def build_model():
global yan_chi, images, labels
global acc, loss, c_loss
global train_op, lr, global_step
global x_train, y_train
global x_test, y_test
yan_chi = tf.placeholder(tf.float32)
images = tf.placeholder(tf.float32, [None, 24, 24, 3])
labels = tf.placeholder(tf.float32, [None, 10])
x_train, y_train = LeNet5.train_input()
x_test, y_test = LeNet5.test_input()
logits = LeNet5.inference(images)
acc = LeNet5.get_acc(logits, labels)
c_loss, loss = LeNet5.get_loss(logits, labels)
train_op, lr, global_step = LeNet5.get_op(yan_chi, loss)
def evaluate_one_image():
# 存放的是我从百度下载的猫狗图片路径
train ='D:/workspace/python_dir/tersonflowdemo/dogvscat/dataset/data/test1/'
image_array = get_one_image(train)
with tf.Graph().as_default():
BATCH_SIZE = 1 # 因为只读取一副图片 所以batch 设置为1
N_CLASSES = 2 # 2个输出神经元，［1，0］ 或者 ［0，1］猫和狗的概率
# 转化图片格式
image = tf.cast(image_array, tf.float32)
# 图片标准化
# image = tf.image.per_image_standardization(image)#这行不要加，我们训练的图片并没有做标准化，这行改了我两天
# 图片原来是三维的 [208, 208, 3] 重新定义图片形状 改为一个4D 四维的 tensor
image = tf.reshape(image, [1, 208, 208, 3])
logit = model.inference(image, BATCH_SIZE, N_CLASSES)
# 因为 inference 的返回没有用激活函数，所以在这里对结果用softmax 激活
logit = tf.nn.softmax(logit)
# 用最原始的输入数据的方式向模型输入数据 placeholder
x = tf.placeholder(tf.float32, shape=[208, 208, 3])
# 我门存放模型的路径
logs_train_dir = 'D:/workspace/python_dir/tersonflowdemo/dogvscat/result/'
# 定义saver
saver = tf.train.Saver()
with tf.Session() as sess:
print("从指定的路径中加载模型。。。。")
# 将模型加载到sess
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('模型加载成功, 训练的步数为 %s' % global_step)
else:
print('模型加载失败，，，文件没有找到')
# 将图片输入到模型计算
prediction = sess.run(logit, feed_dict={x: image_array})
# 获取输出结果中最大概率的索引
max_index = np.argmax(prediction)
print('猫的概率 %.6f' % prediction[:, 0])
print('狗的概率 %.6f' % prediction[:, 1])
MAX_STEP = 8000
learning_rate = 0.0001 # 小于0.001
print("I'm OK")
train_dir = 'D:/workspace/python_dir/tersonflowdemo/dogvscat/dataset/data/train/' # 训练图片文件夹
logs_train_dir = 'D:/workspace/python_dir/tersonflowdemo/dogvscat/result/' # 保存训练结果文件夹
train, train_label = test.get_files(train_dir)
train_batch, train_label_batch = test.get_batch(train, train_label, IMG_W,
IMG_H, BATCH_SIZE, CAPACITY)
# 训练操作定义
sess = tf.Session()
train_logits = model.inference(train_batch, BATCH_SIZE, N_CLASSES) #网络结构
train_loss = model.losses(train_logits, train_label_batch) #反向传播结构
train_op = model.trainning(train_loss, learning_rate)
train_acc = model.evaluation(train_logits, train_label_batch)
# train_label_batch = tf.one_hot(train_label_batch,2,1,0)
# 测试操作定义
summary_op = tf.summary.merge_all()
# 产生一个writer来写log文件
train_writer = tf.summary.FileWriter(logs_train_dir, sess.graph)
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer())
coord = tf.train.Coordinator()