)
if not isinstance(outs, list):
outs = [outs]
for l, o in zip(out_labels, outs):
batch_logs[l] = o
callbacks.on_batch_end(batch_index, batch_logs)
batch_index += 1
steps_done += 1
# Epoch finished.
if steps_done >= steps_per_epoch and do_validation:
if val_gen:
val_outs = evaluate_generator(
model, validation_generator, validation_steps, workers=0)
else:
# No need for try/except because
# data has already been validated.
val_outs = model.evaluate(
val_x,
val_y,
batch_size=batch_size,
sample_weight=val_sample_weights,
verbose=0)
if not isinstance(val_outs, list):
val_outs = [val_outs]
# Same labels assumed.
for l, o in zip(out_labels, val_outs):
epoch_logs['val_' + l] = o
if callback_model.stop_training:
break
callbacks.on_epoch_end(epoch, epoch_logs)
epoch += 1
if callback_model.stop_training:
break
finally:
try:
if enqueuer is not None:
enqueuer.stop()
finally:
if val_enqueuer is not None:
val_enqueuer.stop()
callbacks.on_train_end()
return model.history
def fit_loop(model,
inputs,
targets,
sample_weights=None,
batch_size=None,
epochs=100,
verbose=1,
callbacks=None,
val_inputs=None,
val_targets=None,
val_sample_weights=None,
shuffle=True,
callback_metrics=None,
initial_epoch=0,
steps_per_epoch=None,
validation_steps=None):
"""Abstract fit function for arrays of data.
Arguments:
model: Keras Model instance.
inputs: List of input arrays.
targets: List of target arrays.
sample_weights: Optional list of sample weight arrays.
batch_size: Integer batch size or None if unknown.
epochs: Number of times to iterate over the data
verbose: Verbosity mode, 0, 1 or 2
callbacks: List of callbacks to be called during training
val_inputs: List of input arrays.
val_targets: List of target arrays.
val_sample_weights: Optional list of sample weight arrays.
shuffle: Whether to shuffle the data at the beginning of each epoch
callback_metrics: List of strings, the display names of the metrics
passed to the callbacks. They should be the
concatenation of list the display names of the outputs of
`f` and the list of display names of the outputs of `f_val`.
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. Ignored with the default value of `None`.
validation_steps: Number of steps to run validation for
(only if doing validation from data tensors).
Ignored with the default value of `None`.
Returns:
`History` object.
Raises:
ValueError: in case of invalid arguments.
"""
model._make_train_function()
f = model.train_function
sample_weights = sample_weights or []
val_sample_weights = val_sample_weights or []
if model.uses_learning_phase and not isinstance(K.learning_phase(), int):
ins = inputs + targets + sample_weights + [1]
if val_inputs:
val_ins = val_inputs + val_targets + val_sample_weights + [1]
else:
ins = inputs + targets + sample_weights
if val_inputs:
val_ins = val_inputs + val_targets + val_sample_weights
if not val_inputs:
val_ins = []
do_validation = False
if val_inputs:
do_validation = True
if verbose and inputs and hasattr(inputs[0], 'shape') and hasattr(
val_inputs[0], 'shape'):
print('Train on %d samples, validate on %d samples' %
(inputs[0].shape[0], val_inputs[0].shape[0]))
if validation_steps:
do_validation = True
if steps_per_epoch is None:
raise ValueError('Can only use `validation_steps` '
'when doing step-wise '
'training, i.e. `steps_per_epoch` '
'must be set.')
out_labels = model.metrics_names
if do_validation:
callback_metrics = copy.copy(out_labels) + [
'val_' + n for n in out_labels
]
else:
callback_metrics = copy.copy(out_labels)
num_train_samples = training_utils.check_num_samples(
ins, batch_size, steps_per_epoch, 'steps_per_epoch')
if num_train_samples is not None:
index_array = np.arange(num_train_samples)
model.history = cbks.History()
all_callbacks = [
cbks.BaseLogger(stateful_metrics=model.stateful_metric_names)
]
if verbose:
if steps_per_epoch is not None:
count_mode = 'steps'
else:
count_mode = 'samples'
all_callbacks.append(
cbks.ProgbarLogger(count_mode,
stateful_metrics=model.stateful_metric_names))
all_callbacks += (callbacks or []) + [model.history]
callbacks = cbks.CallbackList(all_callbacks)
out_labels = out_labels or []
# it's possible to callback a different model than self
# (used by Sequential models)
if hasattr(model, 'callback_model') and model.callback_model:
callback_model = model.callback_model
else:
callback_model = model
callbacks.set_model(callback_model)
callbacks.set_params({
'batch_size': batch_size,
'epochs': epochs,
'steps': steps_per_epoch,
'samples': num_train_samples,
'verbose': verbose,
'do_validation': do_validation,
'metrics': callback_metrics or [],
})
callbacks.on_train_begin()
callback_model.stop_training = False
for cbk in callbacks:
