dg_mnist.py 文件源码

def Minibatch_Discriminator(input, num_kernels=100, dim_per_kernel=5, init=False, name='MD'):
    num_inputs=df_dim*4
    theta = tf.get_variable(name+"/theta",[num_inputs, num_kernels, dim_per_kernel], initializer=tf.random_normal_initializer(stddev=0.05))
    log_weight_scale = tf.get_variable(name+"/lws",[num_kernels, dim_per_kernel], initializer=tf.constant_initializer(0.0))
    W = tf.mul(theta, tf.expand_dims(tf.exp(log_weight_scale)/tf.sqrt(tf.reduce_sum(tf.square(theta),0)),0))
    W = tf.reshape(W,[-1,num_kernels*dim_per_kernel])
    x = input
    x=tf.reshape(x, [batchsize,num_inputs])
    activation = tf.matmul(x, W)
    activation = tf.reshape(activation,[-1,num_kernels,dim_per_kernel])
    abs_dif = tf.mul(tf.reduce_sum(tf.abs(tf.sub(tf.expand_dims(activation,3),tf.expand_dims(tf.transpose(activation,[1,2,0]),0))),2),
                                                1-tf.expand_dims(tf.constant(np.eye(batchsize),dtype=np.float32),1))
    f = tf.reduce_sum(tf.exp(-abs_dif),2)/tf.reduce_sum(tf.exp(-abs_dif))
    print(f.get_shape())
    print(input.get_shape())
    return tf.concat(1,[x, f])