https://github.com/alwaysdhruv/image_classification

Hi their my self Dhruv. So this repository or project are developed on C++ and Python for image recognize. C++ are main engine and python are work preprocessing only. more information are in README file.
https://github.com/alwaysdhruv/image_classification

artificial-intelligence backpropagation bias convolution cpp filesystem filters flatten fully-convolutional-networks image machine-learning-algorithms maths maxpooling preprocessing python vector weigths

Last synced: 7 months ago
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Host: GitHub
URL: https://github.com/alwaysdhruv/image_classification
Owner: AlwaysDhruv
Created: 2025-06-07T20:26:39.000Z (7 months ago)
Default Branch: main
Last Pushed: 2025-06-07T21:18:30.000Z (7 months ago)
Last Synced: 2025-06-07T21:27:20.509Z (7 months ago)
Topics: artificial-intelligence, backpropagation, bias, convolution, cpp, filesystem, filters, flatten, fully-convolutional-networks, image, machine-learning-algorithms, maths, maxpooling, preprocessing, python, vector, weigths
Language: C++
Homepage: https://github.com/AlwaysDhruv/Image_Preprocessing
Size: 0 Bytes
Stars: 1
Watchers: 0
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md

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README

Architecture
=========
![1_7_BCJFzekmPXmJQVRdDgwg](https://github.com/user-attachments/assets/f9ac7351-7472-4ea7-89ae-ab69868457a7)
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Preprocessing 🔗 https://github.com/AlwaysDhruv/Image_Classification/blob/main/preprocess.h
=========
First of all preprocessing are programme with two multiple languages but not have connected together C++ and Python. C++ are work for preprocessing to create commad to execute and
create pixels forders and in that given dataset have multiple folders for multiple objects so same to same in C++ And Python also C++ create commad with dataset path and python will
create pixels folder in current directory and make .txt file of multiple objects in count of limit and stores the normalized pixels in that.
Now Let's How to create pixels folder that collection of given dataset multiple object images pixels to normalized and create txt files.
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Selecting Dataset :-

![Screenshot 2025-06-08 021930](https://github.com/user-attachments/assets/3491308b-1e83-4c86-8adf-3ed6c9318e85)
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After Press Ok The Programme Will show the message that created the folder.

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How It's Look After Preprocessing :-
![Screenshot 2025-06-08 022604](https://github.com/user-attachments/assets/f8476abd-d34e-4c87-9b26-ebc3e6cd7c11)
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Now as you can see we have created the pixels folder and cretes .txt file that have image normalized pixels.

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Why 6 Line :-
![image](https://github.com/user-attachments/assets/3feba4f0-2cfb-4e53-bd65-f50a1ce77ccf)
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As you can you seen the down side of my pixels folder image that are 6 line there means what? when i give path dataset in that two folders have 5 images in each. So my progamme will one poxels in one with heigth * widths = one_line eg. 64 * 64 = 4096 without ",".

Preprocessing I have already developed separatly This Link 🔗 https://github.com/AlwaysDhruv/Image_Preprocessing
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One Hot Encoding :-

![image](https://github.com/user-attachments/assets/cfa1b828-6fb3-4bba-ae5c-56582578fdbb)

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The Given Image i have used member() of preprocess class by using pre objetc. I have give Two arguments first labels that in stores the one hot values in double dimentional vector. Labels are type of true output that are used optimiza loss and make the suitbale weigths and biases value to predict the output using gradients and decents method.

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How it's look :-

![image-1](https://github.com/user-attachments/assets/aae855b5-951d-424e-b3e9-e8e270172511)

Here you can see the 2x2 matrix are there and why it's 2x2 or how it's develop? When we are preprocessing you have watched the we have give the dataset folder to the model and in that dataset have two folders that defines two objects. so when the first folder will came out for one values with their folder name so programme will give one hot value like [1 0]. why 1 are first because there first folder are came output and when second folder will cam out so one value like that [0 1] and size one value 2 why bacause there are two objects.

[1 0] folder1
[0 1] folder2

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Weights Biases For Convolutional Layer 🔗 https://github.com/AlwaysDhruv/Image_Classification/blob/main/Kernal.h
==============
Now will talk about weigths and biases whet is this? This one type value are suitable for predict the given input after training and so optimization using gradients decents method.

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Convoluational Parameters :-

![image-3](https://github.com/user-attachments/assets/d41ec49a-b0da-429d-a1b3-72040980037f)

Here you can see i have used values member function of Kernal class using kenral objetc. In that methos i have six arguments first convo_weigths for weigths, convo_bias for bias, 5, 5 for filter dimention, 1 for input channel means image are grayscale there are only one layer pixels are there. for eg when it's come to the RGB stands for red, green and blue so in image there three layer of pixels are there. and last 2 means 5x5 filter will genrate 2 menas 5x5x2 = 25,2 = 50 value for weigths. For bias 2 value will genrated why becaue each filter have sum with one bias value so filter.size() == bias.size().

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How It's Look :-

![image-4](https://github.com/user-attachments/assets/0a954d1b-9a2c-41d1-9830-9e134086fae2)

Here you can see 5x5 kenal are there 2. bias are 2. In That one image will make it two different value of two image using filter.

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Source Code :-

![image-7](https://github.com/user-attachments/assets/9ca3753f-2d85-4935-b0f5-3eb40f8458e8)

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Convoluationl Operation 🔗 https://github.com/AlwaysDhruv/Image_Classification/blob/main/convolutional.h
==============

Now the topic are came out Convolluationl are working for extract the features from images using filters.

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1.Convoluation Operation :-

Formula :-

![unnamed](https://github.com/user-attachments/assets/b93b4251-25ab-4c54-aa84-81a7f5cede11)

Source Code :-

![image-5](https://github.com/user-attachments/assets/3de19d65-f1b9-47f5-9c61-e5dcbc4081ae)

flatten the values for fully conneted layer for eg. 64x64 image are their. so filter shape are 5x5x2 for convolution the output shape
are 64 - 5 + 1 = 60 for column and 64 - 5 + 1 = 60 so convoluation output will generate 60x60x2 there are 2 filter are their that's why.

1.1 Relu activation() :-

Formula :-

![Relu-activation-function](https://github.com/user-attachments/assets/4c1ada4c-f7c8-49e3-97cb-b988b78021ec)

Source Code :-

![image-8](https://github.com/user-attachments/assets/0c6689b8-2674-4eb6-81f7-e399f71192b0)

Relu Are used for raplace negative value to zero for not will generate infinity.

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2. Max Pooling :-

Formula :-

![MaxpoolSample2](https://github.com/user-attachments/assets/49755136-ea90-420d-88c4-c33d39d36d21)

Source Code :-

![image-6](https://github.com/user-attachments/assets/263a9b7c-ece0-4eb2-9e9a-5c08b02e3aef)

after maxpooling 2x2 maxpooling and stride are 2. In that what is the stride? stride are type value that define how many pixels value move
after in convoluation output. 60x60x2 when we apply 2x2 max pooling and stride = 2 so output will generate 30x30x2.

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3. Flatten :-

Style :-

![73_blog_image_1](https://github.com/user-attachments/assets/de665b14-9341-4bcf-848c-95da23a0b03d)

For Source Code i have apply single dimention vector for flattening.

