一个Tensorflow搭建的网络可视化的例子（有代码）

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from __future__ import print_function 

import tensorflow as tf 

import numpy as np 



def add_layer(inputs, in_size, out_size, n_layer, activation_function=None): 

# add one more layer and return the output of this layer 

    layer_name = 'layer%s' % n_layer 

    with tf.name_scope(layer_name): 

        with tf.name_scope('weights'): 

            Weights = tf.Variable(tf.random_normal([in_size, out_size]), name='W') 

            tf.summary.histogram(layer_name + '/weights', Weights) 

        with tf.name_scope('biases'): 

            biases = tf.Variable(tf.zeros([1, out_size]) + 0.1, name='b') 

            tf.summary.histogram(layer_name + '/biases', biases) 

        with tf.name_scope('Wx_plus_b'): 

            Wx_plus_b = tf.add(tf.matmul(inputs, Weights), biases) 

        if activation_function is None: 

            outputs = Wx_plus_b 

        else: 

            outputs = activation_function(Wx_plus_b, ) 

        tf.summary.histogram(layer_name + '/outputs', outputs) 

    return outputs 











# Make up some real data 

x_data = np.linspace(-1, 1, 300)[:, np.newaxis] 

noise = np.random.normal(0, 0.05, x_data.shape) 

y_data = np.square(x_data) - 0.5 + noise 







# define placeholder for inputs to network 

with tf.name_scope('inputs'): 

    xs = tf.placeholder(tf.float32, [None, 1], name='x_input') 

    ys = tf.placeholder(tf.float32, [None, 1], name='y_input') 









# add hidden layer 

l1 = add_layer(xs, 1, 10, n_layer=1, activation_function=tf.nn.relu) 







# add output layer 

prediction = add_layer(l1, 10, 1, n_layer=2, activation_function=None) 





# the error between prediciton and real data 

with tf.name_scope('loss'): 

    loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction), reduction_indices=[1])) 

    tf.summary.scalar('loss', loss) 





with tf.name_scope('train'): 

    train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss) 



sess = tf.Session() 

merged = tf.summary.merge_all() 

writer = tf.summary.FileWriter("logs/", sess.graph) 

init = tf.global_variables_initializer() 

sess.run(init) 



for i in range(1000): 

    sess.run(train_step, feed_dict={xs: x_data, ys: y_data}) 

    if i % 50 == 0: 

        result = sess.run(merged, feed_dict={xs: x_data, ys: y_data}) 

        writer.add_summary(result, i)