tensorflow实现BP模型，保存与重新恢复操作

痛定思痛。 2022-03-06 07:20 300阅读 0赞

前言：这种方法保存和重新恢复模型参数，是必须要重新定义BP模型的。

还有一种不需要重新定义模型结果的方法，请先参考其他文章，我做了后再重新贴上了。

BP模型的建立和训练：

from __future__ import print_function
    import tensorflow as tf
    import numpy as np
    import matplotlib.pyplot as plt
    
    # Parameters
    learning_rate = 0.01
    num_steps = 1000
    batch_size = 100
    display_step = 100
    
    # Network Parameters建立的是五层，可定义节点数的BP神经网络，层数可以自行修改
    
    n_hidden_1 = 10  # 1st layer number of neurons
    n_hidden_2 = 10  # 2nd layer number of neurons
    n_hidden_3 = 10  # 3rd layer number of neurons
    n_hidden_4 = 10  # 4th layer number of neurons
    n_hidden_5 = 10  # 5th layer number of neurons
    num_input = 2  # data input (img shape: 2)
    num_classes = 2  # total classes (2 digits)
    
    # tf Graph input
    X = tf.placeholder("float", [None, num_input])
    Y = tf.placeholder("float", [None, num_classes])
    
    # Store layers weight & bias
    with tf.name_scope('parameters'):
        weights = {
            'h1': tf.Variable(tf.random_normal([num_input, n_hidden_1])),
            'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),
            'h3': tf.Variable(tf.random_normal([n_hidden_2, n_hidden_3])),
            'h4': tf.Variable(tf.random_normal([n_hidden_3, n_hidden_4])),
            'h5': tf.Variable(tf.random_normal([n_hidden_4, n_hidden_5])),
            'out': tf.Variable(tf.random_normal([n_hidden_5, num_classes]))
        }
        biases = {
            'b1': tf.Variable(tf.random_normal([n_hidden_1])),
            'b2': tf.Variable(tf.random_normal([n_hidden_2])),
            'b3': tf.Variable(tf.random_normal([n_hidden_3])),
            'b4': tf.Variable(tf.random_normal([n_hidden_4])),
            'b5': tf.Variable(tf.random_normal([n_hidden_5])),
            'out': tf.Variable(tf.random_normal([num_classes]))
        }
    
    
    # Create model
    def neural_net(x):
        layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['h1']), biases['b1']))
    
        layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['h2']), biases['b2']))
    
        layer_3 = tf.nn.sigmoid(tf.add(tf.matmul(layer_2, weights['h3']), biases['b3']))
    
        layer_4 = tf.nn.sigmoid(tf.add(tf.matmul(layer_3, weights['h4']), biases['b4']))
    
        layer_5 = tf.nn.sigmoid(tf.add(tf.matmul(layer_4, weights['h5']), biases['b5']))
    
        out_layer = tf.matmul(layer_5, weights['out']) + biases['out']
        return out_layer
    
    
    def classifier(x, y):
        #    input the expression you want
        if x ** 2 + y ** 2 < 0.1 or 5 * (x - 1.1) ** 4 < y:#两个**代表乘方y^2
            return True
        return False
    
    
    # Construct model
    logits = neural_net(X)
    prediction = tf.nn.sigmoid(logits)
    # Define loss and optimizer
    loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=Y))
    optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
    train_op = optimizer.minimize(loss_op)
    
    # Evaluate model
    correct_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
    
    # Initialize the variables (i.e. assign their default value)
    init = tf.global_variables_initializer()#初始化参数
    
    #定义保存模型参数句柄
    saver_path = './model/BP_model.ckpt'
    saver = tf.train.Saver()
    
    loss_data = []
    acc_data = []
    x_data = range(1, num_steps + 1)
    
    # Start training
    with tf.Session() as sess:
        # Run the initializer
        sess.run(init)
    
        for step in range(1, num_steps + 1):
            batch_x = np.random.random((batch_size, 2))
            batch_y = np.zeros((batch_size, 2))
            for i in range(batch_size):
                if classifier(batch_x[i, 0], batch_x[i, 1]):
                    batch_y[i, 0] = 1
                else:
                    batch_y[i, 1] = 1
            learning_rate = np.exp(-0.1 * step) * 0.1
    
            # Run optimization op (backprop)
            train, loss, acc = sess.run([train_op, loss_op, accuracy], feed_dict={X: batch_x, Y: batch_y})
            loss_data.append(loss)
            acc_data.append(acc)
    
        print("Optimization Finished!")
    
