Keras LSTM multiclass classification

Question

I have this code that works for binary classification. I have tested it for keras imdb dataset.

    model = Sequential()
    model.add(Embedding(5000, 32, i         


        

Answer 1

Yes, you need one hot target, you can use to_categorical to encode your target or a short way:

model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

here is the full code:

from keras.models import Sequential
from keras.layers import *

model = Sequential()
model.add(Embedding(5000, 32, input_length=500))
model.add(LSTM(100, dropout=0.2, recurrent_dropout=0.2))
model.add(Dense(7, activation='softmax'))
model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

model.summary()

Summary

Using TensorFlow backend.
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
embedding_1 (Embedding)      (None, 500, 32)           160000    
_________________________________________________________________
lstm_1 (LSTM)                (None, 100)               53200     
_________________________________________________________________
dense_1 (Dense)              (None, 7)                 707       
=================================================================
Total params: 213,907
Trainable params: 213,907
Non-trainable params: 0
_________________________________________________________________