import os import cv2 import numpy as np import tensorflow as tf h_pool2_flat = 12288 n_classes = 1 # 设置路径 data_home = 'data/imgdata/' # 标签映射字典 image_to_label = {} with open('data/label.txt', 'r') as fr: lines = fr.readlines() for line in lines: line = line.strip() key, value = line.split(' ') image_to_label[key] = value # 构造训练集 X_train = [] y_train = [] for jpg in os.listdir(data_home): image = cv2.imread(data_home + jpg) x = cv2.resize(image, (64, 64)) X_train.append(x) y = image_to_label[jpg] y_train.append(y) X_train = np.array(X_train).reshape((1000, -1)) y_train = np.array(y_train).reshape((-1, 1)) # print(X_train.shape) # print(y_train) def get_biases(shape): ''' 函数的偏置定义。tf.get_variable(name,shape，dtype，initializer) 参数 1.name:“biases” 2.shape：[size]，即[输出维度] 3.dtype:tf.float32 4.initializer：初始化方式initial，我们选择常量初始化 语法：tf.constant_initializer(value) 参数： 1）value:常数。0.1 :param shape: 偏置输出维度 :return: 偏置变量 ''' # TODO：1.使用tf.constant_initializer(0.1)函数定义初始化方式 initial = tf.constant_initializer(0.1) # TODO：2.使用tf.get_variable函数定义偏置变量 biases = tf.get_variable("biases", shape=shape, dtype=tf.float32, initializer=initial) return biases def get_weights(shape): ''' 定义参数，卷积核/权值 语法：tf.get_variable(name,shape，dtype，initializer) 参数： 1.name:“weights” 2.shape：根据不同的变量，输入不同的shape 3.dtype:tf.float32 4.initializer：初始化方式initial，我们选择初始化方式为截断的正太分布 语法：tf.truncated_normal_initializer(stddev,dtype) 参数： 1）stedev:要生成的随机值的标准偏差。可设置stedev=0.005 2) dtype：数据类型。只支持浮点类型 可设置dtype=tf.float32。 :param shape:参数shape :return: 变量参数 ''' # TODO：1.使用tf.truncated_normal_initializer定义变量的初始化方式 initial = tf.truncated_normal_initializer(stddev=0.005, dtype=tf.float32) # TODO：2.使用tf.get_variable函数定义变量, weights = tf.get_variable("weights", shape=shape, dtype=tf.float32, initializer=initial) return weights # 一层全连接 def add_fc_layer(inputs, insize, outsize, activation_function=None): ''' 添加全连接层 1.定义权值weights 2.定义偏置bias 3.完成全连接操作：inputs*weights+bias 4.如果没有激活函数，返回softmax结果 如果有激活函数则，返回activation_function结果 :param inputs: 输入图像 :param insize: 输入维度 :param outsize: 输出维度 :param activation_function: 激活函数 :return: 计算结果 ''' # TODO：1.调用get_weights()获取权值 weights = get_weights([insize, outsize]) # TODO：2.调用get_biases()获取偏置 bias = get_biases([outsize]) # TODO：3.使用tf.matmul完成inputs和weights乘法运算;使用+完成与bias的加法运算 wx_plus_b = tf.matmul(inputs, weights) + bias # TODO：4.如果激活函数None，返回tf.nn.softmax结果，否则返回activation_function结果 if activation_function == None: outputs = tf.nn.softmax(wx_plus_b) else: outputs = activation_function(wx_plus_b) return outputs # 全部模型 def inference(inputs): # TODO：1.调用add_fc_layer完成全连接操作1 with tf.variable_scope("fc1", reuse=tf.AUTO_REUSE) as scope: # h_pool2_flat = tf.reshape(inputs, shape=[-1, 12288]) fc1 = add_fc_layer(inputs, h_pool2_flat, 128, tf.nn.relu) # TODO：2.调用add_fc_layer完成全连接操作2 with tf.variable_scope("fc2", reuse=tf.AUTO_REUSE) as scope: fc2 = add_fc_layer(fc1, 128, 64, tf.nn.tanh) # TODO：3.调用add_fc_layer完成全连接操作3 with tf.variable_scope("fc3", reuse=tf.AUTO_REUSE) as scope: fc3 = add_fc_layer(fc2, 64, 32, tf.nn.sigmoid) # TODO：4.调用Dropout激活概率为0.5 fc3_drop = tf.nn.dropout(fc3, 0.5) # TODO：5.调用add_fc_layer完成全连接操作4 with tf.variable_scope("fc4", reuse=tf.AUTO_REUSE) as scope: fc4 = add_fc_layer(fc3_drop, 32, n_classes, tf.nn.relu) return fc4 # 占位符 x_pl = tf.placeholder(tf.float32) y_pl = tf.placeholder(tf.float32) # 预测值 y_pred = inference(x_pl) # 损失值、优化方法 k = (y_train - y_pred) loss = tf.reduce_mean(tf.square(k)) optimizer = tf.train.AdamOptimizer(learning_rate=0.01) train_op = optimizer.minimize(loss) # 模型训练 saver = tf.train.Saver() with tf.Session() as sess: sess.run(tf.global_variables_initializer()) for epoch in range(150000): _, train_loss = sess.run([train_op, loss], feed_dict={x_pl: X_train, y_pl: y_train}) if epoch % 1000 == 0: print('epoch:{}, train_losses:{}'.format(epoch, train_loss)) saver.save(sess, "model/model.ckpt", global_step=epoch) print('epoch:{}, save model'.format(epoch))
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