驗證准確性並未提高 CNN

Question

我對深度學習還很陌生，現在正嘗試根據 EEG 數據預測消費者的選擇。 整個數據集包含 1045 個 EEG 記錄，每個記錄對應一個 label，表示對產品的喜歡或不喜歡。 課程分布如下（44% 喜歡，56% 不喜歡）。 我讀到卷積神經網絡適合處理原始 EEG 數據，因此我嘗試使用以下結構實現基於 keras 的 a.network：

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(full_data, target, test_size=0.20, random_state=42)

y_train = np.asarray(y_train).astype('float32').reshape((-1,1))
y_test = np.asarray(y_test).astype('float32').reshape((-1,1))


# X_train.shape = ((836, 512, 14))
# y_train.shape = ((836, 1))

from keras.optimizers import Adam
from keras.optimizers import SGD
from keras.layers import MaxPooling1D
model = Sequential()

model.add(Conv1D(16, kernel_size=3, activation="relu", input_shape=(512,14)))

model.add(MaxPooling1D())

model.add(Conv1D(8, kernel_size=3, activation="relu"))

model.add(MaxPooling1D())

model.add(Flatten())

model.add(Dense(1, activation="sigmoid"))

model.compile(optimizer=Adam(lr = 0.001), loss='binary_crossentropy', metrics=['accuracy'])

model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=20, batch_size = 64)

但是，當我適合 model 時，以下 output 的驗證准確性根本沒有改變：

Epoch 1/20
14/14 [==============================] - 0s 32ms/step - loss: 292.6353 - accuracy: 0.5383 - val_loss: 0.7884 - val_accuracy: 0.5407
Epoch 2/20
14/14 [==============================] - 0s 7ms/step - loss: 1.3748 - accuracy: 0.5598 - val_loss: 0.8860 - val_accuracy: 0.5502
Epoch 3/20
14/14 [==============================] - 0s 6ms/step - loss: 1.0537 - accuracy: 0.5598 - val_loss: 0.7629 - val_accuracy: 0.5455
Epoch 4/20
14/14 [==============================] - 0s 6ms/step - loss: 0.8827 - accuracy: 0.5598 - val_loss: 0.7010 - val_accuracy: 0.5455
Epoch 5/20
14/14 [==============================] - 0s 6ms/step - loss: 0.7988 - accuracy: 0.5598 - val_loss: 0.8689 - val_accuracy: 0.5407
Epoch 6/20
14/14 [==============================] - 0s 6ms/step - loss: 1.0221 - accuracy: 0.5610 - val_loss: 0.6961 - val_accuracy: 0.5455
Epoch 7/20
14/14 [==============================] - 0s 6ms/step - loss: 0.7415 - accuracy: 0.5598 - val_loss: 0.6945 - val_accuracy: 0.5455
Epoch 8/20
14/14 [==============================] - 0s 6ms/step - loss: 0.7381 - accuracy: 0.5574 - val_loss: 0.7761 - val_accuracy: 0.5455
Epoch 9/20
14/14 [==============================] - 0s 6ms/step - loss: 0.7326 - accuracy: 0.5598 - val_loss: 0.6926 - val_accuracy: 0.5455
Epoch 10/20
14/14 [==============================] - 0s 6ms/step - loss: 0.7338 - accuracy: 0.5598 - val_loss: 0.6917 - val_accuracy: 0.5455
Epoch 11/20
14/14 [==============================] - 0s 7ms/step - loss: 0.7203 - accuracy: 0.5610 - val_loss: 0.6916 - val_accuracy: 0.5455
Epoch 12/20
14/14 [==============================] - 0s 6ms/step - loss: 0.7192 - accuracy: 0.5610 - val_loss: 0.6914 - val_accuracy: 0.5455
Epoch 13/20
14/14 [==============================] - 0s 6ms/step - loss: 0.7174 - accuracy: 0.5610 - val_loss: 0.6912 - val_accuracy: 0.5455
Epoch 14/20
14/14 [==============================] - 0s 6ms/step - loss: 0.7155 - accuracy: 0.5610 - val_loss: 0.6911 - val_accuracy: 0.5455
Epoch 15/20
14/14 [==============================] - 0s 6ms/step - loss: 0.7143 - accuracy: 0.5610 - val_loss: 0.6910 - val_accuracy: 0.5455
Epoch 16/20
14/14 [==============================] - 0s 6ms/step - loss: 0.7129 - accuracy: 0.5610 - val_loss: 0.6909 - val_accuracy: 0.5455
Epoch 17/20
14/14 [==============================] - 0s 6ms/step - loss: 0.7114 - accuracy: 0.5610 - val_loss: 0.6907 - val_accuracy: 0.5455
Epoch 18/20
14/14 [==============================] - 0s 6ms/step - loss: 0.7103 - accuracy: 0.5610 - val_loss: 0.6906 - val_accuracy: 0.5455
Epoch 19/20
14/14 [==============================] - 0s 6ms/step - loss: 0.7088 - accuracy: 0.5610 - val_loss: 0.6906 - val_accuracy: 0.5455
Epoch 20/20
14/14 [==============================] - 0s 6ms/step - loss: 0.7075 - accuracy: 0.5610 - val_loss: 0.6905 - val_accuracy: 0.5455

提前感謝您的任何見解！

Answer 1

您遇到的現象稱為underfitting 。 當我們的訓練數據質量不足，或者您的網絡架構太小而無法學習問題時，就會發生這種情況。

嘗試規范化您的輸入數據並嘗試不同的網絡架構、學習率和激活函數。

正如@Muhammad Shahzad 在他的評論中所說，在扁平化之后添加一些密集層將是您應該嘗試的具體架構調整。

Answer 2

您還可以增加紀元並且必須增加數據集。 你也可以使用 -

train_datagen= ImageDataGenerator(
        rescale=1./255,
        shear_range=0.2,
        zoom_range=0.2,
        horizontal_flip=True,
        vertical_flip = True,
        channel_shift_range=0.2,
        fill_mode='nearest'
        )

為 model 提供更多數據，我希望您可以提高 validation_accuracy。