Defining a Model in PyTorch QUIZ (MCQ QUESTIONS AND ANSWERS)

Sequential model

Custom model

Functional API

Ensemble model

initializers.predefined()

weights.initialize()

keras.initializers.Constant()

keras.weights.set_values()

Initializing weights

Introducing non-linearity

Adding biases

Defining layer shapes

plot(history)

visualize(history)

plot_history(history)

history.plot()

Initializing model weights

Defining the model architecture

Preprocessing input data

Configuring the model for training

Sequential model

Custom model

Ensemble model

Functional API

Adjusting model parameters

Making predictions on new data

Visualizing the model

Evaluating model performance

Displaying model predictions

Visualizing training data

Describing model architecture

Summarizing model performance

model.save_weights() and model.load_weights()

save_model() and load_model()

save_weights() and load_model()

save() and load()

Introducing non-linearity

Initializing weights

Adding a constant value to the output

Setting the learning rate

verify_input()

validate_input()

assert_input_shape()

check_input()

Defining the model architecture

Initializing model parameters

Adjusting weights using training data

Visualizing the model

Initializing weights with predefined values

Penalizing large weights during training

Adjusting learning rates based on weights

Automatically updating weights

model.add()

add_layer(model)

append_layer(model)

insert_layer(model)

Number of training epochs

Types of layers and their arrangement

Size of the training dataset

Model hyperparameters

Sequential and Custom

Supervised and Unsupervised

Linear and Non-linear

Gradient Descent and Stochastic Gradient Descent

Features of the input data

Weights and biases

Output predictions

Learning rates

keras.graph

keras.vis

keras.plot

keras.utils

Initializing weights

Defining layer shapes

Introducing non-linearity

Setting learning rates

model.info()

model.inspect()

model.layers

model.summary()

Sequential layer stacking

Custom layer definition

Highly customized model architectures

Automatic weight initialization

Improved model performance

Easier debugging

Faster training times

Automatic hyperparameter tuning

Training accuracy

Layer types and shapes

Testing time

Model predictions

Set to zero

Randomly initialized

User-defined values

Determined by the activation function

Sequential model

Custom model

Functional API

Keras built-in models

Learning rate

Data labels

Model parameters

Validation set

Flexibility in defining complex architectures

Ease of adding layers sequentially

Ability to handle custom layers

Improved performance for large datasets

print(model)

model.show()

plot_model(model)

visualize(model)

Visualizing training data

Summarizing model performance

Describing model architecture

Displaying model predictions

Automatically initialized by the framework

Set to zero initially

Manually provided by the user

Randomly initialized and adjusted during training