import torch import numpy as np import torch.nn as nn from torch.autograd import Variable import torch.optim as optim from torch.utils.data import DataLoader from torchvision import datasets, transforms batch_size = 64 learning_rate = 1e-2 num_epoche
BP神经网络的手写数字识别 ANN 人工神经网络算法在实践中往往给人难以琢磨的印象,有句老话叫“出来混总是要还的”,大概是由于具有很强的非线性模拟和处理能力,因此作为代价上帝让它“黑盒”化了.作为一种general purpose的学**算法,如果你实在不想去理会其他类型算法的理论基础,那就请使用ANN吧.本文为笔者使用BP神经网络进行手写数字识别的整体思路和算法实现,由于近年来神经网络在深度学**,尤其是无监督特征学**上的成功,理解神经网络的实现机制也许可以让“黑盒”变得不再神秘. 首先,作
题目: 1)In the first step, apply the Convolution Neural Network method to perform the training on one single CPU and testing 2)In the second step, try the distributed training on at least two CPU/GPUs and evaluate the training time. 一.单机单卡实现mnist_CNN 1
莫烦视频网址 这个代码实现了预测和可视化 import os # third-party library import torch import torch.nn as nn import torch.utils.data as Data import torchvision import matplotlib.pyplot as plt # torch.manual_seed() # reproducible # Hyper Parameters EPOCH = # train the tra
import numpy as np import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data import os %matplotlib inline import matplotlib.pyplot as plt mnist = input_data.read_data_sets('MNIST_data', one_hot=True) class ConvModel(object):
The human visual system is one of the wonders of the world. Consider the following sequence of handwritten digits: Most people effortlessly recognize those digits as 504192. That ease is deceptive. In each hemisphere of our brain, humans have a prima