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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