Keras model not learning

Question

我的Keras模型沒有學習任何東西，我不明白爲什麼。我甚至將訓練集大小減少到5個元素，並且該模型仍然不適合訓練數據。

loss function visualized with TensorBoard

這裏是我的代碼：

model = Sequential() 
model.add(Conv1D(30, filter_length=3, activation='relu', input_shape=(50, 1))) 
model.add(Conv1D(40, filter_length=(3), activation='relu')) 
model.add(Conv1D(120, filter_length=(3), activation='relu')) 
model.add(Flatten()) 
model.add(Dense(1024, activation='relu')) 
model.add(Dense(256, activation='relu')) 
model.add(Dense(32, activation='relu')) 
model.add(Dense(1, activation='relu')) 
model.summary() 
model.compile(loss='mse', 
       optimizer=keras.optimizers.adam()) 


train_limit = 5 
batch_size = 4096 
tb = keras.callbacks.TensorBoard(log_dir='./logs/' + run_name + '/', 
    histogram_freq=0, write_images=False) 
tb.set_model(model) 
model.fit(X_train[:train_limit], y_train[:train_limit], 
      batch_size=batch_size, 
      nb_epoch=10**4, 
      verbose=0, 
      validation_data=(X_val[:train_limit], y_val[:train_limit]), 
      callbacks=[tb]) 
score = model.evaluate(X_test, y_test, verbose=0) 
print('Test loss:', score) 
print('Test accuracy:', score) 

任何幫助，不勝感激！

Answer 1

這似乎是一個迴歸問題。我注意到的一件事是您的最後一層仍然具有ReLU激活功能。我會建議把ReLU放在最後一層。