python 3.x - Zero accuracy training a neural network in Keras

Question

I train a Neural Network of Regression Problem in Keras. Why the output is only one Dimension, the accuracy in each Epoch always show acc: 0.0000e+00?

like this:

1000/199873 [..............................] - ETA: 5s - loss: 0.0057 - acc: 0.0000e+00

2000/199873 [..............................] - ETA: 4s - loss: 0.0058 - acc: 0.0000e+00

3000/199873 [..............................] - ETA: 3s - loss: 0.0057 - acc: 0.0000e+00

4000/199873 [..............................] - ETA: 3s - loss: 0.0060 - acc: 0.0000e+00 ...

198000/199873 [============================>.] - ETA: 0s - loss: 0.0055 - acc: 0.0000e+00

199000/199873 [============================>.] - ETA: 0s - loss: 0.0055 - acc: 0.0000e+00

199873/199873 [==============================] - 4s - loss: 0.0055 - acc: 0.0000e+00 - val_loss: 0.0180 - val_acc: 0.0000e+00

Epoch 50/50

But if the output are two Dimension or above, no problem for accuracy.

My model as below:`

input_dim = 14
batch_size = 1000
nb_epoch = 50
lrelu = LeakyReLU(alpha = 0.1)

model = Sequential()
model.add(Dense(126, input_dim=input_dim)) #Dense(output_dim(also hidden wight), input_dim = input_dim)
model.add(lrelu) #Activation

model.add(Dense(252))
model.add(lrelu)
model.add(Dense(1))
model.add(Activation('linear'))

model.compile(loss= 'mean_squared_error', optimizer='Adam', metrics=['accuracy'])
model.summary()
history = model.fit(X_train_1, y_train_1[:,0:1],
                    batch_size=batch_size,
                    nb_epoch=nb_epoch,
                    verbose=1,
                    validation_split=0.2)

loss = history.history.get('loss')
acc = history.history.get('acc')
val_loss = history.history.get('val_loss')
val_acc = history.history.get('val_acc')

'''saving model'''
from keras.models import load_model
model.save('XXXXX')
del model

'''loading model'''
model = load_model('XXXXX')

'''prediction'''
pred = model.predict(X_train_1, batch_size, verbose=1)
ans = [np.argmax(r) for r in y_train_1[:,0:1]]

Answer 1

The problem is that your final model output has a linear activation, making the model a regression, not a classification problem. "Accuracy" is defined when the model classifies data correctly according to class, but "accuracy" is effectively not defined for a regression problem, due to its continuous property.

Either get rid of accuracy as a metric and switch over to fully regression, or make your problem into a classification problem, using loss='categorical_crossentropy' and activation='softmax'.

This is a similar problem to yours: Link

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