efficient.py

# we create a list of the indices of words in vocabulary as the input to network
training_reviews = []
for review in training_reviews_raw:
    indices = set()
    for word in review.split(" "):
        if word in word2index.keys():
            indices.add(self.word2index[word])
    training_reviews.append(list(indices))
# forward pass for each data point in the batch(index i)
self.layer_1 *= 0
for index in review:
    self.layer_1 += self.weights_0_1[index]
output = self.sigmoid(np.dot(self.layer_1,self.weights_1_2))
# weight updates for each data point in the batch(index i)
self.weights_1_2 += (self.learning_rate)*np.dot(self.layer_1.T,output_error_term)
for index in training_reviews[i]:
    self.weights_0_1[index] += (self.learning_rate)*(hidden_error_term[0])