머신러닝스터디/2016/2016_06_11

Difference between r1.3 and the current

@@ -1,4 +1,5 @@

[~~[pagelist(^(~~머신러닝스터디/2016))]]

[머신러닝스터디/2016]

[머신러닝스터디/2016/목차]

== 내용 ==
* keras 사용
* mnist

@@ -28,7 +29,8 @@

optimizer='adagrad',
metrics=['accuracy'])

# y_train and y_test is simple integer of 0 to 9

# Need to be transformed to array

y_train_array = np.zeros((60000, 10))
y_test_array = np.zeros((10000, 10))
for i in range(60000):

@@ -46,6 +48,19 @@

print(score)
}}}
== 후기 ==

* [서지혜]: 맥에어에서 돌렸더니 엄청 오래걸렸다.. 클라우드 세팅해야될거같음. 아래는 결과물. 다 돌리고 출력을 어떻게 해야할지 모르겠네.

{{{

Using Theano backend.

Epoch 1/3

60000/60000 [==============================] - 3122s - loss: 14.4306 - acc: 0.1047

Epoch 2/3

60000/60000 [==============================] - 3055s - loss: 14.4370 - acc: 0.1043

Epoch 3/3

60000/60000 [==============================] - 3135s - loss: 14.4453 - acc: 0.1038

10000/10000 [==============================] - 398s

[14.461154937744141, 0.10279999673366547]

}}}

== 다음 시간에는 ==
* Week 6 보기
== 더 보기 ==

머신러닝스터디/2016
머신러닝스터디/2016/목차

내용 ¶

keras 사용
mnist
keras mnist 예제파일 위치: https://s3.amazonaws.com/img-datasets/mnist.pkl.gz
- 코드 실행하면 자동으로 받아짐

코드 ¶

from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
from keras.datasets import mnist
from keras.layers.core import Reshape
import numpy as np

(X_train, y_train), (X_test, y_test) = mnist.load_data()

model = Sequential()
model.add(Reshape((28*28,), input_shape=(28,28)))
model.add(Dense(60000, input_dim=28*28, activation='relu'))

model.add(Dropout(0.5))
model.add(Dense(64, activation='relu'))

model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))

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

# y_train and y_test is simple integer of 0 to 9
# Need to be transformed to array
y_train_array = np.zeros((60000, 10))
y_test_array = np.zeros((10000, 10))
for i in range(60000):
  y_train_array[i][y_train[i]] = 1
for i in range(10000):
  y_test_array[i][y_test[i]] = 1

model.fit(X_train, y_train_array,
          nb_epoch=3,
          batch_size=16)

score = model.evaluate(X_test, y_test_array, batch_size=10000)

# TODO
print(score)

후기 ¶

서지혜: 맥에어에서 돌렸더니 엄청 오래걸렸다.. 클라우드 세팅해야될거같음. 아래는 결과물. 다 돌리고 출력을 어떻게 해야할지 모르겠네.

Using Theano backend.
Epoch 1/3
60000/60000 [==============================] - 3122s - loss: 14.4306 - acc: 0.1047     
Epoch 2/3
60000/60000 [==============================] - 3055s - loss: 14.4370 - acc: 0.1043     
Epoch 3/3
60000/60000 [==============================] - 3135s - loss: 14.4453 - acc: 0.1038     

10000/10000 [==============================] - 398s
[14.461154937744141, 0.10279999673366547]

다음 시간에는 ¶