Simple Tensorflow Implementation of Spectral Normalization for Generative Adversarial Networks (ICLR 2018)
> python main.py --dataset mnist --sn True
def spectral_norm(w, iteration=1): w_shape = w.shape.as_list() w = tf.reshape(w, [-1, w_shape[-1]]) u = tf.get_variable("u", [1, w_shape[-1]], initializer=tf.random_normal_initializer(), trainable=False) u_hat = u v_hat = None for i in range(iteration): """ power iteration Usually iteration = 1 will be enough """ v_ = tf.matmul(u_hat, tf.transpose(w)) v_hat = tf.nn.l2_normalize(v_) u_ = tf.matmul(v_hat, w) u_hat = tf.nn.l2_normalize(u_) u_hat = tf.stop_gradient(u_hat) v_hat = tf.stop_gradient(v_hat) sigma = tf.matmul(tf.matmul(v_hat, w), tf.transpose(u_hat)) with tf.control_dependencies([u.assign(u_hat)]): w_norm = w / sigma w_norm = tf.reshape(w_norm, w_shape) return w_norm
w = tf.get_variable("kernel", shape=[kernel, kernel, x.get_shape()[-1], channels]) b = tf.get_variable("bias", [channels], initializer=tf.constant_initializer(0.0)) x = tf.nn.conv2d(input=x, filter=spectral_norm(w), strides=[1, stride, stride, 1]) + b
Junho Kim