convolution

问题 My team is training a CNN in Tensorflow for binary classification of damaged/acceptable parts. We created our code by modifying the cifar10 example code. In my prior experience with Neural Networks, I always trained until the loss was very close to 0 (well below 1). However, we are now evaluating our model with a validation set during training (on a separate GPU), and it seems like the precision stopped increasing after about 6.7k steps, while the loss is still dropping steadily after over

问题 My team is training a CNN in Tensorflow for binary classification of damaged/acceptable parts. We created our code by modifying the cifar10 example code. In my prior experience with Neural Networks, I always trained until the loss was very close to 0 (well below 1). However, we are now evaluating our model with a validation set during training (on a separate GPU), and it seems like the precision stopped increasing after about 6.7k steps, while the loss is still dropping steadily after over

问题 For my project, I've written a naive C implementation of direct 3D convolution with periodic padding on the input. Unfortunately, since I'm new to C, the performance isn't so good... here's the code: int mod(int a, int b) { // calculate mod to get the correct index with periodic padding int r = a % b; return r < 0 ? r + b : r; } void convolve3D(const double *image, const double *kernel, const int imageDimX, const int imageDimY, const int imageDimZ, const int stencilDimX, const int stencilDimY

问题 I'm just beginning my ML journey and have done a few tutorials. One thing that's not clear (to me) is how the 'filter' parameter is determined for Keras Conv2D. Most sources I've read simply set the parameter to 32 without explanation. Is this just a rule of thumb or do the dimensions of the input images play a part? For example, the images in CIFAR-10 are 32x32 Specifically: model = Sequential() filters = 32 model.add(Conv2D(filters, (3, 3), padding='same', input_shape=x_train.shape[1:]))

问题 I am referring to the implementation of "Text classification using CNN" on this link https://richliao.github.io/supervised/classification/2016/11/26/textclassifier-convolutional. Here in section "A simplified Convolutional", they have used the following layer of keras: Conv1D(128, 5, activation='relu') As per my understanding, no of parameters should be 5*100*128=64,000. But the model summary is showing 64,128 parameters. Could someone help me to understand where am i wrong in my calculation?

问题 I am currently seeing the API of theano, theano.tensor.nnet.conv2d(input, filters, input_shape=None, filter_shape=None, border_mode='valid', subsample=(1, 1), filter_flip=True, image_shape=None, **kwargs) where the filter_shape is a tuple of (num_filter, num_channel, height, width) , I am confusing about this because isn't that the number of filter decided by the stride while sliding the filter window on the image? How can I specify on filter number just like this? It would be reasonable to

问题 I'm using opencv . Having a base image ( 640x640 ) I want to know if applying a rectangular white shape mask of (100x100), in another words, a ROI is more time consuming compared to cropping the image in the same rectangular shape (100x100). What I consider as being a ROI mask = np.zeros_like(original_image) shape = np.array([[a,b,c,d]]) cv2.fillPoly(mask, shape, (255,255,255)) cv2.bitwise_and(original_image, mask) The way of cropping cropped = original_image[x:y, z:t] I want to apply a

问题 Currently learning about computer vision and machine learning through the free online course by stanford CS131. Came across some heavy math formulas and was wondering if anyone could explain to me how one would go on about in implementing a naive 4 nested for loops for the convolution algorithm using only knowing the image height, width and kernel height and width. I was able to come up with this solution by researching online. image_padded = np.zeros((image.shape[0] + 2, image.shape[1] + 2))

问题 I am very new to ML using Big Data and I have played with Keras generic convolutional examples for the dog/cat classification before, however when applying a similar approach to my set of images, I run into memory issues. My dataset consists of very long images that are 10048 x1687 pixels in size. To circumvent the memory issues, I am using a batch size of 1, feeding in one image at a time to the model. The model has two convolutional layers, each followed by max-pooling which together make

问题 Im trying to find out how to type fig. 1 into maple: because when i do it myself i get fig. 2 This is about forced periodic vibrations. 回答1: It looks like you are integrating over "Tao" assuming everything else constant for integration. Why don't you redefine a new variable, say y = w*T and let maple evaluate? If it is just for typing as a text, you can first type in Math format and comment it out with # sign. 来源： https://stackoverflow.com/questions/36304861/maple-integration-of-two-variables