Binary Classifiation

Binary Classification

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import tensorflow as tf import keras import numpy as np from keras.datasets import imdb (train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=10000) #print("train_data[0] : ") #print(train_data[0]) print(max([max(sequence) for sequence in train_data])) word_index = imdb.get_word_index() reverse_word_index = dict([value, key] for(key, value) in word_index.items()) decoded_review = ''.join([reverse_word_index.get(i-3, '?') for i in train_data[0]]) print(decoded_review) def vectorize_sequences(sequences, dimension=10000): results = np.zeros((len(sequences), dimension)) for i, sequence in enumerate(sequences): results[i, sequence] = 1. return results x_train = vectorize_sequences(train_data) x_test = vectorize_sequences(test_data) print(x_train)