Fashion MNIST
Library Import
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import datetime
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.keras.models import Sequential, Model
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.layers import Conv2D, MaxPool2D, Flatten, Dense, Dropout, Input
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.datasets.fashion_mnist import load_data
Data Load & Image Preprocessing
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# Data Load
(X_train, y_train), (X_test, y_test) = load_data()
print(X_train.shape)
print(X_test.shape)
# 축 추가
X_train = X_train[..., np.newaxis]
X_test = X_test[..., np.newaxis]
# Normalization
X_train = X_train / 255.0
X_test = X_test / 255.0
print(X_train.shape)
print(X_test.shape)
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Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz
29515/29515 [==============================] - 0s 0us/step
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz
26421880/26421880 [==============================] - 3s 0us/step
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz
5148/5148 [==============================] - 0s 0us/step
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz
4422102/4422102 [==============================] - 0s 0us/step
(60000, 28, 28)
(10000, 28, 28)
(60000, 28, 28, 1)
(10000, 28, 28, 1)
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class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
Model
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# Model 정의
def build_model():
model = Sequential()
input = Input(shape=(28, 28, 1))
output = Conv2D(filters=32, kernel_size=(3, 3))(input)
output = Conv2D(filters=64, kernel_size=(3, 3))(output)
output = Conv2D(filters=64, kernel_size=(3, 3))(output)
output = Flatten()(output)
output = Dense(units=128, activation='relu')(output)
output = Dense(units=64, activation='relu')(output)
output = Dense(units=10, activation='softmax')(output)
model = Model(inputs=input, outputs=output)
# Model Compile
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
return model
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model_1 = build_model()
model_1.summary()
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Model: "model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_4 (InputLayer) [(None, 28, 28, 1)] 0
conv2d_18 (Conv2D) (None, 26, 26, 32) 320
conv2d_19 (Conv2D) (None, 24, 24, 64) 18496
conv2d_20 (Conv2D) (None, 22, 22, 64) 36928
flatten_7 (Flatten) (None, 30976) 0
dense_15 (Dense) (None, 128) 3965056
dense_16 (Dense) (None, 64) 8256
dense_17 (Dense) (None, 10) 650
=================================================================
Total params: 4,029,706
Trainable params: 4,029,706
Non-trainable params: 0
_________________________________________________________________
accuracy는 오르고 있지만 validation accuracy가 오르지 않는 Overfitting 현상 발생
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# Model fit
history1 = model_1.fit(X_train, y_train,
batch_size=128,
epochs=25,
validation_split=0.3)
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Epoch 1/25
329/329 [==============================] - 6s 17ms/step - loss: 0.3269 - accuracy: 0.8804 - val_loss: 0.3522 - val_accuracy: 0.8736
Epoch 2/25
329/329 [==============================] - 4s 13ms/step - loss: 0.2733 - accuracy: 0.8995 - val_loss: 0.3382 - val_accuracy: 0.8782
Epoch 3/25
329/329 [==============================] - 5s 15ms/step - loss: 0.2323 - accuracy: 0.9140 - val_loss: 0.3505 - val_accuracy: 0.8827
Epoch 4/25
329/329 [==============================] - 5s 14ms/step - loss: 0.1983 - accuracy: 0.9265 - val_loss: 0.3772 - val_accuracy: 0.8753
Epoch 5/25
329/329 [==============================] - 5s 14ms/step - loss: 0.1712 - accuracy: 0.9358 - val_loss: 0.4028 - val_accuracy: 0.8741
Epoch 6/25
