跑AlexNet猫狗遇到IndexError: too many indices for array的问题

小弟最近在看OpenCV+TensorFlow这本书的案例
照着输进去了结果发现运行不下去,
问题应该是出现在第二块内容,但是真的不太明白!求各位大神赐教,如何修改!
图片说明

第一块,修改照片尺寸,为啥呀

import cv2
import os

def resize(dir):
    for root, dirs, files in os.walk(dir):
        for file in files:
            filepath = os.path.join(root, file)
            try:
                image = cv2.imread(filepath)
                dim = (227, 227)
                resized = cv2.resize(image, dim)
                path = "C:\\Users\\Telon_Hu\\Desktop\\ANNs\\train1\\" + file
                cv2.imwrite(path, resized)
            except:
                print(filepath)
                # os.remove(filepath)
            cv2.waitKey(0)

resize('C:\\Users\\Telon_Hu\\Desktop\\ANNs\\train')
import os
import numpy as np
import tensorflow as tf
import cv2


def get_file(file_dir):
    images=[]
    temp=[]
    for root,sub_folders,files in os.walk(file_dir):
        '''
        os.walk(path)---返回的是一个三元组(root,dirs,files):
         root 所指的是当前正在遍历的这个文件夹的本身的地址
         dirs 是一个 list ,内容是该文件夹中所有的目录的名字(不包括子目录)
         files 同样是 list , 内容是该文件夹中所有的文件(不包括子目录)
         '''
        for name in files:
            images.append(os.path.join(root,name))
        for name in sub_folders:
            temp.append(os.path.join(root,name))
    labels=[]
    for one_folder in temp:
        n_img=len(os.listdir(one_folder)) #s.listdir() 方法用于返回指定的文件夹包含的文件或文件夹的名字的列表
        letter=one_folder.split('\\')[-1] #split() 通过指定分隔符对字符串进行切片,默认为-1, 即分隔所有。
        if letter=='cat':
            labels=np.append(labels,n_img*[0])
        else:
            labels=np.append(labels,n_img*[1])


    temp=np.array([images, labels])
    temp=temp.transpose() #矩阵转置
    np.random.shuffle(temp) #随机排序

    image_list=list(temp[:, 0])
    label_list=list(temp[:, 1])
    label_list=[int(float(i)) for i in label_list]
    return image_list,label_list



def get_batch(image_list,label_list,img_width,img_height,batch_size,capacity):
    image=tf.cast(image_list,tf.string)
    label=tf.cast(label_list,tf.int32)

    input_queue=tf.train.slice_input_producer([image,label])

    label=input_queue[1]
    image_contents=tf.read_file(input_queue[0]) #通过图片地址读取图片
    image=tf.image.decode_jpeg(image_contents,channels=3) #解码图片成矩阵

    image=tf.image.resize_image_with_crop_or_pad(image,img_width,img_height)
    '''
    tf.image.resize_images 不能保证图像的纵横比,这样用来做抓取位姿的识别,可能受到影响
    tf.image.resize_image_with_crop_or_pad可让纵横比不变
    '''
    image=tf.image.per_image_standardization(image) #将图片标准化
    image_batch,label_batch=tf.train.batch([image,label],batch_size=batch_size,num_threads=64,capacity=capacity)
    '''
    tf.train.batch([example, label], batch_size=batch_size, capacity=capacity):
    1.[example, label]表示样本和样本标签,这个可以是一个样本和一个样本标签
    2.batch_size是返回的一个batch样本集的样本个数
    3.num_threads是线程
    4.capacity是队列中的容量。
    '''
    label_batch=tf.reshape(label_batch,[batch_size])

    return image_batch,label_batch

def one_hot(labels):
    '''one-hot 编码'''
    n_sample=len(labels)
    n_class=max(labels)+1
    onehot_labels=np.zeros((n_sample,n_class))
    onehot_labels[np.arange(n_sample),labels]=1
    return onehot_labels

get_file('C:\\Users\\Telon_Hu\\Desktop\\ANNs\\dogs_vs_cats\\')
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import time
import os
import sys
import creat_and_read_TFReacord as reader

x_train,y_train=reader.get_file('dogs_vs_cats')

image_batch,label_batch=reader.get_batch(x_train,y_train,227,227,50,2048)

