def predict_loop(model, inputs,
batch_size=32,
verbose=0,
steps=None):
"""Predict function for eager execution.
Arguments:
model: Instance of `Model`.
inputs: List of input arrays.
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).
"""
with backend.learning_phase_scope(0):
if steps is not None:
return iterator_predict_loop(model, inputs, steps, verbose=verbose)
else:
return batch_predict_loop(
model, inputs, batch_size=batch_size, verbose=verbose)
def test_loop(model, inputs, targets,
sample_weights=None,
batch_size=None,
verbose=0,
steps=None):
"""Test function for eager execution.
Arguments:
model: Model instance that is being evaluated in Eager mode.
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.
"""
with backend.learning_phase_scope(0):
if steps is not None:
return iterator_test_loop(model, inputs, steps, verbose=verbose)
else:
return batch_test_loop(
model,
inputs,
targets,
batch_size=batch_size,
sample_weights=sample_weights,
verbose=verbose)
def predict_loop(model, inputs, batch_size=32, verbose=0, steps=None):
"""Predict function for eager execution.
Arguments:
model: Instance of `Model`.
inputs: List of input arrays.
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).
"""
with backend.learning_phase_scope(0):
if steps is not None:
return iterator_predict_loop(model, inputs, steps, verbose=verbose)
else:
return batch_predict_loop(model,
inputs,
batch_size=batch_size,
verbose=verbose)
def test_loop(model, inputs, targets,
sample_weights=None,
batch_size=None,
verbose=0,
steps=None):
"""Test function for eager execution.
Arguments:
model: Model instance that is being evaluated in Eager mode.
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.
"""
with backend.learning_phase_scope(0):
if steps is not None:
return iterator_test_loop(model, inputs, steps, verbose=verbose)
else:
return batch_test_loop(
model,
inputs,
targets,
batch_size=batch_size,
sample_weights=sample_weights,
verbose=verbose)
def _process_single_batch(model,
inputs,
targets,
sample_weights=None,
training=False):
"""Calculate the loss and gradient for one input batch.
The model weights are updated if training is set to True.
Arguments:
model: Model whose loss has to be calculated.
inputs: List of input arrays.
targets: List of target arrays.
sample_weights: Optional list of sample weight arrays.
training: The boolean represents if the weights of the model are updated.
'fit' methods will set this to True while 'evaluate' methods will
set this to False.
Returns:
output of the model, total loss and the loss associated with each output.
Raises:
ValueError: If the model has no loss to optimize.
"""
with backend.learning_phase_scope(1 if training else 0):
with GradientTape() as tape:
outs, loss, loss_metrics = _model_loss(
model,
inputs,
targets,
sample_weights=sample_weights,
training=training)
if loss is None:
raise ValueError('The model cannot be run '
'because it has no loss to optimize.')
if training:
if not model._collected_trainable_weights:
logging.warning(
'The list of trainable weights is empty. Make sure that'
' you are not setting model.trainable to False before '
'compiling the model.')
else:
grads = tape.gradient(loss, model._collected_trainable_weights)
model.optimizer.apply_gradients(
zip(grads, model._collected_trainable_weights))
return outs, loss, loss_metrics
def _process_single_batch(model,
inputs,
targets,
sample_weights=None,
training=False):
"""Calculate the loss and gradient for one input batch.
The model weights are updated if training is set to True.
Arguments:
model: Model whose loss has to be calculated.
inputs: List of input arrays.
targets: List of target arrays.
sample_weights: Optional list of sample weight arrays.
training: The boolean represents if the weights of the model are updated.
'fit' methods will set this to True while 'evaluate' methods will
set this to False.
Returns:
output of the model, total loss and the loss associated with each output.
Raises:
ValueError: If the model has no loss to optimize.
