实践：auto encoder生成手写数字

说明：

课程小作业，简单记录

有用链接：

• keras
  画Loss曲线
  自己设计一个类画Loss
  提前停止
  自行设计的提前停止
• 画图
  fig、ax
• auto encoder
  torch的代码
  李宏毅代码

做法：

1. 加载包

import tensorflow as tf
from tensorflow import keras
import numpy as np
from tensorflow.keras import layers, optimizers, datasets, Model
from tensorflow.keras.layers import Dense, Dropout, Flatten
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Input, Activation

2. 手写数字数据

mnist = datasets.mnist
(x0, y0), (x1, y1) = datasets.mnist.load_data()
train_x = x0.reshape(-1,784)
train_y = x0.reshape(-1,784)
test_x = x1.reshape(-1,784)
test_y = x1.reshape(-1,784)

3. 简单的encoder-decoder
   （784-1000-500-250-2-250-500-1000-784）的前向NN。

#encoder
enc_input = Input([784,])
x = Dense(1000,activation= 'relu')(input_x)
x = Dense(500,activation= 'relu')(x) 
x = Dense(250,activation= 'relu')(x) 
enc_output = Dense(2)(x) 
encoder = Model(input_x,enc_output)
#decoder
dec_input = Input([2,])
x = Dense(250,activation= 'relu')(dec_input) 
x = Dense(500,activation= 'relu')(x) 
x = Dense(1000,activation= 'relu')(x) 
x = Dense(784)(x) 
dec_output = x
decoder = Model(dec_input,dec_output)
#合并
input_x = Input([784,])
code = encoder(input_x )
output = decoder(code)

model = Model(input_x,output)

4. 训练，提前停止，画loss曲线

• 训练

optimizer = optimizers.Adam(0.00001)
model.compile(optimizer=optimizer,
              loss='mse')
early_stopping=tf.keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=1,
                              patience=5, verbose=0, mode='auto',
                              baseline=None, restore_best_weights=False)
history = model.fit(train_x,train_y,validation_data = (test_x,test_y),batch_size =128,epochs = 300,callbacks = [early_stopping])

• 画图

plt.figure()
epochs=range(len(history.history['loss']))
plt.plot(epochs,history.history['loss'],'b',label='Training loss')
plt.plot(epochs,history.history['val_loss'],'r',label='Validation val_loss')
plt.title('Traing and Validation loss')
plt.legend()
plt.show()

5. 查看encoder结果（二维）的散点图

inp = test_x[-1000:].reshape((-1,784))
code_x = encoder(inp).numpy()
dt = []
for i in range(10):
    dt.append(code_x[y1[-1000:]==i])
color =['c', 'b', 'g', 'r', 'orange', 'y', 'k', 'silver','pink','purple']
plt.figure(figsize=(12,12))
for i in range(10):
    plot = dt[i]
    plt.scatter(plot[:,0],plot[:,1],c=color[i],label = i)
    plt.legend(loc = 'best')
plt.show()

6. 查看decoder生成的效果

import random
decode = np.array(random.sample(list(code_x),20))
print(decode.shape)
pred_dec = decoder(decode).numpy().reshape(-1,28,28)
print(pred_dec.shape)
fig, axes = plt.subplots(4, 5,figsize=(5, 5))
for j in range(4):
    for i in range(5):
        axes[j][i].imshow(pred_dec[i*(j+1)])
        axes[j][i].axis('off')
plt.show()

还可以尝试：

• VAE
• GAN