knowledge Transfer

Here, we will be able to see our network graph. One of TensorBoard’s strengths is its ability to visualize complex model structures.

How to Collect and review metrics during the training of your deep learning models and how to plots from the data collected during training

The network training converges faster if its weights are linearly transformed to have zero means and unit variances.

we have only one if statement in here and that’s to define our breakpoints on whether we’re doing WideLayout versus and NarrowLayout

The set_weights() method of keras accepts a list of NumPy arrays. The shape of this should be the same as the shape of the output of get_weights() on the same layer.

we’ll see how we can initialize and get the biases in a keras model. Let’s create a small neural network with one Convolutional layer and one Dense layer containing 10 nodes, and an output layer with 1 node.

We’re gonna check instant m if it’s convolution layer then we can initialize with a variety of different initialization techniques we’re just gonna do the kaiming_uniform_ on the weight of that specific module and we’re only gonna do if it’s a conv2d.

we need to understand whether or not the layer contains biases for each layer. If it is, then we simply add the number of biases. The number of biases will be equal to the number of nodes(filters) in the layer.

It is a data structure of PyTorch that allows the model to operate only up to the exact length of a given sequence without adding padding.

The idea of visualizing a feature map for a specific input image would be to understand what features of the input are detected or preserved in the feature maps.