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MarkDaoust and Copybara-Service Set verbose=2 when evaluating using numpy arrays.
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TensorFlow 1.x tutorials (archived)

Note: Please use the latest tutorials at

TensorFlow is an open-source machine learning library for research and production. TensorFlow offers APIs for beginners and experts to develop for desktop, mobile, web, and cloud. See the sections below to get started.

Learn and use ML

The high-level Keras API provides building blocks to create and train deep learning models. Start with these beginner-friendly notebook examples, then read the TensorFlow Keras guide.

import tensorflow as tf

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

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.Dense(10, activation=tf.nn.softmax)

              metrics=['accuracy']), y_train, epochs=5)
model.evaluate(x_test, y_test)

Run this code in Google's interactive notebook.

Research and experimentation

Eager execution provides an imperative, define-by-run interface for advanced operations. Write custom layers, forward passes, and training loops with auto‑differentiation. Start with these notebooks, then read the eager execution guide.

ML at production scale

Estimators can train large models on multiple machines in a production environment. TensorFlow provides a collection of pre-made Estimators to implement common ML algorithms. See the Estimators guide.

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