Jupyter TensorFlow 範例¶

在 Kubeflow Notebooks 中使用 Jupyter 和 TensorFlow 的範例。

Mnist Example¶

1. 創建 "Notebook" 時，選擇一個安裝了 Jupyter 和 TensorFlow 的容器鏡像

   • 例如, jupyter-tensorflow-full:v1.7.0

2. 使用 Jupyter 的界面創建一個新的 Python 3 筆記本。

   

3. 複製以下程碼並將其粘貼到您的筆記本中：

   • 參考: TensorFlow 2 quickstart for beginners

   # Set up TensorFlow
   import tensorflow as tf
   
   print("TensorFlow version:", tf.__version__)
   
   # Load a dataset
   mnist = tf.keras.datasets.mnist
   
   (x_train, y_train), (x_test, y_test) = mnist.load_data()
   
   x_train, x_test = x_train / 255.0, x_test / 255.0
   
   # Build a tf.keras.Sequential model
   model = tf.keras.models.Sequential([
   tf.keras.layers.Flatten(input_shape=(28, 28)),
   tf.keras.layers.Dense(128, activation='relu'),
   tf.keras.layers.Dropout(0.2),
   tf.keras.layers.Dense(10)
   ])
   
   predictions = model(x_train[:1]).numpy()
   
   tf.nn.softmax(predictions).numpy()
   
   loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
   
   loss_fn(y_train[:1], predictions).numpy()
   
   model.compile(optimizer='adam',
               loss=loss_fn,
               metrics=['accuracy'])
   
   # Train and evaluate your model
   model.fit(x_train, y_train, epochs=5)
   
   model.evaluate(x_test,  y_test, verbose=2)
   
   probability_model = tf.keras.Sequential([
   model,
   tf.keras.layers.Softmax()
   ])
   
   probability_model(x_test[:5])