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Here Fully Explanation With Example :-

![cnn-digit-2](https://github.com/user-attachments/assets/0a70fccd-e69e-4faf-9a92-d7f31c5cdb0c)

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Image 64x64 :-
==============
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0.7 0.71 0.71 0.72 0.73 0.68 0.5 0.49 0.58 0.8 0.78 0.71 0.73 0.62 0.77 0.91 0.96 0.97 0.91 0.9 0.92 0.81 0.73 0.74 0.74 0.74 0.74 0.74 0.74 0.74 0.74 0.74 0.74 0.75 0.75 0.69 0.72 0.78 0.8 0.8 0.79 0.79 0.82 0.84 0.83 0.8 0.79 0.78 0.78 0.79 0.8 0.84 0.88 0.89 0.89 0.9 0.9 0.86 0.71 0.48 0.27 0.22 0.34 0.8
0.71 0.72 0.73 0.73 0.71 0.45 0.44 0.68 0.73 0.7 0.58 0.51 0.67 0.53 0.8 0.91 0.91 0.91 0.92 0.95 0.94 0.93 0.85 0.75 0.74 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.76 0.78 0.51 0.44 0.59 0.71 0.79 0.83 0.85 0.85 0.85 0.84 0.82 0.81 0.8 0.79 0.79 0.79 0.8 0.81 0.79 0.77 0.75 0.7 0.38 0.27 0.22 0.21 0.27 0.35 0.81
0.73 0.73 0.73 0.75 0.6 0.36 0.53 0.72 0.62 0.54 0.44 0.45 0.7 0.53 0.66 0.71 0.79 0.8 0.81 0.77 0.67 0.9 0.97 0.88 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.76 0.76 0.77 0.79 0.54 0.39 0.45 0.47 0.52 0.65 0.67 0.59 0.55 0.55 0.52 0.47 0.41 0.36 0.34 0.38 0.4 0.4 0.27 0.28 0.22 0.33 0.2 0.25 0.26 0.22 0.27 0.35 0.85
0.73 0.74 0.74 0.73 0.46 0.36 0.51 0.62 0.6 0.37 0.45 0.66 0.69 0.51 0.58 0.73 0.81 0.8 0.72 0.64 0.57 0.77 0.82 0.93 0.83 0.75 0.76 0.75 0.76 0.76 0.76 0.77 0.78 0.79 0.79 0.47 0.37 0.48 0.53 0.48 0.36 0.31 0.3 0.25 0.27 0.25 0.27 0.17 0.14 0.16 0.31 0.38 0.37 0.25 0.25 0.18 0.29 0.25 0.29 0.25 0.22 0.28 0.33 0.83
0.74 0.75 0.75 0.65 0.37 0.38 0.4 0.43 0.35 0.33 0.59 0.58 0.6 0.5 0.6 0.76 0.82 0.8 0.75 0.67 0.55 0.56 0.61 0.8 0.83 0.76 0.77 0.77 0.77 0.77 0.78 0.79 0.8 0.82 0.81 0.45 0.39 0.48 0.54 0.5 0.29 0.33 0.33 0.34 0.33 0.32 0.31 0.18 0.16 0.18 0.34 0.39 0.36 0.27 0.33 0.22 0.43 0.24 0.29 0.23 0.22 0.28 0.28 0.71
0.75 0.75 0.72 0.54 0.34 0.33 0.35 0.31 0.32 0.42 0.55 0.6 0.51 0.35 0.4 0.64 0.76 0.78 0.64 0.42 0.45 0.49 0.42 0.62 0.79 0.89 0.85 0.82 0.8 0.78 0.79 0.81 0.82 0.84 0.82 0.49 0.41 0.49 0.55 0.46 0.28 0.41 0.33 0.33 0.31 0.29 0.27 0.15 0.16 0.18 0.35 0.39 0.35 0.25 0.34 0.21 0.44 0.24 0.27 0.21 0.2 0.27 0.29 0.67
0.76 0.75 0.7 0.41 0.3 0.29 0.31 0.21 0.27 0.41 0.61 0.51 0.36 0.28 0.26 0.42 0.64 0.72 0.7 0.58 0.49 0.41 0.35 0.56 0.77 0.85 0.89 0.88 0.87 0.85 0.83 0.81 0.81 0.81 0.83 0.54 0.38 0.5 0.54 0.38 0.29 0.4 0.3 0.33 0.32 0.25 0.23 0.09 0.12 0.16 0.33 0.41 0.36 0.25 0.34 0.2 0.48 0.23 0.27 0.17 0.18 0.27 0.29 0.76
0.77 0.76 0.63 0.34 0.3 0.27 0.23 0.15 0.27 0.54 0.55 0.36 0.28 0.27 0.26 0.28 0.52 0.7 0.72 0.67 0.6 0.47 0.48 0.58 0.53 0.77 0.9 0.87 0.87 0.87 0.85 0.86 0.87 0.86 0.88 0.53 0.35 0.51 0.5 0.29 0.31 0.42 0.28 0.29 0.31 0.26 0.27 0.16 0.14 0.12 0.34 0.38 0.32 0.18 0.35 0.19 0.49 0.2 0.26 0.19 0.18 0.29 0.31 0.81
0.78 0.76 0.56 0.36 0.33 0.25 0.2 0.23 0.34 0.55 0.41 0.3 0.28 0.27 0.27 0.27 0.46 0.69 0.72 0.63 0.72 0.69 0.54 0.36 0.58 0.7 0.95 0.9 0.86 0.87 0.88 0.89 0.91 0.91 0.93 0.51 0.27 0.49 0.46 0.26 0.33 0.44 0.27 0.09 0.12 0.22 0.26 0.18 0.15 0.13 0.3 0.15 0.13 0.05 0.17 0.17 0.5 0.22 0.26 0.2 0.16 0.29 0.3 0.79
0.79 0.76 0.55 0.48 0.33 0.26 0.24 0.29 0.33 0.43 0.35 0.29 0.29 0.28 0.27 0.26 0.35 0.58 0.57 0.37 0.42 0.42 0.35 0.35 0.61 0.6 0.88 0.89 0.9 0.88 0.88 0.89 0.92 0.94 0.96 0.48 0.26 0.51 0.36 0.26 0.28 0.36 0.11 0.03 0.06 0.07 0.21 0.18 0.13 0.14 0.15 0.08 0.16 0.19 0.04 0.07 0.27 0.24 0.25 0.2 0.13 0.29 0.29 0.8
0.8 0.77 0.69 0.5 0.34 0.27 0.28 0.31 0.3 0.33 0.31 0.29 0.29 0.29 0.29 0.28 0.28 0.44 0.57 0.32 0.31 0.35 0.34 0.34 0.55 0.46 0.64 0.87 0.94 0.91 0.86 0.86 0.9 0.93 0.96 0.45 0.33 0.46 0.31 0.28 0.21 0.2 0.07 0.24 0.24 0.08 0.14 0.15 0.09 0.09 0.04 0.27 0.22 0.16 0.18 0.03 0.26 0.24 0.21 0.18 0.12 0.28 0.28 0.76
0.84 0.85 0.78 0.4 0.35 0.29 0.31 0.31 0.3 0.35 0.31 0.28 0.28 0.29 0.29 0.27 0.31 0.47 0.51 0.2 0.43 0.45 0.31 0.32 0.39 0.37 0.39 0.63 0.76 0.93 0.92 0.92 0.92 0.94 0.94 0.46 0.39 0.36 0.3 0.29 0.22 0.12 0.21 0.18 0.12 0.16 0.16 0.13 0.14 0.11 0.1 0.31 0.25 0.11 0.33 0.04 0.28 0.23 0.14 0.15 0.11 0.25 0.27 0.74
