When it comes to convolutional networks, we have two ways of knowing what the model sees. On the one hand these are filters (weights), on the other hand they are feature cards (activation cards). In this lesson we visualize function maps in a convolutionary neural network.

The idea of viewing a function card for a particular input image would be to understand which input functions are recognized or saved in function cards. Plant charts are supposed to recognize fine or fine-grained details. We start by importing all libraries and modules.

import flare
import flare.nn as nn
import flare.optim as optim
import flare.nn as F

from torch.utils.dataLoader
import from torchvision import models

import torchvision.transformms as transformation
import torchvision.datasets as dataset

import matplotlib.pyplot as plt
import numpy as np
import cv2 as cv

Temporary VGG train model

We need a CNN model to visualize the opportunity map. Instead of modifying a model from scratch, we can use an advanced model to classify the pre-trip images.

PyTorch offers many powerful image layout models developed by various research groups for ImageNet. An example of this is the VGG-16, which performed best in the 2014 competition. It is a good visualization model because it has a simple and homogeneous structure of evolutionary layers and the grouping of layers arranged sequentially.

modelVGG = models.vgg16 (pre-trained= true)
output (modelVGG)

When you run the example, the model weights are loaded into memory and a summary of the loaded model is printed.

It is deep with 16 learned layers, and it works very well, which means that filters and derived function maps will capture useful functions.

To explore the function maps, we need the VGG16 input, which enables us to create activations. We will use a simple image of a bee.

We need an image of a when loading with the size predicted by the model, in this case 224×224. Next, we need to convert the image object into an array of NumPy pixel data and expand from a 3D array to a 4D array with dimensions [samples, lines, passes, channels] where we have only one sample.


transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])

Access to the evolution layer

We need to maintain all convolutionary layers of the VGG network. We’re going through all these nests to take out the folds. The following code shows you how to take out all the evolutionary layers.



for the child in model_children:
if type(child)==nn.Conv2d:
elif type(child)==nn.Order :
for the layer in child.children() :
as type(layer)==nn.Conv2d :

First we initialize the no_of_layers variable to register the number of convolutional layers. Next, we will look at all layers of the VGG16 model.

results = [conv_layers[0](img)] for i in the interval(1, len(conv_layers)):
outputs = results

Give the input image to the first evolutionary layer, then we use the loop to pass the output from the last layer to the next layer until we reach the last evolutionary layer.

View the feature map

We know that the number of function charts (e.g. depth or number of channels) in deeper layers is much more than 1. B. 64, 256 or 512. We draw only 16 2D images in the form of a 4×4 square.

for num_layer in the range (len(outputs)):
plt.figure(figsize=(50, 10))
layer_viz = outputs [num_layer][0,:,:,] layer_viz = layer_viz.data
for i, filter in enumeration(layer_viz):
if i === 16:
plt.subplot(2, 8, i + 1)
plt.imshow(filter, cmap=’grey’)

Feature maps are the result of applying filters to imported images. Characteristic maps derived from previous layers can provide insight into the internal representation of a model with a certain input at a certain point in the model.

Run this code in Google colab

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