-
Notifications
You must be signed in to change notification settings - Fork 192
/
2-4-deep-cnn-3.py
73 lines (61 loc) · 3.68 KB
/
2-4-deep-cnn-3.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
import numpy as np
import matplotlib.pyplot as plt
from keras.datasets import mnist
from keras.utils.np_utils import to_categorical
from keras.models import Sequential
from keras import optimizers
from keras.layers import Dense, Activation, Flatten, Conv2D, MaxPooling2D
from keras.layers import BatchNormalization, Dropout
(X_train, y_train), (X_test, y_test) = mnist.load_data()
# reshaping X data: (n, 28, 28) => (n, 28, 28, 1)
X_train = X_train.reshape((X_train.shape[0], X_train.shape[1], X_train.shape[2], 1))
X_test = X_test.reshape((X_test.shape[0], X_test.shape[1], X_test.shape[2], 1))
# converting y data into categorical (one-hot encoding)
y_train = to_categorical(y_train)
y_test = to_categorical(y_test)
def deep_cnn_advanced_nin():
model = Sequential()
model.add(Conv2D(input_shape = (X_train.shape[1], X_train.shape[2], X_train.shape[3]), filters = 50, kernel_size = (3,3), strides = (1,1), padding = 'same', kernel_initializer='he_normal'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Conv2D(filters = 50, kernel_size = (3,3), strides = (1,1), padding = 'same', kernel_initializer='he_normal'))
model.add(Conv2D(filters = 25, kernel_size = (1,1), strides = (1,1), padding = 'valid', kernel_initializer='he_normal'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size = (2,2)))
model.add(Conv2D(filters = 50, kernel_size = (3,3), strides = (1,1), padding = 'same', kernel_initializer='he_normal'))
model.add(Conv2D(filters = 25, kernel_size = (1,1), strides = (1,1), padding = 'valid', kernel_initializer='he_normal'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Conv2D(filters = 50, kernel_size = (3,3), strides = (1,1), padding = 'same', kernel_initializer='he_normal'))
model.add(Conv2D(filters = 25, kernel_size = (1,1), strides = (1,1), padding = 'valid', kernel_initializer='he_normal'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size = (2,2)))
model.add(Conv2D(filters = 50, kernel_size = (3,3), strides = (1,1), padding = 'same', kernel_initializer='he_normal'))
model.add(Conv2D(filters = 25, kernel_size = (1,1), strides = (1,1), padding = 'valid', kernel_initializer='he_normal'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Conv2D(filters = 50, kernel_size = (3,3), strides = (1,1), padding = 'same', kernel_initializer='he_normal'))
model.add(Conv2D(filters = 25, kernel_size = (1,1), strides = (1,1), padding = 'valid', kernel_initializer='he_normal'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size = (2,2)))
# prior layer should be flattend to be connected to dense layers
model.add(Flatten())
# dense layer with 50 neurons
model.add(Dense(50, activation = 'relu', kernel_initializer='he_normal'))
model.add(Dropout(0.5))
# final layer with 10 neurons to classify the instances
model.add(Dense(10, activation = 'softmax', kernel_initializer='he_normal'))
adam = optimizers.Adam(lr = 0.001)
model.compile(loss = 'categorical_crossentropy', optimizer = adam, metrics = ['accuracy'])
return model
model = deep_cnn_advanced_nin()
history = model.fit(X_train, y_train, batch_size = 50, validation_split = 0.2, epochs = 100, verbose = 0)
# plt.plot(history.history['acc'])
# plt.plot(history.history['val_acc'])
# plt.legend(['training', 'validation'], loc = 'upper left')
# plt.show()
results = model.evaluate(X_test, y_test)
print('Test accuracy: ', results[1])