What activation function did LeNet use?

The first layer is the input layer with feature map size 32X32X1. Then we have the first convolution layer with 6 filters of size 5X5 and stride is 1. The activation function used at his layer is tanh. The output feature map is 28X28X6.

How many layers does LeNet-5 have?

7 layers
By modern standards, LeNet-5 is a very simple network. It only has 7 layers, among which there are 3 convolutional layers (C1, C3 and C5), 2 sub-sampling (pooling) layers (S2 and S4), and 1 fully connected layer (F6), that are followed by the output layer. Convolutional layers use 5 by 5 convolutions with stride 1.

How many parameters does LeNet have?

LeNet-1 was a small CNN, which merely included five layers. The network was developed to accommodate minute, single-channel images of size (28×28). It boasted a total of 3,246 trainable parameters and 139,402 connections.

What is meant by LeNet-5?

In general, LeNet refers to LeNet-5 and is a simple convolutional neural network. Convolutional neural networks are a kind of feed-forward neural network whose artificial neurons can respond to a part of the surrounding cells in the coverage range and perform well in large-scale image processing.

How many parameters does LeNet-5 have?

This lenet-5 has only 60k parameters. But today we use anywhere from 100m to 100m parameters.

What is LeNet architecture in CNN?

LeNet-5 CNN architecture is made up of 7 layers. The layer composition consists of 3 convolutional layers, 2 subsampling layers and 2 fully connected layers. The input layer is built to take in 32×32, and these are the dimensions of images that are passed into the next layer.

What is the size of input image in LeNet-5 architecture?

32×32
The input for LeNet-5 is a 32×32 grayscale image which passes through the first convolutional layer with 6 feature maps or filters having size 5×5 and a stride of one. The image dimensions changes from 32x32x1 to 28x28x6.

How many neurons are in LeNet?

LeNet-1 Detailed Design This represents 784 input neurons (+ the bias neuron).

What is AlexNet architecture in CNN?

AlexNet Architecture AlexNet was the first convolutional network which used GPU to boost performance. 1. AlexNet architecture consists of 5 convolutional layers, 3 max-pooling layers, 2 normalization layers, 2 fully connected layers, and 1 softmax layer.

Why are kernels of different size used in Google LeNet architecture?

The idea is that with a given convolutional receptive field, multiple stacked smaller size kernel is better than the one with a larger size kernel because multiple non-linear layers increases the depth of the network which enables it to learn more complex features at a lower cost because it has lower number of learning …

How many fully connected layers does AlexNet have?

3 fully connected layers
The Alexnet has eight layers with learnable parameters. The model consists of five layers with a combination of max pooling followed by 3 fully connected layers and they use Relu activation in each of these layers except the output layer.

What is the output of AlexNet?

The second layer of AlexNet was a max-pooling layer that accepted output from the layer C1, a (55×55×96) tensor, as its input. It performed a zero-padded sub-sampling operation using a (3×3) kernel with a stride of two. This produced a (27×27×96) output tensor that was passed on to the next layer.