from skimage import io, transform  # skimage模块下的io transform(图像的形变与缩放)模块
import glob  # glob 文件通配符模块
import os  # os 处理文件和目录的模块
import tensorflow as tf
import numpy as np  # 多维数据处理模块
import time
import matplotlib.pyplot as plt
# 数据集地址
#path = './flower_photos/'
path = '../dataset/train/'
# 模型保存地址
model_path = './cnn1_model.ckpt'# 将所有的图片resize成100*100
w = 100
h = 100
c = 3# 读取图片+数据处理
def read_img(path):# os.listdir(path) 返回path指定的文件夹包含的文件或文件夹的名字的列表# os.path.isdir(path)判断path是否是目录# b = [x+x for x in list1 if x+x<15 ]  列表生成式,循环list1，当if为真时，将x+x加入列表bcate = [path + x for x in os.listdir(path) if os.path.isdir(path + x)]imgs = []labels = []print("开始读入图片和标签。。。。")for idx, folder in enumerate(cate):# glob.glob(s+'*.py') 从目录通配符搜索中生成文件列表for im in glob.glob(folder + '/*.png'):# 输出读取的图片的名称#print('reading the images:%s' % (im))# io.imread(im)读取单张RGB图片 skimage.io.imread(fname,as_grey=True)读取单张灰度图片# 读取的图片img = io.imread(im)# skimage.transform.resize(image, output_shape)改变图片的尺寸img = transform.resize(img, (w, h))# 将读取的图片数据加载到imgs[]列表中imgs.append(img)# 将图片的label加载到labels[]中，与上方的imgs索引对应labels.append(idx)# 将读取的图片和labels信息，转化为numpy结构的ndarr(N维数组对象（矩阵）)数据信息print("读入图片和标签完毕。。。。")return np.asarray(imgs, np.float32), np.asarray(labels, np.int32)# 调用读取图片的函数，得到图片和labels的数据集
data, label = read_img(path)# 打乱顺序
# 读取data矩阵的第一维数（图片的个数）
num_example = data.shape[0]
# 产生一个num_example范围，步长为1的序列
arr = np.arange(num_example)
# 调用函数，打乱顺序
np.random.shuffle(arr)
# 按照打乱的顺序，重新排序
data = data[arr]
label = label[arr]# 将所有数据分为训练集和验证集
ratio = 0.8
s = np.int(num_example * ratio)
x_train = data[:s]
y_train = label[:s]
x_val = data[s:]
y_val = label[s:]# -----------------构建网络----------------------
# 本程序cnn网络模型，共有7层，前三层为卷积层，后三层为全连接层，前三层中，每层包含卷积、激活、池化层
# 占位符设置输入参数的大小和格式
x = tf.placeholder(tf.float32, shape=[None, w, h, c], name='x')
y_ = tf.placeholder(tf.int32, shape=[None, ], name='y_')def inference(input_tensor, train, regularizer):# -----------------------第一层----------------------------with tf.variable_scope('layer1-conv1'):# 初始化权重conv1_weights为可保存变量，大小为5x5,3个通道（RGB），数量为32个conv1_weights = tf.get_variable("weight", [5, 5, 3, 32],initializer=tf.truncated_normal_initializer(stddev=0.1))# 初始化偏置conv1_biases，数量为32个conv1_biases = tf.get_variable("bias", [32], initializer=tf.constant_initializer(0.0))# 卷积计算，tf.nn.conv2d为tensorflow自带2维卷积函数，input_tensor为输入数据，# conv1_weights为权重，strides=[1, 1, 1, 1]表示左右上下滑动步长为1，padding='SAME'表示输入和输出大小一样，即补0conv1 = tf.nn.conv2d(input_tensor, conv1_weights, strides=[1, 1, 1, 1], padding='SAME')# 激励计算，调用tensorflow的relu函数relu1 = tf.nn.relu(tf.nn.bias_add(conv1, conv1_biases))with tf.name_scope("layer2-pool1"):# 池化计算，调用tensorflow的max_pool函数，strides=[1,2,2,1]，表示池化边界，2个对一个生成，padding="VALID"表示不操作。pool1 = tf.nn.max_pool(relu1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="VALID")# -----------------------第二层----------------------------with tf.variable_scope("layer3-conv2"):# 同上，不过参数的有变化，根据卷积计算和通道数量的变化，设置对应的参数conv2_weights = tf.get_variable("weight", [5, 5, 32, 64],initializer=tf.truncated_normal_initializer(stddev=0.1))conv2_biases = tf.get_variable("bias", [64], initializer=tf.constant_initializer(0.0))conv2 = tf.nn.conv2d(pool1, conv2_weights, strides=[1, 1, 1, 