        #保存模型参数
        saved_path = saver.save(sess, saver_path)  # 这个保存了三个东西， .meta是图的结构， 还有两个是模型中变量的值
    
        plt.plot(x_data, loss_data)
        plt.show()
        plt.plot(x_data, acc_data)
        plt.show()      #凡是使用matplot的需要使用show函数，才能出现图
    
        xx = np.linspace(0, 1, 100)
        yy = np.linspace(0, 1, 100)
        ZZ = np.zeros((100, 100))
        XX, YY = np.meshgrid(xx, yy)
        rightsum = 0
        for i in range(100):
            for j in range(100):
                inp = np.zeros((1, 2))
                inp[0, 0] = XX[i, j]
                inp[0, 1] = YY[i, j]
                poss = sess.run(prediction, feed_dict={X: inp})
                if poss[0, 0] > poss[0, 1]:
                    ZZ[i, j] = 1
                    if classifier(inp[0, 0], inp[0, 1]):
                        rightsum += 1
                else:
                    ZZ[i, j] = 0
                    if classifier(inp[0, 0], inp[0, 1]) == False:
                        rightsum += 1
        print('Testing Accuracy', rightsum / 100, '%')
        plt.contourf(XX, YY, ZZ, 2, colors=('r', 'w', 'b'))

BP模型的恢复和使用：

#！/usr/bin/env python
    # _*_ coding:utf-8 _*_
    
    from __future__ import print_function
    import tensorflow as tf
    import numpy as np
    import matplotlib.pyplot as plt
    
    # Parameters
    learning_rate = 0.01
    num_steps = 1000
    batch_size = 100
    display_step = 100
    
    # Network Parameters建立的是五层，可定义节点数的BP神经网络，层数可以自行修改
    
    n_hidden_1 = 10  # 1st layer number of neurons
    n_hidden_2 = 10  # 2nd layer number of neurons
    n_hidden_3 = 10  # 3rd layer number of neurons
    n_hidden_4 = 10  # 4th layer number of neurons
    n_hidden_5 = 10  # 5th layer number of neurons
    num_input = 2  # data input (img shape: 2)
    num_classes = 2  # total classes (2 digits)
    
    # tf Graph input
    X = tf.placeholder("float", [None, num_input])
    Y = tf.placeholder("float", [None, num_classes])
    
    # Store layers weight & bias
    with tf.name_scope('parameters'):
        weights = {
            'h1': tf.Variable(tf.random_normal([num_input, n_hidden_1])),
            'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),
            'h3': tf.Variable(tf.random_normal([n_hidden_2, n_hidden_3])),
            'h4': tf.Variable(tf.random_normal([n_hidden_3, n_hidden_4])),
            'h5': tf.Variable(tf.random_normal([n_hidden_4, n_hidden_5])),
            'out': tf.Variable(tf.random_normal([n_hidden_5, num_classes]))
        }
        biases = {
            'b1': tf.Variable(tf.random_normal([n_hidden_1])),
            'b2': tf.Variable(tf.random_normal([n_hidden_2])),
            'b3': tf.Variable(tf.random_normal([n_hidden_3])),
            'b4': tf.Variable(tf.random_normal([n_hidden_4])),
            'b5': tf.Variable(tf.random_normal([n_hidden_5])),
            'out': tf.Variable(tf.random_normal([num_classes]))
        }
    
    
    # Create model
    def neural_net(x):
        layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['h1']), biases['b1']))
    
        layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['h2']), biases['b2']))
    
        layer_3 = tf.nn.sigmoid(tf.add(tf.matmul(layer_2, weights['h3']), biases['b3']))
    
        layer_4 = tf.nn.sigmoid(tf.add(tf.matmul(layer_3, weights['h4']), biases['b4']))
    
        layer_5 = tf.nn.sigmoid(tf.add(tf.matmul(layer_4, weights['h5']), biases['b5']))
    
        out_layer = tf.matmul(layer_5, weights['out']) + biases['out']
        return out_layer
    
    # Construct model
    logits = neural_net(X)
    prediction = tf.nn.sigmoid(logits)
    # Define loss and optimizer
    loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=Y))
    optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
    train_op = optimizer.minimize(loss_op)
    
    # Evaluate model
    correct_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
    
    # Initialize the variables (i.e. assign their default value)
    init = tf.global_variables_initializer()#初始化参数
    
    meta_path = './model/BP_model.ckpt.meta'
    model_path = './model/BP_model.ckpt'
    # saver = tf.train.import_meta_graph(meta_path) # 导入图
    saver = tf.train.Saver()
    
    with tf.Session() as sess:
        saver.restore(sess, model_path)  # 导入变量值
        # graph = tf.get_default_graph()
        # sess.run(init)
        t1 = np.zeros((1, 2))
        t1[0, 0] = 1.0
        t1[0, 1] = 2.0
        print(sess.run(prediction, feed_dict={X: t1}))