import keras import matplotlib.pyplot as plt from keras.datasets import mnist (x_train, _), (x_test, y_test) = mnist.load_data() x_train = x_train.astype(‘float32‘) / 255 x_test = x_test.astype(‘float32‘) / 255 x_train = x_train.reshape(x_train.shape[0], -1) x_test = x_test.reshape(x_test.shape[0], -1) encoding_dim = 2 encoder = keras.models.Sequential([ keras.layers.Dense(128, activation=‘relu‘), keras.layers.Dense(32, activation=‘relu‘), keras.layers.Dense(8, activation=‘relu‘), keras.layers.Dense(encoding_dim) ]) decoder = keras.models.Sequential([ keras.layers.Dense(8, activation=‘relu‘), keras.layers.Dense(32, activation=‘relu‘), keras.layers.Dense(128, activation=‘relu‘), keras.layers.Dense(784, activation=‘tanh‘) ]) AutoEncoder = keras.models.Sequential([ encoder, decoder ]) AutoEncoder.compile(optimizer=‘adam‘, loss=‘mse‘) AutoEncoder.fit(x_train, x_train, epochs=10, batch_size=256) predict = encoder.predict(x_test) plt.scatter(predict[:, 0], predict[:, 1], c=y_test) plt.show()

将数据降到两维以后，得到的图像如下:

keras使用AutoEncoder对mnist数据降维