基于机器学习框架tensorflow的图像分类

结果截图：

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#训练函数：import osimport numpy as npimport tensorflow as tfimport input_dataimport modelN_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 = 208BATCH_SIZE = 32 # 每批数据的大小CAPACITY = 256MAX_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 <= 0.0001def 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()# trainrun_training()#模型和数据输入处理过程见附件啦