# -*- coding: utf-8 -*-
"""nipa_aiwebdev_ai_fashion.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1dLQFkikURzUPWJXz91owv-wlN_wCLZhK

# 인공 신경망

<table align="left">
  <td>
    <a target="_blank" href="https://colab.research.google.com/github/rickiepark/hg-mldl/blob/master/7-1.ipynb"><img src="https://www.tensorflow.org/images/colab_logo_32px.png" />구글 코랩에서 실행하기</a>
  </td>
</table>

## 패션 MNIST

Keras 패션 데이터셋 import
"""

from tensorflow import keras

(train_input, train_target), (test_input, test_target) = keras.datasets.fashion_mnist.load_data()

"""Training Set Shape 확인"""

print(train_input.shape, train_target.shape)

"""Test Set Shape 확인"""

print(test_input.shape, test_target.shape)

"""Fashion Dataset 이미지 표시"""

import matplotlib.pyplot as plt

fig, axs = plt.subplots(1, 10, figsize=(10,10))
for i in range(10):
    axs[i].imshow(train_input[i], cmap='gray_r')
    axs[i].axis('off')
plt.show()

"""Training Set 나열"""

print([train_target[i] for i in range(10)])

"""Training Set에서 중복 원소 없애기"""

import numpy as np

print(np.unique(train_target, return_counts=True))

"""## 로지스틱 회귀로 패션 아이템 분류하기

Logistic Regression을 통해 패션 아이템 분류
"""

train_scaled = train_input / 255.0
train_scaled = train_scaled.reshape(-1, 28*28)

print(train_scaled.shape)

from sklearn.model_selection import cross_validate
from sklearn.linear_model import SGDClassifier

sc = SGDClassifier(loss='log', max_iter=5, random_state=42)

scores = cross_validate(sc, train_scaled, train_target, n_jobs=-1)
print(np.mean(scores['test_score']))

"""## 인공신경망

### 텐서플로와 케라스
"""

import tensorflow as tf

from tensorflow import keras

"""## 인공신경망으로 모델 만들기"""

from sklearn.model_selection import train_test_split

train_scaled, val_scaled, train_target, val_target = train_test_split(
    train_scaled, train_target, test_size=0.2, random_state=42)

print(train_scaled.shape, train_target.shape)

print(val_scaled.shape, val_target.shape)

dense = keras.layers.Dense(10, activation='softmax', input_shape=(784,))

model = keras.Sequential(dense)

"""## 인공신경망으로 패션 아이템 분류하기"""

model.compile(loss='sparse_categorical_crossentropy', metrics='accuracy')

print(train_target[:10])

model.fit(train_scaled, train_target, epochs=5)

model.evaluate(val_scaled, val_target)