https://blog.csdn.net/caicai2526/article/details/75329812

https://blog.csdn.net/caicai2526/article/details/75330192

https://blog.csdn.net/wsLJQian/article/details/78091425

實現貓狗的二分類：

input_data.py

# coding=utf-8

#%%

import tensorflow as tf
import numpy as np
import os

#%%

# you need to change this to your data directory
#train_dir = '/home/kevin/tensorflow/cats_vs_dogs/data/train/'
#train_dir = '/home/twinkle/PycharmProjects/AlexNet_CatVSDog/01 cats vs dogs/data/train/'

def get_files(file_dir):
    '''
    Args:
        file_dir: file directory
    Returns:
        list of images and labels
    '''
    cats = []
    label_cats = []
    dogs = []
    label_dogs = []
    for file in os.listdir(file_dir):
        #name = file.split(sep='.')
        name = file.split('.')
        if name[0]=='cat':
            cats.append(file_dir + file)
            label_cats.append(0)
        else:
            dogs.append(file_dir + file)
            label_dogs.append(1)
    print('There are %d cats\nThere are %d dogs' %(len(cats), len(dogs)))
    
    image_list = np.hstack((cats, dogs))
    label_list = np.hstack((label_cats, label_dogs))
    
    temp = np.array([image_list, label_list])
    temp = temp.transpose()
    np.random.shuffle(temp)
    
    image_list = list(temp[:, 0])
    label_list = list(temp[:, 1])
    label_list = [int(i) for i in label_list]
    
    
    return image_list, label_list


#%%

def get_batch(image, label, image_W, image_H, batch_size, capacity):
    '''
    Args:
        image: list type
        label: list type
        image_W: image width
        image_H: image height
        batch_size: batch size
        capacity: the maximum elements in queue
    Returns:
        image_batch: 4D tensor [batch_size, width, height, 3], dtype=tf.float32
        label_batch: 1D tensor [batch_size], dtype=tf.int32
    '''
    
    image = tf.cast(image, tf.string)
    label = tf.cast(label, tf.int32)

    # make an input queue
    input_queue = tf.train.slice_input_producer([image, label])
    
    label = input_queue[1]
    image_contents = tf.read_file(input_queue[0])
    image = tf.image.decode_jpeg(image_contents, channels=3)
    
    ######################################
    # data argumentation should go to here
    ######################################
    
    image = tf.image.resize_image_with_crop_or_pad(image, image_W, image_H)
    
    # if you want to test the generated batches of images, you might want to comment the following line.
    # 如果想看到正常的圖片，請註釋掉111行（標準化）和 126行（image_batch = tf.cast(image_batch, tf.float32)）
    # 訓練時不要註釋掉！
    image = tf.image.per_image_standardization(image)
    
    image_batch, label_batch = tf.train.batch([image, label],
                                                batch_size= batch_size,
                                                num_threads= 64, 
                                                capacity = capacity)
    
    #you can also use shuffle_batch 
#    image_batch, label_batch = tf.train.shuffle_batch([image,label],
#                                                      batch_size=BATCH_SIZE,
#                                                      num_threads=64,
#                                                      capacity=CAPACITY,
#                                                      min_after_dequeue=CAPACITY-1)
    
    label_batch = tf.reshape(label_batch, [batch_size])
    image_batch = tf.cast(image_batch, tf.float32)
    
    return image_batch, label_batch


 
#%% TEST
# To test the generated batches of images
# When training the model, DO comment the following codes




#import matplotlib.pyplot as plt
#
#BATCH_SIZE = 2
#CAPACITY = 256
#IMG_W = 208
#IMG_H = 208
#
#train_dir = '/home/kevin/tensorflow/cats_vs_dogs/data/train/'
#
#image_list, label_list = get_files(train_dir)
#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])
#            
#            # just test one 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

# coding=utf-8


#%%

import tensorflow as tf

#%%
def inference(images, batch_size, n_classes):
    '''Build the model
    Args:
        images: image batch, 4D tensor, tf.float32, [batch_size, width, height, channels]
    Returns:
        output tensor with the computed logits, float, [batch_size, n_classes]
    '''
    #conv1, shape = [kernel size, kernel size, channels, kernel numbers]
    
    with tf.variable_scope('conv1') as scope:
        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)
    
    #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=[3,3,16,16],
                                  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')
    
    
    #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,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):
    '''Compute loss from logits and labels
    Args:
        logits: logits tensor, float, [batch_size, n_classes]
        labels: label tensor, tf.int32, [batch_size]
        
    Returns:
        loss tensor of float type
    '''
    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):
    '''Training ops, the Op returned by this function is what must be passed to 
        'sess.run()' call to cause the model to train.
        