"""
with backend.learning_phase_scope(1 if training else 0):
with GradientTape() as tape:
outs, loss, loss_metrics = _model_loss(model, inputs, targets,
sample_weights=sample_weights,
training=training)
if loss is None:
raise ValueError('The model cannot be run '
'because it has no loss to optimize.')
if training:
if not model._collected_trainable_weights:
logging.warning('The list of trainable weights is empty. Make sure that'
' you are not setting model.trainable to False before '
'compiling the model.')
else:
grads = tape.gradient(loss, model._collected_trainable_weights)
model.optimizer.apply_gradients(zip(grads,
model._collected_trainable_weights))
return outs, loss, loss_metrics
def predict_loop(model, inputs,
batch_size=32,
verbose=0,
steps=None):
"""Abstract method to loop over some data in batches.
Arguments:
model:
inputs: List of input arrays.
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).
"""
with backend.learning_phase_scope(0):
num_samples = training_utils.check_num_samples(
inputs, batch_size, steps, 'steps')
if verbose == 1:
if steps is not None:
progbar = generic_utils.Progbar(target=steps)
else:
progbar = generic_utils.Progbar(target=num_samples)
outs = []
batches = generic_utils.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]
inputs_batch = slice_arrays(inputs, batch_ids)
inputs_batch = [
ops.convert_to_tensor(val, dtype=backend.floatx())
for val in inputs_batch]
if len(inputs_batch) == 1:
if model._expects_training_arg:
batch_outs = model.call(inputs_batch[0], training=False)
else:
batch_outs = model.call(inputs_batch[0])
else:
if model._expects_training_arg:
batch_outs = model.call(inputs_batch, training=False)
else:
batch_outs = model.call(inputs_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:
dims = batch_out.shape[1:].dims
dims_list = [d.value for d in dims]
shape = (num_samples,) + tuple(dims_list)
outs.append(np.zeros(shape, dtype=batch_out.dtype.as_numpy_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 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: Model instance that is being evaluated in Eager mode.
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.
"""
with backend.learning_phase_scope(0):
feed_data = inputs + targets
if sample_weights:
feed_data += sample_weights
num_samples = training_utils.check_num_samples(
feed_data, batch_size=batch_size, steps=steps, steps_name='steps')
outs = []
if verbose == 1:
progbar = generic_utils.Progbar(target=num_samples)
batches = generic_utils.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]
inputs_batch = slice_arrays(inputs, batch_ids)
targets_batch = slice_arrays(targets, batch_ids)
if sample_weights:
sample_weights_batch = slice_arrays(sample_weights, batch_ids)
else:
sample_weights_batch = None
inputs_batch = [
ops.convert_to_tensor(val, dtype=backend.floatx())
for val in inputs_batch]
targets_batch = [
ops.convert_to_tensor(val, dtype=backend.floatx())
for val in targets_batch]
if sample_weights:
sample_weights_batch = [
ops.convert_to_tensor(val, dtype=backend.floatx())
if val is not None else None
for val in sample_weights_batch]
loss_outs, loss, loss_metrics = _model_loss(
model,
inputs_batch,
targets_batch,
sample_weights=sample_weights_batch,
training=False)
metrics_results = _eager_metrics_fn(model, loss_outs, targets_batch)
batch_outs = []
for _, v in zip(model.metrics_names,
[backend.mean(loss)] + loss_metrics + metrics_results):
batch_outs.append(tensor_util.constant_value(v))
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):
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)):
outs[i] /= num_samples
if len(outs) == 1:
return outs[0]
return outs
def fit_loop(
model,
inputs,
targets,
sample_weights=None,
val_inputs=None,
val_targets=None,
val_sample_weights=None,
batch_size=None,
epochs=100,
verbose=1,
callbacks=None,
shuffle=True,
callback_metrics=None,
initial_epoch=0,
steps_per_epoch=None,
validation_steps=None):
"""Abstract fit function for eager execution.
Arguments:
model: Instance of the model that is being executed in Eager mode.
inputs: List of input arrays.
targets: List of target arrays.
sample_weights: Optional list of sample weight arrays.
val_inputs: Input data for validation.
val_targets: Target data for validation.
val_sample_weights: Sample weight data for validation.
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
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 default value of `None`.
Returns:
`History` object.
Raises:
ValueError: In case of invalid argument values.
