def clone_optimizer(optimizer):
# Requires Keras 1.0.7 since get_config has breaking changes.
params = dict([(k, v) for k, v in optimizer.get_config().items()])
config = {
'class_name': optimizer.__class__.__name__,
'config': params,
}
clone = optimizer_from_config(config)
return clone
def clone_optimizer(optimizer):
if type(optimizer) is str:
return get(optimizer)
params = dict([(k, v) for k, v in optimizer.get_config().items()])
config = {
"class_name": optimizer.__class__.__name__,
"config": params
}
clone = optimizer_from_config(config)
return clone
def clone_optimizer(optimizer):
from keras.optimizers import optimizer_from_config
if isinstance(optimizer, str):
return get(optimizer)
params = dict([(k, v) for k, v in optimizer.get_config().items()])
config = {
'class_name': optimizer.__class__.__name__,
'config': params,
}
clone = optimizer_from_config(config)
return clone
def clone_optimizer(optimizer):
if type(optimizer) is str:
return optimizers.get(optimizer)
# Requires Keras 1.0.7 since get_config has breaking changes.
params = dict([(k, v) for k, v in optimizer.get_config().items()])
config = {
'class_name': optimizer.__class__.__name__,
'config': params,
}
if hasattr(optimizers, 'optimizer_from_config'):
# COMPATIBILITY: Keras < 2.0
clone = optimizers.optimizer_from_config(config)
else:
clone = optimizers.deserialize(config)
return clone
def clone_optimizer(optimizer):
if type(optimizer) is str:
return optimizers.get(optimizer)
# Requires Keras 1.0.7 since get_config has breaking changes.
params = dict([(k, v) for k, v in optimizer.get_config().items()])
config = {
'class_name': optimizer.__class__.__name__,
'config': params,
}
if hasattr(optimizers, 'optimizer_from_config'):
# COMPATIBILITY: Keras < 2.0
clone = optimizers.optimizer_from_config(config)
else:
clone = optimizers.deserialize(config)
return clone
def load_model(data):
# instantiate model
model_config = data['model_config']
if model_config is None:
raise ValueError('No model found in config file.')
model = model_from_config(model_config)
if hasattr(model, 'flattened_layers'):
# Support for legacy Sequential/Merge behavior.
flattened_layers = model.flattened_layers
else:
flattened_layers = model.layers
filtered_layers = []
for layer in flattened_layers:
weights = layer.weights
if weights:
filtered_layers.append(layer)
flattened_layers = filtered_layers
layer_names = data['layer_names']
filtered_layer_names = []
for name in layer_names:
weight_dict = data['model_weights'][name]
weight_names = weight_dict['weight_names']
if len(weight_names):
filtered_layer_names.append(name)
layer_names = filtered_layer_names
if len(layer_names) != len(flattened_layers):
raise ValueError('You are trying to load a weight file '
'containing ' + str(len(layer_names)) +
' layers into a model with ' +
str(len(flattened_layers)) + ' layers.')
# We batch weight value assignments in a single backend call
# which provides a speedup in TensorFlow.
weight_value_tuples = []
for k, name in enumerate(layer_names):
weight_dict = data['model_weights'][name]
weight_names = weight_dict['weight_names']
weight_values = weight_dict['weight_values']
layer = flattened_layers[k]
symbolic_weights = layer.weights
if len(weight_values) != len(symbolic_weights):
raise ValueError('Layer #' + str(k) + ' (named "' + layer.name +
'" in the current model) was found to '
'correspond to layer ' + name +
' in the save file. '
'However the new layer ' + layer.name +
' expects ' + str(len(symbolic_weights)) +
' weights, but the saved weights have ' +
str(len(weight_values)) + ' elements.')
if layer.__class__.__name__ == 'Convolution1D':
# This is for backwards compatibility with
# the old Conv1D weights format.
w = weight_values[0]
shape = w.shape
if shape[:2] != (layer.filter_length,
1) or shape[3] != layer.nb_filter:
# Legacy shape:
# (self.nb_filter, input_dim, self.filter_length, 1)
assert shape[0] == layer.nb_filter and shape[2:] == (
layer.filter_length, 1)
w = np.transpose(w, (2, 3, 1, 0))
weight_values[0] = w
weight_value_tuples += zip(symbolic_weights, weight_values)
K.batch_set_value(weight_value_tuples)
# instantiate optimizer
training_config = data.get('training_config')
if training_config is None:
warnings.warn('No training configuration found in save file: '
'the model was *not* compiled. Compile it manually.')
return model
optimizer_config = training_config['optimizer_config']
optimizer = optimizer_from_config(optimizer_config)
# recover loss functions and metrics
loss = training_config['loss']
metrics = training_config['metrics']
sample_weight_mode = training_config['sample_weight_mode']
loss_weights = training_config['loss_weights']
# compile model
model.compile(optimizer=optimizer,
loss=loss,
metrics=metrics,
loss_weights=loss_weights,
sample_weight_mode=sample_weight_mode)
# set optimizer weights
if 'optimizer_weights' in data:
# build train function (to get weight updates)
if isinstance(model, Sequential):
model.model._make_train_function()
else:
model._make_train_function()
optimizer_weights_dict = data['optimizer_weights']
optimizer_weight_names = optimizer_weights_dict['weight_names']
optimizer_weight_values = optimizer_weights_dict['weight_values']
model.optimizer.set_weights(optimizer_weight_values)
return model
