Keras：Binary_crossentropy具有負值

Question

我跟隨this tutorial（第6部分：將它捆綁在一起），用我自己的數據集。我可以通過提供的示例數據集來獲得教程中的示例，沒有任何問題。

我收到了一個二進制交叉熵錯誤，它是負面的，並且隨着時代的發展而沒有任何改進。我很確定二元交叉熵應該總是正面的，我應該看到損失有所改善。我已將截止到下面的示例輸出（和代碼調用）截至5個時期。其他人在培訓CNN時似乎遇到了類似的問題，但我沒有看到明確的解決方案。有誰知道爲什麼會發生這種情況？

輸出示例：

Creating TensorFlow device (/gpu:2) -> (device: 2, name: GeForce GTX TITAN Black, pci bus id: 0000:84:00.0) 
10240/10240 [==============================] - 2s - loss: -5.5378 - acc: 0.5000 - val_loss: -7.9712 - val_acc: 0.5000 
Epoch 2/5 
10240/10240 [==============================] - 0s - loss: -7.9712 - acc: 0.5000 - val_loss: -7.9712 - val_acc: 0.5000 
Epoch 3/5 
10240/10240 [==============================] - 0s - loss: -7.9712 - acc: 0.5000 - val_loss: -7.9712 - val_acc: 0.5000 
Epoch 4/5 
10240/10240 [==============================] - 0s - loss: -7.9712 - acc: 0.5000 - val_loss: -7.9712 - val_acc: 0.5000 
Epoch 5/5 
10240/10240 [==============================] - 0s - loss: -7.9712 - acc: 0.5000 - val_loss: -7.9712 - val_acc: 0.5000 

我的代碼：

import numpy as np 
import keras 
from keras.models import Sequential 
from keras.layers import Dense 
from keras.callbacks import History 

history = History() 
seed = 7 
np.random.seed(seed) 

dataset = np.loadtxt('train_rows.csv', delimiter=",") 

#print dataset.shape (10240, 64) 

# split into input (X) and output (Y) variables 
X = dataset[:, 0:(dataset.shape[1]-2)] #0:62 (63 of 64 columns) 
Y = dataset[:, dataset.shape[1]-1] #column 64 counting from 0 

#print X.shape (10240, 62) 
#print Y.shape (10240,) 

testset = np.loadtxt('test_rows.csv', delimiter=",") 

#print testset.shape (2560, 64) 

X_test = testset[:,0:(testset.shape[1]-2)] 
Y_test = testset[:,testset.shape[1]-1] 

#print X_test.shape (2560, 62) 
#print Y_test.shape (2560,) 

num_units_per_layer = [100, 50] 

### create model 
model = Sequential() 
model.add(Dense(100, input_dim=(dataset.shape[1]-2), init='uniform', activation='relu')) 
model.add(Dense(50, init='uniform', activation='relu')) 
model.add(Dense(1, init='uniform', activation='sigmoid')) 

model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) 
## Fit the model 
model.fit(X, Y, validation_data=(X_test, Y_test), nb_epoch=5, batch_size=128) 

Answer 1

我應該打印出來我的響應變量。類別被標記爲1和2而不是0和1，這使分類器困惑。