advanced.ipynb

Kernel: Python 3

Copyright 2019 The TensorFlow Authors.

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Primeiros passos do TensorFlow 2 para especialistas

Veja em TensorFlow.org

Executar no Google Colab

Ver fonte em GitHub

Baixar notebook

Este é um arquivo de notebook do Google Colaboratory. Os programas em Python são executados diretamente no navegador — uma ótima maneira de aprender e usar o TensorFlow. Para seguir este tutorial, execute o notebook no Google Colab clicando no botão no topo desta página.

1. No Colab, conecte a um runtime do Python: no canto superior direito da barra de menu, selecione CONNECT (CONECTAR).

2. Execute todas as células de código do notebook: selecione Runtime > Run all (Executar tudo).

Baixe e instale o TensorFlow 2. Importe o TensorFlow para seu programa:

Observação: atualize o pip para instalar o pacote do TensorFlow 2. Confira mais detalhes no guia de instalação.

Importe o TensorFlow para seu programa:

import tensorflow as tf
print("TensorFlow version:", tf.__version__)

from tensorflow.keras.layers import Dense, Flatten, Conv2D
from tensorflow.keras import Model

Carregue e prepare o dataset 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")

Use tf.data para dividir o dataset em lotes e misturá-lo:

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)

Crie o modelo tf.keras usando a API de subclasses do modelo:

class MyModel(Model):
  def __init__(self):
    super().__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()

Escolha um otimizador e a função de perda para o treinamento:

loss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)

optimizer = tf.keras.optimizers.Adam()

Selecione métricas para medir a perda e a exatidão do modelo. Essas métricas acumulam os valores das épocas e imprimem o resultado geral.

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')

Use tf.GradientTape para treinar o modelo:

@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)

Teste o modelo:

@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}'
  )

O classificador de imagens já está treinado com cerca de 98% de exatidão nesse dataset. Para saber mais, leia os tutoriais do TensorFlow.