I'm trying to fit an RNN in Keras using sequences that have varying time lengths. My data is in a Numpy array with format (sample, time, feature) = (20631, max_time, 24)
where max_time
is determined at run-time as the number of time steps available for the sample with the most time stamps. I've padded the beginning of each time series with 0
, except for the longest one, obviously.
I've initially defined my model like so...
model = Sequential()
model.add(Masking(mask_value=0., input_shape=(max_time, 24)))
model.add(LSTM(100, input_dim=24))
model.add(Dense(2))
model.add(Activation(activate))
model.compile(loss=weibull_loglik_discrete, optimizer=RMSprop(lr=.01))
model.fit(train_x, train_y, nb_epoch=100, batch_size=1000, verbose=2, validation_data=(test_x, test_y))
For completeness, here's the code for the loss function:
def weibull_loglik_discrete(y_true, ab_pred, name=None):
y_ = y_true[:, 0]
u_ = y_true[:, 1]
a_ = ab_pred[:, 0]
b_ = ab_pred[:, 1]
hazard0 = k.pow((y_ + 1e-35) / a_, b_)
hazard1 = k.pow((y_ + 1) / a_, b_)
return -1 * k.mean(u_ * k.log(k.exp(hazard1 - hazard0) - 1.0) - hazard1)
And here's the code for the custom activation function:
def activate(ab):
a = k.exp(ab[:, 0])
b = k.softplus(ab[:, 1])
a = k.reshape(a, (k.shape(a)[0], 1))
b = k.reshape(b, (k.shape(b)[0], 1))
return k.concatenate((a, b), axis=1)
When I fit the model and make some test predictions, every sample in the test set gets exactly the same prediction, which seems fishy.
Things get better if I remove the masking layer, which makes me think there's something wrong with the masking layer, but as far as I can tell, I've followed the documentation exactly.
Is there something mis-specified with the masking layer? Am I missing something else?
The way you implemented masking should be correct. If you have data with the shape (samples, timesteps, features), and you want to mask timesteps lacking data with a zero mask of the same size as the features argument, then you add Masking(mask_value=0., input_shape=(timesteps, features))
. See here: keras.io/layers/core/#masking
Your model could potentially be too simple, and/or your number of epochs could be insufficient for the model to differentiate between all of your classes. Try this model:
model = Sequential()
model.add(Masking(mask_value=0., input_shape=(max_time, 24)))
model.add(LSTM(256, input_dim=24))
model.add(Dense(1024))
model.add(Dense(2))
model.add(Activation(activate))
model.compile(loss=weibull_loglik_discrete, optimizer=RMSprop(lr=.01))
model.fit(train_x, train_y, nb_epoch=100, batch_size=1000, verbose=2, validation_data=(test_x, test_y))
If that does not work, try doubling the epochs a few times (e.g. 200, 400) and see if that improves the results.
I could not validate without actual data, but I had a similar experience with an RNN. In my case normalization solved the issue. Add a normalization layer to your model.
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