How to get started with TensorFlow

· Category: AI & Machine Learning

Short answer

TensorFlow is an open-source framework for numerical computation with strong support for deep learning through its high-level Keras API.

Steps

  1. Install TensorFlow via pip and verify GPU support if available.
  2. Learn the basics of tensors, operations, and automatic differentiation.
  3. Build models using tf.keras.Sequential or the functional API.
  4. Compile models with optimizers, loss functions, and metrics.
  5. Train using model.fit and evaluate with model.evaluate on test data.

Tips

  • Use the Keras functional API for multi-input or multi-output architectures.
  • Leverage tf.data for efficient input pipelines with prefetching and caching.
  • Enable mixed precision with tf.keras.mixed_precision for faster training on modern GPUs.
  • Use TensorBoard to visualize training curves and model graphs.

Common issues

  • Version mismatches between TensorFlow, CUDA, and cuDNN causing GPU errors.
  • Eager execution versus graph mode confusion when debugging.
  • Memory leaks from creating too many tensors in loops without releasing them.
  • Difficulty in customizing training loops when relying solely on high-level APIs.

Example

import tensorflow as tf

model = tf.keras.Sequential([
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(10, activation='softmax')
])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy')
model.fit(x_train, y_train, epochs=5)

This minimal example builds a neural network with Keras, compiles it, and trains for five epochs on prepared data.