将这段python代码转换为c语言伪代码形式checkpoint_path = './model_pth/dnn.h5' checkpoint = tf.keras.callbacks.ModelCheckpoint(checkpoint_path, monitor='val_accuracy', verbose=1,save_best_only=True, mode='max') early_stopping = tf.keras.callbacks.EarlyStopping(monitor ='val_loss', patience=200, verbose=1) optimizer = tf.keras.optimizers.Adam(learning_rate=1e-3, epsilon=1e-7) DNN_model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy']) callbacks_list = [checkpoint, early_stopping] start = time.time() history = DNN_model.fit(X_train, y_train, validation_split = 0.1, epochs=1000, batch_size=32, verbose=2,callbacks = callbacks_list) end = time.time() print('DNN模型训练时间: ',end - start)

定义变量：

char* checkpoint_path = "./model_pth/dnn.h5";
float learning_rate = 1e-3;
float epsilon = 1e-7;
int patience = 200;
int epochs = 1000;
int batch_size = 32;
float* X_train;
float* y_train;
float* validation_split = 0.1;
float* history;
float start, end;

定义模型：

ModelCheckpoint checkpoint = {checkpoint_path, "val_accuracy", 1, 1, "max"};
EarlyStopping early_stopping = {"val_loss", patience, 1};
Adam optimizer = {learning_rate, epsilon};
compile(DNN_model, "categorical_crossentropy", optimizer, {"accuracy"});

Callback* callbacks_list[2] = {&checkpoint, &early_stopping};

训练模型：

start = time();
history = fit(DNN_model, X_train, y_train, validation_split, epochs, batch_size, callbacks_list, 2);
end = time();
printf("DNN模型训练时间: %f", end - start);