Critics use keras own data sets positive evaluation of whether or not dichotomous training

 1 from keras.datasets import imdb
 2 from keras import layers
 3 from keras import models
 4 from keras import optimizers
 5 import matplotlib.pyplot as plt
 6 import numpy as np
 7 
 8 def vectorize_data(x, dim = 10000):
 9     res = np.zeros([len(x), dim])
10     for i, string in enumerate(x):
11         res[i, string] = 1
12     return res
13 def main():
14     (train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=10000)
15     x_train = vectorize_data(train_data)
16     y_train = np.asanyarray(train_labels).astype('float32')
17     x_test = vectorize_data(test_data)
18     y_test = np.asarray(test_labels).astype('float32')
19 
20     network = models.Sequential()
21     network.add(layers.Dense(16, activation = 'relu', input_shape = (10000, )))
22     network.add(layers.Dense(16, activation = 'relu'))
23     network.add(layers.Dense(1, activation = 'sigmoid'))
24 
25     network.compile(optimizer = 'rmsprop', loss = 'binary_crossentropy', metrics = ['accuracy'])
26     history = network.fit(x_train, y_train, batch_size = 512, epochs = 4)
27 
28     loss, acc = network.evaluate(x_test, y_test)
29 
30     print('acc == ', acc)
31 if __name__ == "__main__":
32     main()