目录 1、保存和加载变量 2、建立、训练、保存和加载模型
1、保存和加载变量
(1)保存变量
import tensorflow as tf # The file path to save the data # 文件保存路径 save_file = './model.ckpt' # Two Tensor Variables: weights and bias # 两个 Tensor 变量:权重和偏置项 weights = tf.Variable(tf.truncated_normal([2, 3])) bias = tf.Variable(tf.truncated_normal([3])) # Class used to save and/or restore Tensor Variables # 用来存取 Tensor 变量的类 saver = tf.train.Saver() with tf.Session() as sess: # Initialize all the Variables # 初始化所有变量 sess.run(tf.global_variables_initializer()) # Show the values of weights and bias # 显示变量和权重 print('Weights:') print(sess.run(weights)) print('Bias:') print(sess.run(bias)) # Save the model # 保存模型 saver.save(sess, save_file)
(2)加载变量
# Remove the previous weights and bias # 移除之前的权重和偏置项 tf.reset_default_graph() # Two Variables: weights and bias # 两个变量:权重和偏置项 weights = tf.Variable(tf.truncated_normal([2, 3])) bias = tf.Variable(tf.truncated_normal([3])) # Class used to save and/or restore Tensor Variables # 用来存取 Tensor 变量的类 saver = tf.train.Saver() with tf.Session() as sess: # Load the weights and bias # 加载权重和偏置项 saver.restore(sess, save_file) # Show the values of weights and bias # 显示权重和偏置项 print('Weight:') print(sess.run(weights)) print('Bias:') print(sess.run(bias))
2、建立模型和保存模型
(1)建立模型
# Remove previous Tensors and Operations # 移除之前的 Tensors 和运算 tf.reset_default_graph() from tensorflow.examples.tutorials.mnist import input_data import numpy as np learning_rate = 0.001 n_input = 784 # MNIST 数据输入 (图片尺寸: 28*28) n_classes = 10 # MNIST 总计类别 (数字 0-9) # Import MNIST data # 加载 MNIST 数据 mnist = input_data.read_data_sets('.', one_hot=True) # Features and Labels # 特征和标签 features = tf.placeholder(tf.float32, [None, n_input]) labels = tf.placeholder(tf.float32, [None, n_classes]) # Weights & bias # 权重和偏置项 weights = tf.Variable(tf.random_normal([n_input, n_classes])) bias = tf.Variable(tf.random_normal([n_classes])) # Logits - xW + b logits = tf.add(tf.matmul(features, weights), bias) # Define loss and optimizer # 定义损失函数和优化器 cost = tf.reduce_mean(\ tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=labels)) optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\ .minimize(cost) # Calculate accuracy # 计算准确率 correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(labels, 1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
(2)训练和保存模型
import math save_file = './train_model.ckpt' batch_size = 128 n_epochs = 100 saver = tf.train.Saver() # Launch the graph # 启动图 with tf.Session() as sess: sess.run(tf.global_variables_initializer()) # Training cycle # 训练循环 for epoch in range(n_epochs): total_batch = math.ceil(mnist.train.num_examples / batch_size) # Loop over all batches # 遍历所有 batch for i in range(total_batch): batch_features, batch_labels = mnist.train.next_batch(batch_size) sess.run( optimizer, feed_dict={features: batch_features, labels: batch_labels}) # Print status for every 10 epochs # 每运行10个 epoch 打印一次状态 if epoch % 10 == 0: valid_accuracy = sess.run( accuracy, feed_dict={ features: mnist.validation.images, labels: mnist.validation.labels}) print('Epoch {:<3} - Validation Accuracy: {}'.format( epoch, valid_accuracy)) # Save the model # 保存模型 saver.save(sess, save_file) print('Trained Model Saved.')
(3)加载模型
saver = tf.train.Saver() # Launch the graph # 加载图 with tf.Session() as sess: saver.restore(sess, save_file) test_accuracy = sess.run( accuracy, feed_dict={features: mnist.test.images, labels: mnist.test.labels}) print('Test Accuracy: {}'.format(test_accuracy))
3、命名报错问题
变量保存时,如果没指定name,则系统会根据定义的先后取名。这种情况下,使用的时候,变量顺序要跟保存时保持一致,否则会出现变量命名不匹配的问题。