0

I am trying to train a network on the Swiss Roll dataset with three features X = [x1, x2, x3] for the classification task. There are four classes with labels 1, 2, 3, 4, and the vector y contains the labels for all the data.

A row in the X matrix looks like this:

-5.2146470e+00   7.0879738e+00   6.7292474e+00

The shape of X is (100, 3), and the shape of y is (100,).

I want to use Radial Basis Functions to train this model. I have used the custom RBFLayer from this StackOverflow answer (also see this explanation) to build the RBFLayer. I want to use a couple of Keras Dense layers to build the network for classification.

What I have tried so far

I have used a Dense layer for the first layer, followed by the custom RBFLayer, and two other Dense layers. Here's the code:

model = Sequential()
model.add((Dense(100, input_dim=3)))
# number of units = 10, gamma = 0.05
model.add(RBFLayer(10,0.05))
model.add(Dense(15, activation='relu'))
model.add(Dense(1, activation='softmax'))

This model gives me zero accuracy. I think there is something wrong with the model architecture, but I can't figure out what is the issue.

Also, I thought the number of units in the last Dense layer should match the number of classes, which is 4 in this case. But when I set the number of units to 4 in the last layer, I get the following error:

ValueError: Shapes (None, 1) and (None, 4) are incompatible

Can you help me with this model architecture?

Zahra Golpa
  • 57
  • 1
  • 7

2 Answers2

1

I faced the same issue while practicing with multi-class classification. Where I had 7 features and the model classifies into 7 classes. I tried encoding the labels and it fixed the issue.

First import LabelEncoder class from sklearn and import to_categorical from tensorflow

from sklearn.preprocessing import LabelEncoder
from tensorflow.keras.utils import to_categorical

Then, initialize an object to the LabelEncoder class and transform your labels before fitting and training the model.

encoder = LabelEncoder()
encoder.fit(y)
y = encoder.transform(y)
y = to_categorical(y)

Note that you have to use np.argmax for getting the actual predicted classification. in my case, the prediction is stored in variable called res

res = np.argmax(res, axis=None, out=None)

You can get your actual predicted class after this line. Looking forward to help you. Hope it solved your problem.

Vigneshwar Ravichandar
  • 78
  • 2
  • 10
0

There are four classes with labels 1, 2, 3, 4, and the vector y contains the labels for all the data.

The simplest solution for input output matching is that you print the shape of the inputs and output for a single batch and then compare.

RBF layer should have no problem because output is taken from last dense layer rather then RBF layer.

With classification problem you must have last nodes equal to classes in regression the last node is 1 sometimes.

you should print

pseudo code

print(input.shape)

compare it with

print(model.input_shape)

then at output

print(output.shape)

then compare it with

print(model.predict(input).shape)

you can find the correct syntax at keras docs these are approx correct syntax / pseudo

Ali Arslan
  • 38
  • 6