"""
if not batch_size:
raise ValueError('With eager execution, `batch_size` should be specified.')
if steps_per_epoch or validation_steps:
raise ValueError('With eager execution, `steps_per_epoch` and '
'`validation_steps` are not valid arguments '
'(set `batch_size` instead).')
# Required for Eager mode
with backend.learning_phase_scope(1):
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:
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.')
do_validation = True
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)
if sample_weights:
feed_data = inputs + targets + sample_weights
else:
feed_data = inputs + targets
num_train_samples = training_utils.check_num_samples(
feed_data,
batch_size=batch_size,
steps=steps_per_epoch,
steps_name='steps_per_epoch')
if num_train_samples is not None:
index_array = np.arange(num_train_samples)
model.history = cbks.History()
callbacks = [cbks.BaseLogger()] + (callbacks or []) + [model.history]
if verbose:
if steps_per_epoch is not None:
count_mode = 'steps'
else:
count_mode = 'samples'
callbacks += [cbks.ProgbarLogger(count_mode)]
callbacks = cbks.CallbackList(callbacks)
# 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:
if not val_inputs:
cbk.validation_data = []
elif val_sample_weights:
cbk.validation_data = val_inputs + val_targets + val_sample_weights
else:
cbk.validation_data = val_inputs + val_targets
for epoch in range(initial_epoch, epochs):
callbacks.on_epoch_begin(epoch)
epoch_logs = {}
if shuffle == 'batch':
index_array = model._batch_shuffle(index_array, batch_size)
elif shuffle:
np.random.shuffle(index_array)
batches = generic_utils.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:
inputs_batch = slice_arrays(inputs, batch_ids,
contiguous=not shuffle)
targets_batch = slice_arrays(targets, batch_ids,
contiguous=not shuffle)
if sample_weights:
sample_weights_batch = slice_arrays(sample_weights, batch_ids,
contiguous=not shuffle)
else:
sample_weights_batch = None
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)
inputs_batch = [
ops.convert_to_tensor(val, dtype=backend.floatx())
for val in inputs_batch]
targets_batch = [
ops.convert_to_tensor(val, dtype=backend.floatx())
for val in targets_batch]
if sample_weights:
sample_weights_batch = [
ops.convert_to_tensor(val, dtype=backend.floatx())
if val is not None else None
for val in sample_weights_batch]
outs, loss, loss_metrics = _process_single_batch(
model,
inputs_batch,
targets_batch,
sample_weights=sample_weights_batch,
training=True)
if not isinstance(outs, list):
outs = [outs]
for l, o in zip(out_labels, outs):
batch_logs[l] = o
# Required for Eager mode
metrics_results = _eager_metrics_fn(model, outs, targets_batch)
batch_logs['loss'] = tensor_util.constant_value(backend.mean(loss))
for k, v in zip(model.metrics_names,
[backend.mean(loss)] + loss_metrics + metrics_results):
batch_logs[k] = tensor_util.constant_value(v)
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 predict_loop(model, inputs, batch_size=32, verbose=0, steps=None):
"""Abstract method to loop over some data in batches.
Arguments:
model:
inputs: List of input arrays.
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).
"""
with backend.learning_phase_scope(0):
num_samples = training_utils.check_num_samples(inputs, batch_size,
steps, 'steps')
if verbose == 1:
if steps is not None:
progbar = generic_utils.Progbar(target=steps)
else:
progbar = generic_utils.Progbar(target=num_samples)
outs = []
batches = generic_utils.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]
inputs_batch = slice_arrays(inputs, batch_ids)
inputs_batch = [
ops.convert_to_tensor(val, dtype=backend.floatx())
for val in inputs_batch
]
if len(inputs_batch) == 1:
if model._expects_training_arg:
batch_outs = model.call(inputs_batch[0], training=False)
else:
batch_outs = model.call(inputs_batch[0])
else:
if model._expects_training_arg:
batch_outs = model.call(inputs_batch, training=False)
else:
batch_outs = model.call(inputs_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:
dims = batch_out.shape[1:].dims
dims_list = [d.value for d in dims]
shape = (num_samples, ) + tuple(dims_list)
outs.append(
np.zeros(shape, dtype=batch_out.dtype.as_numpy_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 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: Model instance that is being evaluated in Eager mode.
