Hualos - 在Keras中動態監視training過程

Posted on |

Hualos - 在Keras中動態監視training過程

Keras本身就有提供callbacks機制，可以讓我們在training過程中，看到一些資訊。
這邊要教如何使用Hualos，讓我們在Web上看到training時的acc、loss等變化。

使用RemoteMonitor

我們可以直接用callbacks.RemoteMonitor()將training時的acc、loss、val_acc、val_loss，POST到我們的Server上。

1
2
3
4
from keras import callbacks
remote = callbacks.RemoteMonitor(root='http://localhost:9000')

model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch, validation_data=(X_test, Y_test), callbacks=[remote])

當然網頁要自己寫太麻煩了，這邊使用Hualos來替我們完成。

安裝Hualos所需套件

由於Hualos需要用到以下兩個Python套件，可以透過pip直接安裝

  • Flask
  • gevent
1
$ pip install flask gevent

下載Hualos

1
$ git clone https://github.com/fchollet/hualos.git

執行Hualos

1
$ python hualos/api.py

開啟瀏覽器，進入localhost:9000

Training

使用mnist為範例，完整程式碼如下

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
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.optimizers import RMSprop
from keras import callbacks

batch_size = 128
num_classes = 10
epochs = 20

(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.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')

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(512, activation='relu', input_shape=(784,)))
model.add(Dropout(0.2))
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(num_classes, activation='softmax'))

model.summary()

model.compile(loss='categorical_crossentropy',
              optimizer=RMSprop(),
              metrics=['accuracy'])

remote = callbacks.RemoteMonitor(root='http://localhost:9000')

model.fit(x_train, y_train,
          batch_size=batch_size,
          epochs=epochs,
          verbose=1,
          validation_data=(x_test, y_test),
          callbacks=[remote])

score = model.evaluate(x_test, y_test, verbose=0)

print('Test loss:', score[0])
print('Test accuracy:', score[1])

回到網頁就能即時看到training時的acc、loss、val_acc、val_loss。

參考

https://github.com/fchollet/hualos