# CJSHS AI Course Week 1
## 課程學期目標（社團發表）
1. 製作 CJSHS GPT
2. 製作自走車
## 課程須知
- 上課時間: 10:00~11:00
- 動手動腦完成作業時間: 11:00~12:00
- 老師要去開會
- 作業完成找社長、副社長或老師檢查
- 完成作業後，自行利用時間 (可以玩電腦，不能玩手機)
- 不能使用手機
* 校方告知學生一律不能使用手機
## Week 1 課程
### Classification (分類任務)
### 開啟 Colab
- https://colab.research.google.com/
- 設定使用GPU運算
1. 執行階段
2. 變更執行階段類型
3. 選擇 "T4 GPU"
4. 儲存
### Classification (分類任務)
- 匯入程式庫
```python=
# TensorFlow and tf.keras
import tensorflow as tf
# Helper libraries
import numpy as np
import matplotlib.pyplot as plt
print(tf.__version__)
```
- 下載資料集 (fashion MNIST)
資料集網頁: https://www.tensorflow.org/api_docs/python/tf/keras/datasets/fashion_mnist/load_data
```python=
fashion_mnist = tf.keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()
```
- 設定類別名稱
```python=
class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
```
- 資料標準化
```python=
train_images = train_images / 255.0
test_images = test_images / 255.0
```
- 資料範例可視化
```python=
plt.figure(figsize=(10,10))
for i in range(25):
plt.subplot(5,5,i+1)
plt.xticks([])
plt.yticks([])
plt.grid(False)
plt.imshow(train_images[i], cmap=plt.cm.binary)
plt.xlabel(class_names[train_labels[i]])
plt.show()
```
- 建立AI模型
```python=
model = tf.keras.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dense(10)
])
```
- 編譯AI模型
```python=
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
```
- 定義繪圖函式及處理預測結果
```python=
def plot_image(i, predictions_array, true_label, img):
true_label, img = true_label[i], img[i]
plt.grid(False)
plt.xticks([])
plt.yticks([])
plt.imshow(img, cmap=plt.cm.binary)
predicted_label = np.argmax(predictions_array)
if predicted_label == true_label:
color = 'blue'
else:
color = 'red'
plt.xlabel("{} {:2.0f}% ({})".format(class_names[predicted_label],
100*np.max(predictions_array),
class_names[true_label]),
color=color)
def plot_value_array(i, predictions_array, true_label):
true_label = true_label[i]
plt.grid(False)
plt.xticks(range(10))
plt.yticks([])
thisplot = plt.bar(range(10), predictions_array, color="#777777")
plt.ylim([0, 1])
predicted_label = np.argmax(predictions_array)
thisplot[predicted_label].set_color('red')
thisplot[true_label].set_color('blue')
```
- 訓練前模型之預測
```python=
probability_model = tf.keras.Sequential([model, tf.keras.layers.Softmax()])
predictions = probability_model.predict(test_images)
```
- 繪製預測
```python=
num_rows = 5
num_cols = 3
num_images = num_rows*num_cols
plt.figure(figsize=(2*2*num_cols, 2*num_rows))
for i in range(num_images):
plt.subplot(num_rows, 2*num_cols, 2*i+1)
plot_image(i, predictions[i], test_labels, test_images)
plt.subplot(num_rows, 2*num_cols, 2*i+2)
plot_value_array(i, predictions[i], test_labels)
plt.tight_layout()
plt.show()
```
- 訓練模型
```python=
model.fit(train_images, train_labels, epochs=10)
```
- 訓練後模型之預測
```python=
probability_model = tf.keras.Sequential([model, tf.keras.layers.Softmax()])
predictions = probability_model.predict(test_images)
```
- 繪製預測
```python=
num_rows = 5
num_cols = 3
num_images = num_rows*num_cols
plt.figure(figsize=(2*2*num_cols, 2*num_rows))
for i in range(num_images):
plt.subplot(num_rows, 2*num_cols, 2*i+1)
plot_image(i, predictions[i], test_labels, test_images)
plt.subplot(num_rows, 2*num_cols, 2*i+2)
plot_value_array(i, predictions[i], test_labels)
plt.tight_layout()
plt.show()
```
## 作業
1. 成功執行以上程式碼。
2. 成功訓練模型。
3. 繪製預測結果。
### Bonus
- 使用另一個資料集來進行訓練並預測繪製結果
- hint:
- MNIST: https://www.tensorflow.org/api_docs/python/tf/keras/datasets/mnist/load_data
- CIFAR10: https://www.tensorflow.org/api_docs/python/tf/keras/datasets/cifar10/load_data
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