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.
"""
with backend.learning_phase_scope(0):
feed_data = inputs + targets
if sample_weights:
feed_data += sample_weights
num_samples = training_utils.check_num_samples(feed_data,
batch_size=batch_size,
steps=steps,
steps_name='steps')
outs = []
if verbose == 1:
progbar = generic_utils.Progbar(target=num_samples)
batches = generic_utils.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]
inputs_batch = slice_arrays(inputs, batch_ids)
targets_batch = slice_arrays(targets, batch_ids)
if sample_weights:
sample_weights_batch = slice_arrays(sample_weights, batch_ids)
else:
sample_weights_batch = None
inputs_batch = [
ops.convert_to_tensor(val, dtype=backend.floatx())
for val in inputs_batch
]
targets_batch = [
ops.convert_to_tensor(val, dtype=backend.floatx())
for val in targets_batch
]
if sample_weights:
sample_weights_batch = [
ops.convert_to_tensor(val, dtype=backend.floatx())
if val is not None else None
for val in sample_weights_batch
]
loss_outs, loss, loss_metrics = _model_loss(
model,
inputs_batch,
targets_batch,
sample_weights=sample_weights_batch,
training=False)
metrics_results = _eager_metrics_fn(model, loss_outs,
targets_batch)
batch_outs = []
for _, v in zip(model.metrics_names, [backend.mean(loss)] +
loss_metrics + metrics_results):
batch_outs.append(tensor_util.constant_value(v))
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):
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)):
outs[i] /= num_samples
if len(outs) == 1:
return outs[0]
return outs
def fit_loop(model,
inputs,
targets,
sample_weights=None,
val_inputs=None,
val_targets=None,
val_sample_weights=None,
batch_size=None,
epochs=100,
verbose=1,
callbacks=None,
shuffle=True,
callback_metrics=None,
initial_epoch=0,
steps_per_epoch=None,
validation_steps=None):
"""Abstract fit function for eager execution.
Arguments:
model: Instance of the model that is being executed in Eager mode.
inputs: List of input arrays.
targets: List of target arrays.
sample_weights: Optional list of sample weight arrays.
val_inputs: Input data for validation.
val_targets: Target data for validation.
val_sample_weights: Sample weight data for validation.
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
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 default value of `None`.
Returns:
`History` object.
Raises:
ValueError: In case of invalid argument values.
"""
if not batch_size:
raise ValueError(
'With eager execution, `batch_size` should be specified.')
if steps_per_epoch or validation_steps:
raise ValueError('With eager execution, `steps_per_epoch` and '
'`validation_steps` are not valid arguments '
'(set `batch_size` instead).')
# Required for Eager mode
with backend.learning_phase_scope(1):
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:
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.')
do_validation = True
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)
if sample_weights:
feed_data = inputs + targets + sample_weights
else:
feed_data = inputs + targets
num_train_samples = training_utils.check_num_samples(
feed_data,
batch_size=batch_size,
steps=steps_per_epoch,
steps_name='steps_per_epoch')
if num_train_samples is not None:
index_array = np.arange(num_train_samples)
model.history = cbks.History()
callbacks = [cbks.BaseLogger()] + (callbacks or []) + [model.history]
if verbose:
if steps_per_epoch is not None:
count_mode = 'steps'
else:
count_mode = 'samples'
callbacks += [cbks.ProgbarLogger(count_mode)]
callbacks = cbks.CallbackList(callbacks)
# 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:
if not val_inputs:
cbk.validation_data = []
elif val_sample_weights:
cbk.validation_data = val_inputs + val_targets + val_sample_weights
else:
cbk.validation_data = val_inputs + val_targets
for epoch in range(initial_epoch, epochs):
callbacks.on_epoch_begin(epoch)
epoch_logs = {}
if shuffle == 'batch':
index_array = model._batch_shuffle(index_array, batch_size)
elif shuffle:
np.random.shuffle(index_array)
batches = generic_utils.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:
inputs_batch = slice_arrays(inputs,
batch_ids,
contiguous=not shuffle)
targets_batch = slice_arrays(targets,
batch_ids,
contiguous=not shuffle)
if sample_weights:
sample_weights_batch = slice_arrays(
sample_weights, batch_ids, contiguous=not shuffle)
else:
sample_weights_batch = None
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)
inputs_batch = [
ops.convert_to_tensor(val, dtype=backend.floatx())
for val in inputs_batch
]
targets_batch = [