0.84 0.85 0.65 0.35 0.33 0.32 0.32 0.3 0.29 0.3 0.28 0.27 0.33 0.33 0.31 0.42 0.49 0.56 0.45 0.18 0.46 0.78 0.42 0.34 0.41 0.34 0.34 0.42 0.52 0.8 0.97 0.96 0.95 0.95 0.94 0.52 0.35 0.25 0.24 0.23 0.25 0.18 0.2 0.14 0.13 0.16 0.09 0.15 0.14 0.13 0.06 0.2 0.29 0.18 0.35 0.03 0.26 0.22 0.2 0.16 0.13 0.23 0.27 0.72
0.83 0.84 0.59 0.34 0.33 0.33 0.31 0.32 0.3 0.29 0.3 0.41 0.49 0.55 0.51 0.45 0.48 0.67 0.63 0.32 0.68 0.97 0.8 0.79 0.84 0.58 0.36 0.35 0.36 0.54 0.87 0.94 0.97 0.96 0.95 0.54 0.29 0.28 0.2 0.22 0.34 0.28 0.05 0.22 0.24 0.07 0.11 0.19 0.14 0.11 0.18 0.08 0.26 0.17 0.33 0.07 0.26 0.17 0.23 0.21 0.13 0.24 0.25 0.68
0.85 0.88 0.55 0.33 0.33 0.33 0.31 0.31 0.29 0.27 0.27 0.39 0.56 0.6 0.55 0.51 0.61 0.79 0.81 0.38 0.73 0.96 0.95 0.95 0.95 0.96 0.85 0.67 0.53 0.38 0.43 0.63 0.95 0.97 0.96 0.53 0.24 0.3 0.3 0.27 0.34 0.39 0.09 0.04 0.06 0.05 0.19 0.2 0.16 0.1 0.29 0.24 0.16 0.07 0.35 0.13 0.29 0.17 0.22 0.22 0.13 0.27 0.26 0.64
0.89 0.89 0.49 0.33 0.33 0.33 0.31 0.3 0.27 0.33 0.39 0.41 0.36 0.32 0.48 0.66 0.83 0.88 0.88 0.47 0.75 0.96 0.94 0.94 0.94 0.95 0.96 0.98 0.95 0.49 0.36 0.53 0.69 0.98 0.95 0.47 0.16 0.3 0.31 0.28 0.33 0.41 0.31 0.14 0.12 0.19 0.24 0.19 0.13 0.09 0.31 0.42 0.28 0.23 0.41 0.15 0.29 0.23 0.24 0.23 0.16 0.27 0.26 0.62
0.92 0.82 0.39 0.31 0.3 0.29 0.29 0.25 0.25 0.33 0.28 0.25 0.3 0.65 0.8 0.78 0.76 0.75 0.89 0.52 0.75 0.96 0.94 0.94 0.94 0.94 0.94 0.94 0.95 0.89 0.85 0.95 0.58 0.84 0.94 0.42 0.16 0.27 0.29 0.27 0.33 0.4 0.35 0.3 0.29 0.25 0.19 0.08 0.04 0.05 0.33 0.4 0.31 0.29 0.48 0.17 0.29 0.24 0.24 0.24 0.16 0.27 0.26 0.6
0.92 0.69 0.36 0.35 0.29 0.29 0.29 0.27 0.28 0.27 0.27 0.41 0.5 0.76 0.85 0.68 0.71 0.75 0.89 0.61 0.75 0.96 0.94 0.94 0.95 0.95 0.95 0.95 0.95 0.96 0.96 0.96 0.81 0.62 0.93 0.38 0.2 0.29 0.31 0.25 0.33 0.38 0.33 0.28 0.29 0.21 0.06 0.08 0.09 0.02 0.24 0.4 0.31 0.3 0.5 0.17 0.32 0.23 0.23 0.23 0.15 0.25 0.25 0.56
0.91 0.73 0.38 0.33 0.3 0.32 0.3 0.28 0.29 0.29 0.29 0.46 0.45 0.54 0.87 0.74 0.73 0.69 0.9 0.74 0.78 0.94 0.93 0.93 0.94 0.94 0.94 0.94 0.94 0.95 0.95 0.94 0.94 0.8 0.91 0.38 0.23 0.31 0.31 0.25 0.3 0.38 0.32 0.24 0.27 0.13 0.05 0.13 0.15 0.07 0.11 0.4 0.31 0.3 0.53 0.17 0.33 0.22 0.22 0.23 0.14 0.24 0.25 0.56
0.88 0.47 0.34 0.33 0.33 0.31 0.29 0.3 0.29 0.3 0.26 0.28 0.24 0.21 0.64 0.82 0.76 0.67 0.82 0.94 0.85 0.93 0.93 0.93 0.94 0.94 0.94 0.94 0.94 0.94 0.94 0.94 0.95 0.97 0.93 0.38 0.22 0.31 0.31 0.25 0.29 0.39 0.31 0.27 0.28 0.11 0.12 0.11 0.1 0.11 0.09 0.33 0.31 0.3 0.55 0.18 0.32 0.18 0.21 0.21 0.16 0.25 0.25 0.54
0.87 0.42 0.22 0.31 0.33 0.29 0.29 0.28 0.27 0.3 0.29 0.25 0.31 0.26 0.33 0.79 0.76 0.59 0.74 0.96 0.91 0.92 0.93 0.93 0.93 0.94 0.94 0.94 0.94 0.95 0.95 0.95 0.95 0.96 0.92 0.38 0.22 0.31 0.31 0.25 0.28 0.38 0.32 0.3 0.28 0.09 0.11 0.12 0.09 0.13 0.08 0.29 0.3 0.31 0.53 0.18 0.29 0.19 0.21 0.22 0.15 0.23 0.23 0.5
0.88 0.56 0.31 0.33 0.32 0.29 0.31 0.3 0.27 0.27 0.32 0.42 0.31 0.42 0.22 0.71 0.81 0.5 0.71 0.95 0.93 0.93 0.93 0.93 0.94 0.94 0.94 0.95 0.95 0.95 0.95 0.95 0.95 0.96 0.91 0.38 0.23 0.31 0.29 0.22 0.27 0.35 0.32 0.31 0.28 0.15 0.06 0.15 0.16 0.08 0.1 0.35 0.29 0.29 0.5 0.18 0.3 0.22 0.22 0.23 0.15 0.24 0.24 0.44
0.88 0.59 0.29 0.33 0.32 0.3 0.29 0.31 0.29 0.29 0.3 0.62 0.73 0.69 0.27 0.56 0.85 0.53 0.63 0.93 0.93 0.93 0.93 0.93 0.93 0.93 0.93 0.94 0.94 0.94 0.94 0.94 0.94 0.95 0.89 0.37 0.25 0.31 0.29 0.22 0.27 0.35 0.33 0.31 0.33 0.29 0.12 0.05 0.08 0.03 0.25 0.41 0.33 0.33 0.51 0.17 0.3 0.23 0.25 0.27 0.15 0.21 0.25 0.43
0.71 0.39 0.23 0.33 0.32 0.3 0.29 0.31 0.27 0.29 0.33 0.32 0.42 0.37 0.4 0.42 0.84 0.65 0.51 0.85 0.93 0.91 0.92 0.92 0.93 0.92 0.93 0.93 0.93 0.93 0.93 0.94 0.95 0.94 0.88 0.35 0.21 0.29 0.29 0.22 0.26 0.33 0.33 0.31 0.33 0.33 0.24 0.11 0.09 0.12 0.36 0.41 0.31 0.31 0.48 0.16 0.29 0.24 0.25 0.27 0.17 0.22 0.24 0.38
