TensorFlow는 머신러닝을 위한 엔드 투 엔드 오픈소스 플랫폼입니다.
TensorFlow를 사용하면 초보자와 전문가 모두 머신러닝 모델을 쉽게 만들 수 있습니다. 시작하려면 아래의 섹션을 참조하세요.
초보자용
The best place to start is with the user-friendly Sequential API. You can create models by plugging together building blocks. Run the “Hello World” example below, then visit the tutorials to learn more.
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import tensorflow as tf
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
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, activation='softmax')
])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.fit(x_train, y_train, epochs=5)
model.evaluate(x_test, y_test)
전문가용
The Subclassing API provides a define-by-run interface for advanced research. Create a class for your model, then write the forward pass imperatively. Easily author custom layers, activations, and training loops. Run the “Hello World” example below, then visit the tutorials to learn more.
class MyModel(tf.keras.Model):
def __init__(self):
super(MyModel, self).__init__()
self.conv1 = Conv2D(32, 3, activation='relu')
self.flatten = Flatten()
self.d1 = Dense(128, activation='relu')
self.d2 = Dense(10, activation='softmax')
def call(self, x):
x = self.conv1(x)
x = self.flatten(x)
x = self.d1(x)
return self.d2(x)
model = MyModel()
with tf.GradientTape() as tape:
logits = model(images)
loss_value = loss(logits, labels)
grads = tape.gradient(loss_value, model.trainable_variables)
optimizer.apply_gradients(zip(grads, model.trainable_variables))
일반적인 문제 해결책
프로젝트에 도움이 되는 단계별 튜토리얼을 탐색하세요.
Keras Subclassing API를 사용하여 스페인어에서 영어로 번역하도록 sequence-to-sequence 모델을 학습시키세요.