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Get Started with TensorFlow 1.x

<table class="tfo-notebook-buttons" align="left"> <td> <a target="_blank" href="https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/r1/tutorials/_index.ipynb">Run in Google Colab</a> </td> <td> <a target="_blank" href="https://github.com/tensorflow/docs/blob/master/site/en/r1/tutorials/_index.ipynb">View source on GitHub</a> </td> </table>

Note: This is an archived TF1 notebook. These are configured to run in TF2's compatibility mode but will run in TF1 as well. To use TF1 in Colab, use the %tensorflow_version 1.x magic.

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:

import tensorflow.compat.v1 as tf

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

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:

model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(input_shape=(28, 28)),
  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:

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

model.evaluate(x_test,  y_test, verbose=2)

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