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GAN MINST prediction 1/6

Data preperation link copied

First, we reshape the MNIST data from -1 to 1. The reason for reshaping the MNIST data into the range of -1 to 1 is to scale the input data to a range suitable for training a GAN model. Scaling the data to this range helps the model converge faster and produces better results. Additionally, some GAN models are more sensitive to the scale of the input data, so normalizing the data is a good practice to ensure optimal performance.

Let’s examine the generator and discriminator to understand the relationship between the shape and the input latent dimension.

GAN MINST prediction 2/6

Generator link copied

The generator in a GAN typically takes a random noise vector as input and produces a synthetic image as output. The input dimension of this noise vector is usually referred to as the latent dimension and determines the complexity of the generated images. A higher latent dimension allows for more diversity in the generated images, while a lower latent dimension restricts the range of possible outputs.

This model needs to divide a 28*28 image by 4 and then multiply by 256 to make the input size of layer 7*7*256 because the generator architecture in this model requires the size of the input layer to be 7*7*256. This step helps to reshape the image data to match the desired input size.

The choice of 7 as the size of the image is often a design decision made by the network architect. This specific size was probably chosen to balance the computational cost of the network and its ability to generate high-quality images. After the image is down-sampled to 7×7, batch normalization, leaky ReLU activation, upsampling, 2D convolutional, and reshape operations are used to generate the final image in the generator. These operations help increase the image’s spatial resolution and add fine-grained details. These operations are commonly used in GAN architectures to improve image generation capabilities.

• Batch normalization: is a normalization technique used to speed up the training of deep neural networks. It helps stabilize the activation values distribution during the forward pass.
• Leaky ReLU activation function: is a variation of the ReLU activation function that allows a slight, non-zero gradient when the input is negative. This helps to prevent the vanishing gradient problem in deep neural networks.
• Upsampling: It is an operation in computer vision and image processing that increases an image’s size by inserting zeros between the existing pixels. In deep learning, it is often used in the generator part of a GAN.
• 2D convolutional: is a convolution operation in which the convolutional filters are applied to the input in a 2-dimensional manner. It is widely used in image processing and computer vision tasks.
• Reshape: is an operation in deep learning that changes the shape of a tensor, for example, from a 2D matrix to a 1D vector.

The 2D convolutional layer in a neural network uses a filter (also known as a kernel) to extract features from the input image. The padding argument determines the number of pixels added to the input tensor along the height and width dimensions. The “same” padding option adds the same number of pixels to both the top and bottom and the left and right of the tensor so that the height and width of the output tensor remain the same as the input tensor. This helps preserve the input image’s spatial resolution and prevents shrinking and losing information in the spatial dimensions during the convolution operation. The number of filters determines the number of feature maps the 2D convolutional layer will learn. Sixty-four filters mean the layer will learn 64 different feature maps. The size of the kernel (3×3) determines the size of the window that slides over the input to extract features, and the stride of 1 means that the kernel will move one pixel at a time. This setting is used to preserve the spatial resolution of the input, as the “same” padding replicates the border pixels to maintain the spatial dimensions of the output tensor.

The whole definition of the network structure is connected to a Keras model. The learning rate is set to 0.0002, which determines how fast the model learns from the data. The beta1 parameter of the Adam optimizer is set to 0.5, which influences the gradient descent algorithm used to optimize the model. The output of this process is a Pug object, which can be used to perform predictions and evaluate the network’s performance. The choice of learning rate and beta1 parameters can impact the stability and speed of the training process, so it is crucial to choose them carefully based on the problem at hand and the characteristics of the data.

GAN MINST prediction 3/6

Discriminator link copied

A discriminator in a GAN is a neural network that separates real data from generated data. It acts as a “critic” to distinguish real from fake data, with the generator’s goal being to produce fake data that is indistinguishable from real data. 

After the input layer, a series of convolutional layers (conv2d), leaky ReLU activation functions, and batch normalization layers are used to build the discriminator network. The purpose of these layers is to extract meaningful features from the input image and normalize the inputs, stabilize the training process, and prevent overfitting.

After the series of Conv2D, LeakyReLU, and BatchNorm layers in a Discriminator, there will typically be a dense layer and a flatten layer. The flatten layer reshapes the previous layer’s output into a 1D tensor, as dense layers require a 1D input. The combination of the dense and flatten layers form the final decision-making stage of the output of a single scalar value representing the probability that the input sample is real.

GAN MINST prediction 4/6

Training process link copied

The generator in a GAN takes a randomly generated tensor as input, which acts as a representation of the latent space. The generator’s goal is to map this input tensor to synthetic data similar to the real data. The input tensor can be adjusted to control the characteristics of the generated synthetic data, in this case, the MNIST dataset. The generator then uses this input to produce a synthetic sample similar to the real data.

GAN MINST prediction 5/6

Conclusion link copied

You now learned how to use the plug-in PUG for a GAN problem in Grasshopper. Here you can find an overview of the script.

Final exercise file

Here you find the final GH script of the tutorial.

GAN MINST prediction 6/6

Useful Links link copied

External resources

Wiki pages:

• Batch normalization
• Leaky ReLU activation function
• Upsampling

Interesting articles:

• 2D convolutional: Intuitively Understanding Convolutions for Deep Learning
• Reshape