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다항 분류

Fashion MNIST

불러오기

import tensorflow as tf
(x_train, y_train), (x_test, y_test) = \
    tf.keras.datasets.fashion_mnist.load_data()

전처리

x_train = x_train / 255
x_test = x_test / 255
  • 이미지 데이터는 0-255로 밝기를 표현
  • 신경망에서 사용하는 시그모이드 등의 함수는 지나치게 큰 값이 들어오면 경사가 0에 가까워져서 학습이 잘 되지 않음
  • 255로 나눠서 0-1 범위로 재조정

모형

from tensorflow.keras.layers import *
model = tf.keras.Sequential([
    Flatten(),
    Dense(10, activation='softmax')
])

소프트맥스 함수

tf.nn.softmax([-1. ,  0.5,  2. ])

<tf.Tensor: shape=(3,), dtype=float32, numpy=array([0.03911257, 0.17529039, 0.785597  ], dtype=float32)>

시그모이드 vs. 소프트맥스

시그모이드

x = 1.0
tf.nn.sigmoid(x)

<tf.Tensor: shape=(), dtype=float32, numpy=0.7310586>

소프트맥스

tf.nn.softmax([0, x]) 

<tf.Tensor: shape=(2,), dtype=float32, numpy=array([0.26894143, 0.7310586 ], dtype=float32)>

훈련

설정

model.compile(
    loss=tf.keras.losses.sparse_categorical_crossentropy,
    optimizer=tf.keras.optimizers.Adam(),
    metrics=['accuracy'])

훈련

model.fit(x_train, y_train, epochs=100, validation_split=0.1,
          callbacks=[tf.keras.callbacks.EarlyStopping(patience=2)])

Epoch 1/100
1688/1688 [==============================] - 1s 716us/step - loss: 0.6092 - accuracy: 0.7953 - val_loss: 0.4744 - val_accuracy: 0.8382
Epoch 2/100
1688/1688 [==============================] - 1s 612us/step - loss: 0.4664 - accuracy: 0.8416 - val_loss: 0.4627 - val_accuracy: 0.8413
Epoch 3/100
1688/1688 [==============================] - 1s 625us/step - loss: 0.4391 - accuracy: 0.8499 - val_loss: 0.4335 - val_accuracy: 0.8493
Epoch 4/100
1688/1688 [==============================] - 1s 625us/step - loss: 0.4256 - accuracy: 0.8532 - val_loss: 0.4350 - val_accuracy: 0.8473
Epoch 5/100
1688/1688 [==============================] - 1s 642us/step - loss: 0.4164 - accuracy: 0.8581 - val_loss: 0.4098 - val_accuracy: 0.8592
Epoch 6/100
1688/1688 [==============================] - 1s 624us/step - loss: 0.4096 - accuracy: 0.8583 - val_loss: 0.4167 - val_accuracy: 0.8560
Epoch 7/100
1688/1688 [==============================] - 1s 646us/step - loss: 0.4040 - accuracy: 0.8604 - val_loss: 0.4218 - val_accuracy: 0.8518

<keras.callbacks.History at 0x1f4167de2f0>

평가

정확도

model.evaluate(x_test, y_test)

313/313 [==============================] - 0s 810us/step - loss: 0.4535 - accuracy: 0.8431

[0.4534875452518463, 0.8431000113487244]

예측

prob = model.predict(x_test)

313/313 [==============================] - 0s 693us/step

가장 확률이 높은 카테고리

y_pred = prob.argmax(axis=1)

혼동행렬

from sklearn.metrics import confusion_matrix
confusion_matrix(y_test, y_pred)

array([[827,   1,  14,  72,   5,   0,  69,   0,  12,   0],
       [  3, 952,   2,  35,   5,   0,   1,   0,   2,   0],
       [ 29,   5, 782,  19, 102,   1,  57,   0,   5,   0],
       [ 22,   7,  12, 914,  15,   0,  26,   0,   4,   0],
       [  0,   0, 156,  68, 697,   0,  71,   0,   8,   0],
       [  0,   0,   0,   1,   0, 937,   0,  46,   2,  14],
       [153,   4, 143,  71,  93,   0, 515,   0,  21,   0],
       [  0,   0,   0,   0,   0,  43,   0, 938,   0,  19],
       [  8,   1,  10,  14,   2,   6,  13,   5, 941,   0],
       [  0,   0,   0,   0,   0,  20,   0,  51,   1, 928]], dtype=int64)