0.65 0.22 0.2 0.32 0.31 0.31 0.28 0.28 0.29 0.24 0.29 0.25 0.48 0.36 0.29 0.33 0.84 0.81 0.45 0.77 0.76 0.73 0.75 0.77 0.77 0.76 0.81 0.8 0.77 0.8 0.74 0.74 0.8 0.73 0.67 0.31 0.18 0.28 0.29 0.24 0.29 0.36 0.31 0.26 0.31 0.33 0.26 0.14 0.13 0.16 0.36 0.42 0.3 0.28 0.51 0.18 0.3 0.22 0.24 0.25 0.16 0.22 0.23 0.36
0.49 0.09 0.18 0.32 0.29 0.29 0.31 0.28 0.28 0.27 0.24 0.43 0.39 0.62 0.26 0.26 0.76 0.75 0.42 0.73 0.65 0.6 0.61 0.62 0.6 0.58 0.61 0.6 0.59 0.59 0.57 0.56 0.58 0.59 0.53 0.25 0.2 0.26 0.29 0.24 0.25 0.33 0.32 0.25 0.3 0.32 0.25 0.14 0.15 0.16 0.29 0.4 0.31 0.26 0.4 0.18 0.3 0.2 0.22 0.2 0.14 0.2 0.23 0.31
0.36 0.05 0.23 0.33 0.28 0.28 0.31 0.31 0.26 0.34 0.28 0.37 0.47 0.53 0.31 0.2 0.65 0.7 0.41 0.71 0.65 0.62 0.65 0.65 0.65 0.63 0.65 0.65 0.65 0.65 0.65 0.63 0.63 0.65 0.59 0.2 0.21 0.27 0.29 0.23 0.25 0.32 0.31 0.29 0.31 0.32 0.25 0.16 0.2 0.15 0.32 0.43 0.33 0.26 0.28 0.17 0.3 0.15 0.17 0.2 0.13 0.19 0.23 0.24
0.2 0.08 0.25 0.33 0.3 0.29 0.29 0.35 0.49 0.35 0.61 0.32 0.53 0.55 0.37 0.12 0.43 0.71 0.5 0.64 0.57 0.54 0.59 0.61 0.58 0.53 0.49 0.54 0.61 0.58 0.58 0.6 0.61 0.64 0.57 0.19 0.18 0.26 0.26 0.24 0.25 0.33 0.29 0.16 0.14 0.25 0.26 0.17 0.15 0.14 0.31 0.25 0.17 0.22 0.29 0.17 0.28 0.17 0.17 0.19 0.14 0.16 0.22 0.24
0.08 0.15 0.27 0.32 0.31 0.3 0.3 0.28 0.6 0.53 0.45 0.58 0.4 0.73 0.38 0.2 0.32 0.75 0.62 0.61 0.44 0.4 0.45 0.44 0.44 0.36 0.25 0.42 0.56 0.55 0.49 0.48 0.52 0.58 0.54 0.2 0.21 0.25 0.26 0.24 0.25 0.33 0.15 0.01 0.03 0.04 0.16 0.18 0.17 0.16 0.08 0.02 0.02 0.04 0.25 0.17 0.28 0.21 0.18 0.18 0.14 0.15 0.22 0.22
0.04 0.22 0.29 0.32 0.32 0.31 0.3 0.29 0.31 0.65 0.67 0.45 0.49 0.61 0.56 0.32 0.34 0.67 0.74 0.66 0.63 0.62 0.61 0.58 0.55 0.53 0.53 0.53 0.51 0.53 0.5 0.42 0.54 0.58 0.45 0.2 0.23 0.27 0.27 0.22 0.24 0.28 0.04 0.14 0.25 0.15 0.06 0.13 0.18 0.13 0.03 0.22 0.24 0.08 0.12 0.17 0.29 0.19 0.17 0.21 0.15 0.15 0.25 0.23
0.11 0.22 0.21 0.29 0.33 0.31 0.28 0.28 0.35 0.49 0.82 0.86 0.69 0.54 0.69 0.36 0.23 0.5 0.85 0.73 0.67 0.69 0.69 0.69 0.69 0.67 0.67 0.67 0.67 0.67 0.65 0.65 0.65 0.66 0.53 0.2 0.24 0.27 0.28 0.21 0.24 0.22 0.05 0.16 0.12 0.22 0.07 0.11 0.16 0.07 0.11 0.25 0.16 0.18 0.05 0.11 0.27 0.17 0.17 0.2 0.15 0.15 0.25 0.23
0.18 0.23 0.22 0.27 0.32 0.55 0.62 0.67 0.77 0.76 0.69 0.51 0.77 0.77 0.73 0.59 0.09 0.2 0.67 0.76 0.69 0.69 0.7 0.7 0.7 0.7 0.7 0.7 0.69 0.7 0.7 0.69 0.69 0.72 0.58 0.21 0.23 0.26 0.29 0.21 0.26 0.19 0.06 0.12 0.05 0.18 0.1 0.1 0.15 0.05 0.19 0.16 0.07 0.15 0.11 0.09 0.25 0.14 0.16 0.18 0.14 0.15 0.24 0.22
0.21 0.22 0.18 0.45 0.36 0.32 0.46 0.39 0.4 0.53 0.9 0.75 0.47 0.69 0.87 0.51 0.14 0.23 0.48 0.6 0.67 0.69 0.67 0.67 0.68 0.69 0.69 0.69 0.7 0.72 0.73 0.72 0.72 0.74 0.59 0.19 0.24 0.25 0.27 0.21 0.27 0.21 0.03 0.16 0.15 0.18 0.04 0.11 0.18 0.05 0.12 0.24 0.12 0.15 0.13 0.09 0.24 0.15 0.17 0.17 0.13 0.16 0.24 0.21
0.22 0.21 0.17 0.59 0.55 0.24 0.45 0.51 0.31 0.25 0.52 0.94 0.85 0.71 0.82 0.44 0.3 0.29 0.5 0.44 0.64 0.68 0.66 0.67 0.66 0.65 0.67 0.65 0.65 0.66 0.68 0.69 0.71 0.73 0.56 0.17 0.21 0.19 0.25 0.21 0.26 0.26 0.07 0.07 0.17 0.09 0.04 0.15 0.22 0.1 0.03 0.21 0.24 0.09 0.12 0.11 0.24 0.15 0.14 0.16 0.13 0.16 0.24 0.21
0.24 0.22 0.19 0.55 0.83 0.6 0.46 0.65 0.84 0.75 0.51 0.78 0.82 0.85 0.74 0.41 0.43 0.38 0.51 0.36 0.6 0.64 0.65 0.69 0.69 0.67 0.66 0.65 0.67 0.65 0.64 0.66 0.67 0.68 0.52 0.17 0.17 0.17 0.27 0.2 0.25 0.27 0.19 0.04 0.01 0.02 0.14 0.18 0.2 0.15 0.11 0.02 0.03 0.01 0.13 0.15 0.23 0.16 0.13 0.16 0.13 0.15 0.23 0.2
0.25 0.23 0.18 0.46 0.85 0.85 0.84 0.75 0.82 0.89 0.87 0.85 0.89 0.84 0.63 0.36 0.47 0.36 0.43 0.32 0.6 0.63 0.62 0.64 0.65 0.65 0.64 0.65 0.66 0.67 0.66 0.64 0.65 0.69 0.51 0.16 0.16 0.2 0.25 0.19 0.26 0.27 0.25 0.15 0.15 0.17 0.22 0.16 0.18 0.16 0.27 0.21 0.09 0.1 0.21 0.15 0.22 0.18 0.13 0.16 0.13 0.14 0.2 0.15
0.25 0.23 0.18 0.43 0.78 0.75 0.72 0.81 0.77 0.78 0.7 0.82 0.89 0.85 0.7 0.29 0.47 0.35 0.33 0.39 0.67 0.65 0.64 0.64 0.64 0.65 0.65 0.62 0.61 0.62 0.62 0.6 0.62 0.67 0.49 0.15 0.16 0.2 0.25 0.18 0.24 0.27 0.2 0.16 0.23 0.22 0.23 0.14 0.16 0.14 0.29 0.35 0.27 0.21 0.21 0.1 0.18 0.16 0.13 0.18 0.12 0.13 0.22 0.16