1], padding='SAME')relu2 = tf.nn.relu(tf.nn.bias_add(conv2, conv2_biases))with tf.name_scope("layer4-pool2"):pool2 = tf.nn.max_pool(relu2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')# -----------------------第三层----------------------------# 同上，不过参数的有变化，根据卷积计算和通道数量的变化，设置对应的参数with tf.variable_scope("layer5-conv3"):conv3_weights = tf.get_variable("weight", [3, 3, 64, 128],initializer=tf.truncated_normal_initializer(stddev=0.1))conv3_biases = tf.get_variable("bias", [128], initializer=tf.constant_initializer(0.0))conv3 = tf.nn.conv2d(pool2, conv3_weights, strides=[1, 1, 1, 1], padding='SAME')relu3 = tf.nn.relu(tf.nn.bias_add(conv3, conv3_biases))with tf.name_scope("layer6-pool3"):pool3 = tf.nn.max_pool(relu3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')# -----------------------第四层----------------------------# 同上，不过参数的有变化，根据卷积计算和通道数量的变化，设置对应的参数with tf.variable_scope("layer7-conv4"):conv4_weights = tf.get_variable("weight", [3, 3, 128, 128],initializer=tf.truncated_normal_initializer(stddev=0.1))conv4_biases = tf.get_variable("bias", [128], initializer=tf.constant_initializer(0.0))conv4 = tf.nn.conv2d(pool3, conv4_weights, strides=[1, 1, 1, 1], padding='SAME')relu4 = tf.nn.relu(tf.nn.bias_add(conv4, conv4_biases))with tf.name_scope("layer8-pool4"):pool4 = tf.nn.max_pool(relu4, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')nodes = 6 * 6 * 128reshaped = tf.reshape(pool4, [-1, nodes])# 使用变形函数转化结构# -----------------------第五层---------------------------with tf.variable_scope('layer9-fc1'):# 初始化全连接层的参数，隐含节点为1024个fc1_weights = tf.get_variable("weight", [nodes, 1024],initializer=tf.truncated_normal_initializer(stddev=0.1))if regularizer != None: tf.add_to_collection('losses', regularizer(fc1_weights))  # 正则化矩阵fc1_biases = tf.get_variable("bias", [1024], initializer=tf.constant_initializer(0.1))# 使用relu函数作为激活函数fc1 = tf.nn.relu(tf.matmul(reshaped, fc1_weights) + fc1_biases)# 采用dropout层，减少过拟合和欠拟合的程度，保存模型最好的预测效率if train: fc1 = tf.nn.dropout(fc1, 0.5)# -----------------------第六层----------------------------with tf.variable_scope('layer10-fc2'):# 同上，不过参数的有变化，根据卷积计算和通道数量的变化，设置对应的参数fc2_weights = tf.get_variable("weight", [1024, 512],initializer=tf.truncated_normal_initializer(stddev=0.1))if regularizer != None: tf.add_to_collection('losses', regularizer(fc2_weights))fc2_biases = tf.get_variable("bias", [512], initializer=tf.constant_initializer(0.1))fc2 = tf.nn.relu(tf.matmul(fc1, fc2_weights) + fc2_biases)if train: fc2 = tf.nn.dropout(fc2, 0.5)# -----------------------第七层----------------------------with tf.variable_scope('layer11-fc3'):# 同上，不过参数的有变化，根据卷积计算和通道数量的变化，设置对应的参数fc3_weights = tf.get_variable("weight", [512, 5],initializer=tf.truncated_normal_initializer(stddev=0.1))if regularizer != None: tf.add_to_collection('losses', regularizer(fc3_weights))fc3_biases = tf.get_variable("bias", [5], initializer=tf.constant_initializer(0.1))logit = tf.matmul(fc2, fc3_weights) + fc3_biases  # matmul矩阵相乘# 返回最后的计算结果return logit# ---------------------------网络结束---------------------------
# 设置正则化参数为0.0001
regularizer = tf.contrib.layers.l2_regularizer(0.0001)
# 将上述构建网络结构引入
logits = inference(x, False, regularizer)# (小处理)将logits乘以1赋值给logits_eval，定义name，方便在后续调用模型时通过tensor名字调用输出tensor
