Convolutional neural network

As explained in Learn Generative AI with PyTorch

A neural network architecture using convolution operations, here applied to multidimensional music data to extract spatial features.

A convolutional neural network (CNN) processes grid-like data by connecting each neuron to a local input region and reusing shared weights across positions. This reduces parameters compared with fully connected networks and helps detect spatial patterns efficiently. section 4.2

A CNN’s main operations separate feature extraction from spatial resizing:

• Convolutional layers apply learnable filters, or kernels, to detect patterns such as edges and textures at different spatial scales. section 4.2
• Kernel size, stride, and zero-padding determine how convolution changes the spatial dimensions and degree of downsampling. section 4.0 section 4.2
• Transposed convolutional layers perform the complementary task of upsampling, producing higher-resolution feature maps for image generation. section 4.6
• Two-dimensional batch normalization normalizes each feature channel to stabilize and accelerate training. section 4.6

High-resolution image generation

A discriminator can use convolutional layers to extract increasingly hierarchical features from color images, while a generator mirrors those stages with transposed convolutions to produce high-resolution outputs. This approach supports anime-face generation and CNN-based VAE models for human face images. section 4.0 section 7.3.2

Figure 7.6 The architecture of a VAE and the steps to train one to generate human face images. A VAE consists of an encoder (middle upper left) and a decoder (middle bottom right). In each iteration of training, human face images are fed to the encoder (step 1). The encoder compresses the images to probabilistic points in the latent space (step 2; since we assume normal distributions, each probability point is characterized by a vector of means and a vector of standard deviations). We then sample encodings from the distribution and present them to the decoder. The decoder takes sampled encodings (step 3) and reconstructs images (step 4). The VAE adjusts its parameters to minimize the sum of reconstruction loss and the KL divergence. The KL divergence measures the difference between the encoder’s output and a standard normal distribution.

Trace how CNN-based encoder and decoder components fit into a VAE training loop: encode images, sample latent representations, reconstruct images, and update parameters. section 7.3.2

How Learn Generative AI with PyTorch covers it

How deep it goes

Explains local connectivity, shared weights, filters, kernel size, stride, padding, upsampling, and batch normalization without detailed derivations.

Examples

Demonstrates anime-face generation, high-resolution color images, and human-face reconstruction with an eyeglasses dataset.

What you can do after

Build it from scratch into image-generation systems using convolutional and transposed convolutional layers.

How it connects

Connects convolutional layers with fully connected layers, feature extraction, transposed convolution, batch normalization, GANs, and variational autoencoders.

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Related concepts

• language modelkind of · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.
• convolutional layeruses · A convolutional layer is a neural-network layer that applies shared learned filters to local regions of structured input to produce feature representations.
• poolinguses · Pooling is an operation that aggregates values or representations across regions or axes to produce a reduced or summarized representation.
• convolution kerneluses · A convolution kernel is a small array or tensor of weights applied across local input regions to compute weighted responses and detect or transform patterns.
• U-Netincludes · U-Net is an encoder-decoder neural-network architecture with downsampling and upsampling paths connected by skip connections for combining features at multiple spatial scales.
• LeNetincludes · LeNet is an early convolutional neural-network architecture designed primarily for handwritten-digit and character recognition.

This explainer was generated by Manning's knowledge graph from the text of Learn Generative AI with PyTorch. The section links point to the parts of the book it draws on.