CNN-简单图片分类

import os

from PIL import Image
#矩阵运算库
import numpy as np
import tensorflow as tf

# 数据文档夹
data_dir = "data"
test_data_dir = "test_data"
# 训练还是测试,true为训练
train = False
# 模型文档路径
model_path = "model/image_model"


# 从文档夹读取图片和标签到numpy数组中
# 标签信息在文档名中，例如1_40.jpg表示该图片的标签为1
def (data_dir,test_data_dir):
    datas = []
    test_datas = []
    labels = []
    test_labels = []
    fpaths = []
    test_fpaths = []
    # 读入图片信息，并且从图片名中得到类别
    for fname in os.listdir(data_dir):
        fpath = os.path.join(data_dir, fname)
        fpaths.append(fpath)    # 向尾部添加
        image = Image.open(fpath)
        data = np.array(image) / 255.0
        label = int(fname.split("_")[0])
        datas.append(data)
        labels.append(label)

    for fname in os.listdir(test_data_dir):
        fpath = os.path.join(test_data_dir, fname)
        test_fpaths.append(fpath)  # 向尾部添加
        image = Image.open(fpath)
        data = np.array(image) / 255.0
        label = int(fname.split("_")[0])
        test_datas.append(data)
        test_labels.append(label)

    datas = np.array(datas)
    test_datas = np.array(test_datas)
    labels = np.array(labels)
    test_labels = np.array(test_labels)
    # 输出矩阵维度
    print("shape of datas: {}tshape of labels: {}".format(datas.shape, labels.shape))
    return fpaths, test_fpaths, datas, labels, test_labels, test_datas


fpaths, test_fpaths, datas, labels, test_labels, test_datas = read_data(data_dir,test_data_dir)

# 计算有多少类图片
num_classes = len(set(labels))

# 定义Placeholder，存放输入和标签
datas_placeholder = tf.placeholder(tf.float32, [None, 32, 32, 3])
labels_placeholder = tf.placeholder(tf.int32, [None])

# 存放DropOut参数的容器，训练时为0.25，测试时为0
dropout_placeholdr = tf.placeholder(tf.float32)

tf.placeholder(
    dtype,	# 数据类型。常用的是tf.float32,tf.float64等数值类型
    shape=None,	#数据形状。默认是None，就是一维值，也可以是多维（比如[2,3], [None, 3]表示列是3，行不定）
    name=None  #名称
)

# 定义卷积层, 20个卷积核, 卷积核大小为5，用Relu激活
conv0 = tf.layers.conv2d(datas_placeholder, 20, 5, activation=tf.nn.relu)
# 定义max-pooling层，pooling窗口为2x2，步长为2x2
pool0 = tf.layers.max_pooling2d(conv0, [2, 2], [2, 2])

# 定义卷积层, 40个卷积核, 卷积核大小为4，用Relu激活
conv1 = tf.layers.conv2d(pool0, 40, 4, activation=tf.nn.relu)
# 定义max-pooling层，pooling窗口为2x2，步长为2x2
pool1 = tf.layers.max_pooling2d(conv1, [2, 2], [2, 2])

# 将3维特征转换为1维向量
flatten = tf.layers.flatten(pool1)

# 全连接层，转换为长度为100的特征向量
fc = tf.layers.dense(flatten, 400, activation=tf.nn.relu)

# 加上DropOut，防止过拟合
dropout_fc = tf.layers.dropout(fc, dropout_placeholdr)

# 未激活的输出层
logits = tf.layers.dense(dropout_fc, num_classes)
# 预测输出
predicted_labels = tf.arg_max(logits, 1)

arg_max(a, axis=None, out=None)
# a 表示array
# axis 表示指定的轴，默认是None，表示把array平铺，
# out 默认为None，如果指定，那么返回的结果会插入其中

# 利用交叉熵定义损失
losses = tf.nn.softmax_cross_entropy_with_logits(
    labels=tf.one_hot(labels_placeholder, num_classes),
    logits=logits
)
# 平均损失
mean_loss = tf.reduce_mean(losses)

# 定义优化器，指定要优化的损失函数
optimizer = tf.train.AdamOptimizer(learning_rate=1e-2).minimize(losses)

# 用于保存和载入模型
saver = tf.train.Saver()

with tf.Session() as sess:

    if train:
        print("训练模式")
        # 如果是训练，初始化参数
        sess.run(tf.global_variables_initializer())
        # 定义输入和Label以填充容器，训练时dropout为0.25
        train_feed_dict = {
            datas_placeholder: datas,
            labels_placeholder: labels,
            dropout_placeholdr: 0.25
        }
        for step in range(136):
            _, mean_loss_val = sess.run([optimizer, mean_loss], feed_dict=train_feed_dict)

            if step % 10 == 0:
                print("step = {}tmean loss = {}".format(step, mean_loss_val))
        saver.save(sess, model_path)
        print("训练结束，保存模型到{}".format(model_path))
    else:
        print("测试模式")
        # 如果是测试，载入参数
        saver.restore(sess, model_path)
        print("从{}载入模型".format(model_path))
        # label和名称的对照关系
        label_name_dict = {
            0: "飞机",
            1: "汽车",
            2: "鸟"
        }
        # 定义输入和Label以填充容器，测试时dropout为0
        test_feed_dict = {
            datas_placeholder: test_datas,
            labels_placeholder: labels,
            dropout_placeholdr: 0
        }
        predicted_labels_val = sess.run(predicted_labels, feed_dict=test_feed_dict)
        # 真实label与模型预测label
        for fpath, real_label, predicted_label in zip(test_fpaths, test_labels, predicted_labels_val):
            # 将label id转换为label名
            real_label_name = label_name_dict[real_label]
            predicted_label_name = label_name_dict[predicted_label]
            print("{}t{} => {}".format(fpath, real_label_name, predicted_label_name))