-
Notifications
You must be signed in to change notification settings - Fork 6
/
Copy pathCNN_Visualization_Using_tSNE_CIFAR10.py
146 lines (118 loc) · 5.74 KB
/
CNN_Visualization_Using_tSNE_CIFAR10.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
# -*- coding: utf-8 -*-
"""
Visualizing the hidden Layer in Neural Networks @ CIFAR10 dataset
Created on Sat Dec 28 19:49:26 2019
@author: liujie
# Credit:
# Visualizing Layer Representations in Neural Networks
# https://becominghuman.ai/visualizing-representations-bd9b62447e38
"""
from __future__ import print_function
import numpy as np
from keras.utils import np_utils
import tensorflow as tf
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
import matplotlib.pyplot as plt
#-----------------------------模型超参数------------------------------
num_epochs = 10
batch_size = 100
learning_rate = 0.01
#-----------------------------数据获取及预处理------------------------------
#tf.keras.datasets
class CIFARLoader():
def __init__(self):
cifar = tf.keras.datasets.cifar10
(self.train_data, self.train_label), (self.test_data, self.test_label) = cifar.load_data()
# MNIST中的图像默认为uint8（0-255的数字）。以下代码将其归一化到0-1之间的浮点数，并在最后增加一维作为颜色通道
self.train_data = self.train_data.astype(np.float32) / 255.0 # [60000, 28, 28, 1]
self.test_data = self.test_data.astype(np.float32) / 255.0 # [10000, 28, 28, 1]
self.train_label = self.train_label.astype(np.int32) # [60000]
self.test_label = self.test_label.astype(np.int32) # [10000]
self.num_train_data, self.num_test_data = self.train_data.shape[0], self.test_data.shape[0]
def get_batch(self, batch_size):
# 从数据集中随机取出batch_size个元素并返回
index = np.random.randint(0, np.shape(self.train_data)[0], batch_size)
return self.train_data[index, :], self.train_label[index]
# 测试CIFARLoader()
data_loader = CIFARLoader()
print(data_loader.train_data.shape)
print(data_loader.train_label.shape)
#-----------------------------模型的构建------------------------------
#Keras Functional API 模式建立模型
inputs = tf.keras.Input(shape=(32, 32, 3))
x = tf.keras.layers.Conv2D(32, kernel_size=(3, 3), activation='relu')(inputs)
x = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(x)
x = tf.keras.layers.Conv2D(64, kernel_size=(3, 3), activation='relu')(x)
x = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(x)
x = tf.keras.layers.Conv2D(64, kernel_size=(3, 3), activation='relu')(x)
x = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(x)
x = tf.keras.layers.Dropout(0.25)(x)
x = tf.keras.layers.Flatten()(x)
x = tf.keras.layers.Dense(units=100, activation=tf.nn.relu)(x)
x = tf.keras.layers.Dense(units=10)(x)
outputs = tf.keras.layers.Softmax()(x)
model = tf.keras.Model(inputs=inputs, outputs=outputs)
model.compile(
optimizer=tf.keras.optimizers.Adam(learning_rate),
loss=tf.keras.losses.sparse_categorical_crossentropy,
metrics=[tf.keras.metrics.sparse_categorical_accuracy]
)
'''
#-----------------------------模型构建的第2种写法------------------------------
#Keras Sequential API 模式建立模型
from tensorflow.keras import layers, models
model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10, activation='softmax'))
model.summary()
model.compile(
optimizer=tf.keras.optimizers.Adam(learning_rate),
loss=tf.keras.losses.sparse_categorical_crossentropy,
metrics=[tf.keras.metrics.sparse_categorical_accuracy]
)
'''
#-----------------------------数据读取和Fit模型------------------------------
data_loader = CIFARLoader()
model.fit(data_loader.train_data, data_loader.train_label, epochs=num_epochs, batch_size=batch_size)
#-------------------------------获取模型最后一层的数据--------------------------------
# 获取x = tf.keras.layers.Flatten()(x)数据
# Reference:https://becominghuman.ai/visualizing-representations-bd9b62447e38
def create_truncated_model(trained_model):
inputs = tf.keras.Input(shape=(32, 32, 3))
x = tf.keras.layers.Conv2D(32, kernel_size=(3, 3), activation='relu')(inputs)
x = tf.keras.layers.Conv2D(64, kernel_size=(3, 3), activation='relu')(x)
x = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(x)
x = tf.keras.layers.Dropout(0.25)(x)
x = tf.keras.layers.Flatten()(x)
for i, layer in enumerate(model.layers):
layer.set_weights(trained_model.layers[i].get_weights())
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
return model
truncated_model = create_truncated_model(model)
hidden_features = truncated_model.predict(data_loader.train_data)
#-------------------------------PCA,tSNE降维分析--------------------------------
pca = PCA(n_components=9)# 总的类别
pca_result = pca.fit_transform(hidden_features)
print('Variance PCA: {}'.format(np.sum(pca.explained_variance_ratio_)))
#Run T-SNE on the PCA features.
tsne = TSNE(n_components=2, verbose = 1)
tsne_results = tsne.fit_transform(pca_result[:5000])
#-------------------------------可视化--------------------------------
y_test_cat = np_utils.to_categorical(data_loader.train_label[:5000], num_classes = 10)# 总的类别
color_map = np.argmax(y_test_cat, axis=1)
plt.figure(figsize=(10,10))
for cl in range(10):# 总的类别
indices = np.where(color_map==cl)
indices = indices[0]
plt.scatter(tsne_results[indices,0], tsne_results[indices, 1], label=cl)
plt.legend()
plt.show()