TensorFlow

Warnings

Install

Install compatibility stale last tested: 2026-05-12

Imports

• keras
  wrong

  # Mixing tf.keras and keras objects causes ValueError:
  import keras
  import tensorflow_hub as hub
  layer = hub.KerasLayer(url)  # hub uses tf.keras, not standalone keras
  model = keras.Sequential([layer])  # ValueError: not a keras.Layer instance

  correct

  # Option 1: Use standalone Keras 3 (recommended for new code)
  import keras
  model = keras.Sequential([keras.layers.Dense(64, activation='relu')])
  
  # Option 2: Access via tf.keras (same Keras 3 in TF 2.16+)
  import tensorflow as tf
  model = tf.keras.Sequential([tf.keras.layers.Dense(64)])

  Since TF 2.16, tf.keras and import keras both point to Keras 3 — but third-party libraries like tensorflow_hub, tensorflow_probability may still use the old bundled keras, causing isinstance failures.

• tf.function
  wrong

  # Calling model.fit() for a single step — inefficient
  # Using sess.run() — TF 1.x session API removed in TF 2.0

  correct

  @tf.function
  def train_step(x, y):
      with tf.GradientTape() as tape:
          pred = model(x, training=True)
          loss = loss_fn(y, pred)
      grads = tape.gradient(loss, model.trainable_variables)
      optimizer.apply_gradients(zip(grads, model.trainable_variables))
      return loss

  TF 1.x session-based API (sess = tf.Session(), sess.run()) fully removed in TF 2.0. Use eager execution or @tf.function for graph compilation.

Quickstart stale last tested: 2026-04-23

Keras 3 model with TensorFlow backend. Use .keras format for saving.

import tensorflow as tf
import keras

# Build model (Keras 3)
model = keras.Sequential([
    keras.layers.Dense(64, activation='relu', input_shape=(10,)),
    keras.layers.Dense(1)
])

model.compile(
    optimizer='adam',
    loss='mse',
    metrics=['mae']
)

# Train
model.fit(X_train, y_train, epochs=10, batch_size=32, validation_split=0.2)

# Save / load (.keras format recommended)
model.save('model.keras')
loaded = keras.models.load_model('model.keras')