ops.convert_to_tensor(val, dtype=backend.floatx())
for val in targets_batch
]
if sample_weights:
sample_weights_batch = [
ops.convert_to_tensor(val, dtype=backend.floatx())
if val is not None else None
for val in sample_weights_batch
]
outs, loss, loss_metrics = _process_single_batch(
model,
inputs_batch,
targets_batch,
sample_weights=sample_weights_batch,
training=True)
if not isinstance(outs, list):
outs = [outs]
for l, o in zip(out_labels, outs):
batch_logs[l] = o
# Required for Eager mode
metrics_results = _eager_metrics_fn(model, outs, targets_batch)
batch_logs['loss'] = tensor_util.constant_value(
backend.mean(loss))
for k, v in zip(model.metrics_names, [backend.mean(loss)] +
loss_metrics + metrics_results):
batch_logs[k] = tensor_util.constant_value(v)
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_loop(model,
inputs,
targets,
sample_weights=None,
class_weight=None,
val_inputs=None,
val_targets=None,
val_sample_weights=None,
batch_size=None,
epochs=1,
verbose=1,
callbacks=None,
shuffle=True,
callback_metrics=None,
initial_epoch=0,
steps_per_epoch=None,
validation_steps=None):
"""Fit function for eager execution.
Arguments:
model: Instance of the model that is being executed in Eager mode.
inputs: List of input arrays.
targets: List of target arrays.
sample_weights: Optional list of sample weight arrays.
class_weight: Optional class-weight array to weight the importance of
samples in `inputs` based on the class they belong to, as conveyed by
`targets`.
val_inputs: Input data for validation.
val_targets: Target data for validation.
val_sample_weights: Sample weight data for validation.
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
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 default value of `None`.
Returns:
`History` object.
Raises:
ValueError: In case of invalid argument values.
"""
# Required for eager execution
with backend.learning_phase_scope(1):
do_validation = False
if val_inputs:
do_validation = True
if (steps_per_epoch is None and 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]))
num_train_samples = None
out_labels = None
if steps_per_epoch is None or model._is_compiled:
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)
if steps_per_epoch is None:
if sample_weights:
feed_data = inputs + targets + sample_weights
else:
feed_data = inputs + targets
num_train_samples = training_utils.check_num_samples(
feed_data,
batch_size=batch_size,
steps=steps_per_epoch,
steps_name='steps_per_epoch')
if num_train_samples is not None:
index_array = np.arange(num_train_samples)
model.history = cbks.History()
callbacks = [cbks.BaseLogger()] + (callbacks or []) + [model.history]
if verbose:
if steps_per_epoch is not None:
count_mode = 'steps'
else:
count_mode = 'samples'
callbacks += [cbks.ProgbarLogger(count_mode)]
callbacks = cbks.CallbackList(callbacks)
# 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:
if not val_inputs:
cbk.validation_data = []
elif isinstance(val_inputs, iterator_ops.EagerIterator):
cbk.validation_data = val_inputs
elif val_sample_weights:
cbk.validation_data = val_inputs + val_targets + val_sample_weights
else:
cbk.validation_data = val_inputs + val_targets
for epoch in range(initial_epoch, epochs):
callbacks.on_epoch_begin(epoch)
epoch_logs = {}
if steps_per_epoch is not None:
iterator_fit_loop(
model,
inputs,
class_weight,
steps_per_epoch=steps_per_epoch,
callback_model=callback_model,
out_labels=out_labels,
epoch_logs=epoch_logs,
val_inputs=val_inputs,
val_targets=val_targets,
val_sample_weights=val_sample_weights,
epochs=epochs,
verbose=verbose,
callbacks=callbacks,
callback_metrics=callback_metrics,
validation_steps=validation_steps,
do_validation=do_validation)
else:
batch_fit_loop(
model,
inputs,
targets,
epoch_logs=epoch_logs,
index_array=index_array,
out_labels=out_labels,
callback_model=callback_model,
batch_size=batch_size,
sample_weights=sample_weights,
val_inputs=val_inputs,
val_targets=val_targets,
val_sample_weights=val_sample_weights,
callbacks=callbacks,
shuffle=shuffle,
num_train_samples=num_train_samples,
do_validation=do_validation)
callbacks.on_epoch_end(epoch, epoch_logs)
if callback_model.stop_training:
break
callbacks.on_train_end()
return model.history
def fit_loop(model,
inputs,
targets,
sample_weights=None,
class_weight=None,
val_inputs=None,
val_targets=None,
val_sample_weights=None,
batch_size=None,
epochs=1,
verbose=1,
callbacks=None,
shuffle=True,
callback_metrics=None,
initial_epoch=0,
steps_per_epoch=None,
validation_steps=None):
"""Fit function for eager execution.
