早上好，我构建了一个神经网络来预测物理量。我想运行模型10次以观察模型的稳定性。我如何创建一个包含所有损失函数（包括验证和训练）的DataFrame，这些损失函数是在10次尝试中评估的？
for i in range(10): #per in numero di esperimenti test_size = 0.2 dataset = pd.read_csv('CompleteDataSet_original_Clean_TP.csv', decimal=',', delimiter = ";") label = dataset.iloc[:,-1] features = dataset[feat_labels] y_max_pre_normalize = max(label) y_min_pre_normalize = min(label) def denormalize(y): final_value = y*(y_max_pre_normalize-y_min_pre_normalize)+y_min_pre_normalize return final_value X_train1, X_test1, y_train1, y_test1 = train_test_split(features, label, test_size = test_size, shuffle = True) y_test2 = y_test1.to_frame() y_train2 = y_train1.to_frame() scaler1 = preprocessing.MinMaxScaler() scaler2 = preprocessing.MinMaxScaler() X_train = scaler1.fit_transform(X_train1) X_test = scaler2.fit_transform(X_test1) scaler3 = preprocessing.MinMaxScaler() scaler4 = preprocessing.MinMaxScaler() y_train = scaler3.fit_transform(y_train2) y_test = scaler4.fit_transform(y_test2) from keras import backend as K # ============================================================================= # Creo la rete # ============================================================================= optimizer = tf.keras.optimizers.Adam(lr=0.001) model = Sequential() model.add(Dense(100, input_shape = (X_train.shape[1],), activation = 'relu',kernel_initializer='glorot_uniform')) model.add(Dropout(0.2)) model.add(Dense(100, activation = 'relu',kernel_initializer='glorot_uniform')) model.add(Dropout(0.2)) model.add(Dense(100, activation = 'relu',kernel_initializer='glorot_uniform')) model.add(Dropout(0.2)) model.add(Dense(100, activation = 'relu',kernel_initializer='glorot_uniform')) model.add(Dense(1,activation = 'linear',kernel_initializer='glorot_uniform')) model.compile(loss = 'mse', optimizer = optimizer, metrics = ['mse', r2_score]) history = model.fit(X_train, y_train, epochs = 200, validation_split = 0.1, shuffle=False, batch_size=250) history_dict = history.history loss_values = history_dict['loss'] val_loss_values = history_dict['val_loss'] y_train_pred = model.predict(X_train) y_test_pred = model.predict(X_test) y_train_pred = denormalize(y_train_pred) y_test_pred = denormalize(y_test_pred) from sklearn.metrics import r2_score from sklearn import metrics r2_test.append(r2_score(y_test_pred, y_test1)) r2_train.append(r2_score(y_train_pred, y_train1)) # Measure MSE error. MSE_test.append(metrics.mean_squared_error(y_test_pred,y_test1)) MSE_train.append(metrics.mean_squared_error(y_train_pred,y_train1)) RMSE_test.append(np.sqrt(metrics.mean_squared_error(y_test_pred,y_test1))) RMSE_train.append(np.sqrt(metrics.mean_squared_error(y_train_pred,y_train1)))回答：
即使不使用Pandas，也可以将它们添加到列表中。如果确实需要DataFrame，可以使用pd.DataFrame()构造函数。
loss_and_val_loss = []for i in range(...): # ... loss_values = history_dict['loss'] val_loss_values = history_dict['val_loss'] loss_and_val_loss.append((loss_values, val_loss_values))# ...假设这两个_values都是按每轮次的数字列表，可以如下方式将其转换为DataFrame：
# (示例数据，两个试验，每个试验有3个轮次)loss_and_val_loss = [ ([1, 2, 3], [4, 5, 6]), ([7, 8, 9], [10, 11, 12]),]losses, val_losses = zip(*loss_and_val_loss)losses_df = pd.DataFrame(losses)val_losses_df = pd.DataFrame(val_losses)