假设,有一张大小为32×32×3的输入图片,这是一张RGB模式的图片,想做手写体数字识别。32×32×3的RGB图片中含有某个数字,比如7,想识别它是从0-9这10个数字中的哪一个,构建一个神经网络来实现这个功能。 用的这个网络模型和经典网络LeNet-5非常相似,灵感也来源于此。LeNet-5是多年前Yann LeCun创建的,所...
1.2 边缘检测示例(Edge detection example) 检测图像垂直边缘过滤器,卷积运算用“*”来表示: 过滤器做垂直边缘检测的原理: 明亮像素在左深色像素右,被视为一个垂直边缘,卷积运算提供了一个方便的方法来发现它, 1.3 更多边缘检测内容(More edge detection) 水平边缘过渡: Sobel过滤器和Scharr过滤器: 将过滤器矩阵的...
平均池化 卷积神经网络示例(Convolutional neural network example) 在神经网络中,另一种常见模式就是一个或多个卷积后面跟随一个池化层,然后一个或多个卷积层后面再跟一个池化层,然后是几个全连接层,最后是一个softmax。这是神经网络的另一种常见模式。 接下来我们讲讲神经网络的激活值形状,激活值大小和参数数量。
Demonstrates a convolutional neural network (CNN) example with the use of convolution, ReLU activation, pooling and fully-connected functions. Model definition: The CNN used in this example is based on CIFAR-10 example from Caffe [1]. The neural network consists of 3 convolution layers intersperse...
Example 1: Example 2:If you have 10 filters that are3 \times 3 \times 3in one layer of a neural network, how many parameters does that layer have? 10\times(3\times3\times3+1)=280 "1": bias for each filter Notation for one convolution layer ...
1.10 卷积神经网络示例(Convolutional neural network example) 1.11 为什么使用卷积?(Why convolutions?) 1.1 计算机视觉(Computer vision) 欢迎参加这次的卷积神经网络课程,计算机视觉是一个飞速发展的一个领域,这多亏了深度学习。深度学习与计算机视觉可以帮助汽车,查明周围的行人和汽车...
Example: \(a^{[l]}_i\) denotes the \(i^{th}\) entry of the activations in layer \(l\), assuming this is a fully connected (FC) layer. \(n_H\), \(n_W\) and \(n_C\) denote respectively the height, width and number of channels of a given layer. If you want to refere...
Build and train a convolutional neural network with TensorFlow. This example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/) Author: Aymeric Damien Project: https://github.com/aymericdamien/TensorFlow-Examples/ ...
Convolutional Neural Network (CNN) 我自己写的代码和该教程略有不一样,有三处改动,第一个地方是用归一化(均值为0,方差为1)代替数值缩放([0, 1]),代替的理由是能提升准确率 第二处改动是添加了正则化,在Conv2D和Dense Layer中均有添加,可以抑制模型过拟合,提升val_acc...
Inception modules in CNNs allow for deeper and larger conv layers while also speeding up computation. This is done by using 1×1 convolutions with small feature map size, for example, 192 28×28 sized feature maps can be reduced to 64 28×28 feature maps through 64 1×1 convolutions. ...