一、構建計算圖

準備訓練數據

定義前向計算過程 Inference

定義loss(loss，accuracy等scalar用tensorboard展示)

定義訓練方法

變量初始化

保存計算圖

二、創建會話

summary對象處理

喂入數據，得到觀測的loss，accuracy等

用測試數據測試模型

import tensorflow as tf import numpy as np import os from tensorflow.examples.tutorials.mnist import input_data os.environ['TF_CPP_MIN_LOG_LEVEl']='3' # tf.reset_default_graph() mnist = input_data.read_data_sets('D:\MyData\zengxf\.keras\datasets\MNIST_data',one_hot=True) xq,yq = mnist.train.next_batch(2)# (2, 784),(2,10)# Inputh1 = 100 h2 = 10with tf.name_scope("Input"):X = tf.placeholder("float",[None,784],name='X')Y_true = tf.placeholder("float",[None,10],name='Y_true') with tf.name_scope("Inference"):with tf.name_scope("hidden1"):W1 = tf.Variable(tf.random_normal([784, h1])*0.1, name='W1')# W1 = tf.Variable(tf.zeros([784, h1]), name='W1')b1 = tf.Variable(tf.zeros([h1]), name='b1')y_1 = tf.nn.sigmoid(tf.matmul(X, W1)+b1)# (None,h1)with tf.name_scope("hidden2"):W2 = tf.Variable(tf.random_normal([h1, h2])*0.1, name='W2')# W2 = tf.Variable(tf.zeros([h1, h2]), name='W2')b2 = tf.Variable(tf.zeros([h2]), name='b2')y_2 = tf.nn.sigmoid(tf.matmul(y_1, W2)+b2)# (h1,h2)with tf.name_scope("Output"):W3 = tf.Variable(tf.truncated_normal([h2, 10])*0.1, name='W3')# W3 = tf.Variable(tf.zeros([h2, 10]), name='W3')b3 = tf.Variable(tf.zeros([10]), name='b3')y = tf.nn.softmax(tf.matmul(y_2, W3)+ b3)# (None,10) with tf.name_scope("Loss"):loss = tf.reduce_mean(-tf.reduce_sum(tf.multiply(Y_true,tf.log(y))))loss_scalar = tf.summary.scalar('loss',loss)accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(y,1),tf.argmax(Y_true,1)),tf.float32))accuracy_scalar = tf.summary.scalar('accuracy', accuracy)# loss = tf.reduce_mean(-tf.reduce_sum(Y_true * tf.log(y)))# l = tf.multiply(Y_true,tf.log(y))with tf.name_scope("Trian"):# optimizer = tf.train.AdamOptimizer(learning_rate=0.05)optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01)train_op = optimizer.minimize(loss)init = tf.global_variables_initializer() merge_summary_op = tf.summary.merge_all() # tf.merge_all_summaries() writer = tf.summary.FileWriter('logs', tf.get_default_graph()) sess = tf.Session() sess.run(init)# for step in range(5000):# train_summary = sess.run(merge_summary_op,feed_dict = {...})#調用sess.run運行圖，生成一步的訓練過程數據train_x,train_y = mnist.train.next_batch(500)_,summary_op,train_loss,acc= sess.run([train_op,merge_summary_op,loss,accuracy],feed_dict={X:train_x,Y_true:train_y})if step%100==99:print('loss=',train_loss)# print(sess.run(y,feed_dict={X:train_x}))# summary_op = sess.run(merge_summary_op)writer.add_summary(summary_op,step) #測試集上預測 print(sess.run(accuracy, feed_dict={X: mnist.test.images, Y_true: mnist.test.labels})) # 0.9185 writer.close() # #正確的預測結果 # correct_prediction = tf.equal(tf.argmax(Y_true, 1), tf.argmax(y, 1)) # # 計算預測準確率，它們都是Tensor # accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) # # 在Session中運行Tensor可以得到Tensor的值 # # 這里是獲取最終模型的正確率

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