TensorFlow 1.x tutorials (archived)
Note: Please use the latest tutorials at https://www.tensorflow.org/tutorials
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.Dropout(0.2), tf.keras.layers.Dense(10, activation=tf.nn.softmax) ]) model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) model.fit(x_train, 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.
- Eager execution basics
- Automatic differentiation and gradient tape
- Custom training: basics
- Custom layers
- Custom training: walkthrough
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.