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Fix for tf.keras: Cast input_dims to int. #4

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Fix for tf.keras: Cast input_dims to int. #4

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12 changes: 6 additions & 6 deletions SpectralNormalizationKeras.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,7 +17,7 @@
class DenseSN(Dense):
def build(self, input_shape):
assert len(input_shape) >= 2
input_dim = input_shape[-1]
input_dim = int(input_shape[-1])
self.kernel = self.add_weight(shape=(input_dim, self.units),
initializer=self.kernel_initializer,
name='kernel',
Expand Down Expand Up @@ -127,7 +127,7 @@ def build(self, input_shape):
if input_shape[channel_axis] is None:
raise ValueError('The channel dimension of the inputs '
'should be defined. Found `None`.')
input_dim = input_shape[channel_axis]
input_dim = int(input_shape[channel_axis])
kernel_shape = self.kernel_size + (input_dim, self.filters)

self.kernel = self.add_weight(shape=kernel_shape,
Expand Down Expand Up @@ -278,7 +278,7 @@ def build(self, input_shape):
if input_shape[channel_axis] is None:
raise ValueError('The channel dimension of the inputs '
'should be defined. Found `None`.')
input_dim = input_shape[channel_axis]
input_dim = int(input_shape[channel_axis])
kernel_shape = self.kernel_size + (input_dim, self.filters)

self.kernel = self.add_weight(shape=kernel_shape,
Expand Down Expand Up @@ -357,7 +357,7 @@ def build(self, input_shape):
if input_shape[channel_axis] is None:
raise ValueError('The channel dimension of the inputs '
'should be defined. Found `None`.')
input_dim = input_shape[channel_axis]
input_dim = int(input_shape[channel_axis])
kernel_shape = self.kernel_size + (input_dim, self.filters)

self.kernel = self.add_weight(shape=kernel_shape,
Expand Down Expand Up @@ -435,7 +435,7 @@ def build(self, input_shape):
if input_shape[channel_axis] is None:
raise ValueError('The channel dimension of the inputs '
'should be defined. Found `None`.')
input_dim = input_shape[channel_axis]
input_dim = int(input_shape[channel_axis])
kernel_shape = self.kernel_size + (input_dim, self.filters)

self.kernel = self.add_weight(shape=kernel_shape,
Expand Down Expand Up @@ -569,7 +569,7 @@ def build(self, input_shape):
if input_shape[channel_axis] is None:
raise ValueError('The channel dimension of the inputs '
'should be defined. Found `None`.')
input_dim = input_shape[channel_axis]
input_dim = int(input_shape[channel_axis])
kernel_shape = self.kernel_size + (self.filters, input_dim)

self.kernel = self.add_weight(shape=kernel_shape,
Expand Down