深度神经网络(Deep Neural Networks)深度神经网络是机器学习领域中一种技术。目前经常使用的深度神经网络模型主要有卷积神经网络(CNN) 、递归神经网络(RNN)、深信度网络(DBN) 、深度自动编码器(AutoEncoder) 和生成对抗网络(GAN) 等。 递归神经网络实际.上包含了两种神经网络。一种是循环神经网络(Recurrent Neural...
4.0)#set default size of plotsplt.rcParams['image.interpolation'] ='nearest'plt.rcParams['image.cmap'] ='gray'#load_ext autoreload#autoreload 2np.random.seed(1)
num_px= train_x_orig.shape[1] m_test=test_x_orig.shape[0]print("Number of training examples:"+str(m_train))print("Number of testing examples:"+str(m_test))print("Each image is of size: ("+ str(num_px) +","+ str(num_px) +", 3)")print("train_x_orig shape:"+str(train...
传统的进化算法只能够针对一个目标或者是一小部分目标,比如,优化图像去匹配ImageNet的一个类。在这里,我们使用了一个新的算法,叫做:multi-dimensional archive of phenotypic elites MAP-Elites,这个算法可以让我们同时的进化出一个群体,如:ImageNet的1000个类。 MAP-Elites的工作方式:在每一轮迭代中,随机选择一个样...
这次作业使用到的函数工具都是Building your Deep Neural Network: Step by Step这次作业中的函数 import time import numpy as np import h5py importmatplotlib.pyplot as plt import scipy from PIL import Image from scipy import ndimage from dnn_app_utils import * ...
You will use use the functions you'd implemented in the previous assignment to build a deep network, and apply it to cat vs non-cat classification. After this assignment you will be able to: Build and apply a deep neural network to supervised learning. ...
image quality measures (IQMs)分为FR和NR,以及介于两者中间的RR(reduced)。传统的做法是利用自然图像的统计特性以及HVS(人类视觉系统)的计算模型。现在的深度学习方法则是纯粹数据驱动。本文中的是10层conv,5个pooling,以及两个fc的CNN,采用了Siamese network的结构,也就是孪生网络。这种结构可以用来做FR 的IQM问题...
Image classification schemes implemented using optical and electronic neural networks. Extended Data Fig. 5 Scalability and computation time enhancement methods. a, An N-layer photonic neural network, in which each layer has its dedicated supply light, allowing scalability to a deep network with a la...
Deep convolutional neural networks [22, 21] have led to a series of breakthroughs for image classification [21, 49, 39]. Deep networksnaturally integrate low/mid/high-level features [49] and classifiers in an end-to-end multi-layer fashion, and the “levels” of features can be enriched ...
本文具体的贡献如下:我们在ILSVRC-2010和ILSVRC-2012[2]的ImageNet子集上训练了到目前为止最大的神经网络之一,并取得了迄今为止在这些数据集上报道过的最好结果。我们编写了高度优化的2D卷积GPU实现以及训练卷积神经网络内部的所有其它操作,我们把它公开了。我们的网络包含许多新的不寻常的特性,这些特性提高了神经网络的...