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你好，下面是一个keras的softmax分类器+自编码器的python代码。你需要安装e5a48de588b662616964757a686964616f31333431343665最新的theano1.0.4才可以跑。import os;

os.environ['KERAS_BACKEND'] = 'theano'

import keras

from keras.datasets import mnist

from keras.models import Model

from keras.layers import Input, Conv2D, MaxPooling2D, Flatten, Dense, UpSampling2D

batch_size = 128

num_classes = 10

epochs = 12

# input image dimensions

img_rows, img_cols = 28, 28

# Data

(x_train, y_train), (x_test, y_test) = mnist.load_data()

x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1).astype('float32') / 255

x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1).astype('float32') / 255

y_train = keras.utils.to_categorical(y_train, num_classes)

y_test = keras.utils.to_categorical(y_test, num_classes)

# Convolutional Encoder

input_img = Input(shape=(img_rows, img_cols, 1))

conv_1 = Conv2D(16, (3, 3), activation='relu', padding='same')(input_img)

pool_1 = MaxPooling2D((2, 2), padding='same')(conv_1)

conv_2 = Conv2D(8, (3, 3), activation='relu', padding='same')(pool_1)

pool_2 = MaxPooling2D((2, 2), padding='same')(conv_2)

conv_3 = Conv2D(8, (3, 3), activation='relu', padding='same')(pool_2)

encoded= MaxPooling2D((2, 2), padding='same')(conv_3)

# Classification

flatten = Flatten()(encoded)

fc = Dense(128, activation='relu')(flatten)

softmax = Dense(num_classes, activation='softmax', name='classification')(fc)

# Decoder

conv_4 = Conv2D(8, (3, 3), activation='relu', padding='same')(encoded)

up_1 = UpSampling2D((2, 2))(conv_4)

conv_5 = Conv2D(8, (3, 3), activation='relu', padding='same')(up_1)

up_2 = UpSampling2D((2, 2))(conv_5)

conv_6 = Conv2D(16, (3, 3), activation='relu')(up_2)

up_3 = UpSampling2D((2, 2))(conv_6)

decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same', name='autoencoder')(up_3)

model = Model(inputs=input_img, outputs=[softmax, decoded])

model.compile(loss={'classification': 'categorical_crossentropy',

'autoencoder': 'binary_crossentropy'},

optimizer='adam',

metrics={'classification': 'accuracy'})

model.fit(x_train,

{'classification': y_train, 'autoencoder': x_train},

batch_size=batch_size,

epochs=epochs,

validation_data= (x_test, {'classification': y_test, 'autoencoder': x_test}),

verbose=1)