Copyright 2018 The TensorFlow Authors.¶

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Get Started with TensorFlow¶

View on TensorFlow.org

Run in Google Colab

View source on GitHub

This is a Google Colaboratory notebook file. Python programs are run directly in the browser—a great way to learn and use TensorFlow. To run the Colab notebook:

Connect to a Python runtime: At the top-right of the menu bar, select CONNECT.
Run all the notebook code cells: Select Runtime > Run all.

For more examples and guides (including details for this program), see Get Started with TensorFlow.

Let's get started, import the TensorFlow library into your program:

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import tensorflow as tf

Load and prepare the MNIST dataset. Convert the samples from integers to floating-point numbers:

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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

Build the tf.keras model by stacking layers. Select an optimizer and loss function used for training:

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model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(),
  tf.keras.layers.Dense(512, activation=tf.nn.relu),
  tf.keras.layers.Dropout(0.2),
  tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])

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

Train and evaluate model:

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model.fit(x_train, y_train, epochs=5)

model.evaluate(x_test, y_test)

You’ve now trained an image classifier with ~98% accuracy on this dataset. See Get Started with TensorFlow to learn more.