1. X像素取值范围是[-1.0, 1.0]
2. _________________________________________________________________
3. Layer (type)                 Output Shape              Param #   
4. =================================================================
5. dense_1 (Dense)              (None, 1024)              103424
6. _________________________________________________________________
7. activation_1 (Activation)    (None, 1024)              0
8. _________________________________________________________________
9. dense_2 (Dense)              (None, 6272)              6428800
10. _________________________________________________________________
11. batch_normalization_1 (Batch (None, 6272)              25088
12. _________________________________________________________________
13. activation_2 (Activation)    (None, 6272)              0
14. _________________________________________________________________
15. reshape_1 (Reshape)          (None, 7, 7, 128)         0
16. _________________________________________________________________
17. up_sampling2d_1 (UpSampling2 (None, 14, 14, 128)       0
18. _________________________________________________________________
19. conv2d_1 (Conv2D)            (None, 14, 14, 64)        204864
20. _________________________________________________________________
21. activation_3 (Activation)    (None, 14, 14, 64)        0
22. _________________________________________________________________
23. up_sampling2d_2 (UpSampling2 (None, 28, 28, 64)        0
24. _________________________________________________________________
25. conv2d_2 (Conv2D)            (None, 28, 28, 1)         1601
26. _________________________________________________________________
27. activation_4 (Activation)    (None, 28, 28, 1)         0
28. =================================================================
29. Total params: 6,763,777
30. Trainable params: 6,751,233
31. Non-trainable params: 12,544
32. _________________________________________________________________
33. _________________________________________________________________
34. Layer (type)                 Output Shape              Param #   
35. =================================================================
36. conv2d_3 (Conv2D)            (None, 28, 28, 64)        1664
37. _________________________________________________________________
38. activation_5 (Activation)    (None, 28, 28, 64)        0
39. _________________________________________________________________
40. max_pooling2d_1 (MaxPooling2 (None, 14, 14, 64)        0
41. _________________________________________________________________
42. conv2d_4 (Conv2D)            (None, 10, 10, 128)       204928
43. _________________________________________________________________
44. activation_6 (Activation)    (None, 10, 10, 128)       0
45. _________________________________________________________________
46. max_pooling2d_2 (MaxPooling2 (None, 5, 5, 128)         0
47. _________________________________________________________________
48. flatten_1 (Flatten)          (None, 3200)              0
49. _________________________________________________________________
50. dense_3 (Dense)              (None, 1024)              3277824
51. _________________________________________________________________
52. activation_7 (Activation)    (None, 1024)              0
53. _________________________________________________________________
54. dense_4 (Dense)              (None, 1)                 1025
55. _________________________________________________________________
56. activation_8 (Activation)    (None, 1)                 0
57. =================================================================
58. Total params: 3,485,441
59. Trainable params: 3,485,441
60. Non-trainable params: 0
61. _________________________________________________________________
62. 2020-11-24 21:53:56.659897: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
63. (25, 28, 28, 1)

1. def generator_model():
2.     model = Sequential()
3.     model.add(Dense(input_dim=100, units=1024))     #  1034  1024
4.     model.add(Activation('tanh'))
5.     model.add(Dense(128*7*7))
6.     model.add(BatchNormalization())
7.     model.add(Activation('tanh'))
8.     model.add(Reshape((7, 7, 128), input_shape=(128*7*7,)))
9.     model.add(UpSampling2D(size=(2, 2)))
10.     model.add(Conv2D(64, (5, 5), padding='same'))
11.     model.add(Activation('tanh'))
12.     model.add(UpSampling2D(size=(2, 2)))
13.     model.add(Conv2D(1, (5, 5), padding='same'))
14.     model.add(Activation('tanh'))
15. return model
16. def discriminator_model():     # 定义鉴别网络：输入一张图像，输出0(伪造)/1(真实)
17.     model = Sequential()
18.     model.add(
19.             Conv2D(64, (5, 5),
20.             padding='same',
21.             input_shape=(28, 28, 1))
22.             )
23.     model.add(Activation('tanh'))
24.     model.add(MaxPooling2D(pool_size=(2, 2)))
25.     model.add(Conv2D(128, (5, 5)))
26.     model.add(Activation('tanh'))
27.     model.add(MaxPooling2D(pool_size=(2, 2)))
28.     model.add(Flatten())
29.     model.add(Dense(1024))
30.     model.add(Activation('tanh'))
31.     model.add(Dense(1))
32.     model.add(Activation('sigmoid'))
33. return model
34. 
35. 
36. g = generator_model()
37. g.summary()
38. 
39. d = discriminator_model()
40. d.summary()