Keras data augmentation before validation

#KERAS DATA AUGMENTATION BEFORE VALIDATION CODE#

NumPy arrays (if your data is small and fits in memory) or tf.data Dataset When passing data to the built-in training loops of a model, you should either use Guide to multi-GPU & distributed training. This guide doesn't cover distributed training, which is covered in our Sequential models, models built with the Functional API, and models written from In general, whether you are using built-in loops or writing your own, model training &Р”В®valuation works strictly in the same way across every kind of Keras model. If you are interested in writing your own training & evaluation loops from If you are interested in leveraging fit() while specifying your When using built-in APIs for training & validation (such as Model.fit(), This guide covers training, evaluation, and prediction (inference) models L2_regularization_13 = lambda2 * torch.Import tensorflow as tf from tensorflow import keras from tensorflow.keras import layers L2_regularization_9 = lambda2 * torch.norm(all_9_params, 2) L2_regularization_5 = lambda2 * torch.norm(all_5_params, 2) L2_regularization_1 = lambda2 * torch.norm(all_1_params, 2) Out, out1, out5, out9, out13 = model(images)Р”Р„ll_1_params = torch.cat()Р”Р„ll_5_params = torch.cat()Р”Р„ll_9_params = torch.cat()Р”Р„ll_13_params = torch.cat() for i, (images, labels) in enumerate(trainloader): But I am not sure if it is correct (I didРІР‚в„ўt use out1, out5, and out5?) and if there is a cleaner way to do it. Also, is there a way to disable the weight decay for batch_normalization layerРІР‚в„ўs learnable parameters? Now I am testing the weight initialized version. I use padding=1 normally, I just forgot to add it there Validation_steps = X_test.shape //batch_size) Validation_data=test_datagen.flow(X_test,Y_test, batch_size =batch_size), Steps_per_epoch=X_train.shape // batch_size, Keras_model.fit_generator(generator = train_datagen, Train_datagen = MixupGenerator(X_train, Y_train, batch_size=batch_size, alpha=1.0, datagen=train_datagen_pre)() Opt_algo = optimizers.SGD(lr = 0.01, momentum = 0.9) Kernel_regularizer = regularizers.l2(weight_decay) Kernel_regularizer = regularizers.l2(weight_decay)) Here is a scratch of keras model, I am not allowed to share it as it is. So I will try to implement glorot_uniform now, thanks! (Is it the same as Xavier?)

#KERAS DATA AUGMENTATION BEFORE VALIDATION CODE#

The source code says conv and dense layer kernels are initialized with glorot_uniform. There is no specific weight initialization for the keras model. Loss_loc = lam * self.loss(output, y_a) + (1 - lam) * self.loss(output, y_b) X, y_a, y_b, lam = utils.mixup_data(x, y, self.alpha) Root='./data', train=False, transform=test_transform, download=True)Р”Р„nd in the original script, in train() I use mixup as below (alpha=1.0): def train(self, x_val, y_val): Root='./data', train=True, transform=train_transform, download=True) Train_(Cutout(n_holes=n_holes, length=length)) Return lambda criterion, pred: lam * criterion(pred, y_a) + (1 - lam) * criterion(pred, y_b)Р”Р„nd my prepare_data() method: def prepare_data(batch_size=128, valid_frac=0.1, manual_seed=0): Index = randperm(batch_size).cuda() if is_cuda else randperm(batch_size) Lam = np.random.beta(alpha, alpha) if alpha > 0. X2 = int(np.clip(x + self.length / 2, 0, w))Р”Вef mixup_data(x, y, alpha=1.0, is_cuda=True): X1 = int(np.clip(x - self.length / 2, 0, w)) Y2 = int(np.clip(y + self.length / 2, 0, h)) Y1 = int(np.clip(y - self.length / 2, 0, h)) Tensor: Image with n_holes of dimension length x length cut out of it. Img (Tensor): Tensor image of size (C, H, W). Length (int): The length (in pixels) of each square patch. N_holes (int): Number of patches to cut out of each image. Randomly mask out one or more patches from an image. These are the data augmentation methods, maybe the problem is here: class Cutout(object): There must still be something that I am missing. And if I donРІР‚в„ўt use mixup, cutout, or random affine, pytorch models can get around 60%. On the other hand keras modelРІР‚в„ўs average accuracy for 20 models is 64.4. After the change 8 modelsРІР‚в„ў average accuracy is 52.78. Before this change 24 different modelsРІР‚в„ў average validation accuracy was 48,4. Note: in keras implementation convolution and dense layers have L2 kernel regularization, in pytorch implementation only the optimizer has L2. However, when I train this network on keras for 20 epochs, using the same data augmentation methods, I can reach over 70% validation accuracy.

I know if the modelРІР‚в„ўs capacity is low it is possible. I am training this network for 20 epochs, and I use the below data augmentation methods.Р”В¤- random affine for horizontal and vertical translationР”В®ach time I add a new data augmentation after normalization(4,5,6), my validation accuracy decreases from 60% to 50%.

I have a cnn as below for cifar10: self.layer1 = nn.Sequential(

Keras data augmentation before validation