具有更多功能/类别的LSTM

Question

How can I use more than one feature/class as input/output on a LSTM using Sequential from Keras Models in Python?

To be more specific, I would like to use as input and output to the network: [FeatureA][FeatureB][FeatureC]. FeatureA is a categorial class with 100 different possible values indicating the sensor which collected the data; FeatureB is a on/off indicator, being 0 or 1; FeatureC is a categorial class too having 5 unique values.

Data Example:

 1. 40,1,5
 2. 58,1,2
 3. 57,1,5
 4. 40,0,1
 5. 57,1,4
 6. 23,0,3

When using the raw data and loss='categorical_crossentropy' on model.compile, the loss is over than 10.0.

When normalisating the data to values between 0-1 and using mean_squared_error on loss, it gets an average of 0.27 on loss. But when testing it on prediction, the results does not makes any sense.

Any suggestions here or tutorials I could consult? Thanks in advance.

Answer 1

1. You need to convert FeatureC to a binary category. Mean squared error is for regression and as best I can tell you are trying to predict which class a certain combination of sensors and states belongs to. Since there's 5 possible classes you can kind of think that you're trying to predict if the class is Red, Green, Blue, Yellow, or Purple. Right now those are represented by numbers but for a regression you model is going to be predicting values like 3.24 which doesn't make sense.

   In effect you're converting values of FeatureC to 5 columns of binary values. Since it seems like the classes are exclusive there should be a single 1 and the rest of the columns of a row would be 0s. So if the first row is 'red' it would be [1, 0, 0, 0, 0]

2. For best results you should also convert FeatureA to binary categorical features. For the same reason as above, sensor 80 is not 4x more than sensor 20, but instead a different entity.

3. The last layer of your model should be of softmax type with 5 neurons. Basically your model is going to try to be predicting the probability of each class, in the end.

It looks like you are working with a dataframe since there's an index. Therefore I would try:

import keras
import numpy as np
import pandas as pd # assume that this has probably already been done though

feature_a = data.loc[:, "FeatureA"].values  # pull out feature A
labels = data.loc[:, "FeatureC"].values   # y are the labels that  we're trying to predict
feature_b = data.loc[:, "FeatureB"].values  # pull out B to make things more clear later

# make sure that feature_b.shape = (rows, 1) otherwise reset the shape
# so hstack works later
feature_b = feature_b.reshape(feature_b.shape[0], 1)

labels -= 1  # subtract 1 from all labels values to zero base (0 - 4)
y = keras.utils.to_categorical(labels)
# labels.shape should be (rows, 5)

# convert 1-100 to binary columns
# zero base again
feature_a -= 1  

# Before: feature_a.shape=(rows, 1)
feature_a_data = keras.utils.to_categorical(feature_a)
# After: feature_a_data.shape=(rows, 100)

data = np.hstack([feature_a_data, feature_b])
# data.shape should be (rows, 101)
# y.shape should be (rows, 5)

Now you're ready to train/test split and so on.

Here's something to look at which has multi-class prediction:

https://github.com/keras-team/keras/blob/master/examples/mnist_cnn.py