deep-learning

问题 I'm very new to deep learning models, and trying to train a multiple time series model using LSTM with Keras Sequential. There are 25 observations per year for 50 years = 1250 samples, so not sure if this is even possible to use LSTM for such small data. However, I have thousands of feature variables, not including time lags. I'm trying to predict a sequence of the next 25 time steps of data. The data is normalized between 0 and 1. My problem is that, despite trying many obvious adjustments,

问题 Basically pygame.surfarray.pixels3d returns a (672,672,3) shaped array which gives the error: Resources exhausted but it works when I pass an array of (6,30,30) . Any help would be appreciated. import numpy import random from DeepRTS import PyDeepRTS from Algorithms.DQN2.DQN import DQN # Start the game g = PyDeepRTS('21x21-2v2.json') # Add players player1 = g.add_player() player2 = g.add_player() #player3 = g.add_player() #player4 = g.add_player() # Set FPS and UPS limits g.set_max_fps

问题 I am working to create a multi-input Convolutional Neural Network (CNN) model in Keras for Images Classification that takes two images and gives one output which is the class of the two images. I have two datasets: type1 and type2, and each dataset contains the same classes. The model should take one image from Type1 dataset and one image from Type2 dataset and then classify these images to one class (ClassA or ClassB or------). I want to create a model that predict the two images and then

问题 I have a matrix g of shape [4, 4, 2, 2] where I need to find the rank of g[0, 0] , g[1, 1] , g[2, 2] and g[3, 3] which are all 2x2 matrices. I used the tf.rank operator but it treats g as a single array and computes the rank and returns a single value for the whole matrix. What I need is a 2x2 matrix of ranks of the corresponding g[i, j] 's. Following is a MWE: import tensorflow as tf import numpy as np a = np.array([ [[[ 0., 0.], [ 0., 0.]], [[-1., -1.], [-1., -1.]], [[-2., -2.], [-2., -2.]]

问题 if sample_rate != sr: waveform = torchaudio.transforms.Resample(sample_rate, sr)(waveform) sample_rate = sr I was wondering how this Resamle works in there. So took a look at the docs of torchaudio. I thought there would be __call__ function. Because Resample is used as a function. I mean that Resample()(waveform) . But inside, there are only __init__ and forward function. I think the forward function is the working function but I don't know why it is named 'forward' not __call__. What am I

问题 I'm trying to restore the pretrained model provided here and continue training on a different dataset. The pretrained models available there are trained on tensorflow_gpu-1.1.0. But I have tensorflow_gpu-1.13.1 . When I try restoring the model, I get the below error. NotFoundError (see above for traceback): Restoring from checkpoint failed. This is most likely due to a Variable name or other graph key that is missing from the checkpoint. Please ensure that you have not altered the graph

问题 I'm trying to restore the pretrained model provided here and continue training on a different dataset. The pretrained models available there are trained on tensorflow_gpu-1.1.0. But I have tensorflow_gpu-1.13.1 . When I try restoring the model, I get the below error. NotFoundError (see above for traceback): Restoring from checkpoint failed. This is most likely due to a Variable name or other graph key that is missing from the checkpoint. Please ensure that you have not altered the graph

问题 Good day! I have a celebrity dataset on which I want to fine-tune a keras built-in model. SO far what I have explored and done, we remove the top layers of the original model (or preferably, pass the include_top=False) and add our own layers, and then train our newly added layers while keeping the previous layers frozen. This whole thing is pretty much like intuitive. Now what I require is, that my model learns to identify the celebrity faces, while also being able to detect all the other

问题 Closed . This question is opinion-based. It is not currently accepting answers. Want to improve this question? Update the question so it can be answered with facts and citations by editing this post. Closed 2 months ago . Improve this question I'm trying to train a Regression Model on Inception V3. Inputs are images of size (96,320,3). There are a total of 16k+ images out of which 12k+ are for training and the rest for validation. I have frozen all layers in Inception, but unfreezing them

问题 Closed . This question is opinion-based. It is not currently accepting answers. Want to improve this question? Update the question so it can be answered with facts and citations by editing this post. Closed 2 months ago . Improve this question I'm trying to train a Regression Model on Inception V3. Inputs are images of size (96,320,3). There are a total of 16k+ images out of which 12k+ are for training and the rest for validation. I have frozen all layers in Inception, but unfreezing them