Weigths 5x5 And bias 2x1 :-
==============
0.06 0.26 0.2 0.11 0.13

-0.01 -0.25 -0.28 -0.02 -0.18

0 0.21 0.05 0.03 -0.07

0.15 0 0.18 0.27 -0.19

0.23 0.2 0.03 -0.14 -0.18

0.26 -0.02

Convolution Operation With Relu 60x60 :-
==============
1.5441 1.5472 1.5496 1.5514 1.5593 1.5632 1.5684 1.5781 1.5838 1.5897 1.5968 1.5986 1.603 1.6003 1.5976 1.6017 1.6056 1.6109 1.6177 1.6137 1.6143 1.6125 1.6138 1.6145 1.6131 1.619 1.6208 1.6227 1.6254 1.6242 1.6287 1.6312 1.6278 1.6232 1.626 1.6291 1.6283 1.637 1.6372 1.6375 1.6429 1.6445 1.6489 1.6515 1.6568 1.662 1.658 1.6619 1.6617 1.6635 1.67 1.6696 1.6734 1.6826 1.6779 1.6816 1.6849 1.6922 1.6963 1.6983
1.5531 1.5522 1.5577 1.5575 1.5604 1.5714 1.579 1.5883 1.5888 1.5913 1.5921 1.5966 1.6013 1.6036 1.607 1.6113 1.6178 1.6222 1.6285 1.6186 1.6162 1.6206 1.623 1.629 1.6282 1.6283 1.6293 1.6269 1.6318 1.6325 1.6327 1.6351 1.6348 1.636 1.6301 1.6329 1.6373 1.634 1.6382 1.6409 1.6425 1.6467 1.6515 1.6551 1.6595 1.6605 1.6577 1.663 1.6641 1.668 1.673 1.6766 1.6778 1.68 1.6857 1.6854 1.6886 1.6932 1.6953 1.7024
1.5552 1.5606 1.5624 1.5689 1.5768 1.5843 1.592 1.6018 1.6044 1.6113 1.6143 1.6154 1.6185 1.6174 1.6204 1.626 1.6304 1.631 1.6325 1.628 1.63 1.6283 1.6299 1.6354 1.6363 1.6351 1.6327 1.631 1.6319 1.6339 1.6363 1.6366 1.6373 1.6347 1.638 1.6396 1.6368 1.648 1.6458 1.6462 1.6495 1.65 1.6582 1.6585 1.6666 1.6705 1.6709 1.6772 1.6789 1.6807 1.6782 1.6767 1.6842 1.687 1.6872 1.6938 1.6918 1.6946 1.703 1.7038
1.5612 1.5604 1.5707 1.5762 1.584 1.5949 1.6022 1.6099 1.6129 1.6169 1.62 1.6229 1.6266 1.6282 1.6252 1.6263 1.6268 1.6271 1.6269 1.6222 1.6309 1.6372 1.6395 1.6452 1.6452 1.6459 1.6407 1.6335 1.6399 1.6465 1.6472 1.6374 1.6394 1.6423 1.64 1.645 1.6493 1.6452 1.6444 1.6475 1.6494 1.6562 1.658 1.6597 1.664 1.6657 1.671 1.6767 1.6776 1.679 1.6821 1.6817 1.6867 1.6942 1.6977 1.6933 1.6967 1.7019 1.7034 1.708
1.5755 1.5811 1.5848 1.5874 1.5912 1.6007 1.607 1.6159 1.6237 1.6277 1.6236 1.6249 1.6255 1.6249 1.6325 1.6372 1.6408 1.6413 1.646 1.648 1.6473 1.6459 1.6457 1.6492 1.6479 1.6792 1.6209 1.6155 1.6226 1.6417 1.7086 1.6633 1.6541 1.652 1.651 1.6499 1.6483 1.653 1.6505 1.6561 1.6576 1.6611 1.6661 1.6676 1.6716 1.6747 1.6727 1.6803 1.6797 1.6816 1.6851 1.6884 1.6925 1.6927 1.6956 1.703 1.7024 1.6995 1.7058 1.709
1.58 1.5855 1.5977 1.6003 1.6056 1.6143 1.6172 1.6234 1.6249 1.6287 1.6344 1.6376 1.6436 1.6447 1.6482 1.6497 1.6525 1.6533 1.6522 1.6496 1.652 1.6494 1.6462 1.6559 1.6565 1.7308 1.6295 1.658 1.6311 1.5721 1.6701 1.6772 1.6535 1.6503 1.6546 1.6582 1.6574 1.6583 1.6597 1.6624 1.6687 1.6738 1.6704 1.6758 1.6778 1.679 1.6875 1.6864 1.6899 1.6945 1.6948 1.6936 1.6981 1.7022 1.6995 1.7008 1.7027 1.7086 1.7111 1.7161
1.5891 1.5936 1.6027 1.6051 1.6109 1.6221 1.6263 1.6354 1.6429 1.645 1.643 1.6497 1.653 1.6556 1.6601 1.6576 1.6574 1.6555 1.6553 1.6538 1.6524 1.6524 1.657 1.6628 1.6575 1.733 1.5875 1.563 1.5918 1.6438 1.6731 1.7118 1.7117 1.6657 1.6616 1.6655 1.6689 1.6663 1.668 1.6696 1.668 1.6725 1.6779 1.6739 1.6784 1.6862 1.6842 1.6956 1.6941 1.6933 1.6988 1.7026 1.7042 1.7012 1.7056 1.7114 1.7114 1.7159 1.7205 1.7204
1.5968 1.6051 1.6144 1.6183 1.6223 1.6308 1.6331 1.6457 1.6532 1.6451 1.6197 1.6357 1.6676 1.6952 1.6893 1.6608 1.6623 1.6665 1.6676 1.6696 1.6722 1.6721 1.6706 1.6739 1.6709 1.736 1.5946 1.6775 1.5582 1.6277 1.7535 1.6856 1.7383 1.6886 1.6733 1.6742 1.672 1.6706 1.6712 1.6729 1.6783 1.6822 1.681 1.6839 1.6863 1.692 1.6949 1.6914 1.6922 1.6954 1.6972 1.6992 1.7053 1.7057 1.7085 1.7121 1.7172 1.7232 1.723 1.7263
1.6133 1.6164 1.6211 1.6248 1.6306 1.6389 1.6486 1.6556 1.6614 1.6329 1.5861 1.62 1.6787 1.7652 1.7942 1.7195 1.6661 1.6685 1.6688 1.6708 1.6731 1.6713 1.6742 1.6763 1.6664 1.7262 1.6193 1.7358 1.6959 1.7314 1.7654 1.7099 1.7119 1.6782 1.6724 1.6716 1.6789 1.6794 1.6829 1.6901 1.6881 1.6906 1.6901 1.6882 1.694 1.6957 1.6977 1.7045 1.7031 1.6977 1.7053 1.7109 1.7108 1.7124 1.7183 1.7187 1.7164 1.7171 1.7229 1.7244
1.6184 1.62 1.6278 1.6336 1.6431 1.6516 1.6553 1.6635 1.7253 1.7107 1.6481 1.6487 1.6574 1.6791 1.7379 1.7453 1.6873 1.6857 1.6754 1.6738 1.6808 1.6875 1.6905 1.6849 1.6807 1.6967 1.5478 1.6579 1.5502 1.6668 1.7014 1.6557 1.6869 1.6838 1.6875 1.6847 1.688 1.687 1.6881 1.6916 1.6967 1.6971 1.6965 1.6921 1.6952 1.6985 1.7012 1.7032 1.7052 1.7105 1.7144 1.72 1.721 1.7203 1.719 1.7163 1.7247 1.7274 1.7286 1.7315
