def inspect_model(model):
"""Print the structure of Keras `model`."""
for i, l in enumerate(model.layers):
print(i, 'cls={} name={}'.format(type(l).__name__, l.name))
weights = l.get_weights()
print_str = ''
for weight in weights:
print_str += str_shape(weight) + ' '
print(print_str)
print()
def inspect_model(model):
"""Print the structure of Keras `model`."""
for i, l in enumerate(model.layers):
print(i, 'cls={} name={}'.format(type(l).__name__, l.name))
weights = l.get_weights()
print_str = ''
for weight in weights:
print_str += str_shape(weight) + ' '
print(print_str)
print()
def load_weights(model, filepath):
"""Load all weights possible into model from filepath.
This is a modified version of keras load_weights that loads as much as it can
if there is a mismatch between file and model. It returns the weights
of the first layer in which the mismatch has happened
"""
print('Loading', filepath, 'to', model.name)
with h5py.File(filepath, mode='r') as f:
# new file format
layer_names = [n.decode('utf8') for n in f.attrs['layer_names']]
# we batch weight value assignments in a single backend call
# which provides a speedup in TensorFlow.
weight_value_tuples = []
for name in layer_names:
print(name)
g = f[name]
weight_names = [n.decode('utf8') for n in g.attrs['weight_names']]
if len(weight_names):
weight_values = [
g[weight_name] for weight_name in weight_names
]
try:
layer = model.get_layer(name=name)
except:
layer = None
if not layer:
print('failed to find layer', name, 'in model')
print('weights',
' '.join(str_shape(w) for w in weight_values))
print('stopping to load all other layers')
weight_values = [np.array(w) for w in weight_values]
break
symbolic_weights = layer.trainable_weights + layer.non_trainable_weights
weight_value_tuples += zip(symbolic_weights, weight_values)
weight_values = None
K.batch_set_value(weight_value_tuples)
return weight_values
def load_weights(model, filepath):
"""Load all weights possible into model from filepath.
This is a modified version of keras load_weights that loads as much as it can
if there is a mismatch between file and model. It returns the weights
of the first layer in which the mismatch has happened
"""
print('Loading', filepath, 'to', model.name)
with h5py.File(filepath, mode='r') as f:
# new file format
layer_names = [n.decode('utf8') for n in f.attrs['layer_names']]
# we batch weight value assignments in a single backend call
# which provides a speedup in TensorFlow.
weight_value_tuples = []
for name in layer_names:
print(name)
g = f[name]
weight_names = [n.decode('utf8') for n in g.attrs['weight_names']]
if len(weight_names):
weight_values = [g[weight_name] for weight_name in weight_names]
try:
layer = model.get_layer(name=name)
except:
layer = None
if not layer:
print('failed to find layer', name, 'in model')
print('weights', ' '.join(str_shape(w) for w in weight_values))
print('stopping to load all other layers')
weight_values = [np.array(w) for w in weight_values]
break
symbolic_weights = layer.trainable_weights + layer.non_trainable_weights
weight_value_tuples += zip(symbolic_weights, weight_values)
weight_values = None
K.batch_set_value(weight_value_tuples)
return weight_values