tensorflow13《TensorFlow實戰Google深度學習架構》筆記-06-02mnist LeNet5卷積神經網路 code

01 LeNet5卷積神經網路前向傳播

# 《TensorFlow實戰Google深度學習架構》06 Image Recognition與卷積神經網路# win10 Tensorflow1.0.1 python3.5.3# CUDA v8.0 cudnn-8.0-windows10-x64-v5.1# filename:LeNet5_infernece.py # LeNet5前向傳播import tensorflow as tf# 1. 設定神經網路的參數INPUT_NODE = 784OUTPUT_NODE = 10IMAGE_SIZE = 28NUM_CHANNELS = 1NUM_LABELS = 10CONV1_DEEP = 32CONV1_SIZE = 5CONV2_DEEP = 64CONV2_SIZE = 5FC_SIZE = 512# 2. 定義前向傳播的過程def inference(input_tensor, train, regularizer):    with tf.variable_scope('layer1-conv1'):        conv1_weights = tf.get_variable(            "weight", [CONV1_SIZE, CONV1_SIZE, NUM_CHANNELS, CONV1_DEEP],            initializer=tf.truncated_normal_initializer(stddev=0.1))        conv1_biases = tf.get_variable("bias", [CONV1_DEEP], initializer=tf.constant_initializer(0.0))        conv1 = tf.nn.conv2d(input_tensor, conv1_weights, strides=[1, 1, 1, 1], padding='SAME')        relu1 = tf.nn.relu(tf.nn.bias_add(conv1, conv1_biases))    with tf.name_scope("layer2-pool1"):        pool1 = tf.nn.max_pool(relu1, ksize = [1,2,2,1],strides=[1,2,2,1],padding="SAME")    with tf.variable_scope("layer3-conv2"):        conv2_weights = tf.get_variable(            "weight", [CONV2_SIZE, CONV2_SIZE, CONV1_DEEP, CONV2_DEEP],            initializer=tf.truncated_normal_initializer(stddev=0.1))        conv2_biases = tf.get_variable("bias", [CONV2_DEEP], initializer=tf.constant_initializer(0.0))        conv2 = tf.nn.conv2d(pool1, conv2_weights, strides=[1, 1, 1, 1], padding='SAME')        relu2 = tf.nn.relu(tf.nn.bias_add(conv2, conv2_biases))    with tf.name_scope("layer4-pool2"):        pool2 = tf.nn.max_pool(relu2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')        pool_shape = pool2.get_shape().as_list()        nodes = pool_shape[1] * pool_shape[2] * pool_shape[3]        reshaped = tf.reshape(pool2, [pool_shape[0], nodes])    with tf.variable_scope('layer5-fc1'):        fc1_weights = tf.get_variable("weight", [nodes, FC_SIZE],                                      initializer=tf.truncated_normal_initializer(stddev=0.1))        if regularizer != None: tf.add_to_collection('losses', regularizer(fc1_weights))        fc1_biases = tf.get_variable("bias", [FC_SIZE], initializer=tf.constant_initializer(0.1))        fc1 = tf.nn.relu(tf.matmul(reshaped, fc1_weights) + fc1_biases)        if train: fc1 = tf.nn.dropout(fc1, 0.5)    with tf.variable_scope('layer6-fc2'):        fc2_weights = tf.get_variable("weight", [FC_SIZE, NUM_LABELS],                                      initializer=tf.truncated_normal_initializer(stddev=0.1))        if regularizer != None: tf.add_to_collection('losses', regularizer(fc2_weights))        fc2_biases = tf.get_variable("bias", [NUM_LABELS], initializer=tf.constant_initializer(0.1))        logit = tf.matmul(fc1, fc2_weights) + fc2_biases    return logit

