Tensorflow细节-P196-输入数据处理框架

要点

1、filename_queue = tf.train.string_input_producer(files, shuffle=False) 表示创建一个队列来维护列表

2、min_after_dequeue = 10000queue runner线程要保证队列中至少剩下min_after_dequeue个数据。

如果min_after_dequeue设置的过少，则即使shuffle为true，也达不到好的混合效果。

3、·sess.run((tf.global_variables_initializer(),

tf.local_variables_initializer()))· 记得要加一个tf.local_variables_initializer(）

import tensorflow as tf

files = tf.train.match_filenames_once("output.tfrecords")  # 把文件读进来output.tfrecords
filename_queue = tf.train.string_input_producer(files, shuffle=False)  # 创建一个队列来维护列表
# 读取文件。
reader = tf.TFRecordReader()
_, serialized_example = reader.read(filename_queue)
# 解析读取的样例。
features = tf.parse_single_example(
    serialized_example,
    features={
        'image_raw': tf.FixedLenFeature([], tf.string),
        'pixels': tf.FixedLenFeature([], tf.int64),
        'label': tf.FixedLenFeature([], tf.int64)
    })

decoded_images = tf.decode_raw(features['image_raw'],tf.uint8)  # 解码图像
retyped_images = tf.cast(decoded_images, tf.float32)  # 将图像转换为整数
labels = tf.cast(features['label'], tf.int32)
#pixels = tf.cast(features['pixels'],tf.int32)
images = tf.reshape(retyped_images, [784])

min_after_dequeue = 10000
batch_size = 100
capacity = min_after_dequeue + 3 * batch_size
image_batch, label_batch = tf.train.shuffle_batch([images, labels], batch_size=batch_size,
                                                capacity=capacity, min_after_dequeue=min_after_dequeue)

def inference(input_tensor, weights1, biases1, weights2, biases2):
    layer1 = tf.nn.relu(tf.matmul(input_tensor, weights1) + biases1)
    return tf.matmul(layer1, weights2) + biases2

# 模型相关的参数
INPUT_NODE = 784
OUTPUT_NODE = 10
LAYER1_NODE = 500
REGULARAZTION_RATE = 0.0001
TRAINING_STEPS = 5000

weights1 = tf.Variable(tf.truncated_normal([INPUT_NODE, LAYER1_NODE], stddev=0.1))
biases1 = tf.Variable(tf.constant(0.1, shape=[LAYER1_NODE]))

weights2 = tf.Variable(tf.truncated_normal([LAYER1_NODE, OUTPUT_NODE], stddev=0.1))
biases2 = tf.Variable(tf.constant(0.1, shape=[OUTPUT_NODE]))

# 计算交叉熵及其平均值
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y, labels=label_batch)
cross_entropy_mean = tf.reduce_mean(cross_entropy)

# 损失函数的计算
regularizer = tf.contrib.layers.l2_regularizer(REGULARAZTION_RATE)
regularaztion = regularizer(weights1) + regularizer(weights2)
loss = cross_entropy_mean + regularaztion

# 优化损失函数
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(loss)

# 初始化会话，并开始训练过程。
with tf.Session() as sess:
    # tf.global_variables_initializer().run()
    sess.run((tf.global_variables_initializer(),
              tf.local_variables_initializer()))  # 记得要加一个tf.local_variables_initializer()
    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(sess=sess, coord=coord)
    # 循环的训练神经网络。
    for i in range(TRAINING_STEPS):
        if i % 1000 == 0:
            print("After %d training step(s), loss is %g " % (i, sess.run(loss)))
        sess.run(train_step)

    coord.request_stop()
    coord.join(threads)