Arguments:
model: Instance of the model that is being executed in Eager mode.
inputs: List of input arrays.
targets: List of target arrays.
sample_weights: Optional list of sample weight arrays.
class_weight: Optional class-weight array to weight the importance of
samples in `inputs` based on the class they belong to, as conveyed by
`targets`.
val_inputs: Input data for validation.
val_targets: Target data for validation.
val_sample_weights: Sample weight data for validation.
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
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 default value of `None`.
Returns:
`History` object.
Raises:
ValueError: In case of invalid argument values.
"""
# Required for eager execution
with backend.learning_phase_scope(1):
do_validation = False
if val_inputs:
do_validation = True
if (steps_per_epoch is None and 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]))
num_train_samples = None
out_labels = None
if steps_per_epoch is None or model._is_compiled:
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)
if steps_per_epoch is None:
if sample_weights:
feed_data = inputs + targets + sample_weights
else:
feed_data = inputs + targets
num_train_samples = training_utils.check_num_samples(
feed_data,
batch_size=batch_size,
steps=steps_per_epoch,
steps_name='steps_per_epoch')
if num_train_samples is not None:
index_array = np.arange(num_train_samples)
model.history = cbks.History()
callbacks = [cbks.BaseLogger()] + (callbacks or []) + [model.history]
if verbose:
if steps_per_epoch is not None:
count_mode = 'steps'
else:
count_mode = 'samples'
callbacks += [cbks.ProgbarLogger(count_mode)]
callbacks = cbks.CallbackList(callbacks)
# 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:
if not val_inputs:
cbk.validation_data = []
elif isinstance(val_inputs, iterator_ops.EagerIterator):
cbk.validation_data = val_inputs
elif val_sample_weights:
cbk.validation_data = val_inputs + val_targets + val_sample_weights
else:
cbk.validation_data = val_inputs + val_targets
for epoch in range(initial_epoch, epochs):
callbacks.on_epoch_begin(epoch)
epoch_logs = {}
if steps_per_epoch is not None:
iterator_fit_loop(model,
inputs,
class_weight,
steps_per_epoch=steps_per_epoch,
callback_model=callback_model,
out_labels=out_labels,
epoch_logs=epoch_logs,
val_inputs=val_inputs,
val_targets=val_targets,
val_sample_weights=val_sample_weights,
epochs=epochs,
verbose=verbose,
callbacks=callbacks,
callback_metrics=callback_metrics,
validation_steps=validation_steps,
do_validation=do_validation)
else:
batch_fit_loop(model,
inputs,
targets,
epoch_logs=epoch_logs,
index_array=index_array,
out_labels=out_labels,
callback_model=callback_model,
batch_size=batch_size,
sample_weights=sample_weights,
val_inputs=val_inputs,
val_targets=val_targets,
val_sample_weights=val_sample_weights,
callbacks=callbacks,
shuffle=shuffle,
num_train_samples=num_train_samples,
do_validation=do_validation)
callbacks.on_epoch_end(epoch, epoch_logs)
if callback_model.stop_training:
break
callbacks.on_train_end()
return model.history
