Keras Cheat Sheet
High-level Keras API reference covering Sequential and Functional model building, compiling, training with callbacks, and common layer types.
2 PagesBeginnerApr 8, 2026
Sequential API
Stack layers linearly and compile.
python
from tensorflow import kerasfrom tensorflow.keras import layersmodel = keras.Sequential([ layers.Dense(128, activation="relu", input_shape=(784,)), layers.Dropout(0.3), layers.Dense(10, activation="softmax"),])model.compile( optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"],)
Functional API
Build models with non-linear topology.
python
inputs = keras.Input(shape=(784,))x = layers.Dense(128, activation="relu")(inputs)x = layers.Dense(64, activation="relu")(x)outputs = layers.Dense(10, activation="softmax")(x)model = keras.Model(inputs=inputs, outputs=outputs)
Train, Evaluate & Save
Fit the model and persist it to disk.
python
history = model.fit( X_train, y_train, validation_split=0.2, epochs=20, batch_size=32, callbacks=[keras.callbacks.EarlyStopping(patience=3, restore_best_weights=True)],)loss, acc = model.evaluate(X_test, y_test)model.save("my_model.keras")model2 = keras.models.load_model("my_model.keras")
Common Layers
Building blocks used across most models.
- Dense- fully connected layer
- Conv2D- 2D convolution for image inputs
- MaxPooling2D- downsamples feature maps
- LSTM / GRU- recurrent layers for sequence data
- Dropout- randomly zeroes inputs for regularization
- BatchNormalization- normalizes layer inputs for stable training
- Embedding- maps integer tokens to dense vectors
- Flatten- reshapes multi-dimensional input to 1D
Pro Tip
Combine EarlyStopping with ModelCheckpoint so you persist the best-performing weights seen during training, not just whatever the final epoch happens to produce.
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#Keras#KerasCheatSheet#DataScience#Beginner#SequentialAPI#FunctionalAPI#TrainEvaluateSave#CommonLayers#MachineLearning#APIs#CheatSheet#SkillVeris
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