TensorFlow学习——CIFAR-10（二）代码实现

搜罗CIFAR-10资料时发现Kevin的视频教程系列与网上的资料略有出路

网上关于CIFAR-10的程序大致相同，以下博客都是相同的程序代码及解释：

http://www.cnblogs.com/lixiaoran/p/6740022.html

http://shartoo.github.io/tensorflow-sourcecode-input/

http://blog.csdn.net/diligent_321/article/details/53130913

程序链接：https://github.com/tensorflow/models/blob/master/tutorials/image/cifar10

下载到的cifar10程序共包括

1. cifar10.py

2. cifar10_eval.py
3. cifar10_input.py
4. cifar10_multi_gpu_train.py
5. cifar10_train.py
只讨论CPU版本则可以自动忽略cifar10_multi_gpu_train.py文件。另外，为加快程序调试，避免在程序运行时再去自动下载资源，可以提前去http://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz下好图片压缩文件，放在默认位置/tmp/cifar10_data 中即可

本文针对Kevin的视频教程做学习笔录，则代码这块主要如下：

（不懂的函数查找tensonflow官方网站的API手册：https://www.tensorflow.org/versions/r0.12/api_docs/python/nn.html#neural-network）

1. cifar10_input.py

#By @Kevin Xu#[e-mail]#Youtube: https://www.youtube.com/channel/UCVCSn4qQXTDAtGWpWAe4Plw#Chinese weibo: http://bit.ly/2nAmOcO#The aim of this project is to use TensorFlow to process our own data.#    - cifar10_input.py:  read in data and generate batches#    - cifar10.py: build the model architecture, train, evaluate# I used Ubuntu with Python 3.5, TensorFlow 1.0*, other OS should also be good.# I didn't use data argumentation, I spent less than 30 mins with 10K steps.# data: cifar10 binary version# https://www.cs.toronto.edu/~kriz/cifar.html# data size: ~184M# How to run?# 0. you need to change the data directory# 1. run cifar10.py# 2. call train() in the console to train the model# 3. call evaluate() in the console to test on the test data# Note: # it is suggested to restart your kenel to train the model multiple times # (in order to clear all the variables in the memory)# Otherwise errors may occur: conv1/weights/biases already exist......#%%import tensorflow as tfimport numpy as npimport os#%% Reading datadef read_cifar10(data_dir, is_train, batch_size, shuffle):    """Read CIFAR10        Args:        data_dir: the directory of CIFAR10        is_train: boolen        batch_size:        shuffle:           Returns:        label: 1D tensor, tf.int32        image: 4D tensor, [batch_size, height, width, 3], tf.float32        """    img_width = 32    img_height = 32    img_depth = 3    label_bytes = 1    image_bytes = img_width*img_height*img_depth            with tf.name_scope('input'):                if is_train:            filenames = [os.path.join(data_dir, 'data_batch_%d.bin' %ii)                                        for ii in np.arange(1, 6)]        else:            filenames = [os.path.join(data_dir, 'test_batch.bin')]                  filename_queue = tf.train.string_input_producer(filenames)            reader = tf.FixedLengthRecordReader(label_bytes + image_bytes)            key, value = reader.read(filename_queue)                   record_bytes = tf.decode_raw(value, tf.uint8)                label = tf.slice(record_bytes, [0], [label_bytes])           label = tf.cast(label, tf.int32)                image_raw = tf.slice(record_bytes, [label_bytes], [image_bytes])             image_raw = tf.reshape(image_raw, [img_depth, img_height, img_width])             image = tf.transpose(image_raw, (1,2,0)) # convert from D/H/W to H/W/D               image = tf.cast(image, tf.float32)#        # data argumentation#        image = tf.random_crop(image, [24, 24, 3])# randomly crop the image size to 24 x 24#        image = tf.image.random_flip_left_right(image)#        image = tf.image.random_brightness(image, max_delta=63)#        image = tf.image.random_contrast(image,lower=0.2,upper=1.8)                image = tf.image.per_image_standardization(image) #substract off the mean and divide by the variance         if shuffle:            images, label_batch = tf.train.shuffle_batch(                                    [image, label],                                     batch_size = batch_size,                                    num_threads= 16,                                    capacity = 2000,                                    min_after_dequeue = 1500)        else:            images, label_batch = tf.train.batch(                                    [image, label],                                    batch_size = batch_size,                                    num_threads = 16,                                    capacity= 2000)        #        return images, tf.reshape(label_batch, [batch_size])## ONE-HOT              n_classes = 10        label_batch = tf.one_hot(label_batch, depth= n_classes)                        return images, tf.reshape(label_batch, [batch_size, n_classes])    #%%   TEST# To test the generated batches of images# When training the model, DO comment the following codesimport matplotlib.pyplot as plt#data_dir = '/home/kevin/tensorflow/CIFAR10/data/cifar-10-batches-bin/'data_dir = 'D:/cat_VS_dog/CIFAR10/data/cifar-10-batches-bin/'  #My dir for data binBATCH_SIZE = 10image_batch, label_batch = read_cifar10(data_dir,                                        is_train=True,                                        batch_size=BATCH_SIZE,                                         shuffle=True)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])                        # just test one batch            for j in np.arange(BATCH_SIZE):                #print('label: %d' %label[j])  #20170730 remark by ciky for error when running                plt.imshow(img[j,:,:,:])                plt.show()            i+=1                except tf.errors.OutOfRangeError:        print('done!')    finally:        coord.request_stop()    coord.join(threads)