#Batch_Normalization正则化
def batch_norm(inputs,is_train,is_conv_out=True,decay=0.999):
    scale=tf.Variable(tf.ones([inputs.get_shape()[-1]]))
    beta = tf.Variable(tf.zeros([inputs.get_shape()[-1]]))
    pop_mean = tf.Variable(tf.zeros([inputs.get_shape()[-1]]), trainable=False)
    pop_var = tf.Variable(tf.ones([inputs.get_shape()[-1]]), trainable=False)

    if is_train:
        if is_conv_out:
            batch_mean, batch_var = tf.nn.moments(inputs, [0, 1, 2])
        else:
            batch_mean, batch_var = tf.nn.moments(inputs, [0])

        train_mean = tf.assign(pop_mean, pop_mean * decay + batch_mean * (1 - decay))
        train_var = tf.assign(pop_var, pop_var * decay + batch_var * (1 - decay))

        with tf.control_dependencies([train_mean, train_var]):
            return tf.nn.batch_normalization(inputs,
                                             batch_mean, batch_var, beta, scale, 0.001)
    else:
        return tf.nn.batch_normalization(inputs,
                                         pop_mean, pop_var, beta, scale, 0.001)


with tf.device('/gpu:0'):
    # 模型参数
    learning_rate = 1e-4
    training_iters = 200
    batch_size = 50
    display_step = 5
    n_classes = 2
    n_fc1 = 4096
    n_fc2 = 2048

    # 构建模型
    x = tf.placeholder(tf.float32, [None, 227, 227, 3])
    y = tf.placeholder(tf.float32, [None, n_classes])

    W_conv = {
        'conv1': tf.Variable(tf.truncated_normal([11, 11, 3, 96], stddev=0.0001)),
        'conv2': tf.Variable(tf.truncated_normal([5, 5, 96, 256], stddev=0.01)),
        'conv3': tf.Variable(tf.truncated_normal([3, 3, 256, 384], stddev=0.01)),
        'conv4': tf.Variable(tf.truncated_normal([3, 3, 384, 384], stddev=0.01)),
        'conv5': tf.Variable(tf.truncated_normal([3, 3, 384, 256], stddev=0.01)),
        'fc1': tf.Variable(tf.truncated_normal([6 * 6 * 256, n_fc1], stddev=0.1)),
        'fc2': tf.Variable(tf.truncated_normal([n_fc1, n_fc2], stddev=0.1)),
        'fc3': tf.Variable(tf.truncated_normal([n_fc2, n_classes], stddev=0.1))
    }
    b_conv = {
        'conv1': tf.Variable(tf.constant(0.0, dtype=tf.float32, shape=[96])),
        'conv2': tf.Variable(tf.constant(0.1, dtype=tf.float32, shape=[256])),
        'conv3': tf.Variable(tf.constant(0.1, dtype=tf.float32, shape=[384])),
        'conv4': tf.Variable(tf.constant(0.1, dtype=tf.float32, shape=[384])),
        'conv5': tf.Variable(tf.constant(0.1, dtype=tf.float32, shape=[256])),
        'fc1': tf.Variable(tf.constant(0.1, dtype=tf.float32, shape=[n_fc1])),
        'fc2': tf.Variable(tf.constant(0.1, dtype=tf.float32, shape=[n_fc2])),
        'fc3': tf.Variable(tf.constant(0.0, dtype=tf.float32, shape=[n_classes]))
    }

    x_image = tf.reshape(x, [-1, 227, 227, 3])

    # 卷积层 1
    conv1 = tf.nn.conv2d(x_image, W_conv['conv1'], strides=[1, 4, 4, 1], padding='VALID')
    conv1 = tf.nn.bias_add(conv1, b_conv['conv1'])
    conv1 = batch_norm(conv1, True)
    conv1 = tf.nn.relu(conv1)
    # 池化层 1
    pool1 = tf.nn.avg_pool(conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='VALID')
    norm1 = tf.nn.lrn(pool1, 5, bias=1.0, alpha=0.001 / 9.0, beta=0.75)

