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train_win.py

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+from datetime import datetime
+import keras
+from model_core import LeNet5Custom
+import matplotlib.pyplot as plt
+
+# 打印模型框架基本信息
+print(keras.__version__)
+
+
+def load_and_preprocess_data():
+    # 加载MNIST数据集
+    (train_images, train_labels), (test_images, test_labels) = keras.datasets.mnist.load_data()
+
+    # 反转像素值：255 - x
+    train_images = 255 - train_images
+    test_images = 255 - test_images
+
+    # 归一化像素值到 [0, 1] 区间
+    train_images = train_images.astype("float32") / 255
+    test_images = test_images.astype("float32") / 255
+
+    # 由于 MNIST 的图像是灰度图像，需要增加一个颜色通道
+    train_images = train_images.reshape(-1, 28, 28, 1)
+    test_images = test_images.reshape(-1, 28, 28, 1)
+
+    # 对标签进行分类编码
+    train_labels = keras.utils.to_categorical(train_labels, 10)
+    test_labels = keras.utils.to_categorical(test_labels, 10)
+
+    return train_images, train_labels, test_images, test_labels
+
+
+def lr_schedule(epoch):
+    lr = 1e-3
+    if epoch > 15:
+        lr *= 0.1
+    elif epoch > 55:
+        lr *= 0.01
+    return lr
+
+
+class TrainingHistory(keras.callbacks.Callback):
+    def on_train_begin(self, logs={}):
+        self.history = {'loss': [], 'accuracy': [], 'val_loss': [], 'val_accuracy': []}
+
+    def on_epoch_end(self, epoch, logs={}):
+        self.history['loss'].append(logs.get('loss'))
+        self.history['accuracy'].append(logs.get('accuracy'))
+        self.history['val_loss'].append(logs.get('val_loss'))
+        self.history['val_accuracy'].append(logs.get('val_accuracy'))
+
+
+
+def train_model_dp(train_images, train_labels, test_images, test_labels, dropout_rates, epochs):
+    lr_scheduler = keras.callbacks.LearningRateScheduler(lr_schedule)
+    early_stopping = keras.callbacks.EarlyStopping(monitor='val_loss',
+                                                   patience=3,
+                                                   restore_best_weights=True)
+    history_callback = TrainingHistory()
+
+    best_accuracy = 0.0
+    best_dropout_rate = None
+    best_model = None
+
+    for dropout_rate in dropout_rates:
+        print(f"Training model with dropout rate: {dropout_rate}")
+
+        model = LeNet5Custom(dropout_rate)
+        model = model.compile_model()
+
+        model.summary()
+
+        model.fit(train_images,
+                  train_labels,
+                  epochs=epochs,
+                  batch_size=256,
+                  validation_data=(test_images, test_labels),
+                  callbacks=[lr_scheduler, early_stopping, history_callback])
+
+        # 评估模型
+        test_loss, test_acc = model.evaluate(test_images, test_labels)
+        print(f'Test Accuracy with dropout rate {dropout_rate}: {test_acc:.4f} with loss {test_loss:.4f}')
+
+        if test_acc > best_accuracy:
+            best_accuracy = test_acc
+            best_dropout_rate = dropout_rate
+            best_model = model
+
+    return best_model, best_dropout_rate, best_accuracy, history_callback.history
+
+
+def plot_history(history):
+    epochs = range(1, len(history['loss']) + 1)
+
+    plt.figure(figsize=(12, 10))
+
+    plt.subplot(2, 1, 1)
+    plt.plot(epochs, history['loss'], label='Training Loss')
+    plt.plot(epochs, history['val_loss'], label='Validation Loss')
+    plt.title('Training and Validation Loss')
+    plt.xlabel('Epochs')
+    plt.ylabel('Loss')
+    plt.legend()
+
+    plt.subplot(2, 1, 2)
+    plt.plot(epochs, history['accuracy'], label='Training Accuracy')
+    plt.plot(epochs, history['val_accuracy'], label='Validation Accuracy')
+    plt.title('Training and Validation Accuracy')
+    plt.xlabel('Epochs')
+    plt.ylabel('Accuracy')
+    plt.legend()
+
+    plt.tight_layout()
+    plt.show()
+
+
+def save_model(model, accuracy):
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")  # 获取当前时间戳并格式化为字符串
+    filepath = f'./model/lenet5_model_best_{timestamp}.h5'
+    model.save(filepath=filepath)
+    print(f"Best model weights saved to {filepath} with test accuracy: {accuracy:.4f}")
+
+
+if __name__ == "__main__":
+    keras.backend.clear_session()
+
+    train_images, train_labels, test_images, test_labels = load_and_preprocess_data()
+
+    dropout_rates = [0.3]  # 0.3 is the best of [0.1, 0.2, 0.3, 0.4, 0.5]
+    epochs = 100
+
+    best_model, best_dropout_rate, best_accuracy, history = train_model_dp(train_images,
+                                                                           train_labels,
+                                                                           test_images,
+                                                                           test_labels,
+                                                                           dropout_rates,
+                                                                           epochs)
+
+    print(f'Best dropout rate: {best_dropout_rate} with test accuracy: {best_accuracy:.4f}')
+
+    save_model(best_model, best_accuracy)
+    plot_history(history)