|
@@ -0,0 +1,47 @@
|
|
1
|
+# -*- encoding:utf-8 -*-
|
|
2
|
+import numpy
|
|
3
|
+from tensorflow.keras.optimizers import SGD, Adam
|
|
4
|
+from keras.models import Sequential
|
|
5
|
+from keras.layers import Dense
|
|
6
|
+from keras.layers import Dropout
|
|
7
|
+from keras.layers import Flatten
|
|
8
|
+from keras.utils import np_utils
|
|
9
|
+from keras import backend as K
|
|
10
|
+from keras.utils import np_utils
|
|
11
|
+import numpy as np
|
|
12
|
+import random
|
|
13
|
+
|
|
14
|
+
|
|
15
|
+
|
|
16
|
+def read_data(path):
|
|
17
|
+ with open(path) as f :
|
|
18
|
+ lines=f.readlines()
|
|
19
|
+ lines=random.sample(lines,int(len(lines)/10))
|
|
20
|
+ lines=[eval(line.strip()) for line in lines]
|
|
21
|
+ X,Y=zip(*lines)
|
|
22
|
+ X=np.array(X)
|
|
23
|
+ X=1.0*X/256
|
|
24
|
+ X=X.reshape(-1,28*28)#784
|
|
25
|
+ Y=np.array(Y)
|
|
26
|
+ Y=np_utils.to_categorical(Y,num_classes)
|
|
27
|
+ return X,Y
|
|
28
|
+
|
|
29
|
+num_classes=11
|
|
30
|
+train_x,train_y=read_data("train_data")
|
|
31
|
+train_x_10,train_y_10=read_data("train_data_10")#reject data
|
|
32
|
+train_x=np.concatenate((train_x,train_x_10))
|
|
33
|
+train_y=np.concatenate((train_y,train_y_10))
|
|
34
|
+model = Sequential()
|
|
35
|
+#input_shape=(输入长,输入宽,输入通道数)
|
|
36
|
+model.add(Dense(units=256,input_dim=28*28, activation='relu',use_bias=True))
|
|
37
|
+model.add(Dense(units=128,input_dim=256,activation='relu',use_bias=True))
|
|
38
|
+model.add(Dense(units=128,input_dim=256,activation='relu',use_bias=True))
|
|
39
|
+model.add(Dense(units=num_classes,input_dim=128,use_bias=True, activation='softmax'))
|
|
40
|
+# Compile model
|
|
41
|
+model.compile(loss='categorical_crossentropy', optimizer="adam", metrics=['accuracy'])
|
|
42
|
+
|
|
43
|
+model.fit(train_x, train_y, batch_size=50, epochs=10,shuffle=True)
|
|
44
|
+model.save("model")
|
|
45
|
+
|
|
46
|
+
|
|
47
|
+
|