如何在 for 循环中保存损失函数？

Question

早上好，我为了预测一个物理量建立了神经网络。我想运行 10次模型，以查看模型的稳定性。我如何创建一个 DataFrame，其中包含在第 10 次尝试中评估的所有损失函数（包括验证和训练）？

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)))

Answer 1

甚至不使用 Pandas，只需将它们附加到列表中即可。如果你真的需要一个数据帧，你可以使用 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 都是数字列表，每个时期一个，这可以按如下方式转换为数据帧：

# (Example data with two trials, each with 3 epochs)
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)

如何在 for 循环中保存损失函数？

Ho can save the loss function in a for cycle?

python

machine-learning

neural-network

spyder