Keras Library CheatSheet

1. Detailed TensorFlow Keras Cheat
Sheet
Comprehensive Guide for Deep
Learning

2. Introduction to TensorFlow Keras
• Keras is a high-level API for building and
training deep learning models.
• Integrated tightly with TensorFlow for efficient
computation.
• Offers Sequential, Functional, and Subclassing
approaches.

3. Sequential API
• Sequential Model: model =
tf.keras.Sequential([...])
• Example:
• model = tf.keras.Sequential([
• tf.keras.layers.Dense(128, activation='relu'),
• tf.keras.layers.Dense(10,
activation='softmax')
• ])
• Best for simple, layer-by-layer stacking.

4. Functional API
• Define complex models with shared layers and
custom outputs.
• Example:
• inputs = tf.keras.Input(shape=(784,))
• x = tf.keras.layers.Dense(128, activation='relu')
(inputs)
• outputs = tf.keras.layers.Dense(10,
activation='softmax')(x)
• model = tf.keras.Model(inputs, outputs)

5. Subclassing Model
• Subclass tf.keras.Model for full control over
architecture.
• Example:
• class MyModel(tf.keras.Model):
• def __init__(self):
• super(MyModel, self).__init__()
• self.dense1 = tf.keras.layers.Dense(128,
activation='relu')
• self.dense2 = tf.keras.layers.Dense(10,

6. Common Layers
• Dense Layer: Fully connected layer.
• Example: tf.keras.layers.Dense(64,
activation='relu')
• Conv2D Layer: 2D convolutional layer.
• Example: tf.keras.layers.Conv2D(32, (3, 3),
activation='relu')
• Dropout Layer: Regularization to prevent
overfitting.
• Example: tf.keras.layers.Dropout(0.5)

7. Model Compilation
• Compile the model with optimizer, loss, and
metrics.
• Example:
• model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
• Loss Functions: 'mse',
'categorical_crossentropy', etc.
• Metrics: ['accuracy', 'mae', etc.]

8. Model Training
• Fit the model to your training data.
• Example:
• model.fit(X_train, y_train, epochs=10,
batch_size=32, validation_data=(X_val, y_val))
• Supports callbacks for dynamic training
adjustments.

9. Model Evaluation
• Evaluate model performance on test data.
• Example:
• loss, accuracy = model.evaluate(X_test, y_test)
• Use metrics like accuracy, precision, and recall.

10. Making Predictions
• Generate predictions for new data.
• Example:
• predictions = model.predict(new_data)
• Post-process predictions for interpretation.

11. Data Preprocessing
• Standardization: tf.keras.layers.Normalization()
• Text Tokenization:
tf.keras.preprocessing.text.Tokenizer()
• Image Augmentation:
tf.keras.layers.RandomFlip(),
tf.keras.layers.RandomRotation()

12. Callbacks
• EarlyStopping: Stop training when
performance stops improving.
• ModelCheckpoint: Save model checkpoints
during training.
• TensorBoard: Monitor training metrics and
logs.

13. Custom Training Loops
• Full control over training steps using
GradientTape.
• Example:
• with tf.GradientTape() as tape:
• predictions = model(X_train)
• loss = loss_fn(y_train, predictions)
• gradients = tape.gradient(loss,
model.trainable_variables)
• optimizer.apply_gradients(zip(gradients,

14. Saving and Loading Models
• Save entire model:
• model.save('model.h5')
• Load saved model:
• model =
tf.keras.models.load_model('model.h5')
• Save weights only:
• model.save_weights('weights.h5')
• Load weights only:
• model.load_weights('weights.h5')

15. Transfer Learning
• Reuse pre-trained models for new tasks.
• Example:
• base_model =
tf.keras.applications.MobileNetV2(include_top
=False, input_shape=(224, 224, 3))
• base_model.trainable = False
• model = tf.keras.Sequential([
• base_model,
• tf.keras.layers.GlobalAveragePooling2D(),

16. Hyperparameter Tuning
• Use libraries like Keras Tuner for automated
optimization.
• Example:
• def model_builder(hp):
• model = tf.keras.Sequential()
• hp_units = hp.Int('units', min_value=32,
max_value=512, step=32)
•
model.add(tf.keras.layers.Dense(units=hp_unit
s, activation='relu'))

17. Optimizers
• Adam: Adaptive Moment Estimation.
• Example:
tf.keras.optimizers.Adam(learning_rate=0.001)
• SGD: Stochastic Gradient Descent.
• Example:
tf.keras.optimizers.SGD(learning_rate=0.01,
momentum=0.9)
• RMSprop: Root Mean Square Propagation.
• Example:
tf.keras.optimizers.RMSprop(learning_rate=0.0

18. Loss Functions
• Mean Squared Error:
tf.keras.losses.MeanSquaredError()
• Sparse Categorical Crossentropy:
tf.keras.losses.SparseCategoricalCrossentropy(
)
• Binary Crossentropy:
tf.keras.losses.BinaryCrossentropy()

19. Metrics
• Accuracy: tf.keras.metrics.Accuracy()
• Precision: tf.keras.metrics.Precision()
• Recall: tf.keras.metrics.Recall()
• Mean Absolute Error:
tf.keras.metrics.MeanAbsoluteError()

20. Custom Layers
• Create layers by subclassing
tf.keras.layers.Layer.
• Example:
• class MyLayer(tf.keras.layers.Layer):
• def __init__(self, units=32):
• super(MyLayer, self).__init__()
• self.units = units
• def build(self, input_shape):
• self.w =