基于机器学习框架tensorflow的图像分类

结果截图：

核心代码：

#训练函数：
import os
import numpy as np
import tensorflow as tf
import input_data
import model

N_CLASSES = 2  # 2个输出神经元，［1，0］ 或者 ［0，1］猫和狗的概率
IMG_W = 208  # 重新定义图片的大小，图片如果过大则训练比较慢
IMG_H = 208
BATCH_SIZE = 32  # 每批数据的大小
CAPACITY = 256
MAX_STEP = 1000  # 训练的步数，应当 >= 10000，因为训练过慢，只以1000次为例
learning_rate = 0.0001  # 学习率，建议刚开始的 learning_rate <= 0.0001


def run_training():
    # 数据集
    train_dir = 'd:/computer_sighting/try2_dogcat/train/'  # 训练集
    # logs_train_dir 存放训练模型的过程的数据，在tensorboard 中查看
    logs_train_dir = 'd:/computer_sighting/try2_dogcat/logs/'

    # 获取图片和标签集
    train, train_label = input_data.get_files(train_dir)
    # 生成批次
    train_batch, train_label_batch = input_data.get_batch(train,
                                                          train_label,
                                                          IMG_W,
                                                          IMG_H,
                                                          BATCH_SIZE,
                                                          CAPACITY)
    # 进入模型
    train_logits = model.inference(train_batch, BATCH_SIZE, N_CLASSES)
    # 获取 loss
    train_loss = model.losses(train_logits, train_label_batch)
    # 训练
    train_op = model.trainning(train_loss, learning_rate)
    # 获取准确率
    train__acc = model.evaluation(train_logits, train_label_batch)
    # 合并 summary
    summary_op = tf.summary.merge_all()
    sess = tf.Session()
    # 保存summary
    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:
                # 每隔2000步保存一下模型，模型保存在 checkpoint_path 中
                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()


# train
run_training()
#模型和数据输入处理过程见附件啦