Training, Saving, Testing in TensorFlow QUIZ (MCQ QUESTIONS AND ANSWERS)

tf.keras.models.fit()

tf.train.Checkpoint.save()

tf.keras.optimizers.Adam()

tf.GradientTape()

tf.keras.optimizers

tf.train.Checkpoint

tf.GradientTape

tf.keras.models.fit()

Saves model architecture

Saves model weights

Saves model hyperparameters

Saves entire training data

Using tf.keras.optimizers

Using tf.GradientTape

Using tf.keras.metrics

Using tf.keras.layers

tf.train.Checkpoint

tf.keras.layers

tf.GradientTape

tf.keras.optimizers

To define model architecture

To compile the model

To monitor and interact with the training process

To make predictions using the model

One-hot encoding

Min-max scaling

Z-score normalization

Principal Component Analysis

To train the model

To evaluate the model on unseen data

To monitor model performance during training

To optimize model parameters

Normalization

Feature selection

One-hot encoding

Handling missing values

To optimize model parameters

To evaluate model performance

To transform raw data into a format suitable for training

To visualize model architecture

Early stopping

Regularization

Data augmentation

Oversampling or undersampling

Using dropout layers

Using regularization

Using callbacks with EarlyStopping

Adjusting the learning rate during training

Accuracy

Precision

Recall

Mean Squared Error

model.add_regularizer()

model.compile(optimizer='adam', loss='regularized_loss')

Using dropout layers

model.set_regularization()

ReLU

Sigmoid

Tanh

LeakyReLU

tf.save_model()

tf.train.Checkpoint.save()

tf.keras.models.save_model()

tf.save_checkpoint()

model.init()

model.build()

model.compile()

model.summary()

model.summary()

model.compile()

model.evaluate()

model.fit()

tf.keras.models.fit()

tf.train.Checkpoint.save()

tf.GradientTape()

tf.keras.optimizers.Adam()

To visualize model architecture

To measure model performance on unseen data

To compute gradients during training

To save training time

tf.keras.models

tf.train.Checkpoint

tf.GradientTape

tf.losses

To compute gradients for training neural networks

To save and restore model checkpoints

To evaluate models on new data

To visualize the training process

To define the architecture of neural networks

To compile models for training

To compute gradients during backpropagation

To optimize model parameters during training

To train the model

To compile the model

To make predictions using the model

To save the model

model.evaluate()

model.fit()

model.predict()

model.compile()

To save the model

To compile the model

To train the model

To evaluate the model

model.compile()

model.build()

model.fit()

model.evaluate()

Using tf.load_model()

Using tf.train.load_checkpoint()

Using tf.keras.models.load_model()

Using tf.restore_model()

SGD

Adam

RMSprop

GradientBoost

Saves model architecture

Saves model weights

Saves model hyperparameters

Saves entire training data