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adding kernel initializer option to FUNITAdain block
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parent
deeb98474b
commit
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2 changed files with 6 additions and 5 deletions
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@ -258,10 +258,10 @@ class FUNIT(object):
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inp, mlp = input
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x = inp
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x = Conv2D(dim, 3, strides=1, padding='valid')(ZeroPadding2D(1)(x))
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x = FUNITAdain()([x,mlp])
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x = FUNITAdain(kernel_initializer='he_normal')([x,mlp])
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x = ReLU()(x)
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x = Conv2D(dim, 3, strides=1, padding='valid')(ZeroPadding2D(1)(x))
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x = FUNITAdain()([x,mlp])
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x = FUNITAdain(kernel_initializer='he_normal')([x,mlp])
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return Add()([x,inp])
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return func
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@ -522,10 +522,11 @@ NLayerDiscriminator = nnlib.NLayerDiscriminator
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so we don't need to slice outter MLP block and assign weights every call, just pass MLP inside.
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also size of dense blocks is calculated automatically
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"""
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def __init__(self, axis=-1, epsilon=1e-5, momentum=0.99, **kwargs):
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def __init__(self, axis=-1, epsilon=1e-5, momentum=0.99, kernel_initializer='glorot_uniform', **kwargs):
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self.axis = axis
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self.epsilon = epsilon
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self.momentum = momentum
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self.kernel_initializer = kernel_initializer
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super(FUNITAdain, self).__init__(**kwargs)
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def build(self, input_shape):
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@ -533,9 +534,9 @@ NLayerDiscriminator = nnlib.NLayerDiscriminator
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x, mlp = input_shape
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units = x[self.axis]
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self.kernel1 = self.add_weight(shape=(units, units), initializer='he_normal', name='kernel1')
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self.kernel1 = self.add_weight(shape=(units, units), initializer=self.kernel_initializer, name='kernel1')
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self.bias1 = self.add_weight(shape=(units,), initializer='zeros', name='bias1')
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self.kernel2 = self.add_weight(shape=(units, units), initializer='he_normal', name='kernel2')
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self.kernel2 = self.add_weight(shape=(units, units), initializer=self.kernel_initializer, name='kernel2')
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self.bias2 = self.add_weight(shape=(units,), initializer='zeros', name='bias2')
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self.built = True
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