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基于Keras 的VGG16神经网络模型的Mnist数据集识别并使用GPU加速

时间:2019-11-13 23:59:24      阅读:45      评论:0      收藏:0      [点我收藏+]

标签:曲线   未使用   datasets   author   util   左右   精度   img   ima   

这段话放在前面:之前一种用的Pytorch,用着还挺爽,感觉挺方便的,但是在最近文献的时候,很多实验都是基于Google 的Keras的,所以抽空学了下Keras,学了之后才发现Keras相比Pytorch而言,基于keras来写神经网络的话太方便,因为Keras高度的封装性,所以基于Keras来搭建神经网络很简单,在Keras下,可以用两种两种方法来搭建网络模型,分别是Sequential()与Model(),对于网络结构简单,层次较少的模型使用sequential方法较好,只需不断地model.add即可,而后者更适用于网络模型复杂的情况,各有各的好处。

论GPU的重要性:在未使用GPU之前,一直用的CPU来训练,那速度,简直是龟速,一个VGG16花了10个小时,费时费力还闹心,然后今天将它加载在实验室的服务器上,只花了不到半个小时就好了。

技术图片

下面给出代码:

  1 #!/usr/bin/env python 3.6
  2 #_*_coding:utf-8 _*_
  3 #@Time    :2019/11/9 15:19
  4 #@Author  :hujinzhou 
  5 #@FileName: My_frist_keras_moudel.py
  6 
  7 #@Software: PyCharm
  8 from keras.datasets import mnist
  9 from call_back import LossHistory
 10 from keras.utils.np_utils import to_categorical
 11 import numpy as np
 12 import cv2
 13 import pylab
 14 from keras.optimizers import Adam,SGD
 15 from matplotlib import pyplot as plt
 16 from keras.utils.vis_utils import plot_model
 17 from keras.layers import Dense, Activation, Flatten,Convolution2D,MaxPool2D,Dropout,BatchNormalization
 18 from keras.models import Sequential, Model
 19 from keras.layers.normalization import BatchNormalization
 20 np.random.seed(10)
 21 """下载mnist数据集,x_train训练集的数据,y_train训练集的标签,测试集依次类推"""
 22 (x_train,y_train),(x_test,y_test)=mnist.load_data()
 23 print(x_train.shape)
 24 print(len(x_train))
 25 print(y_train[0])
 26 "-----------------------------------------------------------------------------------------"
 27 """通过迭代的方法将训练集中的数据整形为32*32"""
 28 x_train4D = [cv2.cvtColor(cv2.resize(i,(32,32)), cv2.COLOR_GRAY2BGR) for i in x_train]
 29 x_train4D = np.concatenate([arr[np.newaxis] for arr in x_train4D]).astype(float32)
 30 
 31 x_test4D = [cv2.cvtColor(cv2.resize(i,(32,32)), cv2.COLOR_GRAY2BGR) for i in x_test]
 32 x_test4D = np.concatenate([arr[np.newaxis] for arr in x_test4D]).astype(float32)
 33 print(x_test4D.shape)
 34 print(x_train4D.shape)
 35 "------------------------------------------------------------------------------------"
 36 plt.imshow(x_train4D[0],cmap=gray)
 37 pylab.show()
 38 #x_train4D = x_train4D.astype(‘float32‘)
 39 #x_test4D = x_test4D.astype(‘float32‘)
 40 """归一化"""
 41 x_test4D_normalize=x_test4D/255
 42 x_train4D_normalize=x_train4D/255
 43 
 44 """one_hot encoding"""
 45 y_trainOnehot=to_categorical(y_train)
 46 y_testOnehot=to_categorical(y_test)
 47 
 48 """建立模型"""
 49 "--------------------------------------------------------------------------"
 50 model=Sequential()
 51 model.add(Convolution2D(filters=64,
 52                         kernel_size=(5,5),
 53                         padding=same,
 54                         input_shape=(32,32,3),
 55 
 56                         kernel_initializer=he_normal,
 57                         name=cnn1
 58 
 59                         )
 60           )#output32*32*64
 61 model.add(BatchNormalization(axis=-1))
 62 model.add(Activation(relu))
 63 
 64 
 65 # model.add(Convolution2D(filters=64,
 66 #                         kernel_size=(5,5),
 67 #                         padding=‘same‘,
 68 #
 69 #                         kernel_initializer=‘he_normal‘,
 70 #                         name=‘cnn2‘
 71 #                         )
 72 #           )#output32*32*64
 73 # model.add(BatchNormalization(axis=-1))
 74 # model.add(Activation(‘relu‘))
 75 model.add(MaxPool2D(pool_size=(2,2),strides=(2, 2)))#output16*16*64
 76 
 77 model.add(Convolution2D(filters=128,
 78                         kernel_size=(5,5),
 79                         padding=same,
 80 
 81                         kernel_initializer=he_normal,
 82                         name=cnn3
 83                         )
 84           )#output16*16*128
 85 model.add(BatchNormalization(axis=-1))
 86 model.add(Activation(relu))
 87 # model.add(Convolution2D(filters=128,
 88 #                         kernel_size=(5,5),
 89 #                         padding=‘same‘,
 90 #
 91 #                         kernel_initializer=‘he_normal‘,
 92 #                         name=‘cnn4‘
 93 #                         )
 94 #           )#output16*16*128
 95 # model.add(BatchNormalization(axis=-1))
 96 # model.add(Activation(‘relu‘))
 97 model.add(MaxPool2D(pool_size=(2,2),strides=(2, 2)))#output8*8*128
 98 
 99 model.add(Convolution2D(filters=256,
100                         kernel_size=(5,5),
101                         padding=same,
102 
103                         kernel_initializer=he_normal,
104                         name=cnn5
105                         )
106           )#output8*8*256
107 model.add(BatchNormalization(axis=-1))
108 model.add(Activation(relu))
109 # model.add(Convolution2D(filters=256,
110 #                         kernel_size=(5,5),
111 #                         padding=‘same‘,
112 #
113 #                         kernel_initializer=‘he_normal‘,
