import matplotlib.pyplot as plt

import numpy as np

a = [[1, 2]]

a = np.array(a)

c = a[:, 0]

b = a[:, 1]

print(c, b)

print(a.shape)

plt.scatter(c, b)

plt.show()

 # coding:utf-8

 import numpy as np

 from keras.datasets import mnist

 from keras.models import Model

 from keras.layers import Dense, Input

 import matplotlib.pyplot as plt

 '''

 1st 加载数据集 x (60000, 28, 28) y (10000, 1)

 '''

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

 # 数据处理-归一化 转 浮点 , 不需要标签

 x_train = x_train.astype('float32')/255.0

 x_test = x_test.astype('float32')/255.0

 # reshape 数据形状 适用于dense层，是input需要的

 x_train = x_train.reshape(-1, 784)

 x_test = x_test.reshape(-1, 784)

 '''

 2nd encoded & decoded

 '''

 # 定义encoding终极维度

 encoding_dim = 4  # 因为我们输出的时候， 需要一个二维坐标，二维坐标只有两个值. 高维无法可视化

 # 定义输入层input可以接受的数据shape，类似TensorFlow 的placeholder

 input_img = Input(shape=(784,))

 # 定义编码层 这里是把数据从大维度降低到小维度 如28*28或784 降低到2个维度

 # 特别注意 keras 这次用API函数模式构建网络层

 # ***第一层编码***

 encoded = Dense(units=128, activation='relu')(input_img)

 # ***第二层编码***

 encoded = Dense(units=64, activation='relu')(encoded)  # 后面的encoded是第一层的

 # ***第三层编码***

 encoded = Dense(units=32, activation='relu')(encoded)  # 后面的encoded是第2层的

 # ***第四层编码***（终极输出）和前面终极维度相对应 encoding_dim = 2,并给出解码层

 encoded_output = Dense(units=encoding_dim)(encoded)  # 这里up主少了(encoded)

 # ***可以输出结果 如果我想的话 2个维度结果*** 改为4个维度 2*2

 # 定义解码层

 # ***第一层解码***

 decoded = Dense(units=32, activation='relu')(encoded_output)

 # ***第二层解码***

 decoded = Dense(units=64, activation='relu')(decoded)

 # ***第三层解码***

 decoded = Dense(units=128, activation='relu')(decoded)

 # ***第四层解码***

 decoded = Dense(units=784, activation='tanh')(decoded)

 '''

 3rd autoencoder

 '''

 # 构建自动编码模型结构

 autoencoder = Model(inputs=input_img, outputs=decoded)

 # 构建编码模型结构

 encoder = Model(inputs=input_img, outputs=encoded_output)

 # 编译模型

 autoencoder.compile(optimizer='adam', loss='mse')  # mean square err

 # adam可以实例化，自行改参数

 # 训练

 autoencoder.fit(

     x=x_train,

     y=x_train,  # 非监督学习，比的不是label，而是自己

     epochs=20,

     batch_size=512,

     shuffle=True,  # 每个训练的epoch完成后，数据打乱每次结构 1 2 3 4 5 6 7 8 9这样的顺序会被打乱

 )

 # 打印结果

 encoded_img = encoder.predict(x_test)

 plt.scatter(x=encoded_img[:, 0], y=encoded_img[:, 1], c=y_test, s=3)  # y_test唯一用的地方

 plt.show()

 # 打印三个图对比

 decoded_img = autoencoder.predict(x_test[1].reshape(1, 784))

 encoded_img = encoder.predict(x_test[1].reshape(1, 784))

 plt.figure(1)

 plt.imshow(decoded_img[0].reshape(28, 28))

 plt.figure(2)

 plt.imshow(encoded_img[0].reshape(2, 2))

 plt.figure(3)

 plt.imshow(x_test[1].reshape(28, 28))

 plt.show()

D:\Anaconda3\envs\tf\python.exe D:/adevelop/keras/autoencoder/ancoder.py

Using TensorFlow backend.

Epoch 1/20

2018-12-20 21:10:36.415134: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2

2018-12-20 21:10:36.589254: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Found device 0 with properties:

name: GeForce GTX 1060 6GB major: 6 minor: 1 memoryClockRate(GHz): 1.7845

pciBusID: 0000:01:00.0

totalMemory: 6.00GiB freeMemory: 4.97GiB

2018-12-20 21:10:36.589537: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1511] Adding visible gpu devices: 0

2018-12-20 21:10:36.928535: I tensorflow/core/common_runtime/gpu/gpu_device.cc:982] Device interconnect StreamExecutor with strength 1 edge matrix:

2018-12-20 21:10:36.928694: I tensorflow/core/common_runtime/gpu/gpu_device.cc:988]      0

2018-12-20 21:10:36.928783: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1001] 0:   N

2018-12-20 21:10:36.928967: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 4720 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1060 6GB, pci bus id: 0000:01:00.0, compute capability: 6.1)

.......

  512/60000 [..............................] - ETA: 0s - loss: 0.0291

 5120/60000 [=>............................] - ETA: 0s - loss: 0.0296

 9728/60000 [===>..........................] - ETA: 0s - loss: 0.0298

14336/60000 [======>.......................] - ETA: 0s - loss: 0.0298

19456/60000 [========>.....................] - ETA: 0s - loss: 0.0297

24064/60000 [===========>..................] - ETA: 0s - loss: 0.0296

28672/60000 [=============>................] - ETA: 0s - loss: 0.0297

33280/60000 [===============>..............] - ETA: 0s - loss: 0.0297

38400/60000 [==================>...........] - ETA: 0s - loss: 0.0297

43008/60000 [====================>.........] - ETA: 0s - loss: 0.0297

47616/60000 [======================>.......] - ETA: 0s - loss: 0.0297

52224/60000 [=========================>....] - ETA: 0s - loss: 0.0298

56832/60000 [===========================>..] - ETA: 0s - loss: 0.0297

60000/60000 [==============================] - 1s 11us/step - loss: 0.0297

Process finished with exit code 0

autoencoder.save('autoencoder.h5')

ex_img1 = Image.open('../mnist/4.png')

ex_img2 = Image.open('../mnist/7.jpg')

ex_img1 = np.array(ex_img1)

ex_img2 = np.array(ex_img2)

encoded_img1 = encoder.predict(ex_img1.reshape(1, 784))

encoded_img2 = encoder.predict(ex_img2.reshape(1, 784))

print(encoded_img1)

print(encoded_img2)

  512/60000 [..............................] - ETA: 0s - loss: 0.0293

 5632/60000 [=>............................] - ETA: 0s - loss: 0.0299

10752/60000 [====>.........................] - ETA: 0s - loss: 0.0299

15360/60000 [======>.......................] - ETA: 0s - loss: 0.0298

19968/60000 [========>.....................] - ETA: 0s - loss: 0.0298

25088/60000 [===========>..................] - ETA: 0s - loss: 0.0298

30208/60000 [==============>...............] - ETA: 0s - loss: 0.0299

34816/60000 [================>.............] - ETA: 0s - loss: 0.0299

39424/60000 [==================>...........] - ETA: 0s - loss: 0.0299

44544/60000 [=====================>........] - ETA: 0s - loss: 0.0299

49664/60000 [=======================>......] - ETA: 0s - loss: 0.0299

54784/60000 [==========================>...] - ETA: 0s - loss: 0.0299

59904/60000 [============================>.] - ETA: 0s - loss: 0.0299

60000/60000 [==============================] - 1s 11us/step - loss: 0.0299

[[  851.9073 -2038.2947  1888.9907 -3720.866 ]]

[[  462.43057 -2609.475    2269.1575  -5579.742  ]]