cbk.validation_data = val_ins
# To prevent a slowdown, we find beforehand the arrays that need conversion.
feed = model._feed_inputs + model._feed_targets + model._feed_sample_weights
indices_for_conversion_to_dense = []
for i in range(len(feed)):
if issparse is not None and issparse(
ins[i]) and not K.is_sparse(feed[i]):
indices_for_conversion_to_dense.append(i)
for epoch in range(initial_epoch, epochs):
# Reset stateful metrics
for m in model.metrics:
if isinstance(m, Layer):
m.reset_states()
# Update callbacks
callbacks.on_epoch_begin(epoch)
epoch_logs = {}
if steps_per_epoch is not None:
for step_index in range(steps_per_epoch):
batch_logs = {}
batch_logs['batch'] = step_index
batch_logs['size'] = 1
callbacks.on_batch_begin(step_index, batch_logs)
try:
outs = f(ins)
except errors.OutOfRangeError:
logging.warning(
'Your dataset iterator ran out of data; '
'interrupting training. Make sure that your dataset '
'can generate at least `steps_per_epoch * epochs` '
'batches (in this case, %d batches).' %
steps_per_epoch * epochs)
break
if not isinstance(outs, list):
outs = [outs]
for l, o in zip(out_labels, outs):
batch_logs[l] = o
callbacks.on_batch_end(step_index, batch_logs)
if callback_model.stop_training:
break
if do_validation:
val_outs = test_loop(model,
val_inputs,
val_targets,
sample_weights=val_sample_weights,
batch_size=batch_size,
steps=validation_steps,
verbose=0)
if not isinstance(val_outs, list):
val_outs = [val_outs]
# Same labels assumed.
for l, o in zip(out_labels, val_outs):
epoch_logs['val_' + l] = o
else:
if shuffle == 'batch':
index_array = training_utils.batch_shuffle(
index_array, batch_size)
elif shuffle:
np.random.shuffle(index_array)
batches = make_batches(num_train_samples, batch_size)
for batch_index, (batch_start, batch_end) in enumerate(batches):
batch_ids = index_array[batch_start:batch_end]
try:
if isinstance(ins[-1], int):
# Do not slice the training phase flag.
ins_batch = slice_arrays(ins[:-1],
batch_ids) + [ins[-1]]
else:
ins_batch = slice_arrays(ins, batch_ids)
except TypeError:
raise TypeError('TypeError while preparing batch. '
'If using HDF5 input data, '
'pass shuffle="batch".')
batch_logs = {}
batch_logs['batch'] = batch_index
batch_logs['size'] = len(batch_ids)
callbacks.on_batch_begin(batch_index, batch_logs)
for i in indices_for_conversion_to_dense:
ins_batch[i] = ins_batch[i].toarray()
outs = f(ins_batch)
if not isinstance(outs, list):
outs = [outs]
for l, o in zip(out_labels, outs):
batch_logs[l] = o
callbacks.on_batch_end(batch_index, batch_logs)
if callback_model.stop_training:
break
if batch_index == len(batches) - 1: # Last batch.
if do_validation:
val_outs = test_loop(model,
val_inputs,
val_targets,
sample_weights=val_sample_weights,
batch_size=batch_size,
verbose=0)
if not isinstance(val_outs, list):
val_outs = [val_outs]
# Same labels assumed.
for l, o in zip(out_labels, val_outs):
epoch_logs['val_' + l] = o
callbacks.on_epoch_end(epoch, epoch_logs)
if callback_model.stop_training:
break
callbacks.on_train_end()
return model.history
def fit_generator(model,
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):
"""See docstring for `Model.fit_generator`."""
wait_time = 0.01 # in seconds
epoch = initial_epoch
do_validation = bool(validation_data)
model._make_train_function()
if do_validation:
model._make_test_function()
is_sequence = isinstance(generator, Sequence)
if not is_sequence and use_multiprocessing and workers > 1:
logging.warning(
UserWarning('Using a generator with `use_multiprocessing=True`'
' and multiple workers may duplicate your data.'
' Please consider using the`keras.utils.Sequence'
' class.'))
if steps_per_epoch is None:
if is_sequence:
steps_per_epoch = len(generator)
else:
raise ValueError('`steps_per_epoch=None` is only valid for a'
' generator based on the `keras.utils.Sequence`'
' class. Please specify `steps_per_epoch` or use'
' the `keras.utils.Sequence` class.')
# python 2 has 'next', 3 has '__next__'
# avoid any explicit version checks
val_gen = (hasattr(validation_data, 'next')
or hasattr(validation_data, '__next__')
or isinstance(validation_data, Sequence))
if (val_gen and not isinstance(validation_data, Sequence)
and not validation_steps):
raise ValueError('`validation_steps=None` is only valid for a'
' generator based on the `keras.utils.Sequence`'
' class. Please specify `validation_steps` or use'
' the `keras.utils.Sequence` class.')