114 #                         name=‘cnn6‘
115 #                         )
116 #           )#output8*8*256
117 # model.add(BatchNormalization(axis=-1))
118 # model.add(Activation(‘relu‘))
119 # model.add(Convolution2D(filters=256,
120 #                         kernel_size=(5,5),
121 #                         padding=‘same‘,
122 #
123 #                         kernel_initializer=‘he_normal‘,
124 #                         name=‘cnn7‘
125 #                         )
126 #           )#output8*8*256
127 # model.add(BatchNormalization(axis=-1))
128 # model.add(Activation(‘relu‘))
129 model.add(MaxPool2D(pool_size=(2,2),strides=(2, 2)))#output4*4*256
130 model.add(Convolution2D(filters=512,
131                         kernel_size=(5,5),
132                         padding=same,
133 
134                         kernel_initializer=he_normal,
135                         name=cnn8
136                         )
137           )#output4*4*512
138 model.add(BatchNormalization(axis=-1))
139 model.add(Activation(relu))
140 # model.add(Convolution2D(filters=512,
141 #                         kernel_size=(5,5),
142 #                         padding=‘same‘,
143 #
144 #                         kernel_initializer=‘he_normal‘,
145 #                         name=‘cnn9‘
146 #                         )
147 #           )#output4*4*512
148 # model.add(BatchNormalization(axis=-1))
149 # model.add(Activation(‘relu‘))
150 # model.add(Convolution2D(filters=512,
151 #                         kernel_size=(5,5),
152 #                         padding=‘same‘,
153 #
154 #                         kernel_initializer=‘he_normal‘,
155 #                         name=‘cnn10‘
156 #                         )
157 #           )#output4*4*512
158 # model.add(BatchNormalization(axis=-1))
159 # model.add(Activation(‘relu‘))
160 model.add(MaxPool2D(pool_size=(2,2),strides=(2, 2)))#output2*2*512
161 model.add(Convolution2D(filters=512,
162                         kernel_size=(5,5),
163                         padding=same,
164 
165                         kernel_initializer=he_normal,
166                         name=cnn11
167                         )
168           )#output2*2*512
169 model.add(BatchNormalization(axis=-1))
170 model.add(Activation(relu))
171 # model.add(Convolution2D(filters=512,
172 #                         kernel_size=(5,5),
173 #                         padding=‘same‘,
174 #
175 #                         kernel_initializer=‘he_normal‘,
176 #                         name=‘cnn12‘
177 #                         )
178 #           )#output2*2*512
179 # model.add(BatchNormalization(axis=-1))
180 # model.add(Activation(‘relu‘))
181 # model.add(Convolution2D(filters=512,
182 #                         kernel_size=(5,5),
183 #                         padding=‘same‘,
184 #
185 #                         kernel_initializer=‘he_normal‘,
186 #                         name=‘cnn13‘
187 #                         )
188 #           )#output2*2*512
189 # model.add(BatchNormalization(axis=-1))
190 # model.add(Activation(‘relu‘))
191 model.add(MaxPool2D(pool_size=(2,2),strides=(2, 2)))#output1*1*512
192 model.add(Dropout(0.5))
193 model.add(Flatten())
194 model.add(Dense(512))
195 model.add(Activation(relu))
196 model.add(Dropout(0.5))
197 model.add(Dense(10))
198 model.add(Activation(softmax))
199 model.summary()
200 plot_model(model,to_file=model4.png,show_shapes=True,show_layer_names=True)
201 # for layer in model.layers:
202 #     layer.trainable=False
203 "--------------------------------------------------------------------------------"
204 """训练模型"""
205 adam=SGD(lr=0.1)
206 model.compile(optimizer=adam,loss=categorical_crossentropy,metrics=[accuracy])
207 epoch=5
208 batchsize=100
209 # from keras.models import load_model
210 # model = load_model(‘./My_keras_model_weight‘)
211 history=model.fit(x=x_train4D_normalize,
212           y=y_trainOnehot,
213           epochs=epoch,
214           batch_size=batchsize,
215           validation_data=(x_test4D_normalize,y_testOnehot))
216 
217 """保存模型"""
218 model.save(./My_keras_model2_weight)
219 
220 #model.load(‘./My_keras_model_weight‘)
221 """画出损失曲线"""
222 training_loss=history.history["loss"]
223 train_acc=history.history["acc"]
224 test_loss=history.history["val_loss"]
225 test_acc=history.history["val_acc"]
226 
227 epoch_count=range(1,len(training_loss)+1)
228 plt.plot(epoch_count,training_loss,r--)
229 plt.plot(epoch_count,test_loss,b--)
230 plt.plot(epoch_count,train_acc,r--)
231 plt.plot(epoch_count,test_acc,b--)
232 plt.legend(["Training_loss","Test_loss","train_acc","test_acc"])
233 plt.xlabel("Epoch")
234 plt.ylabel("loss")
235 plt.show()

结果如下技术图片

最终的精度可以达到0.993左右

loss: 0.0261 - acc: 0.9932 - val_loss: 0.0246 - val_acc: 0.9933

 

基于Keras 的VGG16神经网络模型的Mnist数据集识别并使用GPU加速

标签:曲线   未使用   datasets   author   util   左右   精度   img   ima   

原文地址:https://www.cnblogs.com/hujinzhou/p/guobao2019_6_1.html

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