# 这里有两个callback函数:早停和模型检查点 callbacks_list=[ keras.callbacks.EarlyStopping( monitor="val_accuracy",#监控指标 patience=2 #两轮内不再改善中断训练 ), keras.callbacks.ModelCheckpoint( filepath="checkpoint_path", monitor="val_loss", save_best_only=True ) ] #模型获取 model=get_minist_model() model.compile(optimizer="rmsprop", loss="sparse_categorical_crossentropy", metrics=["accuracy"]) model.fit(train_images,train_labels, epochs=10,callbacks=callbacks_list, #该参数使用回调函数 validation_data=(val_images,val_labels)) test_metrics=model.evaluate(test_images,test_labels)#计算模型在新数据上的损失和指标 predictions=model.predict(test_images)#计算模型在新数据上的分类概率
#也可以在训练完成后手动保存模型,只需调用model.save('my_checkpoint_path')。 #重新加载模型 model_new=keras.models.load_model("checkpoint_path.keras")
on_epoch_begin(epoch, logs) ←----在每轮开始时被调用on_epoch_end(epoch, logs) ←----在每轮结束时被调用on_batch_begin(batch, logs) ←----在处理每个批量之前被调用on_batch_end(batch, logs) ←----在处理每个批量之后被调用on_train_begin(logs) ←----在训练开始时被调用on_train_end(logs ←----在训练结束时被调用
from matplotlib import pyplot as plt # 实现记录每一轮中每个batch训练后的损失,并为每个epoch绘制一个图 class LossHistory(keras.callbacks.Callback): def on_train_begin(self, logs): self.per_batch_losses = [] def on_batch_end(self, batch, logs): self.per_batch_losses.append(logs.get("loss")) def on_epoch_end(self, epoch, logs): plt.clf() plt.plot(range(len(self.per_batch_losses)), self.per_batch_losses, label="Training loss for each batch") plt.xlabel(f"Batch (epoch {epoch})") plt.ylabel("Loss") plt.legend() plt.savefig(f"plot_at_epoch_{epoch}") self.per_batch_losses = [] #清空,方便下一轮的技术
model = get_mnist_model() model.compile(optimizer="rmsprop", loss="sparse_categorical_crossentropy", metrics=["accuracy"]) model.fit(train_images, train_labels, epochs=10, callbacks=[LossHistory()], validation_data=(val_images, val_labels))
def get_minist_model(): inputs=keras.Input(shape=(28*28,)) features=layers.Dense(512,activation="relu")(inputs) features=layers.Dropout(0.5)(features) outputs=layers.Dense(10,activation="softmax")(features) model=keras.Model(inputs,outputs) return model #datset from tensorflow.keras.datasets import mnist (train_images,train_labels),(test_images,test_labels)=mnist.load_data() train_images=train_images.reshape((60000,28*28)).astype("float32")/255 test_images=test_images.reshape((10000,28*28)).astype("float32")/255 train_images,val_images=train_images[10000:],train_images[:10000] train_labels,val_labels=train_labels[10000:],train_labels[:10000]
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