    # 卷积层 2
    conv2 = tf.nn.conv2d(pool1, W_conv['conv2'], strides=[1, 1, 1, 1], padding='SAME')
    conv2 = tf.nn.bias_add(conv2, b_conv['conv2'])
    conv2 = batch_norm(conv2, True)
    conv2 = tf.nn.relu(conv2)
    # 池化层 2
    pool2 = tf.nn.avg_pool(conv2, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='VALID')

    # 卷积层3
    conv3 = tf.nn.conv2d(pool2, W_conv['conv3'], strides=[1, 1, 1, 1], padding='SAME')
    conv3 = tf.nn.bias_add(conv3, b_conv['conv3'])
    conv3 = batch_norm(conv3, True)
    conv3 = tf.nn.relu(conv3)

    # 卷积层4
    conv4 = tf.nn.conv2d(conv3, W_conv['conv4'], strides=[1, 1, 1, 1], padding='SAME')
    conv4 = tf.nn.bias_add(conv4, b_conv['conv4'])
    conv4 = batch_norm(conv4, True)
    conv4 = tf.nn.relu(conv4)

    # 卷积层5
    conv5 = tf.nn.conv2d(conv4, W_conv['conv5'], strides=[1, 1, 1, 1], padding='SAME')
    conv5 = tf.nn.bias_add(conv5, b_conv['conv5'])
    conv5 = batch_norm(conv5, True)
    conv5 = tf.nn.relu(conv5)

    # 池化层5
    pool5 = tf.nn.avg_pool(conv5, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='VALID')
    reshape = tf.reshape(pool5, [-1, 6 * 6 * 256])
    fc1 = tf.add(tf.matmul(reshape, W_conv['fc1']), b_conv['fc1'])
    fc1 = batch_norm(fc1, True, False)
    fc1 = tf.nn.relu(fc1)

    # 全连接层 2
    fc2 = tf.add(tf.matmul(fc1, W_conv['fc2']), b_conv['fc2'])
    fc2 = batch_norm(fc2, True, False)
    fc2 = tf.nn.relu(fc2)
    fc3 = tf.add(tf.matmul(fc2, W_conv['fc3']), b_conv['fc3'])

    # 定义损失
    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=fc3))
    optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(loss)
    # 评估模型
    correct_pred = tf.equal(tf.argmax(fc3,1),tf.argmax(y,1))
    accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

    init = tf.global_variables_initializer()


def onehot(labels):
    '''one-hot 编码'''
    n_sample = len(labels)
    n_class = max(labels) + 1
    onehot_labels = np.zeros((n_sample, n_class))
    onehot_labels[np.arange(n_sample), labels] = 1
    return onehot_labels


save_model = ".//model//AlexNetModel.ckpt"


def train(opech):
    with tf.Session() as sess:
        sess.run(init)

        train_writer = tf.summary.FileWriter(".//log", sess.graph)  # 输出日志的地方
        saver = tf.train.Saver()

        c = []
        start_time = time.time()

        coord = tf.train.Coordinator()
        threads = tf.train.start_queue_runners(coord=coord)
        step = 0
        for i in range(opech):
            step = i
            image, label = sess.run([image_batch, label_batch])

            labels = onehot(label)
            acc=[]

            sess.run(optimizer, feed_dict={x: image, y: labels})
            loss_record = sess.run(loss, feed_dict={x: image, y: labels})
            acc=sess.run(accuracy,feed_dict={x:image,y:labels})
            print("now the loss is %f " % loss_record)
            print("now the accuracy is %f "%acc)
            c.append(loss_record)
            end_time = time.time()
            print('time: ', (end_time - start_time))
            start_time = end_time
            print("---------------%d onpech is finished-------------------" % i)
        print("Optimization Finished!")
        #        checkpoint_path = os.path.join(".//model", 'model.ckpt')  # 输出模型的地方
        saver.save(sess, save_model)
        print("Model Save Finished!")

        coord.request_stop()
        coord.join(threads)
        plt.plot(c)
        plt.xlabel('Iter')
        plt.ylabel('loss')
        plt.title('lr=%f, ti=%d, bs=%d' % (learning_rate, training_iters, batch_size))
        plt.tight_layout()
        plt.savefig('cat_and_dog_AlexNet.jpg', dpi=200)

train(training_iters)

1个回答

35行单独标注一下,到底是哪一行出问题?把得到的image_list这个数组reshape 一下成为下文可以接受的数组类型

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