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Ini adalah file notebook Google Colaboratory . Program Python dijalankan langsung di browser — cara yang bagus untuk mempelajari dan menggunakan TensorFlow. Untuk mengikuti tutorial ini, jalankan notebook di Google Colab dengan mengklik tombol di bagian atas halaman ini.
- Di Colab, sambungkan ke runtime Python: Di kanan atas bilah menu, pilih HUBUNGKAN .
- Jalankan semua sel kode notebook: Pilih Runtime > Run all .
Download dan instal TensorFlow 2. Impor TensorFlow ke dalam program Anda:
Impor TensorFlow ke dalam program Anda:
import tensorflow as tf
from tensorflow.keras.layers import Dense, Flatten, Conv2D
from tensorflow.keras import Model
Muat dan siapkan set data MNIST .
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
# Add a channels dimension
x_train = x_train[..., tf.newaxis].astype("float32")
x_test = x_test[..., tf.newaxis].astype("float32")
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz 11493376/11490434 [==============================] - 0s 0us/step
Gunakan tf.data
untuk tf.data
dan mengacak kumpulan data:
train_ds = tf.data.Dataset.from_tensor_slices(
(x_train, y_train)).shuffle(10000).batch(32)
test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(32)
Bangun model tf.keras
menggunakan API subclassing model Keras:
class MyModel(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)
def call(self, x):
x = self.conv1(x)
x = self.flatten(x)
x = self.d1(x)
return self.d2(x)
# Create an instance of the model
model = MyModel()
Pilih fungsi pengoptimal dan kerugian untuk pelatihan:
loss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
optimizer = tf.keras.optimizers.Adam()
Pilih metrik untuk mengukur kerugian dan akurasi model. Metrik ini mengakumulasi nilai selama beberapa waktu dan kemudian mencetak hasil keseluruhan.
train_loss = tf.keras.metrics.Mean(name='train_loss')
train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')
test_loss = tf.keras.metrics.Mean(name='test_loss')
test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='test_accuracy')
Gunakantf.GradientTape
untuk melatih model:
@tf.function
def train_step(images, labels):
with tf.GradientTape() as tape:
# training=True is only needed if there are layers with different
# behavior during training versus inference (e.g. Dropout).
predictions = model(images, training=True)
loss = loss_object(labels, predictions)
gradients = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(gradients, model.trainable_variables))
train_loss(loss)
train_accuracy(labels, predictions)
Uji modelnya:
@tf.function
def test_step(images, labels):
# training=False is only needed if there are layers with different
# behavior during training versus inference (e.g. Dropout).
predictions = model(images, training=False)
t_loss = loss_object(labels, predictions)
test_loss(t_loss)
test_accuracy(labels, predictions)
EPOCHS = 5
for epoch in range(EPOCHS):
# Reset the metrics at the start of the next epoch
train_loss.reset_states()
train_accuracy.reset_states()
test_loss.reset_states()
test_accuracy.reset_states()
for images, labels in train_ds:
train_step(images, labels)
for test_images, test_labels in test_ds:
test_step(test_images, test_labels)
print(
f'Epoch {epoch + 1}, '
f'Loss: {train_loss.result()}, '
f'Accuracy: {train_accuracy.result() * 100}, '
f'Test Loss: {test_loss.result()}, '
f'Test Accuracy: {test_accuracy.result() * 100}'
)
Epoch 1, Loss: 0.1360151171684265, Accuracy: 95.94332885742188, Test Loss: 0.06302054226398468, Test Accuracy: 97.90999603271484 Epoch 2, Loss: 0.04181966185569763, Accuracy: 98.71833038330078, Test Loss: 0.05256362631917, Test Accuracy: 98.22999572753906 Epoch 3, Loss: 0.020464003086090088, Accuracy: 99.33500671386719, Test Loss: 0.05558345466852188, Test Accuracy: 98.30999755859375 Epoch 4, Loss: 0.012017485685646534, Accuracy: 99.60333251953125, Test Loss: 0.06642905622720718, Test Accuracy: 98.1500015258789 Epoch 5, Loss: 0.00839359499514103, Accuracy: 99.73666381835938, Test Loss: 0.07797500491142273, Test Accuracy: 98.19999694824219
Pengklasifikasi gambar sekarang dilatih untuk ~ 98% akurasi pada set data ini. Untuk mempelajari lebih lanjut, baca tutorial TensorFlow .