def dcgan_encoder(name,
inputs,
n_channels,
latent_dim,
is_training,
mode=None,
nonlinearity=LeakyReLU):
conv2d.set_weights_stdev(0.02)
deconv2d.set_weights_stdev(0.02)
linear.set_weights_stdev(0.02)
output = tf.reshape(inputs, [-1, n_channels, DIM, DIM])
output = Conv2D(name + '.1', 3, DIM, 5, output, stride=2)
output = nonlinearity(output)
output = Conv2D(name + '.2', DIM, 2 * DIM, 5, output, stride=2)
output = Normalize(name + '.BN2', [0, 2, 3], output, is_training, mode)
output = nonlinearity(output)
output = Conv2D(name + '.3', 2 * DIM, 4 * DIM, 5, output, stride=2)
output = Normalize(name + '.BN3', [0, 2, 3], output, is_training, mode)
output = nonlinearity(output)
output = Conv2D(name + '.4', 4 * DIM, 8 * DIM, 5, output, stride=2)
output = Normalize(name + '.BN4', [0, 2, 3], output, is_training, mode)
output = nonlinearity(output)
output = tf.reshape(output, [-1, 4 * 4 * 8 * DIM])
output = Linear(name + '.Output', 4 * 4 * 8 * DIM, latent_dim, output)
conv2d.unset_weights_stdev()
deconv2d.unset_weights_stdev()
linear.unset_weights_stdev()
return output
def dcgan_decoder(name, z, n_channels, is_training, mode=None, nonlinearity=tf.nn.relu):
conv2d.set_weights_stdev(0.02)
deconv2d.set_weights_stdev(0.02)
linear.set_weights_stdev(0.02)
output = Linear(name + '.Input', z.get_shape().as_list()[1], 4*4*8*DIM, z)
output = tf.reshape(output, [-1, 8*DIM, 4, 4])
output = Normalize(name + '.BN1', [0,2,3], output, is_training, mode)
output = nonlinearity(output)
output = Deconv2D(name +'.2', 8*DIM, 4*DIM, 5, output)
output = Normalize(name + '.BN2', [0,2,3], output, is_training, mode)
output = nonlinearity(output)
output = Deconv2D(name +'.3', 4*DIM, 2*DIM, 5, output)
output = Normalize(name + '.BN3', [0,2,3], output, is_training, mode)
output = nonlinearity(output)
output = Deconv2D(name +'.4', 2*DIM, DIM, 5, output)
output = Normalize(name + '.BN4', [0,2,3], output, is_training, mode)
output = nonlinearity(output)
output = Deconv2D(name +'.5', DIM, n_channels, 5, output)
output = tf.reshape(output, [-1, n_channels*DIM*DIM])
conv2d.unset_weights_stdev()
deconv2d.unset_weights_stdev()
linear.unset_weights_stdev()
return output
