详解TensorFlow训练网络两种方式

TensorFlow训练网络有两种方式，一种是基于tensor(array)，另外一种是迭代器

两种方式区别是：

• 第一种是要加载全部数据形成一个tensor，然后调用model.fit()然后指定参数batch_size进行将所有数据进行分批训练
• 第二种是自己先将数据分批形成一个迭代器，然后遍历这个迭代器，分别训练每个批次的数据

方式一：通过迭代器

IMAGE_SIZE = 1000

# step1:加载数据集
(train_images, train_labels), (val_images, val_labels) = tf.keras.datasets.mnist.load_data()

# step2:将图像归一化
train_images, val_images = train_images / 255.0, val_images / 255.0

# step3:设置训练集大小
train_images = train_images[:IMAGE_SIZE]
val_images = val_images[:IMAGE_SIZE]
train_labels = train_labels[:IMAGE_SIZE]
val_labels = val_labels[:IMAGE_SIZE]

# step4:将图像的维度变为(IMAGE_SIZE,28,28,1)
train_images = tf.expand_dims(train_images, axis=3)
val_images = tf.expand_dims(val_images, axis=3)

# step5:将图像的尺寸变为(32,32)
train_images = tf.image.resize(train_images, [32, 32])
val_images = tf.image.resize(val_images, [32, 32])

# step6:将数据变为迭代器
train_loader = tf.data.Dataset.from_tensor_slices((train_images, train_labels)).batch(32)
val_loader = tf.data.Dataset.from_tensor_slices((val_images, val_labels)).batch(IMAGE_SIZE)

# step5:导入模型
model = LeNet5()

# 让模型知道输入数据的形式
model.build(input_shape=(1, 32, 32, 1))

# 结局Output Shape为 multiple
model.call(Input(shape=(32, 32, 1)))

# step6:编译模型
model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

# 权重保存路径
checkpoint_path = "./weight/cp.ckpt"

# 回调函数，用户保存权重
save_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path,
                                                   save_best_only=True,
                                                   save_weights_only=True,
                                                   monitor='val_loss',
                              http://www.cppcns.com                     verbose=0)

EPOCHS = 11

for epoch in range(1, EPOCHS):
    # 每个批次训练集误差
    train_epoch_loss_avg = tf.keras.metrics.Mean()
    # 每个批次训练集精度
    train_http://www.cppcns.comepoch_accuracy = tf.keras.me编程客栈trics.SparseCategoricalAccuracy()
    # 每个批次验证集误差
    val_epoch_loss_avg = tf.keras.metrics.Mean()
    # 每个批次验证集精度
    val_epoch_accuracy = tf.keras.metrics.SparseCategoricalAccuracy()

    for x, y in train_loader:
        history = model.fit(x,
                            y,
                            validation_data=val_loader,
                            callbacks=[save_callback],
                            verbose=0)

        # 更新误差，保留上次
        train_epoch_loss_avg.update_state(history.history['loss'][0])
        # 更新精度，保留上次
        train_epoch_accuracy.update_state(y, model(x, training=True))

        val_epoch_loss_avg.update_state(history.history['val_loss'][0])
        val_epoch_accuracy.update_state(next(iter(val_loader))[1], model(next(iter(val_loader))[0], training=True))

    # 使用.result()计算每个批次的误差和精度结果
    print("Epoch {:d}: trainLoss: {:.3f}, trainAccuracy: {:.3%} valLoss: {:.3f}, valAccuracy: {:.3%}".format(epoch,
                                                                                                             train_epoch_loss_avg.result(),
                                                                                                             train_epoch_accuracy.result(),
                                                                                                             val_epoch_loss_avg.result(),
                                                                                                             val_epoch_accuracy.result()))

方式二：适用model.fit()进行分批训练

import model_sequential

(train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.mnist.load_data()

# step2:将图像归一化
train_images, test_images = train_images / 255.0, test_images / 255.0

# step3:将图像的维度变为(60000,28,28,1)
train_images = tf.expand_dims(train_images, axis=3)
test_images = tf.expand_dims(test_images, axis=3)

# step4:将图像尺寸改为(60000,32,32,1)
train_images = tf.image.resize(train_images, [32, 32])
test_images = tf.image.resize(test_images, [32, 32])

# step5:导入模型
# history = LeNet5()
history = model_sequential.LeNet()

# 让模型知道输入数据的形式
history.build(input_shape=(1, 32, 32, 1))
# history(tf.zeros([1, 32, 32, 1]))

# 结局Output Shape为 IlJIMmultiple
history.call(Input(shape=(32, 32, 1)))
history.summary()

# step6:编译模型
history.compile(optimizer='adam',
                loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
                metrics=['accuracy'])

# 权重保存路径
checkpoint_path = "./weight/cp.ckpt"

# 回调函数，用户保存权重
save_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path,
                                                   save_best_only=True,
                                                   save_weights_only=True,
                                                   monitor='val_loss',
                                                   verbose=1)
# step7:训练模型
history = history.fit(train_images,
                      train_labels,
                      epochs=10,
                      batch_size=32,
                      validation_data=(t编程客栈est_images, test_labels),
                      callbacks=[save_callback])

到此这篇关于详解TensorFlow训练网络两种方式的文章就介绍到这了,更多相关TensorFlow训练网络内容请搜索我们以前的文章或继续浏览下面的相关文章希望大家以后多多支持我们！