TensorFlow（三）可视化

import tensorflow as tfdef add_layer(inputs,in_size,out_size,activation_function=None):    with tf.name_scope('layer'):        with tf.name_scope('Weights'):            Weights=tf.Variable(tf.random_normal([in_size,out_size]),name='w')        with tf.name_scope('biases'):            biases=tf.Variable(tf.zeros([1,out_size])+0.1)        with tf.name_scope('Wx_plus_b'):            Wx_plus_b=tf.matmul(inputs,Weights)+biases        if activation_function is None:           outputs = Wx_plus_b        else:           outputs = activation_function(Wx_plus_b)        return outputswith tf.name_scope('inputs'): xs=tf.placeholder(tf.float32,[None,1],name='x_input') ys=tf.placeholder(tf.float32,[None,1],name='y_input')   l1=add_layer(xs,1,10,activation_function=tf.nn.relu)prediction=add_layer(l1,10,1,activation_function=None)with tf.name_scope('loss'):    loss=tf.reduce_mean(tf.reduce_sum(tf.square(ys-prediction),reduction_indices=[1]))      with tf.name_scope('train'):    train_step=tf.train.GradientDescentOptimizer(0.1).minimize(loss)sess=tf.Session()writer = tf.summary.FileWriter("logs/", sess.graph)sess.run(tf.initialize_all_tables())