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adding kernel initializer option to FUNITAdain block

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This commit is contained in:
Colombo 2019-09-24 21:30:28 +04:00
parent deeb98474b
commit 4a2203cc35
2 changed files with 6 additions and 5 deletions
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4
nnlib/FUNIT.py
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@ -258,10 +258,10 @@ class FUNIT(object):
inp, mlp = input
x = inp
x = Conv2D(dim, 3, strides=1, padding='valid')(ZeroPadding2D(1)(x))
x = FUNITAdain()([x,mlp])
x = FUNITAdain(kernel_initializer='he_normal')([x,mlp])
x = ReLU()(x)
x = Conv2D(dim, 3, strides=1, padding='valid')(ZeroPadding2D(1)(x))
x = FUNITAdain()([x,mlp])
x = FUNITAdain(kernel_initializer='he_normal')([x,mlp])
return Add()([x,inp])
return func

7
nnlib/nnlib.py
View file

@ -522,10 +522,11 @@ NLayerDiscriminator = nnlib.NLayerDiscriminator
so we don't need to slice outter MLP block and assign weights every call, just pass MLP inside.
also size of dense blocks is calculated automatically
"""
def __init__(self, axis=-1, epsilon=1e-5, momentum=0.99, **kwargs):
def __init__(self, axis=-1, epsilon=1e-5, momentum=0.99, kernel_initializer='glorot_uniform', **kwargs):
self.axis = axis
self.epsilon = epsilon
self.momentum = momentum
self.kernel_initializer = kernel_initializer
super(FUNITAdain, self).__init__(**kwargs)
def build(self, input_shape):
@ -533,9 +534,9 @@ NLayerDiscriminator = nnlib.NLayerDiscriminator
x, mlp = input_shape
units = x[self.axis]
self.kernel1 = self.add_weight(shape=(units, units), initializer='he_normal', name='kernel1')
self.kernel1 = self.add_weight(shape=(units, units), initializer=self.kernel_initializer, name='kernel1')
self.bias1 = self.add_weight(shape=(units,), initializer='zeros', name='bias1')
self.kernel2 = self.add_weight(shape=(units, units), initializer='he_normal', name='kernel2')
self.kernel2 = self.add_weight(shape=(units, units), initializer=self.kernel_initializer, name='kernel2')
self.bias2 = self.add_weight(shape=(units,), initializer='zeros', name='bias2')
self.built = True