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Hi everyone. I'm a green hand in GAN.
Seeing the following code:
`def discriminator_network(input_image_tensor):
"""
The discriminator network, Dφ, contains 5 convolution layers and 2 max-pooling layers.
:param input_image_tensor: Input tensor corresponding to an image, either real or refined.
:return: Output tensor that corresponds to the probability of whether an image is real or refined.
"""
x = layers.Convolution2D(96, 3, 3, border_mode='same', subsample=(2, 2), activation='relu')(input_image_tensor)
x = layers.Convolution2D(64, 3, 3, border_mode='same', subsample=(2, 2), activation='relu')(x)
x = layers.MaxPooling2D(pool_size=(3, 3), border_mode='same', strides=(1, 1))(x)
x = layers.Convolution2D(32, 3, 3, border_mode='same', subsample=(1, 1), activation='relu')(x)
x = layers.Convolution2D(32, 1, 1, border_mode='same', subsample=(1, 1), activation='relu')(x)
x = layers.Convolution2D(2, 1, 1, border_mode='same', subsample=(1, 1), activation='relu')(x)
# here one feature map corresponds to `is_real` and the other to `is_refined`,
# and the custom loss function is then `tf.nn.sparse_softmax_cross_entropy_with_logits`
return layers.Reshape((-1, 2))(x)`
How to understand ' here one feature map corresponds to is_real and the other to is_refined'? Usually, the discriminator output only one feature map indicating the confidence probability of true or false.
The text was updated successfully, but these errors were encountered:
Hi everyone. I'm a green hand in GAN.
Seeing the following code:
`def discriminator_network(input_image_tensor):
"""
The discriminator network, Dφ, contains 5 convolution layers and 2 max-pooling layers.
How to understand ' here one feature map corresponds to
is_real
and the other tois_refined
'?Usually, the discriminator output only one feature map indicating the confidence probability of true or false.
The text was updated successfully, but these errors were encountered: