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Small fixes for Neural_Doodle example (#6577)

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* Fixed type conversion in neural_doodle example. Shape returns number of channels as int32 however further calculations require it to be float

* Updated neural doodle example to follow Keras2 API. Renamed ‘border_mode’ argument to ‘padding’.

* Fixed apostrophe for consistency.
This commit is contained in:
rejunity 2017-05-11 23:33:39 +02:00 committed by François Chollet
parent 5f4f234f9b
commit e177397427
1 changed files with 3 additions and 2 deletions
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5
examples/neural_doodle.py
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@ -196,8 +196,8 @@ x = mask_input
for layer in image_model.layers[1:]:
name = 'mask_%s' % layer.name
if 'conv' in layer.name:
x = AveragePooling2D((3, 3), strides=(
1, 1), name=name, border_mode='same')(x)
x = AveragePooling2D((3, 3), padding='same', strides=(
1, 1), name=name)(x)
elif 'pool' in layer.name:
x = AveragePooling2D((2, 2), name=name)(x)
mask_model = Model(mask_input, x)
@ -238,6 +238,7 @@ def region_style_loss(style_image, target_image, style_mask, target_mask):
masked_target = K.permute_dimensions(
target_image, (2, 0, 1)) * target_mask
num_channels = K.shape(style_image)[-1]
num_channels = K.cast(num_channels, dtype='float32')
s = gram_matrix(masked_style) / K.mean(style_mask) / num_channels
c = gram_matrix(masked_target) / K.mean(target_mask) / num_channels
return K.mean(K.square(s - c))