Category : neural-network

I have a simple NN: import torch import torch.nn as nn import torch.optim as optim class Model(nn.Module): def __init__(self): super(Model, self).__init__() self.fc1 = nn.Linear(1, 5) self.fc2 = nn.Linear(5, 10) self.fc3 = nn.Linear(10, 1) def forward(self, x): x = self.fc1(x) x = torch.relu(x) x = torch.relu(self.fc2(x)) x = self.fc3(x) return x net = Model() opt = ..

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I am trying to train a neural network with pyTorch, but I get the error in the title. I followed this tutorial, I just applied some small changes to meet my needs. Here’s the network: class ChordClassificationNetwork(nn.Module): def __init__(self, train_model=False): super(ChordClassificationNetwork, self).__init__() self.train_model = train_model self.flatten = nn.Flatten() self.firstConv = nn.Conv2d(3, 64, (3, 3)) self.secondConv ..

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When I run a train.py file that is suppose to produce semantic segmentation on different images from datasets, I get the following error (QGN) [[email protected] QGN]$ python train.py Input arguments: id ade20k arch_encoder resnet50 arch_decoder QGN_dense_resnet34 weights_encoder weights_decoder fc_dim 2048 list_train ./data/train_ade20k.odgt list_val ./data/validation_ade20k.odgt root_dataset ./data/ num_gpus 1 batch_size_per_gpu 2 num_epoch 20 start_epoch 1 epoch_iters ..

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educated programmers and hobbyists without a diploma! The first time I heard about the concept of machine learning was as a PhD aspirant. During that time, of course Facebook and many others have used some ML strategies like ‘face recognition’ etc. However, there was a scientific article, showing an implementation of neural network in the ..

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I have a numpy classifier based on this book (free to read on the web) and on this code on github. Here is the code for my network: import numpy as np import random import pickle import matplotlib.pyplot as plt import copy def sigmoid(x): return (1/(1+np.exp(-x))) def dsigmoid(x): return sigmoid(x)*(1-sigmoid(x)) def dsigmoidmatrix(x): return np.multiply(sigmoidmatrix(x), (np.subtract(np.ones((x.shape)), ..

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