mirror of
https://github.com/wassname/DenseNet-Keras.git
synced 2026-06-27 18:40:52 +08:00
Felix Yu
75 lines
3.3 KiB
Python
75 lines
3.3 KiB
Python
from keras.engine import Layer, InputSpec
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try:
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from keras import initializations
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except ImportError:
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from keras import initializers as initializations
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import keras.backend as K
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class Scale(Layer):
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'''Custom Layer for DenseNet used for BatchNormalization.
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Learns a set of weights and biases used for scaling the input data.
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the output consists simply in an element-wise multiplication of the input
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and a sum of a set of constants:
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out = in * gamma + beta,
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where 'gamma' and 'beta' are the weights and biases larned.
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# Arguments
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axis: integer, axis along which to normalize in mode 0. For instance,
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if your input tensor has shape (samples, channels, rows, cols),
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set axis to 1 to normalize per feature map (channels axis).
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momentum: momentum in the computation of the
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exponential average of the mean and standard deviation
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of the data, for feature-wise normalization.
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weights: Initialization weights.
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List of 2 Numpy arrays, with shapes:
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`[(input_shape,), (input_shape,)]`
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beta_init: name of initialization function for shift parameter
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(see [initializations](../initializations.md)), or alternatively,
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Theano/TensorFlow function to use for weights initialization.
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This parameter is only relevant if you don't pass a `weights` argument.
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gamma_init: name of initialization function for scale parameter (see
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[initializations](../initializations.md)), or alternatively,
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Theano/TensorFlow function to use for weights initialization.
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This parameter is only relevant if you don't pass a `weights` argument.
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'''
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def __init__(self, weights=None, axis=-1, momentum = 0.9, beta_init='zero', gamma_init='one', **kwargs):
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self.momentum = momentum
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self.axis = axis
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self.beta_init = initializations.get(beta_init)
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self.gamma_init = initializations.get(gamma_init)
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self.initial_weights = weights
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super(Scale, self).__init__(**kwargs)
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def build(self, input_shape):
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self.input_spec = [InputSpec(shape=input_shape)]
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shape = (int(input_shape[self.axis]),)
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# Tensorflow >= 1.0.0 compatibility
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self.gamma = K.variable(self.gamma_init(shape), name='{}_gamma'.format(self.name))
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self.beta = K.variable(self.beta_init(shape), name='{}_beta'.format(self.name))
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#self.gamma = self.gamma_init(shape, name='{}_gamma'.format(self.name))
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#self.beta = self.beta_init(shape, name='{}_beta'.format(self.name))
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self.beta = self.beta_init(shape)
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self.trainable_weights = [self.gamma, self.beta]
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if self.initial_weights is not None:
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self.set_weights(self.initial_weights)
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del self.initial_weights
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def call(self, x, mask=None):
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input_shape = self.input_spec[0].shape
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broadcast_shape = [1] * len(input_shape)
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broadcast_shape[self.axis] = input_shape[self.axis]
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out = K.reshape(self.gamma, broadcast_shape) * x + K.reshape(self.beta, broadcast_shape)
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return out
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def get_config(self):
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config = {"momentum": self.momentum, "axis": self.axis}
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base_config = super(Scale, self).get_config()
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return dict(list(base_config.items()) + list(config.items()))
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