-
Notifications
You must be signed in to change notification settings - Fork 1
/
train.py
55 lines (46 loc) · 1.53 KB
/
train.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
import os, sys, json
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.optimizers import RMSprop
#####################
# parameters
#####################
epochs = int(sys.argv[1])
activate = sys.argv[2]
dropout = float(sys.argv[3])
print(sys.argv)
#####################
batch_size = 128
num_classes = 10
hidden = 512
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train.reshape(60000, 784)
x_test = x_test.reshape(10000, 784)
x_train = x_train[:30000]
y_train = y_train[:30000]
x_test = x_test[:2000]
y_test = y_test[:2000]
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
model = Sequential()
model.add(Dense(hidden, activation='relu', input_shape=(784,)))
model.add(Dropout(dropout))
model.add(Dense(hidden, activation='relu'))
model.add(Dropout(dropout))
model.add(Dense(num_classes, activation=activate))
model.summary()
model.compile(loss='categorical_crossentropy', optimizer=RMSprop(),metrics=['accuracy'])
history = model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs,
validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])