def deserialize(name, custom_objects=None):
'''deserialize Keras Object'''
return deserialize_keras_object(
name,
module_objects=globals(),
custom_objects=custom_objects,
printable_module_name='activation function')
def get_quantizer(identifier):
"""Gets the quantizer.
Args:
identifier: An quantizer, which could be dict, string, or callable function.
Returns:
A quantizer class or quantization function from this file. For example,
Quantizer classes: quantized_bits, quantized_po2, quantized_relu_po2,
binary, stochastic_binary, ternary, stochastic_ternary, etc.
Quantization functions: binary_sigmoid, hard_sigmoid, soft_sigmoid, etc.
Raises:
ValueError: An error occurred when quantizer cannot be interpreted.
"""
if identifier is None:
return None
if isinstance(identifier, dict):
return deserialize_keras_object(identifier,
module_objects=globals(),
printable_module_name="quantizer")
elif isinstance(identifier, six.string_types):
return safe_eval(identifier, globals())
elif callable(identifier):
return identifier
else:
raise ValueError("Could not interpret quantizer identifier: " +
str(identifier))
def callback_from_config(config):
if 'class_name' not in config:
raise ValueError('class_name is needed to define callback')
if 'config' not in config:
# auto add empty config for callback with only class_name
config['config'] = {}
return deserialize_keras_object(config,
module_objects=globals(),
custom_objects=Callbacks().callbacks,
printable_module_name='callback')
def deserialize(config, custom_objects=None):
return deserialize_keras_object(
config,
module_objects=globals(),
custom_objects=custom_objects,
printable_module_name="ProtoFlow Initializers")