# Prepare display labels.
out_labels = model.metrics_names
callback_metrics = out_labels + ['val_%s' % n for n in out_labels]
# prepare callbacks
model.history = cbks.History()
callbacks = [cbks.BaseLogger()] + (callbacks or []) + [model.history]
if verbose:
callbacks += [cbks.ProgbarLogger(count_mode='steps')]
callbacks = cbks.CallbackList(callbacks)
# it's possible to callback a different model than self:
if hasattr(model, 'callback_model') and model.callback_model:
callback_model = model.callback_model
else:
callback_model = model
callbacks.set_model(callback_model)
callbacks.set_params({
'epochs': epochs,
'steps': steps_per_epoch,
'verbose': verbose,
'do_validation': do_validation,
'metrics': callback_metrics,
})
callbacks.on_train_begin()
enqueuer = None
val_enqueuer = None
try:
if do_validation and not val_gen:
# Prepare data for validation
if len(validation_data) == 2:
val_x, val_y = validation_data # pylint: disable=unpacking-non-sequence
val_sample_weight = None
elif len(validation_data) == 3:
val_x, val_y, val_sample_weight = validation_data # pylint: disable=unpacking-non-sequence
else:
raise ValueError('`validation_data` should be a tuple '
'`(val_x, val_y, val_sample_weight)` '
'or `(val_x, val_y)`. Found: ' +
str(validation_data))
val_x, val_y, val_sample_weights = model._standardize_user_data(
val_x, val_y, val_sample_weight)
val_data = val_x + val_y + val_sample_weights
if model.uses_learning_phase and not isinstance(
K.learning_phase(), int):
val_data += [0.]
for cbk in callbacks:
cbk.validation_data = val_data
if workers > 0:
if is_sequence:
enqueuer = OrderedEnqueuer(
generator,
use_multiprocessing=use_multiprocessing,
shuffle=shuffle)
else:
enqueuer = GeneratorEnqueuer(
generator,
use_multiprocessing=use_multiprocessing,
wait_time=wait_time)
enqueuer.start(workers=workers, max_queue_size=max_queue_size)
output_generator = enqueuer.get()
else:
if is_sequence:
output_generator = iter(generator)
else:
output_generator = generator
callback_model.stop_training = False
# Construct epoch logs.
epoch_logs = {}
while epoch < epochs:
callbacks.on_epoch_begin(epoch)
steps_done = 0
batch_index = 0
while steps_done < steps_per_epoch:
generator_output = next(output_generator)
if not hasattr(generator_output, '__len__'):
raise ValueError('Output of generator should be '
'a tuple `(x, y, sample_weight)` '
'or `(x, y)`. Found: ' +
str(generator_output))
if len(generator_output) == 2:
x, y = generator_output
sample_weight = None
elif len(generator_output) == 3:
x, y, sample_weight = generator_output
else:
raise ValueError('Output of generator should be '
'a tuple `(x, y, sample_weight)` '
'or `(x, y)`. Found: ' +
str(generator_output))
# build batch logs
batch_logs = {}
if isinstance(x, list):
batch_size = x[0].shape[0]
elif isinstance(x, dict):
batch_size = list(x.values())[0].shape[0]
else:
batch_size = x.shape[0]
batch_logs['batch'] = batch_index
batch_logs['size'] = batch_size
callbacks.on_batch_begin(batch_index, batch_logs)
outs = model.train_on_batch(x,
y,
sample_weight=sample_weight,
class_weight=class_weight)
if not isinstance(outs, list):
outs = [outs]
for l, o in zip(out_labels, outs):
batch_logs[l] = o
callbacks.on_batch_end(batch_index, batch_logs)
batch_index += 1
steps_done += 1
# Epoch finished.
if steps_done >= steps_per_epoch and do_validation:
if val_gen:
val_outs = evaluate_generator(
model,
validation_data,
validation_steps,
workers=workers,
use_multiprocessing=use_multiprocessing,
max_queue_size=max_queue_size)
else:
# No need for try/except because
# data has already been validated.
val_outs = model.evaluate(
val_x,
val_y,
batch_size=batch_size,
sample_weight=val_sample_weights,
verbose=0)
if not isinstance(val_outs, list):
val_outs = [val_outs]
# Same labels assumed.
for l, o in zip(out_labels, val_outs):
epoch_logs['val_' + l] = o
if callback_model.stop_training:
break
callbacks.on_epoch_end(epoch, epoch_logs)
epoch += 1
if callback_model.stop_training:
break
finally:
try:
if enqueuer is not None:
enqueuer.stop()
finally:
if val_enqueuer is not None:
val_enqueuer.stop()
callbacks.on_train_end()
return model.history