b = tf.constant(value=1, dtype=tf.float32)
logits_eval = tf.multiply(logits, b, name='logits_eval')  # b为1# 设置损失函数，作为模型训练优化的参考标准，loss越小，模型越优
loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=y_)
# 设置整体学习率为α为0.001
train_op = tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss)
# 设置预测精度
correct_prediction = tf.equal(tf.cast(tf.argmax(logits, 1), tf.int32), y_)
acc = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))# 定义一个函数，按批次取数据
def minibatches(inputs=None, targets=None, batch_size=None, shuffle=False):assert len(inputs) == len(targets)if shuffle:indices = np.arange(len(inputs))np.random.shuffle(indices)for start_idx in range(0, len(inputs) - batch_size + 1, batch_size):  #range(start,end,step)if shuffle:excerpt = indices[start_idx:start_idx + batch_size]else:excerpt = slice(start_idx, start_idx + batch_size)yield inputs[excerpt], targets[excerpt]# 训练和测试数据，可将n_epoch设置更大一些# 迭代次数
n_epoch = 30
fig_loss = np.zeros([n_epoch])
fig_acc1 = np.zeros([n_epoch])
fig_acc2= np.zeros([n_epoch])
# 每次迭代输入的图片数据
batch_size = 64
saver = tf.train.Saver(max_to_keep=1)  # 可以指定保存的模型个数，利用max_to_keep=4，则最终会保存4个模型（
with tf.Session() as sess:# 初始化全局参数sess.run(tf.global_variables_initializer())# 开始迭代训练，调用的都是前面设置好的函数或变量for epoch in range(n_epoch):start_time = time.time()# training#训练集train_loss, train_acc, n_batch = 0, 0, 0for x_train_a, y_train_a in minibatches(x_train, y_train, batch_size, shuffle=True):_, err, ac = sess.run([train_op, loss, acc], feed_dict={x: x_train_a, y_: y_train_a})train_loss += errtrain_acc += acn_batch += 1if n_batch%20==0:# print("Epoch:%d After %d batch_size train loss" % (n_epoch,n_batch))# print(err)print("Epoch:%d After %d batch_size average train loss: %f" % (epoch, n_batch, np.sum(train_loss) / n_batch))# print("Epoch:%d After %d batch_size train acc %f" % (epoch, n_batch,ac))print("Epoch:%d After %d batch_size average train acc: %f" % (epoch, n_batch, np.sum(train_acc) / n_batch))#Epoch: 9 After 45  batch_size average train loss: 2.750402 Epoch: 9#After 45 batch_size average train acc: 0.993403fig_loss[epoch] = np.sum(train_loss) / n_batchfig_acc1[epoch] = np.sum(train_acc) / n_batch#validation#验证集val_loss, val_acc, n_batch = 0, 0, 0for x_val_a, y_val_a in minibatches(x_val, y_val, batch_size, shuffle=False):err, ac = sess.run([loss, acc], feed_dict={x: x_val_a, y_: y_val_a})val_loss += errval_acc += acn_batch += 1print("validation loss: %f" % (np.sum(val_loss) / n_batch))print("validation acc: %f" % (np.sum(val_acc) / n_batch))fig_acc2[epoch] = np.sum(val_acc) / n_batch#保存模型及模型参数if epoch % 2 == 0:saver.save(sess, model_path, global_step=epoch)# 训练loss图
fig, ax1 = plt.subplots()
lns1 = ax1.plot(np.arange(n_epoch), fig_loss, label="Loss")
ax1.set_xlabel('iteration')
ax1.set_ylabel('training loss')# 训练和验证两种准确率曲线图放在一张图中
fig2, ax2 = plt.subplots()
ax3 = ax2.twinx()#由ax2图生成ax3图
lns2 = ax2.plot(np.arange(n_epoch), fig_acc1, label="Loss")
lns3 = ax3.plot(np.arange(n_epoch), fig_acc2, label="Loss")ax2.set_xlabel('iteration')
ax2.set_ylabel('training acc')
ax3.set_ylabel('val acc')# 合并图例
lns = lns3 + lns2
labels = ["train acc", "val acc"]
plt.legend(lns, labels, loc=7)plt.show()