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]]
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