Num_words represent the maximum number of words in the review. It represents a collection of movies and its reviews. (x_train, y_train), (x_test, y_test) = imdb.load_data(num_words = 2000) Use 2000 as the maximum number of word in a given sentence.įrom keras.layers import Dense, Embedding ![]() Use 80 as the maximum length of the word. Use binary_crossentropy as loss function. Output layer, Dense consists of 1 unit and ‘sigmoid’ activation function. Input layer using Embedding layer with 128 features.įirst layer, Dense consists of 128 units with normal dropout and recurrent dropout set to 0.2. The core features of the model are as follows − The model for the sequence analysis can be represented as below − ![]() Let us create a LSTM model to analyze the IMDB movie reviews and find its positive/negative sentiment. Sequence Analysis is used frequently in natural language processing to find the sentiment analysis of the given text. Here, the words are considered as values, and first value corresponds to first word, second value corresponds to second word, etc., and the order will be strictly maintained. Reading and understanding a sentence involves reading the word in the given order and trying to understand each word and its meaning in the given context and finally understanding the sentence in a positive or negative sentiment. Let us consider a simple example of reading a sentence. A sequence is a set of values where each value corresponds to a particular instance of time. ![]() In this chapter, let us write a simple Long Short Term Memory (LSTM) based RNN to do sequence analysis.
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