A quick demo of lstms used for classification with dummy data all in keras

lstm_classification_example.py

import numpy as np
from keras.preprocessing.sequence import pad_sequences
from keras.utils import to_categorical
from keras.models import Sequential
from keras.layers import Embedding, Dense, LSTM


def main():


	num_classes = 5 
	max_words = 20
	sentences = ["The cat is in the house",
	                           "The green boy",
	            "computer programs are not alive while the children are"]
	labels = np.random.randint(0, num_classes, len(sentences))
	y = to_categorical(labels, num_classes=num_classes)
	print(y, "Y")
	words = set(w for sent in sentences for w in sent.split())
	word_map = {w : i+1 for (i, w) in enumerate(words)}
	sent_ints = [[word_map[w] for w in sent.split()] for sent in sentences]
	vocab_size = len(words)

	# pad to max_words length and encode with len(words) + 1  
	# + 1 because we'll reserve 0 as the padding sentinel.
	X = np.array([to_categorical(pad_sequences((sent,), max_words),  
	       vocab_size + 1)  for sent in sent_ints])
	print(X.shape) # (3, 20, 16)

	model = Sequential()
	model.add(Dense(512, input_shape=(max_words, vocab_size + 1)))
	model.add(LSTM(128))
	model.add(Dense(num_classes, activation='softmax'))
	model.compile(loss='categorical_crossentropy',
	          optimizer='adam',
	          metrics=['accuracy'])

	model.fit(X, y)

if __name__ == "__main__":
	main()