G = Conv2D(64, (7,7), padding='same', kernel_initializer=init)(in_image) Return g def define_generator(image_shape, n_resnet=9): # concatenate merge channel-wise with input layer G = Conv2D(n_filters, (3,3), padding='same', kernel_initializer=init)(g) G = Conv2D(n_filters, (3,3), padding='same', kernel_initializer=init)(input_layer) CycleGAN Generator def resnet_block(n_filters, input_layer): So, we first define a ResNet block and then our Generator Model. It does this by first downsampling or encoding the input image down to a bottleneck layer, then interpreting the encoding with a number of ResNet layers that use skip connections, followed by a series of layers that upsample or decode the representation to the size of the output image. horse photo) and generates a target image (e.g. The generator is an encoder-decoder model architecture. Return model Step 3: Making the Generator (G) Model Patch_out = Conv2D(1, (4,4), padding='same', kernel_initializer=init)(d) The benefit of this approach is that the same model can be applied to input images of different sizes, e.g. This is called a PatchGAN model and is carefully designed so that each output prediction of the model maps to a 70×70 square or patch of the input image. The discriminator design is based on the effective receptive field of the model, which defines the relationship between one output of the model to the number of pixels in the input image. Two discriminator models are used, one for Domain-A (horses) and one for Domain-B (zebras). It takes a source image as input and predicts the likelihood of whether the target image is a real or fake image. The Discriminator is a deep convolutional neural network that performs image classification. Print('Saved dataset: ', filename) Step 2: Making the Discriminator (D) model Path = './input/horse-to-zebra/horse2zebra/' Pixels = load_img(path + filename, target_size=size) # enumerate filenames in directory, assume all are images # load all images in a directory into memory from os import listdirįrom import img_to_arrayįrom import load_img We will also need helper functions to load & generate real samples and to generate fake samples. Load the Dataset and extract images to convert to an array of images which will be stored in a compresses NumPy array format. Step 1: Dataset Loading and Preprocessing Paired data is harder to find in most domains, and not even possible in some, the unsupervised training capabilities of CycleGAN are quite useful. CycleGAN is interesting because it did not require paired training data. While there has been a great deal of research into this task, most of it has utilised supervised training, where we have access to (x, y) pairs of corresponding images from the two domains we want to learn to translate between. The architecture used in the tutorial will comprise of 2 discriminator & generator models each and this is the very model which was trained in the CycleGAN paper.įor the scope of this tutorial, we will keep the helper functions out, however, they will be available in the notebook at the end of the tutorial. We will be training the model for 20 epochs on Kaggle using GPU and compare the performance of GAN. In this tutorial, we will train a CycleGAN model to translate photos of horses to zebras, and back again to horses. In the architecture below, GAN is trying to generate images of number 5 from MNIST dataset. The ultimate goal is for the Generator to blur the lines between generated and authentic data, and thus succeed in fooling the Discriminator. The Generator generates new classes of data while the Discriminator tries to evaluate the authenticity of this new data. The two networks, the Generator (G) and Discriminator (D) are in a competitive game of min max. General Adversarial Networks or GAN, were first introduced in a paper by Ian Goodfellow and other researchers outlining a new deep neural network architecture comprising of two networks.
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