tensorflow視覺化學習18.11.18
阿新 • • 發佈：2018-12-22
# -*- coding: utf-8 -*- """ Created on Sat Nov 17 21:21:38 2018 @author: asusf """ import tensorflow as tf import numpy as np def add_layer(inputs,in_size,out_size,n_layer,activation_function=None): 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=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 with tf.name_scope('inputs'): xs=tf.placeholder(tf.float32,[None,1]) ys=tf.placeholder(tf.float32,[None,1]) l1=add_layer(xs,1,10,n_layer=1,activation_function=tf.nn.relu) prediction=add_layer(l1,10,1,n_layer=2,activation_function=None) 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) init=tf.global_variables_initializer() with tf.Session() as sess: merged=tf.summary.merge_all() #所有的summary打包合併 writer=tf.summary.FileWriter("logs",sess.graph) 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}) #merged也需要run writer.add_summary(result,i)