import numpy as np np.random.seed(1337) from keras.datasets import mnist from keras.models import Model from keras.layers import Dense, Input import matplotlib.pyplot as plt (x_train,y_train),(x_test,y_test) = mnist.load_data() x_train = x_train.astype('float32') / 255.-0.5 #(-0.5,0.5)的区间 x_test = x_test.astype('float32') / 255.-0.5 x_train = x_train.reshape((x_train.shape[0],-1)) x_test = x_test.reshape((x_test.shape[0],-1)) print(x_train.shape) print(x_test.shape) # 最终压缩成2个 encoding_dim = 2 # 输入 input_img = Input(shape=(784,)) # encoder layers encoded = Dense(128, activation='relu')(input_img) encoded = Dense(64, activation='relu')(encoded) encoded = Dense(10, activation='relu')(encoded) encoder_output = Dense(encoding_dim,)(encoded) # decoder layers decoded = Dense(10,activation='relu')(encoder_output) decoded = Dense(64,activation='relu')(decoded) decoded = Dense(128,activation='relu')(decoded) decoded = Dense(784,activation='tanh')(decoded) # 搭建autoencoder模型 autoencoder = Model(input=input_img,output=decoded) # 搭建encoder model for plotting,encoder是autoencoder的一部分 encoder = Model(input=input_img,output=encoder_output) # 编译 autoencoder autoencoder.compile(optimizer='adam',loss='mse') # 训练 autoencoder.fit(x_train, x_train, nb_epoch=20, batch_size=256, shuffle=True) # plotting encoded_imgs = encoder.predict(x_test) plt.scatter(encoded_imgs[:,0], encoded_imgs[:,1], c=y_test) plt.show()
E:ProgramDataAnaconda3libsite-packagesh5py__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`. from ._conv import register_converters as _register_converters Using TensorFlow backend. (60000, 784) (10000, 784) D:/我的python/用keras搭建神经网络/Autoencoder 自编码.py:38: UserWarning: Update your `Model` call to the Keras 2 API: `Model(inputs=Tensor("in..., outputs=Tensor("de...)` autoencoder = Model(input=input_img,output=decoded) D:/我的python/用keras搭建神经网络/Autoencoder 自编码.py:41: UserWarning: Update your `Model` call to the Keras 2 API: `Model(inputs=Tensor("in..., outputs=Tensor("de...)` encoder = Model(input=input_img,output=encoder_output) D:/我的python/用keras搭建神经网络/Autoencoder 自编码.py:50: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`. shuffle=True) Epoch 1/20 60000/60000 [==============================] - 5s 80us/step - loss: 0.0694 Epoch 2/20 60000/60000 [==============================] - 1s 20us/step - loss: 0.0562 Epoch 3/20 60000/60000 [==============================] - 1s 19us/step - loss: 0.0525 Epoch 4/20 60000/60000 [==============================] - 1s 20us/step - loss: 0.0493 Epoch 5/20 60000/60000 [==============================] - 1s 20us/step - loss: 0.0476 Epoch 6/20 60000/60000 [==============================] - 1s 20us/step - loss: 0.0463 Epoch 7/20 60000/60000 [==============================] - 1s 22us/step - loss: 0.0452 Epoch 8/20 60000/60000 [==============================] - 1s 23us/step - loss: 0.0442 Epoch 9/20 60000/60000 [==============================] - 1s 19us/step - loss: 0.0435 Epoch 10/20 60000/60000 [==============================] - 1s 19us/step - loss: 0.0429 Epoch 11/20 60000/60000 [==============================] - 1s 18us/step - loss: 0.0424 Epoch 12/20 60000/60000 [==============================] - 1s 18us/step - loss: 0.0419 Epoch 13/20 60000/60000 [==============================] - 1s 18us/step - loss: 0.0415 Epoch 14/20 60000/60000 [==============================] - 1s 18us/step - loss: 0.0412 Epoch 15/20 60000/60000 [==============================] - 1s 18us/step - loss: 0.0409 Epoch 16/20 60000/60000 [==============================] - 1s 18us/step - loss: 0.0405 Epoch 17/20 60000/60000 [==============================] - 1s 18us/step - loss: 0.0402 Epoch 18/20 60000/60000 [==============================] - 1s 19us/step - loss: 0.0401 Epoch 19/20 60000/60000 [==============================] - 1s 18us/step - loss: 0.0398 Epoch 20/20 60000/60000 [==============================] - 1s 18us/step - loss: 0.0397
