[Tensorflow] Colab - 초보자를 위한 빠른 시작

Tensorflow 2.0 시작하기 : 초보자용

• https://www.tensorflow.org/tutorials/quickstart/beginner

라이브러리 설치 & Import

pip install -q tensorflow-gpu=2.0.0-rc1
import tensorflow as tf

mnist 데이터셋 준비

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

tf.keras.Sequential 모델 생성(훈련에 사용할 Optimizer와 손실 함수 선택)

model = tf.keras.models.Sequential([
   tf.keras.layers.Flattern(input_shape=(28, 28)),
   tf.keras.layers.Dense(128, activation='relu'),
   tf.keras.layers.Dropout(0.2),
   tf.kears.layers.Dense(10, activation='softmax')
])

model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

모델 훈련 & 평가

model.fit(x_train, y_train, epoch=5)
model.evaludate(x_test, y_test, verbose=2)

출력결과

Train on 60000 samples
Epoch 1/5
WARNING:tensorflow:Entity <function Function._initialize_uninitialized_variables.<locals>.initialize_variables at 0x7f2d11707048> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'
WARNING: Entity <function Function._initialize_uninitialized_variables.<locals>.initialize_variables at 0x7f2d11707048> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'
60000/60000 [==============================] - 4s 68us/sample - loss: 0.2941 - accuracy: 0.9140
Epoch 2/5
60000/60000 [==============================] - 4s 62us/sample - loss: 0.1396 - accuracy: 0.9587
Epoch 3/5
60000/60000 [==============================] - 4s 62us/sample - loss: 0.1046 - accuracy: 0.9680
Epoch 4/5
60000/60000 [==============================] - 4s 62us/sample - loss: 0.0859 - accuracy: 0.9742
Epoch 5/5
60000/60000 [==============================] - 4s 62us/sample - loss: 0.0724 - accuracy: 0.9771
10000/1 - 0s - loss: 0.0345 - accuracy: 0.9788
[0.06729823819857557, 0.9788]