TensorflowLearn/input_files_queue_framework.py at master · linchaolong-learn/TensorflowLearn · GitHub

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import tensorflow as tf

# 创建文件列表，通过文件列表创建输入文件队列，读取文件为本章第一节创建的文件
files = tf.train.match_filenames_once('build/output.tfrecords')
filename_queue = tf.train.string_input_producer(files, shuffle=False)

# 解析TFRecord文件里的数据
reader = tf.TFRecordReader()
_, serialized_example = reader.read(filename_queue)
features = tf.parse_single_example(serialized_example, features={
    'pixels': tf.FixedLenFeature([], tf.int64),
    'label': tf.FixedLenFeature([], tf.int64),
    'image_raw': tf.FixedLenFeature([], tf.string),
})

# 从原始图片解析出像素矩阵
decoded_images = tf.decode_raw(features['image_raw'], tf.uint8)
retyped_images = tf.cast(decoded_images, tf.float32)
labels = tf.cast(features['label'], tf.int32)
# pixels = tf.cast(features['pixels'],tf.int32)
images = tf.reshape(retyped_images, [784])

# 将文件以100个为一组打包
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)


# 训练模型
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]))

y = inference(image_batch, weights1, biases1, weights2, biases2)

# 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()))
    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)