I don't know why I am getting such good results.
Epoch 3/10 2937/2937 [==============================] - 12s 4ms/step -
loss: 0.2836 - acc: 0.4679 - val_loss: 0.1937 - val_acc: 0.1980
Epoch 4/10 2937/2937 [==============================] - 12s 4ms/step -
loss: 0.1355 - acc: 0.4679 - val_loss: 0.0866 - val_acc: 0.1980
>Epoch
5/10 2937/2937 [==============================] - 13s 4ms/step - loss:
0.0580 - acc: 0.4679 - val_loss: 0.0342 - val_acc: 0.1980
Epoch 6/10 2937/2937 [==============================] - 13s 4ms/step -
loss: 0.0223 - acc: 0.4679 - val_loss: 0.0120 - val_acc: 0.1980
Epoch 7/10 2937/2937 [==============================] - 14s 5ms/step -
loss: 0.0082 - acc: 0.4679 - val_loss: 0.0040 - val_acc: 0.1980
My training and label sets are float number arrays in range [-0.05, 0.05] and I am using Keras.sequential.model.lstm
. Why might this be happening? Previously, I had the opposite problem here: loss/val_loss are decreasing but accuracies are the same in LSTM!, but I couldn't understand the problem.
EDIT: I changed my code from:
model.compile(optimizer = 'adam', loss = 'mean_square_error', metrics=['accuracy'])
to:
model.compile(optimizer = 'adam', loss = 'mean_absolute_error', metrics=['accuracy'])
But the result is same.
I then changed the above line of the code to:
model.compile(optimizer = 'adam', loss = 'mean_squared_error', metrics=['mean_squared_error'])
But it didn't work and the result is as follow:
Train on 2937 samples, validate on 735 samples Epoch 1/10 2937/2937
[==============================] - 90s 31ms/step - loss: 1.6645 -
mean_squared_error: 0.0019 - val_loss: 0.7620 -
val_mean_squared_error: 0.0010
Epoch 2/10 2937/2937 [==============================] - 13s 4ms/step -
loss: 0.5503 - mean_squared_error: 0.0019 - val_loss: 0.3890 -
val_mean_squared_error: 0.0010
Epoch 3/10 2937/2937 [==============================] - 13s 4ms/step -
loss: 0.2837 - mean_squared_error: 0.0019 - val_loss: 0.1938 -
val_mean_squared_error: 0.0010
Epoch 4/10 2937/2937 [==============================] - 13s 4ms/step -
loss: 0.1355 - mean_squared_error: 0.0019 - val_loss: 0.0866 -
val_mean_squared_error: 0.0010
Epoch 5/10 2937/2937 [==============================] - 13s 4ms/step -
loss: 0.0580 - mean_squared_error: 0.0019 - val_loss: 0.0342 -
val_mean_squared_error: 0.0010
Epoch 6/10 2937/2937 [==============================] - 13s 4ms/step -
loss: 0.0223 - mean_squared_error: 0.0019 - val_loss: 0.0120 -
val_mean_squared_error: 0.0010
Epoch 7/10 2937/2937 [==============================] - 13s 5ms/step -
loss: 0.0082 - mean_squared_error: 0.0019 - val_loss: 0.0040 -
val_mean_squared_error: 0.0010
Epoch 8/10 2937/2937 [==============================] - 14s 5ms/step -
loss: 0.0035 - mean_squared_error: 0.0019 - val_loss: 0.0017 -
val_mean_squared_error: 0.0010
Epoch 9/10 2937/2937 [==============================] - 13s 5ms/step -
loss: 0.0022 - mean_squared_error: 0.0019 - val_loss: 0.0011 -
val_mean_squared_error: 0.0010
Epoch 10/10 2937/2937 [==============================] - 13s 5ms/step
- loss: 0.0019 - mean_squared_error: 0.0019 - val_loss: 0.0010 - val_mean_squared_error: 0.0010
model.compile(optimizer = 'adam', loss = 'mean_absolute_error', metrics=['accuracy'])
But the result is same! $\endgroup$ – user145959 Apr 13 '19 at 14:52