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