深度学习进度05（TFRecords存储和读取、神经网络）

TFRecords存储和读取：

什么是TFRecords：

 Example结构解析：

 

 

写：

 def write_to_tfrecords(self, image_batch, label_batch):
        """
        将样本的特征值和目标值一起写入tfrecords文件
        :param image:
        :param label:
        :return:
        """
        with tf.compat.v1.python_io.TFRecordWriter("cifar10.tfrecords") as writer:
            # 循环构造example对象，并序列化写入文件
            for i in range(100):
                image = image_batch[i].tostring()
                label = label_batch[i][0]
                # print("tfrecords_image:
", image)
                # print("tfrecords_label:
", label)
                example = tf.train.Example(features=tf.train.Features(feature={
                    "image": tf.train.Feature(bytes_list=tf.train.BytesList(value=[image])),
                    "label": tf.train.Feature(int64_list=tf.train.Int64List(value=[label])),
                }))
                # example.SerializeToString()
                # 将序列化后的example写入文件
                writer.write(example.SerializeToString())

        return None

读：

 def read_tfrecords(self):
        """
        读取TFRecords文件
        :return:
        """
        # 1、构造文件名队列
        file_queue = tf.compat.v1.train.string_input_producer(["cifar10.tfrecords"])

        # 2、读取与解码
        # 读取
        reader = tf.compat.v1.TFRecordReader()
        key, value = reader.read(file_queue)

        # 解析example
        feature = tf.compat.v1.parse_single_example(value, features={
            "image": tf.compat.v1.FixedLenFeature([], tf.string),
            "label": tf.compat.v1.FixedLenFeature([], tf.int64)
        })
        image = feature["image"]
        label = feature["label"]
        print("read_tf_image:
", image)
        print("read_tf_label:
", label)

        # 解码
        image_decoded = tf.compat.v1.decode_raw(image, tf.uint8)
        print("image_decoded:
", image_decoded)
        # 图像形状调整
        image_reshaped = tf.reshape(image_decoded, [self.height, self.width, self.channel])
        print("image_reshaped:
", image_reshaped)

        # 3、构造批处理队列
        image_batch, label_batch = tf.compat.v1.train.batch([image_reshaped, label], batch_size=100, num_threads=2, capacity=100)
        print("image_batch:
", image_batch)
        print("label_batch:
", label_batch)

        # 开启会话
        with tf.compat.v1.Session() as sess:

            # 开启线程
            coord = tf.train.Coordinator()
            threads = tf.compat.v1.train.start_queue_runners(sess=sess, coord=coord)

            image_value, label_value = sess.run([image_batch, label_batch])
            print("image_value:
", image_value)
            print("label_value:
", label_value)

            # 回收资源
            coord.request_stop()
            coord.join(threads)

        return None

 

 神经网络：

 感知机：

 主要用途：

 softmax回归：

 

 交叉熵损失：