1.6277 1.6321 1.6347 1.6466 1.6481 1.6546 1.6654 1.6892 1.81 1.757 1.578 1.4622 1.5095 1.5953 1.6479 1.6542 1.6552 1.7157 1.715 1.6881 1.6888 1.6899 1.6917 1.6914 1.6869 1.7251 1.5835 1.7221 1.6627 1.7221 1.7567 1.7043 1.7022 1.686 1.6908 1.6838 1.6878 1.6896 1.6939 1.703 1.6986 1.701 1.7007 1.7 1.7056 1.7089 1.712 1.7133 1.7171 1.7137 1.7164 1.7185 1.7183 1.7224 1.7274 1.7305 1.7353 1.7345 1.7363 1.7364
1.6309 1.6454 1.6512 1.6549 1.6628 1.6704 1.6735 1.7404 1.8715 1.7485 1.5431 1.3372 1.3513 1.3885 1.5045 1.6258 1.6236 1.714 1.791 1.7356 1.6919 1.6987 1.6986 1.6956 1.7003 1.7169 1.6038 1.7163 1.6919 1.7327 1.7414 1.7068 1.7077 1.698 1.7051 1.6999 1.7027 1.701 1.7005 1.7031 1.7044 1.7015 1.7042 1.7069 1.708 1.7128 1.7181 1.7189 1.7225 1.7186 1.721 1.7222 1.7217 1.7303 1.7337 1.7342 1.7367 1.7361 1.7347 1.7381
1.6456 1.6495 1.6601 1.6688 1.6701 1.6788 1.6817 1.8003 1.8916 1.7888 1.6562 1.5001 1.5554 1.5127 1.47 1.5079 1.6334 1.694 1.7697 1.7984 1.716 1.6984 1.7019 1.7046 1.7195 1.7146 1.6321 1.7243 1.6907 1.7428 1.7433 1.7096 1.7005 1.6932 1.7043 1.7072 1.7168 1.7156 1.7177 1.7172 1.7094 1.7125 1.7146 1.7105 1.7178 1.7194 1.722 1.7279 1.7288 1.7267 1.7309 1.7339 1.7347 1.7372 1.7369 1.7382 1.7419 1.7457 1.7465 1.7466
1.6503 1.6621 1.664 1.6725 1.6815 1.6882 1.6972 1.8636 1.8839 1.7526 1.604 1.4812 1.4892 1.5416 1.5547 1.4654 1.5347 1.6076 1.6834 1.7595 1.7479 1.7018 1.7074 1.7088 1.7409 1.7112 1.6535 1.7259 1.6818 1.7383 1.7332 1.706 1.7052 1.7101 1.7165 1.7176 1.7222 1.7214 1.7204 1.7195 1.7206 1.7218 1.7253 1.7276 1.7295 1.7317 1.7322 1.7334 1.7364 1.7364 1.7395 1.7358 1.7379 1.7411 1.74 1.7432 1.744 1.7445 1.7479 1.7471
1.6619 1.6666 1.678 1.6838 1.6884 1.6952 1.7377 1.9182 1.8304 1.694 1.5314 1.4379 1.3881 1.4283 1.4768 1.4267 1.4166 1.4637 1.5359 1.6636 1.7611 1.722 1.7097 1.7148 1.7587 1.6953 1.6725 1.7152 1.6892 1.7592 1.7308 1.712 1.7137 1.717 1.7163 1.7203 1.7208 1.724 1.7278 1.725 1.7249 1.7285 1.729 1.7295 1.7323 1.7278 1.7323 1.7375 1.7384 1.7395 1.7473 1.743 1.7456 1.7492 1.7504 1.753 1.7554 1.7549 1.7524 1.7534
1.673 1.6821 1.6875 1.6959 1.7009 1.7011 1.7919 1.9202 1.7803 1.6547 1.4616 1.4622 1.3812 1.4303 1.485 1.3856 1.3793 1.4146 1.4262 1.5714 1.7078 1.7351 1.72 1.7161 1.7716 1.6803 1.6996 1.7208 1.7048 1.7705 1.7334 1.7214 1.7187 1.7219 1.7257 1.7307 1.7322 1.7358 1.7342 1.7356 1.7335 1.7395 1.7381 1.7326 1.7385 1.7393 1.7401 1.7435 1.746 1.7469 1.7525 1.7545 1.7588 1.7531 1.7554 1.7584 1.7595 1.763 1.7656 1.7663
1.6852 1.6904 1.6935 1.6957 1.7069 1.7131 1.8684 1.9062 1.7501 1.6188 1.4198 1.4631 1.4094 1.4797 1.5862 1.5065 1.3916 1.4253 1.4221 1.4987 1.6512 1.7195 1.7232 1.7282 1.7844 1.6588 1.7157 1.7377 1.7426 1.7963 1.7344 1.724 1.7293 1.7298 1.7363 1.7405 1.7397 1.7424 1.7424 1.7402 1.7428 1.7421 1.7399 1.7473 1.749 1.7479 1.7518 1.7483 1.7492 1.7539 1.7572 1.7555 1.7556 1.7545 1.757 1.7603 1.7624 1.7624 1.7624 1.7662
1.6892 1.6965 1.7014 1.7105 1.7121 1.7523 1.9239 1.8643 1.7169 1.5584 1.3344 1.415 1.4419 1.4759 1.5247 1.508 1.3935 1.3341 1.3637 1.4398 1.6099 1.7194 1.7384 1.7344 1.8044 1.6904 1.7583 1.6991 1.6826 1.7711 1.7412 1.7337 1.7351 1.7397 1.7462 1.7452 1.7437 1.7454 1.7448 1.7467 1.7484 1.754 1.7556 1.755 1.7559 1.7533 1.7531 1.7579 1.7584 1.7644 1.7653 1.7638 1.7697 1.7682 1.77 1.7693 1.7668 1.7667 1.7667 1.7704
1.7051 1.7118 1.7133 1.7197 1.7235 1.8292 1.9647 1.8341 1.6969 1.4759 1.2436 1.4192 1.5167 1.5746 1.5738 1.3934 1.2724 1.2361 1.3247 1.4787 1.6167 1.7167 1.7433 1.7469 1.8051 1.6733 1.7214 1.7144 1.709 1.7342 1.7406 1.7427 1.7468 1.747 1.7479 1.7474 1.7488 1.7519 1.7487 1.7521 1.7555 1.7573 1.7602 1.7619 1.7563 1.7572 1.7631 1.7635 1.7664 1.7676 1.7676 1.7701 1.7668 1.7649 1.7666 1.771 1.7776 1.7799 1.7802 1.7773
1.7082 1.7182 1.7225 1.7259 1.7472 1.9123 1.9441 1.7858 1.6151 1.3564 1.1794 1.3977 1.5687 1.6629 1.6037 1.445 1.2711 1.2374 1.3549 1.512 1.6675 1.7373 1.7468 1.7494 1.7771 1.7159 1.7179 1.6578 1.7161 1.7444 1.7478 1.7492 1.7471 1.7488 1.7516 1.756 1.7594 1.7633 1.7602 1.7607 1.7629 1.7632 1.7664 1.7692 1.7704 1.7704 1.7693 1.7719 1.7691 1.7772 1.7763 1.7718 1.7704 1.766 1.7661 1.7784 1.7826 1.7794 1.7787 1.7801
