x = Convolution2D(internal, (3, 3), padding="same", use_bias=True)(x)
else:
b, w, h, nb_filters = encoder.get_shape().as_list()
in_shape = x.get_shape().as_list()
// x = Deconvolution2D(internal, 3, 3, output_shape=(None, w * 2, h * 2, internal), border_mode="same",
// subsample=(2, 2), input_shape=in_shape)(x)
x = Deconvolution2D(internal, (3, 3), padding="same", strides=(2, 2), input_shape=in_shape)(x)
x = BatchNormalization(momentum=0.1)(x)
After Change
if not upsample:
x = Convolution2D(internal, (3, 3), padding="same", use_bias=True)(x)
else:
in_shape = K.int_shape(x)
x = Deconvolution2D(internal, (3, 3), padding="same", strides=(2, 2), input_shape=in_shape)(x)
x = BatchNormalization(momentum=0.1)(x)
x = Activation("relu")(x)
