When using the image dataset input layer (with either lmdb or leveldb backend) caffe only supports one integer label per input image. If you want to do regression, and use floating point labels, you should try and use the HDF5 data layer. See for example this question. In python you can use h5py package to … Read more
Found this page on their github repo, I’ll just copy-paste the content here. Recommended approach for saving a model There are two main approaches for serializing and restoring a model. The first (recommended) saves and loads only the model parameters: torch.save(the_model.state_dict(), PATH) Then later: the_model = TheModelClass(*args, **kwargs) the_model.load_state_dict(torch.load(PATH)) The second saves and loads the … Read more
Here’s a potential approach using morphological operations to filter out non-text contours. The idea is: Obtain binary image. Load image, grayscale, then Otsu’s threshold Remove horizontal and vertical lines. Create horizontal and vertical kernels using cv2.getStructuringElement() then remove lines with cv2.drawContours() Remove diagonal lines, circle objects, and curved contours. Filter using contour area cv2.contourArea() and … Read more
Units: The amount of “neurons”, or “cells”, or whatever the layer has inside it. It’s a property of each layer, and yes, it’s related to the output shape (as we will see later). In your picture, except for the input layer, which is conceptually different from other layers, you have: Hidden layer 1: 4 units … Read more
Yes There is! Credit: It was hard to find the information and get it working but here is an example copying from the principles and code found here and here. Requirements: Before we start, there are two requirement for this to be able to succeed. First you need to be able to write your activation … Read more
A quick guide to Caffe’s convert_imageset Build First thing you must do is build caffe and caffe’s tools (convert_imageset is one of these tools). After installing caffe and makeing it make sure you ran make tools as well. Verify that a binary file convert_imageset is created in $CAFFE_ROOT/build/tools. Prepare your data Images: put all images … Read more
The reason for this apparent performance discrepancy between categorical & binary cross entropy is what user xtof54 has already reported in his answer below, i.e.: the accuracy computed with the Keras method evaluate is just plain wrong when using binary_crossentropy with more than 2 labels I would like to elaborate more on this, demonstrate the … Read more
You can easily get the outputs of any layer by using: model.layers[index].output For all layers use this: from keras import backend as K inp = model.input # input placeholder outputs = [layer.output for layer in model.layers] # all layer outputs functors = [K.function([inp, K.learning_phase()], [out]) for out in outputs] # evaluation functions # Testing test … Read more
As a complement to the accepted answer, this answer shows keras behaviors and how to achieve each picture. General Keras behavior The standard keras internal processing is always a many to many as in the following picture (where I used features=2, pressure and temperature, just as an example): In this image, I increased the number … Read more
Yes, you can apply the ML model but before that understanding of your problem statement come into a picture with all of the feature name available in the data set. If you are having big dataset try to convert it into a cluster of 2 or else take a small dataset to analyze what your … Read more