1.7231 1.7252 1.7266 1.7375 1.8208 1.9978 1.8889 1.7261 1.5161 1.2676 1.164 1.3743 1.5641 1.685 1.5932 1.4362 1.2916 1.2863 1.4342 1.5824 1.6927 1.762 1.7562 1.7572 1.7812 1.7728 1.8133 1.807 1.781 1.7608 1.7479 1.7485 1.7481 1.7511 1.7521 1.7573 1.7645 1.7642 1.7655 1.7702 1.7708 1.7685 1.773 1.7703 1.7634 1.769 1.7696 1.7714 1.7742 1.7724 1.7706 1.7713 1.7716 1.7805 1.7972 1.8085 1.805 1.8064 1.8016 1.7959
1.7264 1.7307 1.7315 1.768 1.9073 1.9698 1.7639 1.6505 1.4341 1.2147 1.2371 1.4157 1.651 1.7181 1.6038 1.4172 1.2405 1.2896 1.4046 1.5661 1.6969 1.7635 1.7574 1.7583 1.7405 1.7459 1.7288 1.7327 1.7638 1.761 1.761 1.7591 1.7563 1.7635 1.766 1.7624 1.7589 1.7659 1.7715 1.7747 1.7736 1.7716 1.7716 1.7705 1.7762 1.7779 1.7848 1.7944 1.7933 1.7937 1.7914 1.7918 1.793 1.7938 1.8007 1.8052 1.8042 1.7999 1.8055 1.813
1.7319 1.7372 1.755 1.8597 1.9505 1.7955 1.567 1.4634 1.3785 1.4115 1.4914 1.5639 1.7677 1.6981 1.5976 1.4418 1.3858 1.4253 1.5411 1.6601 1.7312 1.7646 1.7608 1.762 1.7539 1.7497 1.7461 1.7353 1.7416 1.7572 1.7603 1.7619 1.7613 1.7625 1.7598 1.7658 1.7722 1.7782 1.7892 1.7914 1.7828 1.778 1.7828 1.7943 1.804 1.8126 1.8158 1.8056 1.7944 1.7802 1.7779 1.7858 1.7925 1.7986 1.8006 1.8025 1.8344 1.9185 1.9799 1.8645
1.7356 1.7502 1.8125 1.9001 1.8269 1.6058 1.5398 1.6068 1.6458 1.6498 1.6877 1.6644 1.6638 1.5486 1.646 1.5751 1.5501 1.6234 1.7136 1.7537 1.8111 1.7901 1.7732 1.7742 1.7744 1.7735 1.7729 1.7749 1.7755 1.7719 1.7696 1.7744 1.7804 1.7751 1.7498 1.7502 1.7781 1.8042 1.8052 1.7846 1.7812 1.8018 1.8247 1.8351 1.8298 1.8164 1.8049 1.7907 1.7722 1.7689 1.7881 1.7994 1.8065 1.8262 1.8703 1.9479 2.0494 2.0656 1.8805 1.545
1.7501 1.8211 1.8891 1.8295 1.682 1.5272 1.6308 1.7331 1.6741 1.723 1.6007 1.5306 1.6725 1.6886 1.8202 1.7841 1.7901 1.8126 1.8486 1.8793 1.8887 1.8513 1.797 1.7738 1.7735 1.7758 1.7758 1.7758 1.7758 1.7734 1.7704 1.8301 1.8622 1.8311 1.7582 1.6659 1.6749 1.7336 1.7709 1.8025 1.8241 1.8389 1.8413 1.8424 1.8314 1.8148 1.8088 1.7928 1.7919 1.8141 1.8439 1.8719 1.8973 1.9639 2.0306 2.0262 1.9129 1.7279 1.5656 1.2817
1.7836 1.8973 1.8583 1.6994 1.6093 1.5896 1.6732 1.5941 1.3999 1.412 1.3599 1.4996 1.6127 1.6289 1.7503 1.7427 1.7909 1.8248 1.8172 1.8157 1.841 1.888 1.8721 1.8122 1.7808 1.7823 1.7823 1.7805 1.7791 1.775 1.7725 1.878 1.8846 1.7896 1.7156 1.631 1.6155 1.6622 1.7253 1.7948 1.8452 1.8471 1.8265 1.8243 1.829 1.821 1.7945 1.7608 1.7424 1.7739 1.8042 1.8025 1.7647 1.7884 1.7199 1.6399 1.6187 1.5257 1.4812 1.2842
1.8504 1.9481 1.8142 1.623 1.5629 1.5815 1.6657 1.6092 1.4595 1.6218 1.6106 1.616 1.5526 1.4844 1.5109 1.58 1.6898 1.6298 1.6488 1.7278 1.8021 1.8665 1.87 1.8581 1.8016 1.7835 1.7841 1.7801 1.7819 1.7764 1.7749 1.9239 1.9523 1.8323 1.6768 1.5304 1.552 1.6884 1.7575 1.7352 1.6932 1.6784 1.6632 1.6728 1.6834 1.6499 1.5647 1.4792 1.464 1.5408 1.5667 1.585 1.5193 1.5302 1.521 1.547 1.6239 1.617 1.6157 1.3463
1.9166 1.9542 1.7825 1.6509 1.6065 1.6484 1.5682 1.516 1.5893 1.7499 1.6803 1.6585 1.6021 1.6244 1.7612 1.8743 1.9066 1.7748 1.7269 1.6588 1.6946 1.7864 1.8116 1.8385 1.8115 1.7862 1.79 1.7827 1.7824 1.7816 1.788 1.9814 1.9986 1.9062 1.8112 1.6611 1.6607 1.6576 1.6193 1.5734 1.4981 1.4972 1.4946 1.5342 1.528 1.4731 1.3732 1.3211 1.3777 1.5079 1.5623 1.5998 1.5027 1.5805 1.6072 1.6998 1.7398 1.5856 1.4798 1.1696
1.9718 1.9024 1.7486 1.6327 1.5554 1.5326 1.4868 1.5452 1.5569 1.7102 1.7275 1.6588 1.5973 1.652 1.8161 1.992 2.0591 1.9045 1.7981 1.636 1.5992 1.6011 1.6569 1.7723 1.8303 1.8428 1.826 1.8099 1.799 1.7895 1.8078 1.9838 1.9552 1.8416 1.7234 1.6502 1.7873 1.8041 1.7585 1.6811 1.6493 1.7079 1.6961 1.76 1.7486 1.7215 1.6148 1.5833 1.6515 1.7875 1.8199 1.8953 1.7411 1.7774 1.6862 1.6121 1.5662 1.3878 1.3675 1.1452
1.9895 1.8695 1.7354 1.6164 1.521 1.4867 1.3927 1.3928 1.5567 1.685 1.6349 1.5559 1.4853 1.4887 1.6089 1.7948 1.8511 1.856 1.8612 1.6197 1.5293 1.5269 1.5909 1.7345 1.8118 1.8539 1.8492 1.8382 1.8355 1.8258 1.8308 1.992 1.9645 1.858 1.7007 1.5956 1.6818 1.6769 1.7436 1.7267 1.7009 1.7286 1.6883 1.7361 1.6631 1.5764 1.4217 1.3542 1.394 1.4871 1.497 1.5669 1.3904 1.4769 1.4587 1.4883 1.4858 1.3626 1.3637 1.1397
