1 import numpy as np 2 from keras.datasets import reuters 3 from keras import layers 4 from keras import models 5 from keras import optimizers 6 from keras.utils.np_utils import to_categorical 7 import matplotlib.pyplot as plt 8 9 def vectorize_data(x, dim = 10000): 10 res = np.zeros((len(x), dim)) 11 for i, sequence in enumerate(x): 12 res[i, sequence] = 1 13 return res 14 15 def main(): 16 (train_data, train_labels), (test_data, test_labels) = reuters.load_data(num_words=10000) 17 x_train = vectorize_data(train_data) 18 x_test = vectorize_data(test_data) 19 y_train = to_categorical(train_labels) 20 y_test = to_categorical(test_labels) 21 22 network = models.Sequential() 23 network.add(layers.Dense(128, activation = ‘relu‘, input_shape = (10000, ))) 24 network.add(layers.Dense(64, activation = ‘relu‘)) 25 network.add(layers.Dense(46, activation = ‘softmax‘)) 26 27 network.compile(optimizer = ‘rmsprop‘, loss = ‘categorical_crossentropy‘, metrics = [‘accuracy‘]) 28 network.fit(x_train, y_train, epochs = 6, batch_size = 512) 29 30 _, acc = network.evaluate(x_test, y_test) 31 32 print(acc) 33 34 35 if __name__ == "__main__": 36 main()
原文地址:https://www.cnblogs.com/rising-sun/p/11624243.html
时间: 2024-11-06 07:20:30