吴裕雄 python 神经网络——TensorFlow 数据集高层操作

Posted 2021-12-01 tszr

import tempfile
import tensorflow as tf

train_files = tf.train.match_filenames_once("E:\\\\output.tfrecords")
test_files = tf.train.match_filenames_once("E:\\\\output_test.tfrecords")

# 解析一个TFRecord的方法。
def parser(record):
    features = tf.parse_single_example(record,
        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)
    retyped_images = tf.cast(decoded_images, tf.float32)
    images = tf.reshape(retyped_images, [784])
    labels = tf.cast(features[‘label‘],tf.int32)
    #pixels = tf.cast(features[‘pixels‘],tf.int32)
    return images, labels

image_size = 299          # 定义神经网络输入层图片的大小。
batch_size = 100          # 定义组合数据batch的大小。
shuffle_buffer = 10000   # 定义随机打乱数据时buffer的大小。

# 定义读取训练数据的数据集。
dataset = tf.data.TFRecordDataset(train_files)
dataset = dataset.map(parser)

# 对数据进行shuffle和batching操作。这里省略了对图像做随机调整的预处理步骤。
dataset = dataset.shuffle(shuffle_buffer).batch(batch_size)

# 重复NUM_EPOCHS个epoch。
NUM_EPOCHS = 10
dataset = dataset.repeat(NUM_EPOCHS)

# 定义数据集迭代器。
iterator = dataset.make_initializable_iterator()
image_batch, label_batch = iterator.get_next()

# 定义神经网络的结构以及优化过程。这里与7.3.4小节相同。
def inference(input_tensor, weights1, biases1, weights2, biases2):
    layer1 = tf.nn.relu(tf.matmul(input_tensor, weights1) + biases1)
    return tf.matmul(layer1, weights2) + biases2

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)

# 定义测试用的Dataset。
test_dataset = tf.data.TFRecordDataset(test_files)
test_dataset = test_dataset.map(parser)
test_dataset = test_dataset.batch(batch_size)

# 定义测试数据上的迭代器。
test_iterator = test_dataset.make_initializable_iterator()
test_image_batch, test_label_batch = test_iterator.get_next()

# 定义测试数据上的预测结果。
test_logit = inference(test_image_batch, weights1, biases1, weights2, biases2)
predictions = tf.argmax(test_logit, axis=-1, output_type=tf.int32)
 
# 声明会话并运行神经网络的优化过程。
with tf.Session() as sess:  
    # 初始化变量。
    sess.run((tf.global_variables_initializer(),tf.local_variables_initializer()))
    # 初始化训练数据的迭代器。
    sess.run(iterator.initializer)
    # 循环进行训练，直到数据集完成输入、抛出OutOfRangeError错误。
    while True:
        try:
            sess.run(train_step)
        except tf.errors.OutOfRangeError:
            break
    test_results = []
    test_labels = []
    # 初始化测试数据的迭代器。
    sess.run(test_iterator.initializer)
    # 获取预测结果。
    while True:
        try:
            pred, label = sess.run([predictions, test_label_batch])
            test_results.extend(pred)
            test_labels.extend(label)
        except tf.errors.OutOfRangeError:
            break

# 计算准确率
correct = [float(y == y_) for (y, y_) in zip (test_results, test_labels)]
accuracy = sum(correct) / len(correct)
print("Test accuracy is:", accuracy)