The layer that helps in reducing the dimensions of the input image in the discriminator's code is the Conv2D layer with strides.

Here's a step-by-step explanation:

1. The Conv2D layer is a 2D convolution layer, which is commonly used in image processing. It creates a convolution kernel that is convolved with the layer input to produce a tensor of outputs.

2. The 'strides' parameter in the Conv2D layer is a 2-tuple of integers, specifying the strides of the convolution along the height and width. The stride is the amount by which the kernel is shifted when the kernel is passed over the image.

3. A stride of more than 1 will reduce the size of the output spatial dimensions. For example, if the stride is 2, then the output height and width will be roughly half of the input height and width.

4. Therefore, the Conv2D layer with strides is the layer that helps in reducing the dimensions of the input image in the discriminator's code.

Question

The layer that helps in reducing the dimensions of the input image in the discriminator's code is the Conv2D layer with strides.

Here's a step-by-step explanation:

1. The Conv2D layer is a 2D convolution layer, which is commonly used in image processing. It creates a convolution kernel that is convolved with the layer input to produce a tensor of outputs.

2. The 'strides' parameter in the Conv2D layer is a 2-tuple of integers, specifying the strides of the convolution along the height and width. The stride is the amount by which the kernel is shifted when the kernel is passed over the image.

3. A stride of more than 1 will reduce the size of the output spatial dimensions. For example, if the stride is 2, then the output height and width will be roughly half of the input height and width.

4. Therefore, the Conv2D layer with strides is the layer that helps in reducing the dimensions of the input image in the discriminator's code.

Knowee AI · Accepted Answer

The layer that helps in reducing the dimensions of the input image in the discriminator's code is the Conv2D layer with strides.

Here's a step-by-step explanation:

1. The Conv2D layer is a 2D convolution layer, which is commonly used in image processing. It creates a convolution kernel that is convolved with the layer input to produce a tensor of outputs.

2. The 'strides' parameter in the Conv2D layer is a 2-tuple of integers, specifying the strides of the convolution along the height and width. The stride is the amount by which the kernel is shifted when the kernel is passed over the image.

3. A stride of more than 1 will reduce the size of the output spatial dimensions. For example, if the stride is 2, then the output height and width will be roughly half of the input height and width.

4. Therefore, the Conv2D layer with strides is the layer that helps in reducing the dimensions of the input image in the discriminator's code.

In the discriminator's code, which layer helps in reducing the dimensions of the input image?DenseUpSampling2DBatchNormalizationConv2D with stridesReshape

Question

In the discriminator's code, which layer helps in reducing the dimensions of the input image?

Solution

Similar Questions

Upgrade your grade with Knowee