Keras - 有没有办法减少 categorical_accuracy 和 val_categorical_accuracy 之间的价值差距？
Keras - Is There an way to reduce value gap between categorical_accuracy and val_categorical_accuracy?
我正在尝试构建和训练 LSTM 神经网络。
这是我的代码（摘要版）：
X_train, X_test, y_train, y_test = train_test_split(np.array(sequences), to_categorical(labels).astype(int), test_size=0.2)
X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2)
log_dir = os.path.join('Logs')
tb_callback = TensorBoard(log_dir=log_dir)
model = Sequential()
model.add(LSTM(64, return_sequences=True, activation='tanh', input_shape=(60,1662)))
model.add(LSTM(128, return_sequences=True, activation='tanh', dropout=0.31))
model.add(LSTM(64, return_sequences=False, activation='tanh'))
model.add(Dense(32, activation='relu'))
model.add(Dense(len(actions), activation='softmax'))
model.compile(optimizer='Adam', loss='categorical_crossentropy', metrics=['categorical_accuracy'])
val_dataset = tf.data.Dataset.from_tensor_slices((X_val, y_val)) # default slice percentage check
val_dataset = val_dataset.batch(256)
model.fit(X_train, y_train, batch_size=256, epochs=250, callbacks=[tb_callback], validation_data=val_dataset)
模型拟合结果：
Epoch 248/250
8/8 [==============================] - 2s 252ms/step - loss: 0.4563 - categorical_accuracy: 0.8641 - val_loss: 2.1406 - val_categorical_accuracy: 0.6104
Epoch 249/250
8/8 [==============================] - 2s 255ms/step - loss: 0.4542 - categorical_accuracy: 0.8672 - val_loss: 2.2365 - val_categorical_accuracy: 0.5667
Epoch 250/250
8/8 [==============================] - 2s 234ms/step - loss: 0.4865 - categorical_accuracy: 0.8562 - val_loss: 2.1668 - val_categorical_accuracy: 0.5875
我想缩小 categorical_accuracy 和 val_categorical_accuracy 之间的价值差距。
我能知道怎么做吗？
感谢您阅读我的文章。
当您的训练数据和验证数据之间存在如此大的差异时，这意味着您的模型过度拟合。
所以寻找如何防止过度拟合。通常您需要做的 是向您的数据集添加更多数据。
It won’t work every time, but training with more data can help algorithms detect the signal better.
Try to stop before overfit
另一个方面是尝试停止模型并降低学习率
我正在尝试构建和训练 LSTM 神经网络。
这是我的代码（摘要版）：
X_train, X_test, y_train, y_test = train_test_split(np.array(sequences), to_categorical(labels).astype(int), test_size=0.2)
X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2)
log_dir = os.path.join('Logs')
tb_callback = TensorBoard(log_dir=log_dir)
model = Sequential()
model.add(LSTM(64, return_sequences=True, activation='tanh', input_shape=(60,1662)))
model.add(LSTM(128, return_sequences=True, activation='tanh', dropout=0.31))
model.add(LSTM(64, return_sequences=False, activation='tanh'))
model.add(Dense(32, activation='relu'))
model.add(Dense(len(actions), activation='softmax'))
model.compile(optimizer='Adam', loss='categorical_crossentropy', metrics=['categorical_accuracy'])
val_dataset = tf.data.Dataset.from_tensor_slices((X_val, y_val)) # default slice percentage check
val_dataset = val_dataset.batch(256)
model.fit(X_train, y_train, batch_size=256, epochs=250, callbacks=[tb_callback], validation_data=val_dataset)
模型拟合结果：
Epoch 248/250
8/8 [==============================] - 2s 252ms/step - loss: 0.4563 - categorical_accuracy: 0.8641 - val_loss: 2.1406 - val_categorical_accuracy: 0.6104
Epoch 249/250
8/8 [==============================] - 2s 255ms/step - loss: 0.4542 - categorical_accuracy: 0.8672 - val_loss: 2.2365 - val_categorical_accuracy: 0.5667
Epoch 250/250
8/8 [==============================] - 2s 234ms/step - loss: 0.4865 - categorical_accuracy: 0.8562 - val_loss: 2.1668 - val_categorical_accuracy: 0.5875
我想缩小 categorical_accuracy 和 val_categorical_accuracy 之间的价值差距。
我能知道怎么做吗？
感谢您阅读我的文章。
当您的训练数据和验证数据之间存在如此大的差异时，这意味着您的模型过度拟合。
所以寻找如何防止过度拟合。通常您需要做的 是向您的数据集添加更多数据。
It won’t work every time, but training with more data can help algorithms detect the signal better.
Try to stop before overfit
另一个方面是尝试停止模型并降低学习率