All of the given options are used to refine the models during training.

1. Batch Normalization: This is a technique used to increase the stability of a neural network. It normalizes the input layer by adjusting and scaling the activations.

2. Adam Optimizer: This is an optimization algorithm that can be used instead of the classical stochastic gradient descent procedure to update network weights iterative based on training data.

3. Conv2D: This is a 2D Convolution Layer, this layer creates a convolution kernel that is convolved with the layer input to produce a tensor of outputs.

4. LeakyReLU: This is a type of activation function. Like the vanilla ReLU function, Leaky ReLU is also used to add non-linearity to the network but it does not have the dying ReLU problem because it allows small negative values when the input is less than zero.

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All of the given options are used to refine the models during training.

1. Batch Normalization: This is a technique used to increase the stability of a neural network. It normalizes the input layer by adjusting and scaling the activations.

2. Adam Optimizer: This is an optimization algorithm that can be used instead of the classical stochastic gradient descent procedure to update network weights iterative based on training data.

3. Conv2D: This is a 2D Convolution Layer, this layer creates a convolution kernel that is convolved with the layer input to produce a tensor of outputs.

4. LeakyReLU: This is a type of activation function. Like the vanilla ReLU function, Leaky ReLU is also used to add non-linearity to the network but it does not have the dying ReLU problem because it allows small negative values when the input is less than zero.

Knowee AI · Accepted Answer

All of the given options are used to refine the models during training.

1. Batch Normalization: This is a technique used to increase the stability of a neural network. It normalizes the input layer by adjusting and scaling the activations.

2. Adam Optimizer: This is an optimization algorithm that can be used instead of the classical stochastic gradient descent procedure to update network weights iterative based on training data.

3. Conv2D: This is a 2D Convolution Layer, this layer creates a convolution kernel that is convolved with the layer input to produce a tensor of outputs.

4. LeakyReLU: This is a type of activation function. Like the vanilla ReLU function, Leaky ReLU is also used to add non-linearity to the network but it does not have the dying ReLU problem because it allows small negative values when the input is less than zero.

What is used to refine the models during training?Batch NormalizationAdam OptimizerAll of the given optionsConv2DLeakyReLU

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