02 LeNet5 卷積神經網路訓練

# 《TensorFlow實戰Google深度學習架構》06 Image Recognition與卷積神經網路# win10 Tensorflow1.0.1 python3.5.3# CUDA v8.0 cudnn-8.0-windows10-x64-v5.1# filename:LeNet5_train.py # LeNet5訓練import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_dataimport LeNet5_inferneceimport osimport numpy as np# 1. 定義神經網路相關的參數BATCH_SIZE = 100LEARNING_RATE_BASE = 0.01LEARNING_RATE_DECAY = 0.99REGULARIZATION_RATE = 0.0001TRAINING_STEPS = 55000MOVING_AVERAGE_DECAY = 0.99MODEL_SAVE_PATH = "LeNet5_model/" # 在目前的目錄下存在LeNet5_model子檔案夾MODEL_NAME = "LeNet5_model"# 2. 定義訓練過程def train(mnist):    # 定義輸出為4維矩陣的placeholder    x = tf.placeholder(tf.float32, [        BATCH_SIZE,        LeNet5_infernece.IMAGE_SIZE,        LeNet5_infernece.IMAGE_SIZE,        LeNet5_infernece.NUM_CHANNELS],                       name='x-input')    y_ = tf.placeholder(tf.float32, [None, LeNet5_infernece.OUTPUT_NODE], name='y-input')    regularizer = tf.contrib.layers.l2_regularizer(REGULARIZATION_RATE)    y = LeNet5_infernece.inference(x, True, regularizer)    global_step = tf.Variable(0, trainable=False)    # 定義損失函數、學習率、滑動平均操作以及訓練過程。    variable_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY, global_step)    variables_averages_op = variable_averages.apply(tf.trainable_variables())    cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1))    cross_entropy_mean = tf.reduce_mean(cross_entropy)    loss = cross_entropy_mean + tf.add_n(tf.get_collection('losses'))    learning_rate = tf.train.exponential_decay(        LEARNING_RATE_BASE,        global_step,        mnist.train.num_examples / BATCH_SIZE, LEARNING_RATE_DECAY,        staircase=True)    train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_step)    with tf.control_dependencies([train_step, variables_averages_op]):        train_op = tf.no_op(name='train')    # 初始化TensorFlow持久化類。    saver = tf.train.Saver()    with tf.Session() as sess:        tf.global_variables_initializer().run()        for i in range(TRAINING_STEPS):            xs, ys = mnist.train.next_batch(BATCH_SIZE)            reshaped_xs = np.reshape(xs, (                BATCH_SIZE,                LeNet5_infernece.IMAGE_SIZE,                LeNet5_infernece.IMAGE_SIZE,                LeNet5_infernece.NUM_CHANNELS))            _, loss_value, step = sess.run([train_op, loss, global_step], feed_dict={x: reshaped_xs, y_: ys})            if i % 1000 == 0:                print("After %d training step(s), loss on training batch is %g." % (step, loss_value))                saver.save(sess, os.path.join(MODEL_SAVE_PATH, MODEL_NAME), global_step=global_step)# 3. 主程式入口def main(argv=None):    mnist = input_data.read_data_sets("../../../datasets/MNIST_data", one_hot=True)    train(mnist)if __name__ == '__main__':    main()'''...After 49001 training step(s), loss on training batch is 0.589334.After 50001 training step(s), loss on training batch is 0.601423.After 51001 training step(s), loss on training batch is 0.639142.After 52001 training step(s), loss on training batch is 0.610477.After 53001 training step(s), loss on training batch is 0.58531.After 54001 training step(s), loss on training batch is 0.626083.'''

03 LeNet5 卷積神經網路測試

# 《TensorFlow實戰Google深度學習架構》06 Image Recognition與卷積神經網路# win10 Tensorflow1.0.1 python3.5.3# CUDA v8.0 cudnn-8.0-windows10-x64-v5.1# filename:LeNet5_eval.py # 測試import timeimport mathimport tensorflow as tfimport numpy as npfrom tensorflow.examples.tutorials.mnist import input_dataimport LeNet5_inferneceimport LeNet5_traindef evaluate(mnist):    with tf.Graph().as_default() as g:        # 定義輸出為4維矩陣的placeholder        x = tf.placeholder(tf.float32, [            mnist.test.num_examples,            #LeNet5_train.BATCH_SIZE,            LeNet5_infernece.IMAGE_SIZE,            LeNet5_infernece.IMAGE_SIZE,            LeNet5_infernece.NUM_CHANNELS],                           name='x-input')        y_ = tf.placeholder(tf.float32, [None, LeNet5_infernece.OUTPUT_NODE], name='y-input')        validate_feed = {x: mnist.test.images, y_: mnist.test.labels}        global_step = tf.Variable(0, trainable=False)        regularizer = tf.contrib.layers.l2_regularizer(LeNet5_train.REGULARIZATION_RATE)        y = LeNet5_infernece.inference(x, False, regularizer)        correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))        accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))        variable_averages = tf.train.ExponentialMovingAverage(LeNet5_train.MOVING_AVERAGE_DECAY)        variables_to_restore = variable_averages.variables_to_restore()        saver = tf.train.Saver(variables_to_restore)        #n = math.ceil(mnist.test.num_examples / LeNet5_train.BATCH_SIZE)        n = math.ceil(mnist.test.num_examples / mnist.test.num_examples)        for i in range(n):            with tf.Session() as sess:                ckpt = tf.train.get_checkpoint_state(LeNet5_train.MODEL_SAVE_PATH)                if ckpt and ckpt.model_checkpoint_path:                    saver.restore(sess, ckpt.model_checkpoint_path)                    global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]                    xs, ys = mnist.test.next_batch(mnist.test.num_examples)                    #xs, ys = mnist.test.next_batch(LeNet5_train.BATCH_SIZE)                    reshaped_xs = np.reshape(xs, (                        mnist.test.num_examples,                        #LeNet5_train.BATCH_SIZE,                        LeNet5_infernece.IMAGE_SIZE,                        LeNet5_infernece.IMAGE_SIZE,                        LeNet5_infernece.NUM_CHANNELS))                    accuracy_score = sess.run(accuracy, feed_dict={x:reshaped_xs, y_:ys})                    print("After %s training step(s), test accuracy = %g" % (global_step, accuracy_score))                else:                    print('No checkpoint file found')                    return# 主程式def main(argv=None):    mnist = input_data.read_data_sets("../../../datasets/MNIST_data", one_hot=True)    evaluate(mnist)if __name__ == '__main__':    main()'''After 54001 training step(s), test accuracy = 0.9915'''