12/27/2023 0 Comments High resolutiion checkered patterThe DCGAN ModelĪgain we define the DCGAN model architecture by subclass keras.Model and override train_step to define the custom training loops. This means we add one more set of Conv2DTranspose -> BatchNormalization -> ReLU.Īnother change made to the generator is to update kernel size from 5 to 4 to avoid reducing checkerboard artifacts in the generated images (see Figure 2).įigure 6: Discriminator architecture with Keras code (image by the author). Now we are working with a training image size of 64×64, so we upsample a few times as 8 -> 16 -> 32 -> 64. A stride of 2 halves the width and height so you can work backward to figure out the initial image size dimension: for Fashion-MNIST, we upsampled as 7 -> 14 -> 28.We update CHANNELS = 3 for color images instead of 1, which is for grayscale images.Here let’s look at how to adjust the upsampling to generate the desired color image size of 64×64×3: We already went through the details of how to create the generator architecture in my previous DCGAN post. We create the generator architecture with the keras Sequential API in the build_generator function. Finally, we apply the normalization by using the map function of tf.dataset with a lambda function. Same as before, we normalize the images to the range of because the generator’s final layer activation uses tanh. Let’s visualize one training image as an example in Figure 1:įigure 1: 64×64 training image (source: Clothing & Models). Zalando_data_dir, label_mode=None, image_size=(64, 64), batch_size=32) train_images = tf._dataset_from_directory( Finally, we specify the image size of 64×64 and a batch size of 32. ![]() Then we use Keras’ image_dataset_from_directory to create a tf.data.Dataset from the images in the directory, which will be used for training the model later on. zalando_data_dir = "/content/datasets/zalando/zalando/zalando" !unzip datasets/zalando-store-crawl.zip -d datasets/Īfter downloading and unzipping the data, we set a directory where the data are. !kaggle datasets download -d dqmonn/zalando-store-crawl -p datasets Os.environ="enter-your-own-user-name"ĭownload and unzip the data to a directory called dataset. You could either upload the Kaggle json file to Colab or put your Kaggle user name and key in the notebook. To download data from Kaggle, you will need to provide your Kaggle credential. There are six categories and over 16k color images in the size of 606×875, which will be resized to 64×64 for training. We will train the DCGAN with a dataset called Clothing & Models from Kaggle, which is a collection of clothing pieces scraped from. Even training with Fashion-MNIST grayscale images could be tricky. With these changes, you can start training the DCGAN on the color image however, when working with color images or any data other than MNIST or Fashion-MNIST, you will realize how challenging GAN training can be. Discriminator: adjust the input image shape from 28×28×1 to 64×64×3.Generator: adjust how to upsample the model architecture to generate a color image. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |