cifar10读取图片数据用queue队列，训练

input_data.py直接先将图片路径和标签对应为两个列表，然后用Tensorflow的模块生产批次batch

import os
import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as np
 
train_path = ‘D:/python学习/神经网络动物分类/train/’
test_path = ‘D:/python学习/神经网络动物分类/test/’
 
classes = [“airplane”, “automobile”,“bird”,“cat”,“deer”,
           “dog”,“frog”,“horse”,“ship”,“truck”]
 
 
def get_files(file_dir):
    # file_dir: 文件夹路径
    # return: 乱序后的图片和标签
    img_list = []
    label_list = []
    for index, name in enumerate(classes):
        class_path = file_dir + name + “/”
        for img_name in os.listdir(class_path):
            img_path = class_path + img_name
            img_list.append(img_path)
            label_list.append(int(index))
 
    temp = np.array([img_list, label_list])
    temp = temp.transpose()  # 转置
    np.random.shuffle(temp)
    img_list = list(temp[:, 0])
    label_list = list(temp[:, 1])
    label_list = [int(i) for i in label_list]
 
    return img_list, label_list
 
 
def get_batch(image, label, image_W, image_H, batch_size, capacity):
    # image, label: 要生成batch的图像的地址和标签list
    # image_W, image_H: 图片的宽高
    # batch_size: 每个batch有多少张图片
    # capacity: 队列容量
    # return: 图像和标签的batch
 
    # 将python.list类型转换成tf能够识别的格式
    image = tf.cast(image, tf.string)
    label = tf.cast(label, tf.int32)
 
    # 生成队列
    input_queue = tf.train.slice_input_producer([image, label])
 
    image_contents = tf.read_file(input_queue[0])
    label = input_queue[1]
    image = tf.image.decode_jpeg(image_contents, channels=3)
 
    image = tf.image.resize_images(image, [image_H, image_W], method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
    image = tf.cast(image, tf.float32)
 
    image_batch, label_batch = tf.train.batch([image, label],
                                              batch_size=batch_size,
                                              num_threads=64,  # 线程
                                              capacity=capacity)
    return image_batch, label_batch
 
# 测试两个函数是否成功运行
”“”
if __name__ == ‘__main__’:
    BATCH_SIZE = 2
    CAPACITY = 256
    IMG_W = 32
    IMG_H = 32
    image_list, label_list = get_files(train_path)
    image_batch, label_batch = get_batch(image_list, label_list, IMG_W, IMG_H, BATCH_SIZE, CAPACITY)
    with tf.Session() as sess:
        i = 0
        coord = tf.train.Coordinator()
        threads = tf.train.start_queue_runners(coord=coord)
        try:
            while not coord.should_stop() and i < 1:
                img, label = sess.run([image_batch, label_batch])
                for j in np.arange(BATCH_SIZE):
                    print(“label: %d” % label[j])
                    plt.imshow(img[j, :, :, :])
                    plt.show()
                i += 1
        except tf.errors.OutOfRangeError:
            print(“done!”)
        finally:
            coord.request_stop()
        coord.join(threads)
“”“

model.py函数实现了模型以及预测

#coding=utf-8
import tensorflow as tf
 
def inference(images, batch_size, n_classes):
 
    with tf.variable_scope('conv1') as scope:
     # 卷积盒的为 3*3 的卷积盒，图片厚度是3，输出是16个featuremap
        weights = tf.get_variable('weights',
                                  shape=[3, 3, 3, 16],
                                  dtype=tf.float32,
                                  initializer=tf.truncated_normal_initializer(stddev=0.1, dtype=tf.float32))
        biases = tf.get_variable('biases',
                                 shape=[16],
                                 dtype=tf.float32,
                                 initializer=tf.constant_initializer(0.1))
        conv = tf.nn.conv2d(images, weights, strides=[1, 1, 1, 1], padding='SAME')
        pre_activation = tf.nn.bias_add(conv, biases)
        conv1 = tf.nn.relu(pre_activation, name=scope.name)
 
    with tf.variable_scope('pooling1_lrn') as scope:
            pool1 = tf.nn.max_pool(conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME', name='pooling1')
            norm1 = tf.nn.lrn(pool1, depth_radius=4, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name='norm1')
 
    with tf.variable_scope('conv2') as scope:
                weights = tf.get_variable('weights',
                                          shape=[3, 3, 16, 16],
                                          dtype=tf.float32,
                                          initializer=tf.truncated_normal_initializer(stddev=0.1, dtype=tf.float32))
                biases = tf.get_variable('biases',
                                         shape=[16],
                                         dtype=tf.float32,
                                         initializer=tf.constant_initializer(0.1))
                conv = tf.nn.conv2d(norm1, weights, strides=[1, 1, 1, 1], padding='SAME')
                pre_activation = tf.nn.bias_add(conv, biases)
                conv2 = tf.nn.relu(pre_activation, name='conv2')
 
