利用keras自带影评数据集进行评价正面与否的二分类训练

 from keras.datasets import imdb
 from keras import layers
 from keras import models
 from keras import optimizers
 import matplotlib.pyplot as plt
 import numpy as np

 def vectorize_data(x, dim = 10000):
     res = np.zeros([len(x), dim])
     for i, string in enumerate(x):
         res[i, string] = 1
     return res
 def main():
     (train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=10000)
     x_train = vectorize_data(train_data)
     y_train = np.asanyarray(train_labels).astype('float32')
     x_test = vectorize_data(test_data)
     y_test = np.asarray(test_labels).astype('float32')

     network = models.Sequential()
     network.add(layers.Dense(16, activation = 'relu', input_shape = (10000, )))
     network.add(layers.Dense(16, activation = 'relu'))
     network.add(layers.Dense(1, activation = 'sigmoid'))

     network.compile(optimizer = 'rmsprop', loss = 'binary_crossentropy', metrics = ['accuracy'])
     history = network.fit(x_train, y_train, batch_size = 512, epochs = 4)

     loss, acc = network.evaluate(x_test, y_test)

     print('acc == ', acc)
 if __name__ == "__main__":
     main()