Then using zip function, I created the final generators that we can use for training and validation. Also, I’ve just used a parameter in flow_from_directory function to convert to grayscale image. I’ve created two ImageDataGenerator but with same seed. flow_from_directory ( input_dir, color_mode = 'grayscale', class_mode = None, seed = seed, subset = 'validation' ) train_generator = zip ( train_bw_generator, train_color_generator ) validation_generator = zip ( valid_bw_generator, valid_color_generator ) flow_from_directory ( input_dir, class_mode = None, seed = seed, subset = 'validation' ) valid_bw_generator = bw_datagen. flow_from_directory ( input_dir, color_mode = 'grayscale', class_mode = None, seed = seed, subset = 'training' ) valid_color_generator = color_datagen. flow_from_directory ( input_dir, class_mode = None, seed = seed, subset = 'training' ) train_bw_generator = bw_datagen. , width_shift_range = 0.1, height_shift_range = 0.1, validation_split = validation_split ) color_datagen = ImageDataGenerator ( ** data_gen_args ) bw_datagen = ImageDataGenerator ( ** data_gen_args ) train_color_generator = color_datagen. LOAD_TRUNCATED_IMAGES = True data_gen_args = dict ( rescale = 1 / 255.0, zoom_range = 0.2, rotation_range = 30. Input_dir = './input' seed = 1 validation_split = 0.7 from import ImageDataGenerator from PIL import ImageFile ImageFile. Next, we’ll create Keras image generator to load these images and feed them to the model. I used to download 560 images from that sub-reddit. Datasetįor this experiment, I’ve collected images from. They provide the output of each step in encoding phase to the corresponding step in decoding phase so that the decoder can utilize these information as well. That’s where the skip-connections come to the rescue. Because the decoding phase only sees the compressed representation of original input, it might miss important features of image that were lost during the encoding phase. During decoding phase, transposed convolution layers or upsampling layers are typically used. Now using this compressed representation of the input, the decoding phase produces the final output. The basic idea is that the encoding phase takes an input and compresses the input by passing it through different convolutional layers. It extends encoder-decoder model with skip connections (gray arrows in the figure). U-Net was originally proposed for biomedical image segmentation and has shown remarkable results in image segmentation. ![]() In particular, we’ll implement a model called U-Net. In this problem, the input as well as output of the model is an image so we’ll build a fully convolutional neural network. ![]() In this post, we’ll implement a deep neural network that can convert black and white image to color.
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