It’s a simple incompatibility between the output shape of the decoder and the shape of your training data. (Target means output). I see you’ve got 2 MaxPoolings (dividing your image size by 4), and three upsamplings (multiplying the decoder’s input by 8). The final output of the autoencoder is too big and doesn’t match your … Read more
Your test_image and input of tensorflow model is not match. # Your image shape is (, , 3) test_image = cv2.imread(‘media/’+request.FILES[‘test_image’].name) if test_image is not None: test_image = cv2.resize(test_image, (128, 128)) test_image = np.array(test_image) test_image = test_image.astype(‘float32’) test_image /= 255 print(test_image.shape) else: print(‘image didnt load’) # Your image shape is (, , 4) test_image = … Read more
Edit: This solution seems to work for a majority of users, but not all. If you are in this case, see the proposed answer by Sachin Mohan I had the exact same error using yolov5, on windows 10. Reinstalling the library by typing pip uninstall opencv-python then pip install opencv-python worked for me.
The work-around I have been doing is to use two SummaryWriter with different log dir for training set and cross-validation set respectively. And you will see something like this:
You’re confusing the cross-entropy for binary and multi-class problems. Multi-class cross-entropy The formula that you use is correct and it directly corresponds to tf.nn.softmax_cross_entropy_with_logits: -tf.reduce_sum(p * tf.log(q), axis=1) p and q are expected to be probability distributions over N classes. In particular, N can be 2, as in the following example: p = tf.placeholder(tf.float32, shape=[None, … Read more
You can extract the important lines from the load_model and save_model functions. For saving optimizer states, in save_model: # Save optimizer weights. symbolic_weights = getattr(model.optimizer, ‘weights’) if symbolic_weights: optimizer_weights_group = f.create_group(‘optimizer_weights’) weight_values = K.batch_get_value(symbolic_weights) For loading optimizer states, in load_model: # Set optimizer weights. if ‘optimizer_weights’ in f: # Build train function (to get weight … Read more
model.eval() is a kind of switch for some specific layers/parts of the model that behave differently during training and inference (evaluating) time. For example, Dropouts Layers, BatchNorm Layers etc. You need to turn them off during model evaluation, and .eval() will do it for you. In addition, the common practice for evaluating/validation is using torch.no_grad() … Read more
In mathematical sense you need to set: SVC(kernel=”linear”, **kwargs) # by default it uses RBF kernel and LinearSVC(loss=”hinge”, **kwargs) # by default it uses squared hinge loss Another element, which cannot be easily fixed is increasing intercept_scaling in LinearSVC, as in this implementation bias is regularized (which is not true in SVC nor should be … Read more
Don’t use clustering for 1-dimensional data Clustering algorithms are designed for multivariate data. When you have 1-dimensional data, sort it, and look for the largest gaps. This is trivial and fast in 1d, and not possible in 2d. If you want something more advanced, use Kernel Density Estimation (KDE) and look for local minima to … Read more
Although I am still not fully understanding the optimization algorithm, I feed like it will help me greatly. First up, let me briefly explain this part. Bayesian Optimization methods aim to deal with exploration-exploitation trade off in the multi-armed bandit problem. In this problem, there is an unknown function, which we can evaluate in any … Read more