    Args:
        loss: loss tensor, from losses()
        
    Returns:
        train_op: The op for trainning
    '''
    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):
  """Evaluate the quality of the logits at predicting the label.
  Args:
    logits: Logits tensor, float - [batch_size, NUM_CLASSES].
    labels: Labels tensor, int32 - [batch_size], with values in the
      range [0, NUM_CLASSES).
  Returns:
    A scalar int32 tensor with the number of examples (out of batch_size)
    that were predicted correctly.
  """
  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

#%%
 

##############################################

training.py

# coding=utf-8
#%%

import os
import numpy as np
import tensorflow as tf
import input_data
import model

#%%

N_CLASSES = 2
IMG_W = 208  # resize the image, if the input image is too large, training will be very slow.
IMG_H = 208
#BATCH_SIZE = 16
BATCH_SIZE = 16
CAPACITY = 2000
#MAX_STEP = 10000 # with current parameters, it is suggested to use MAX_STEP>10k
MAX_STEP = 1000000 # with current parameters, it is suggested to use MAX_STEP>10k
learning_rate = 0.0001 # with current parameters, it is suggested to use learning rate<0.0001


#%%
def run_training():

    # you need to change the directories to yours.
    train_dir = '/home/twinkle/PycharmProjects/AlexNet_CatVSDog/01 cats vs dogs/data/train/'
    logs_train_dir = '/home/twinkle/PycharmProjects/AlexNet_CatVSDog/01 cats vs dogs/logs/train/'

    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 % 50 == 0:
                print('Step %d, train loss = %.2f, train accuracy = %.2f%%' %(step, tra_loss, tra_acc*100.0))
                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()


#%% Evaluate one image
# when training, comment the following codes.


from PIL import Image
import matplotlib.pyplot as plt

def get_one_image(train):
   '''Randomly pick one image from training data
   Return: ndarray
   '''
   n = len(train)
   ind = np.random.randint(0, n)
   img_dir = train[ind]

   image = Image.open(img_dir)
   #plt.imshow(image)
   image.show(image)
   print('show %d picture' %(ind))

   image = image.resize([208, 208])
   image = np.array(image)
   return image

def evaluate_one_image():
   '''Test one image against the saved models and parameters
   '''

   # you need to change the directories to yours.
   train_dir = '/home/twinkle/PycharmProjects/AlexNet_CatVSDog/01 cats vs dogs/data/train/'
   train, train_label = input_data.get_files(train_dir)
   image_array = get_one_image(train)

   with tf.Graph().as_default():
       BATCH_SIZE = 1
       N_CLASSES = 2

       image = tf.cast(image_array, tf.float32)
       image = tf.image.per_image_standardization(image)
       image = tf.reshape(image, [1, 208, 208, 3])
       logit = model.inference(image, BATCH_SIZE, N_CLASSES)

       logit = tf.nn.softmax(logit)

       x = tf.placeholder(tf.float32, shape=[208, 208, 3])

       # you need to change the directories to yours.
       logs_train_dir = '/home/twinkle/PycharmProjects/AlexNet_CatVSDog/01 cats vs dogs/logs/train/'

       saver = tf.train.Saver()

       with tf.Session() as sess:

           print("Reading checkpoints...")
           ckpt = tf.train.get_checkpoint_state(logs_train_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')

           prediction = sess.run(logit, feed_dict={x: image_array})
           max_index = np.argmax(prediction)
           if max_index==0:
               print('This is a cat with possibility %.6f' %prediction[:, 0])
           else:
               print('This is a dog with possibility %.6f' %prediction[:, 1])


#%%

evaluate_one_image()
#run_training()
 

檔案樹：訓練檔案需要另外下載。

在training.py資料夾所在目錄開啟終端執行training.py

檔案內最後兩句

#evaluate_one_image()
#run_training()

取消註釋可以執行訓練和評價，模型存放在logs中。