The easy answer is don’t use a sequential model for this, use the functional API instead, implementing skip connections (also called residual connections) are then very easy, as shown in this example from the functional API guide: from keras.layers import merge, Convolution2D, Input # input tensor for a 3-channel 256×256 image x = Input(shape=(3, 256, … Read more
For ResNet model, you can use children attribute to access layers since ResNet model in pytorch consist of nn modules. (Tested on pytorch 0.4.1) model = models.resnet152(pretrained=True) newmodel = torch.nn.Sequential(*(list(model.children())[:-1])) print(newmodel) Update: Although there is not an universal answer for the question that can work on all pytorch models, it should work on all well … Read more
Try reducing batch_size attribute to a small number(like 1,2 or 3). Example: train_generator = data_generator.flow_from_directory( ‘path_to_the_training_set’, target_size = (IMG_SIZE,IMG_SIZE), batch_size = 2, class_mode=”categorical” )