https://blog.csdn.net/caicai2526/article/details/75329812
https://blog.csdn.net/caicai2526/article/details/75330192
https://blog.csdn.net/wsLJQian/article/details/78091425
实现猫狗的二分类：
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.
# 如果想看到正常的图片，请注释掉111行（标准化）和 126行（image_batch = tf.cast(image_batch, tf.float32)）
# 训练时不要注释掉！
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()文件树：训练文件需要另外下载。
在training.py文件夹所在目录打开终端执行training.py
文件内最后两句
#evaluate_one_image()
#run_training()
取消注释可以执行训练和评价，模型存放在logs中。