329/329 [==============================] - 5s 16ms/step - loss: 0.1531 - accuracy: 0.9436 - val_loss: 0.4374 - val_accuracy: 0.8751
Epoch 7/25
329/329 [==============================] - 4s 13ms/step - loss: 0.1345 - accuracy: 0.9507 - val_loss: 0.4659 - val_accuracy: 0.8768
Epoch 8/25
329/329 [==============================] - 5s 15ms/step - loss: 0.1212 - accuracy: 0.9552 - val_loss: 0.5363 - val_accuracy: 0.8724
Epoch 9/25
329/329 [==============================] - 5s 14ms/step - loss: 0.1024 - accuracy: 0.9616 - val_loss: 0.5711 - val_accuracy: 0.8724
Epoch 10/25
329/329 [==============================] - 4s 14ms/step - loss: 0.0930 - accuracy: 0.9657 - val_loss: 0.6218 - val_accuracy: 0.8639
Epoch 11/25
329/329 [==============================] - 5s 14ms/step - loss: 0.0864 - accuracy: 0.9681 - val_loss: 0.6617 - val_accuracy: 0.8666
Epoch 12/25
329/329 [==============================] - 5s 16ms/step - loss: 0.0845 - accuracy: 0.9705 - val_loss: 0.7101 - val_accuracy: 0.8617
Epoch 13/25
329/329 [==============================] - 5s 15ms/step - loss: 0.0785 - accuracy: 0.9713 - val_loss: 0.7027 - val_accuracy: 0.8673
Epoch 14/25
329/329 [==============================] - 5s 14ms/step - loss: 0.0685 - accuracy: 0.9761 - val_loss: 0.7472 - val_accuracy: 0.8660
Epoch 15/25
329/329 [==============================] - 4s 13ms/step - loss: 0.0613 - accuracy: 0.9784 - val_loss: 0.8054 - val_accuracy: 0.8684
Epoch 16/25
329/329 [==============================] - 5s 15ms/step - loss: 0.0602 - accuracy: 0.9785 - val_loss: 0.7927 - val_accuracy: 0.8677
Epoch 17/25
329/329 [==============================] - 5s 14ms/step - loss: 0.0576 - accuracy: 0.9794 - val_loss: 0.9067 - val_accuracy: 0.8662
Epoch 18/25
329/329 [==============================] - 4s 13ms/step - loss: 0.0609 - accuracy: 0.9787 - val_loss: 0.8520 - val_accuracy: 0.8731
Epoch 19/25
329/329 [==============================] - 5s 16ms/step - loss: 0.0547 - accuracy: 0.9819 - val_loss: 0.8942 - val_accuracy: 0.8672
Epoch 20/25
329/329 [==============================] - 4s 13ms/step - loss: 0.0522 - accuracy: 0.9810 - val_loss: 0.9241 - val_accuracy: 0.8693
Epoch 21/25
329/329 [==============================] - 4s 13ms/step - loss: 0.0428 - accuracy: 0.9850 - val_loss: 0.9956 - val_accuracy: 0.8685
Epoch 22/25
329/329 [==============================] - 5s 16ms/step - loss: 0.0378 - accuracy: 0.9868 - val_loss: 1.0312 - val_accuracy: 0.8626
Epoch 23/25
329/329 [==============================] - 5s 14ms/step - loss: 0.0475 - accuracy: 0.9833 - val_loss: 1.0124 - val_accuracy: 0.8623
Epoch 24/25
329/329 [==============================] - 5s 16ms/step - loss: 0.0368 - accuracy: 0.9873 - val_loss: 1.1394 - val_accuracy: 0.8602
Epoch 25/25
329/329 [==============================] - 5s 14ms/step - loss: 0.0475 - accuracy: 0.9849 - val_loss: 1.0265 - val_accuracy: 0.8673
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history1.history.keys()
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dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])
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plt.figure(figsize=(12, 4))
# Loss
plt.subplot(1, 2, 1)
plt.plot(history1.history['loss'], 'b--', label='loss')
plt.plot(history1.history['val_loss'], 'r:', label='val_loss')
plt.xlabel('Epochs')
plt.grid()
plt.legend()
# Accuracy
plt.subplot(1, 2, 2)
plt.plot(history1.history['accuracy'], 'b--', label='accuracy')
plt.plot(history1.history['val_accuracy'], 'r:', label='val_accuracy')
plt.xlabel('Epochs')
plt.grid()
plt.legend()
plt.show()
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# 모델 평가
model_1.evaluate(X_test, y_test)
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313/313 [==============================] - 1s 4ms/step - loss: 1.1313 - accuracy: 0.8558
[1.1313254833221436, 0.8557999730110168]
과적합 문제를 해결하기 위한 방법을 다음 포스팅에서 다룰 예정이다.