TensorFlow Cheat Sheet
TensorFlow 2 model building, training loops, and deployment basics.
3 PagesAdvancedApr 22, 2026
Building a Model
Define a simple sequential model.
python
import tensorflow as tffrom tensorflow.keras import layersmodel = tf.keras.Sequential([ layers.Dense(64, activation='relu', input_shape=(784,)), layers.Dense(10, activation='softmax'),])
Compile & Train
Configure and run the training loop.
python
model.compile( optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'],)model.fit(x_train, y_train, epochs=5, validation_split=0.1)
Tensors
Create and manipulate tensors.
python
t = tf.constant([[1, 2], [3, 4]])tf.reduce_sum(t)tf.reshape(t, [4])tf.cast(t, tf.float32)
Save & Deploy
Persist a trained model.
python
model.save('my_model.keras')loaded = tf.keras.models.load_model('my_model.keras')loaded.predict(x_test[:1])
Pro Tip
Use model.summary() after building a model to sanity-check layer shapes before you spend time training.
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