deep-learning

问题 I am building a deconvolution network. I would like to add a layer to it which is the reverse of a softmax. I tried to write a basic python function that returns the inverse of a softmax for a given matrix and put that in a tensorflow Lambda and add it to my model. I have no error but when I doing a predict I only have 0 at the exit. When I don't add this layer to my network I have output something other than zeros. This therefore justifies that they are due to my inv_softmax function which

问题 I am building a deconvolution network. I would like to add a layer to it which is the reverse of a softmax. I tried to write a basic python function that returns the inverse of a softmax for a given matrix and put that in a tensorflow Lambda and add it to my model. I have no error but when I doing a predict I only have 0 at the exit. When I don't add this layer to my network I have output something other than zeros. This therefore justifies that they are due to my inv_softmax function which

问题 I am writing a code on Jupyter notebook using python to recognize the number on the device with 7segment(FND). I used opencv and got the edge of the image. import cv2 import matplotlib.pyplot as plt def detect_edge(image): ''' function Detecting Edges ''' image_with_edges = cv2.Canny(image , 100, 200) images = [image , image_with_edges] location = [121, 122] for loc, img in zip(location, images): plt.subplot(loc) plt.imshow(img, cmap='gray') plt.savefig('edge.png') plt.show() image = cv2

问题 Here's how I used the function: dataset = tf.keras.preprocessing.image_dataset_from_directory( main_directory, labels='inferred', image_size=(299, 299), validation_split=0.1, subset='training', seed=123 ) I'd like to explore the created dataset much like in this example, particularly the part where it was converted to a pandas dataframe. But my minimum goal is to check the labels and the number of files attached to it, just to check if, indeed, it created the dataset as expected (sub

问题 def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'): torch.save(state, filename) if is_best: shutil.copyfile(filename, 'model_best.pth.tar') save_checkpoint({ 'epoch': epoch + 1, 'arch': args.arch, 'state_dict': model.state_dict(), 'best_prec1': best_prec1, 'optimizer': optimizer.state_dict() }, is_best) I am saving my model like this. How can I load back the model so that I can use it in other places, like cnn visualization? This is how I am loading the model now: torch.load(

问题 I was wondering how are layer weights and biases initialized by default? E.g. if I create the linear layer torch.nn.Linear(5,100) How are weights and biases for this layer initialized by default? 回答1: PyTorch 1.0 Most layers are initialized using Kaiming Uniform method. Example layers include Linear, Conv2d, RNN etc. If you are using other layers, you should look up that layer on this doc. If it says weights are initialized using U(...) then its Kaiming Uniform method. Bias is initialized

问题 I am working on a classification problem using CNN where my input image size is 64X64 and I want to use pretrained model such as VGG16 , COCO or any other. But the problem is input image size of pretrained model is 224X224 . How do I sort this issue. Is there any data augmentation way for input image size. If I resize my input image to 224X224 then there is very high chance of image will get blurred and that may impact the training. Please correct me if I am wrong. Another question is related

问题 I had a working deep learning library on CPU Linux Mint ubuntu verion 18.3 with Anaconda 3.6 but something got hosed when I was tinkering around in Theano. https://machinelearningmastery.com/setup-python-environment-machine-learning-deep-learning-anaconda/ Everything was working just fine until I wanted to tinker around with a few .py files with KERAS_BACKEND=theano python program.py and in an attempt to fix something not working in Theano, I did this command from the theano website http:/

问题 I had a working deep learning library on CPU Linux Mint ubuntu verion 18.3 with Anaconda 3.6 but something got hosed when I was tinkering around in Theano. https://machinelearningmastery.com/setup-python-environment-machine-learning-deep-learning-anaconda/ Everything was working just fine until I wanted to tinker around with a few .py files with KERAS_BACKEND=theano python program.py and in an attempt to fix something not working in Theano, I did this command from the theano website http:/

问题 I am try to train a model using Xception /Inception Model of keras library but I face value error Dataset which I use it from kaggle commuinity and Notebook which I refer Notebook But I am try to use different Model like Xception /Inception but silmilar idea not work for me with strategy.scope(): enet = keras.applications.inception_v3.InceptionV3( input_shape=(512, 512, 3), weights='imagenet', include_top=False ) model = tf.keras.Sequential([ enet, tf.keras.layers.GlobalAveragePooling2D(), tf