 Ver em TensorFlow.org
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 Executar no Google Colab
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 Ver código fonte no GitHub
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 Baixar notebook
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Esta breve introdução usa Keras para:
- Criar uma rede neural que classifique imagens.
- Treinar essa rede neural.
- E, finalmente, avaliar a precisão do modelo.
Este é um arquivo de bloco de notas Google Colaboratory. Os programas Python são executados diretamente no navegador - uma ótima maneira de aprender e usar o TensorFlow. Para seguir este tutorial, execute o bloco de anotações no Google Colab clicando no botão na parte superior desta página.
- No Colab, conecte-se a um tempo de execução do Python: No canto superior direito da barra de menus, selecione CONNECT.
- Execute todas as células de código do notebook: Selecione Tempo de execução > Executar tudo.
Baixe e instale o pacote TensorFlow 2. Importe o TensorFlow para o seu programa:
from __future__ import absolute_import, division, print_function, unicode_literals
# Install TensorFlow
try:
# %tensorflow_version only exists in Colab.
%tensorflow_version 2.x
except Exception:
pass
import tensorflow as tf
Carregue e prepare o conjunto de dados MNIST. Converta as amostras de números inteiros em números de ponto flutuante:
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
Crie o modelo tf.keras.Sequential empilhando camadas. Escolha uma função otimizadora e de perda para treinamento:
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'])
Treine e avalie o modelo:
model.fit(x_train, y_train, epochs=5)
model.evaluate(x_test, y_test, verbose=2)
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ETA: 1s - loss: 0.0766 - accuracy: 0.9764 933/1875 [=============>................] - ETA: 1s - loss: 0.0760 - accuracy: 0.9765 966/1875 [==============>...............] - ETA: 1s - loss: 0.0760 - accuracy: 0.9764 1000/1875 [===============>..............] - ETA: 1s - loss: 0.0763 - accuracy: 0.9765 1034/1875 [===============>..............] - ETA: 1s - loss: 0.0766 - accuracy: 0.9765 1068/1875 [================>.............] - ETA: 1s - loss: 0.0765 - accuracy: 0.9765 1101/1875 [================>.............] - ETA: 1s - loss: 0.0762 - accuracy: 0.9766 1135/1875 [=================>............] - ETA: 1s - loss: 0.0757 - accuracy: 0.9767 1169/1875 [=================>............] - ETA: 1s - loss: 0.0756 - accuracy: 0.9769 1203/1875 [==================>...........] - ETA: 0s - loss: 0.0758 - accuracy: 0.9768 1237/1875 [==================>...........] - ETA: 0s - loss: 0.0759 - accuracy: 0.9769 1271/1875 [===================>..........] - ETA: 0s - loss: 0.0758 - accuracy: 0.9768 1305/1875 [===================>..........] - ETA: 0s - loss: 0.0759 - accuracy: 0.9768 1339/1875 [====================>.........] - ETA: 0s - loss: 0.0763 - accuracy: 0.9766 1373/1875 [====================>.........] - ETA: 0s - loss: 0.0762 - accuracy: 0.9765 1407/1875 [=====================>........] - ETA: 0s - loss: 0.0760 - accuracy: 0.9765 1441/1875 [======================>.......] - ETA: 0s - loss: 0.0759 - accuracy: 0.9766 1475/1875 [======================>.......] - ETA: 0s - loss: 0.0760 - accuracy: 0.9766 1509/1875 [=======================>......] - ETA: 0s - loss: 0.0757 - accuracy: 0.9765 1544/1875 [=======================>......] - ETA: 0s - loss: 0.0761 - accuracy: 0.9763 1577/1875 [========================>.....] - ETA: 0s - loss: 0.0755 - accuracy: 0.9765 1610/1875 [========================>.....] - ETA: 0s - loss: 0.0751 - accuracy: 0.9765 1644/1875 [=========================>....] - ETA: 0s - loss: 0.0750 - accuracy: 0.9765 1678/1875 [=========================>....] - ETA: 0s - loss: 0.0749 - accuracy: 0.9766 1713/1875 [==========================>...] - ETA: 0s - loss: 0.0747 - accuracy: 0.9767 1747/1875 [==========================>...] - ETA: 0s - loss: 0.0743 - accuracy: 0.9769 1782/1875 [===========================>..] - ETA: 0s - loss: 0.0744 - accuracy: 0.9769 1816/1875 [============================>.] - ETA: 0s - loss: 0.0745 - accuracy: 0.9768 1851/1875 [============================>.] - ETA: 0s - loss: 0.0751 - accuracy: 0.9766 1875/1875 [==============================] - 3s 1ms/step - loss: 0.0750 - accuracy: 0.9766 313/313 - 0s - loss: 0.0744 - accuracy: 0.9766 [0.07437223196029663, 0.9765999913215637]
O classificador de imagem agora é treinado para ~98% de acurácia neste conjunto de dados. Para saber mais, leia os tutoriais do TensorFlow.