dg_mnist.py 文件源码

def discriminator(image, Reuse=False):
    with tf.variable_scope('disc', reuse=Reuse):
        image = tf.reshape(image, [-1, 28, 28, 1])
        h0 = lrelu(conv(image, 5, 5, 1, df_dim, stridex=2, stridey=2, name='d_h0_conv'))
        h1 = lrelu( batch_norm(conv(h0, 5, 5, df_dim,df_dim*2,stridex=2,stridey=2,name='d_h1_conv'), decay=0.9, scale=True, updates_collections=None, is_training=phase_train, reuse=Reuse, scope='d_bn1'))
        h2 = lrelu(batch_norm(conv(h1, 3, 3, df_dim*2, df_dim*4, stridex=2, stridey=2,name='d_h2_conv'), decay=0.9,scale=True, updates_collections=None, is_training=phase_train, reuse=Reuse, scope='d_bn2'))
        h3 = tf.nn.max_pool(h2, ksize=[1,4,4,1], strides=[1,1,1,1],padding='VALID')
        h6 = tf.reshape(h2,[-1, 4*4*df_dim*4])
        h7 = Minibatch_Discriminator(h3, num_kernels=df_dim*4, name = 'd_MD')
        h8 = dense(tf.reshape(h7, [batchsize, -1]), df_dim*4*2, 1, scope='d_h8_lin')
        return tf.nn.sigmoid(h8), h8