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', 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_epoch_accuracy = tf.keras.metrics.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,
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 multiple 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=(test_images, test_labels), callbacks=[save_callback])
