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