1.在Test中，print('label: %d' %label[j])这句运行时，弹出错误如下：

Traceback (most recent call last):File "<ipython-input-7-5a78e53decff>", line 23, in <module> print('label: %d' %label[j])TypeError: %d format: a number is required, not numpy.ndarray

改成“”print('label: %s' %label[j])“”（或注释掉）未报错，每个显示的图片显示为：label: [ 0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]

2. 显示图片可能看上去较怪，这是因为代码中加了句：

image = tf.image.per_image_standardization(image)#substract off the mean and divide by the variance

如想看原图显示效果，可将该句注释掉，不过显示效果怪不影响训练。

2. cifar10.py

#By @Kevin Xu#[e-mail]#Youtube: https://www.youtube.com/channel/UCVCSn4qQXTDAtGWpWAe4Plw#Chinese weibo: http://bit.ly/2nAmOcO    #The aim of this project is to use TensorFlow to process our own data.#    - cifar10_input.py:  read in data and generate batches#    - cifar10.py: build the model architecture, train, evaluate# I used Ubuntu with Python 3.5, TensorFlow 1.0*, other OS should also be good.# I didn't use data argumentation, I spent less than 30 mins with 10K steps.# data: cifar10 binary version# https://www.cs.toronto.edu/~kriz/cifar.html# data size: ~184M# How to run?# 0. you need to change the data directory# 1. run cifar10.py# 2. call train() in the console to train the model# 3. call evaluate() in the console to test on the test data# Note: # it is suggested to restart your kenel to train the model multiple times # (in order to clear all the variables in the memory)# Otherwise errors may occur: conv1/weights/biases already exist......#%%import osimport os.pathimport mathimport numpy as npimport tensorflow as tfimport cifar10_input#%%BATCH_SIZE = 128learning_rate = 0.05MAX_STEP = 10000 # with this setting, it took less than 30 mins on my laptop to train.#%%def inference(images):    '''    Args:        images: 4D tensor [batch_size, img_width, img_height, img_channel]    Notes:        In each conv layer, the kernel size is:        [kernel_size, kernel_size, number of input channels, number of output channels].        number of input channels are from previuous layer, if previous layer is THE input        layer, number of input channels should be image's channels.                        '''    #conv1, [5, 5, 3, 96], The first two dimensions are the patch size,    #the next is the number of input channels,     #the last is the number of output channels    with tf.variable_scope('conv1') as scope:        weights = tf.get_variable('weights',                                   shape = [3, 3, 3, 96],                                  dtype = tf.float32,                                   initializer=tf.truncated_normal_initializer(stddev=0.05,dtype=tf.float32))         biases = tf.get_variable('biases',                                  shape=[96],                                 dtype=tf.float32,                                  initializer=tf.constant_initializer(0.0))        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)            #pool1 and norm1       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')            #conv2    with tf.variable_scope('conv2') as scope:        weights = tf.get_variable('weights',                                  shape=[3,3,96, 64],                                  dtype=tf.float32,                                  initializer=tf.truncated_normal_initializer(stddev=0.05,dtype=tf.float32))        biases = tf.get_variable('biases',                                 shape=[64],                                  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')            #local3    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,384],                                  dtype=tf.float32,                                  initializer=tf.truncated_normal_initializer(stddev=0.004,dtype=tf.float32))        biases = tf.get_variable('biases',                                 shape=[384],                                 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=[384,192],                                  dtype=tf.float32,                                   initializer=tf.truncated_normal_initializer(stddev=0.004,dtype=tf.float32))        biases = tf.get_variable('biases',                                 shape=[192],                                 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=[192, 10],                                  dtype=tf.float32,                                  initializer=tf.truncated_normal_initializer(stddev=0.004,dtype=tf.float32))        biases = tf.get_variable('biases',                                  shape=[10],                                 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:                labels = tf.cast(labels, tf.int64)                # to use this loss fuction, one-hot encoding is needed!        