将这段代码转换为伪代码形式，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', 优化器=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, 详细=2 ,callbacks = callbacks_list) end = time.time() print('DNN模型培训时间: ',end - start)

设置checkpoint路径为'./model_pth/dnn.h5'
设置ModelCheckpoint回调函数，监控验证集准确率，保存最佳模型权重到checkpoint_path，输出保存信息，最大化监控指标。
设置EarlyStopping回调函数，监控验证集损失，如果连续200个epoch验证集损失没有改善就停止训练，输出停止信息。
设置Adam优化器，学习率为1e-3，epsilon为1e-7。
用交叉熵作为损失函数，Adam优化器进行优化，评价指标为准确率。
设置回调函数列表callbacks_list为[checkpoint, early_stopping]。
记录训练开始时间为start。
对DNN_model进行fit训练，使用X_train和y_train作为训练数据和标签，将10%的训练集用作验证集，训练1000个epoch，每个batch大小为32，输出每个epoch的训练信息，使用callbacks_list中的回调函数进行训练过程中的保存和停止，记录训练历史信息到history。
记录训练结束时间为end。
输出DNN模型训练时间为end - start。

相关问题

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);

将这段代码转换为伪代码形式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)

设置模型保存路径为 './model_pth/dnn.h5'
定义 ModelCheckpoint 回调函数，监控验证集上的准确率，如果有提升则保存最佳的模型权重
定义 EarlyStopping 回调函数，监控验证集上的损失函数，如果连续200个epoch没有改善，则提前停止训练
定义优化器为Adam，设置学习率和epsilon参数
编译模型，设置损失函数和评价指标
创建回调函数列表，将 ModelCheckpoint 和 EarlyStopping 添加到列表中
开始计时
用训练数据训练模型，设置验证集比例、迭代次数、批次大小、回调函数列表等参数
停止计时，输出模型训练时间
将训练过程中的损失函数和评价指标保存在 history 变量中