    # pool2 and norm2
    with tf.variable_scope('pooling2_lrn') as scope:
        norm2 = tf.nn.lrn(conv2, depth_radius=4, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name='norm2')
        pool2 = tf.nn.max_pool(norm2, ksize=[1, 3, 3, 1], strides=[1, 1, 1, 1], padding='SAME', name='pooling2')
 
    with tf.variable_scope('local3') as scope:
        reshape = tf.reshape(pool2, shape=[batch_size, -1])
        dim = reshape.get_shape()[1].value
        weights = tf.get_variable('weights',
                                  shape=[dim, 128],
                                  dtype=tf.float32,
                                  initializer=tf.truncated_normal_initializer(stddev=0.005, dtype=tf.float32))
        biases = tf.get_variable('biases',
                                 shape=[128],
                                 dtype=tf.float32,
                                 initializer=tf.constant_initializer(0.1))
    local3 = tf.nn.relu(tf.matmul(reshape, weights) + biases, name=scope.name)
 
    # local4
    with tf.variable_scope('local4') as scope:
        weights = tf.get_variable('weights',
                                  shape=[128, 128],
                                  dtype=tf.float32,
                                  initializer=tf.truncated_normal_initializer(stddev=0.005, dtype=tf.float32))
        biases = tf.get_variable('biases',
                                 shape=[128],
                                 dtype=tf.float32,
                                 initializer=tf.constant_initializer(0.1))
        local4 = tf.nn.relu(tf.matmul(local3, weights) + biases, name='local4')
 
    # softmax
    with tf.variable_scope('softmax_linear') as scope:
        weights = tf.get_variable('softmax_linear',
                                  shape=[128, n_classes],
                                  dtype=tf.float32,
                                  initializer=tf.truncated_normal_initializer(stddev=0.005, dtype=tf.float32))
        biases = tf.get_variable('biases',
                                 shape=[n_classes],
                                 dtype=tf.float32,
                                 initializer=tf.constant_initializer(0.1))
        softmax_linear = tf.add(tf.matmul(local4, weights), biases, name='softmax_linear')
 
    return softmax_linear
 
 
 
def losses(logits, labels):
    with tf.variable_scope('loss') as scope:
        cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits \
                        (logits=logits, labels=labels, name='xentropy_per_example')
        loss = tf.reduce_mean(cross_entropy, name='loss')
        tf.summary.scalar(scope.name + '/loss', loss)
    return loss
 
def trainning(loss, learning_rate):
    with tf.name_scope('optimizer'):
        optimizer = tf.train.AdamOptimizer(learning_rate= learning_rate)
        global_step = tf.Variable(0, name='global_step', trainable=False)
        train_op = optimizer.minimize(loss, global_step= global_step)
    return train_op
 
def evaluation(logits, labels):
    with tf.variable_scope('accuracy') as scope:
        correct = tf.nn.in_top_k(logits, labels, 1)
        correct = tf.cast(correct, tf.float16)
        accuracy = tf.reduce_mean(correct)
        tf.summary.scalar(scope.name + '/accuracy', accuracy)
    return accuracy

train.py函数实现了训练过程

import os
import numpy as np
import tensorflow as tf
import input_data
import model
 
N_CLASSES = 10
IMG_H = 32
IMG_W = 32
BATCH_SIZE = 200
CAPACITY = 2000
MAX_STEP = 15000
learning_rate = 0.0001
 
def run_training():
 
    train_dir = "D:\\python学习\\神经网络动物分类\\train\\"
    logs_train_dir = "logs\\"
    train, train_label = input_data.get_files(train_dir)
    train_batch, train_label_batch = input_data.get_batch(train,
                                                          train_label,
                                                          IMG_W,
                                                          IMG_H,
                                                          BATCH_SIZE,
                                                          CAPACITY)
    train_logits = model.inference(train_batch, BATCH_SIZE, N_CLASSES)
    train_loss = model.losses(train_logits, train_label_batch)
    train_op = model.trainning(train_loss, learning_rate)
    train_acc = model.evaluation(train_logits, train_label_batch)
 
    summary_op = tf.summary.merge_all()
    sess = tf.Session()
    train_writer = tf.summary.FileWriter(logs_train_dir, sess.graph)
    saver = tf.train.Saver()
 
    sess.run(tf.global_variables_initializer())
    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(sess=sess, coord=coord)
 
    try:
        for step in np.arange(MAX_STEP):
            if coord.should_stop():
                break
            _, tra_loss, tra_acc = sess.run([train_op, train_loss, train_acc])
 
            if step % 100 == 0:
                print("Step %d, train loss = %.2f, train accuracy = %.2f%%" % (step, tra_loss, tra_acc))
                summary_str = sess.run(summary_op)
                train_writer.add_summary(summary_str, step)
            if step % 2000 == 0 or (step + 1) == MAX_STEP:
                checkpoint_path = os.path.join(logs_train_dir, "model.ckpt")
                saver.save(sess, checkpoint_path, global_step=step)
    except tf.errors.OutOfRangeError:
        print("Done training -- epoch limit reached.")
    finally:
        coord.request_stop()
 
    coord.join(threads)
    sess.close()
 
if __name__ == '__main__':
    run_training()

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