cross_entropy = tf.nn.softmax_cross_entropy_with_logits\                        (logits=logits, labels=labels, name='xentropy_per_example')                        #        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#%% Train the model on the training data# you need to change the training data directory belowdef train():        my_global_step = tf.Variable(0, name='global_step', trainable=False)           #data_dir = '/home/kevin/tensorflow/CIFAR10/data/cifar-10-batches-bin/'    data_dir = 'D:/cat_VS_dog/CIFAR10/data/cifar-10-batches-bin/'  #My dir for data bin    #log_dir = '/home/kevin/tensorflow/CIFAR10/logs234/'    log_dir = 'D:/cat_VS_dog/CIFAR10/logs'        images, labels = cifar10_input.read_cifar10(data_dir=data_dir,                                                is_train=True,                                                batch_size= BATCH_SIZE,                                                shuffle=True)    logits = inference(images)        loss = losses(logits, labels)            optimizer = tf.train.GradientDescentOptimizer(learning_rate)    train_op = optimizer.minimize(loss, global_step= my_global_step)        saver = tf.train.Saver(tf.global_variables())    summary_op = tf.summary.merge_all()            init = tf.global_variables_initializer()    sess = tf.Session()    sess.run(init)        coord = tf.train.Coordinator()    threads = tf.train.start_queue_runners(sess=sess, coord=coord)        summary_writer = tf.summary.FileWriter(log_dir, sess.graph)        try:        for step in np.arange(MAX_STEP):            if coord.should_stop():                    break            _, loss_value = sess.run([train_op, loss])                           if step % 50 == 0:                                 print ('Step: %d, loss: %.4f' % (step, loss_value))                            if step % 100 == 0:                summary_str = sess.run(summary_op)                summary_writer.add_summary(summary_str, step)                                if step % 2000 == 0 or (step + 1) == MAX_STEP:                checkpoint_path = os.path.join(log_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()#%% To test the model on the test datadef evaluate():    with tf.Graph().as_default():                log_dir = '/home/kevin/tensorflow/CIFAR10/logs10000/'        test_dir = '/home/kevin/tensorflow/CIFAR10/data/cifar-10-batches-bin/'        n_test = 10000                      # reading test data        images, labels = cifar10_input.read_cifar10(data_dir=test_dir,                                                    is_train=False,                                                    batch_size= BATCH_SIZE,                                                    shuffle=False)        logits = inference(images)        top_k_op = tf.nn.in_top_k(logits, labels, 1)        saver = tf.train.Saver(tf.global_variables())                with tf.Session() as sess:                        print("Reading checkpoints...")            ckpt = tf.train.get_checkpoint_state(log_dir)            if ckpt and ckpt.model_checkpoint_path:                global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]                saver.restore(sess, ckpt.model_checkpoint_path)                print('Loading success, global_step is %s' % global_step)            else:                print('No checkpoint file found')                return                    coord = tf.train.Coordinator()            threads = tf.train.start_queue_runners(sess = sess, coord = coord)                        try:                num_iter = int(math.ceil(n_test / BATCH_SIZE))                true_count = 0                total_sample_count = num_iter * BATCH_SIZE                step = 0                while step < num_iter and not coord.should_stop():                    predictions = sess.run([top_k_op])                    true_count += np.sum(predictions)                    step += 1                    precision = true_count / total_sample_count                print('precision = %.3f' % precision)            except Exception as e:                coord.request_stop(e)            finally:                coord.request_stop()                coord.join(threads)    #%%

待续