1.9658 1.8253 1.7158 1.6026 1.5273 1.4248 1.3502 1.5008 1.6171 1.689 1.67 1.5043 1.3593 1.3538 1.462 1.6324 1.7563 1.789 1.7637 1.6482 1.6253 1.5967 1.6069 1.6889 1.7807 1.8583 1.8779 1.8603 1.8404 1.8278 1.8249 1.9926 2.0002 1.8689 1.7111 1.6284 1.6427 1.5684 1.5812 1.4755 1.4647 1.4678 1.4044 1.4595 1.412 1.3782 1.2659 1.2246 1.3053 1.4509 1.4608 1.5196 1.2777 1.432 1.4103 1.4634 1.4968 1.3499 1.3615 1.1278
1.9263 1.8227 1.6803 1.5577 1.424 1.2852 1.3438 1.489 1.5989 1.6819 1.6099 1.5352 1.4331 1.3722 1.4568 1.6317 1.7726 1.8846 1.8525 1.7608 1.7611 1.5899 1.5113 1.5805 1.6781 1.8174 1.896 1.869 1.8531 1.8527 1.8421 2.0245 2.0579 1.8924 1.7322 1.6308 1.5718 1.5262 1.5669 1.4879 1.53 1.5367 1.4222 1.4289 1.4198 1.4204 1.278 1.2933 1.385 1.4903 1.445 1.4696 1.2356 1.4024 1.4044 1.4927 1.5216 1.3585 1.3529 1.1158
1.8905 1.831 1.6824 1.5419 1.4087 1.3294 1.4185 1.5425 1.584 1.6032 1.5671 1.5357 1.45 1.4004 1.4532 1.5919 1.6781 1.7563 1.7541 1.7052 1.5816 1.5158 1.4452 1.5269 1.709 1.7679 1.8574 1.858 1.853 1.8668 1.8745 2.0951 2.1373 1.8946 1.7576 1.6484 1.5449 1.5426 1.5913 1.5619 1.5284 1.4473 1.336 1.3568 1.3653 1.404 1.3376 1.3778 1.3541 1.3672 1.3391 1.3786 1.2279 1.4183 1.3139 1.4206 1.4779 1.3612 1.3586 1.0868
1.917 1.8668 1.6915 1.5327 1.4242 1.368 1.4579 1.4983 1.511 1.5495 1.5201 1.4799 1.4192 1.3734 1.4299 1.556 1.5834 1.6388 1.5963 1.5263 1.4544 1.4619 1.4535 1.5947 1.6585 1.7142 1.8452 1.8928 1.8858 1.8576 1.8341 2.0926 2.1147 1.8891 1.8156 1.6201 1.5393 1.5672 1.5882 1.4786 1.3835 1.3582 1.2638 1.3326 1.3466 1.32 1.3137 1.3278 1.2528 1.3087 1.3531 1.4032 1.2257 1.3441 1.2946 1.4121 1.4569 1.3334 1.3218 1.0697
1.9856 1.885 1.683 1.4767 1.4289 1.3755 1.4277 1.4545 1.5094 1.5286 1.4643 1.4363 1.4145 1.3391 1.347 1.5584 1.5673 1.5255 1.4836 1.4412 1.3961 1.5431 1.4825 1.4713 1.5393 1.6021 1.6962 1.8029 1.8441 1.869 1.8619 2.1288 2.1012 1.9317 1.8648 1.6056 1.552 1.5378 1.5063 1.4024 1.4189 1.4585 1.39 1.3643 1.3435 1.3315 1.3225 1.2709 1.2953 1.4182 1.44 1.546 1.3484 1.3486 1.3403 1.3605 1.4483 1.3152 1.3103 1.0803
1.9876 1.8314 1.6173 1.4581 1.4429 1.4201 1.469 1.488 1.4823 1.4552 1.4222 1.4083 1.3904 1.3463 1.454 1.7207 1.6724 1.6646 1.6643 1.6126 1.6694 1.6865 1.4888 1.4763 1.4987 1.5069 1.5734 1.666 1.7646 1.875 1.9132 2.1534 2.0909 2.0016 1.8857 1.5804 1.5359 1.5154 1.4657 1.4319 1.4677 1.4224 1.4172 1.4069 1.3475 1.3327 1.3541 1.2725 1.2961 1.349 1.2945 1.4465 1.3702 1.4069 1.4438 1.3881 1.4306 1.317 1.3143 1.0783
1.9352 1.7877 1.5833 1.4719 1.4508 1.445 1.4685 1.4565 1.4132 1.3817 1.3952 1.4228 1.4817 1.4703 1.4945 1.6832 1.5584 1.4629 1.5513 1.5531 1.594 1.7298 1.7033 1.7189 1.7036 1.5198 1.4568 1.5389 1.6659 1.8203 1.9372 2.1694 2.1318 2.0582 1.8496 1.5551 1.4676 1.454 1.455 1.4411 1.4048 1.3032 1.2567 1.2889 1.3257 1.3366 1.3599 1.2766 1.2764 1.3186 1.2059 1.4194 1.3033 1.3579 1.3815 1.3417 1.4144 1.3192 1.3359 1.1046
1.9157 1.7952 1.5938 1.4686 1.4589 1.4516 1.4509 1.3998 1.3541 1.3564 1.4322 1.54 1.5431 1.4581 1.5228 1.7343 1.5698 1.5406 1.5822 1.5232 1.7567 1.8832 1.87 1.8486 1.8718 1.7944 1.6905 1.6593 1.6054 1.6137 1.7379 2.0724 2.1621 2.0718 1.8244 1.5232 1.3896 1.4238 1.4591 1.457 1.4792 1.446 1.3013 1.2513 1.2636 1.2904 1.2847 1.2676 1.2317 1.3178 1.2886 1.4325 1.2511 1.3426 1.365 1.3456 1.4505 1.3416 1.3445 1.1326
1.9853 1.8293 1.5774 1.4722 1.4683 1.4633 1.4354 1.3796 1.3796 1.4773 1.5334 1.4774 1.3961 1.3413 1.462 1.7804 1.6913 1.6056 1.6101 1.5366 1.7499 1.9008 1.9371 1.967 1.907 1.914 1.919 1.8043 1.6178 1.5549 1.6246 1.9618 2.1517 2.0578 1.8 1.5468 1.4121 1.4074 1.4949 1.4737 1.4358 1.4151 1.3164 1.2673 1.3243 1.3744 1.301 1.2762 1.2916 1.3664 1.2874 1.4714 1.3097 1.4246 1.3886 1.3438 1.4163 1.3154 1.342 1.1544
1.9702 1.7706 1.5321 1.4663 1.4692 1.4266 1.4042 1.4003 1.4067 1.3136 1.1804 1.2212 1.3609 1.5411 1.6611 1.8619 1.6448 1.5654 1.5199 1.4532 1.6247 1.6922 1.6923 1.7792 1.9236 2.0786 1.9709 1.7943 1.7443 1.6641 1.7391 2.044 2.0798 1.9768 1.7727 1.4866 1.3546 1.3787 1.4516 1.4496 1.475 1.4192 1.3132 1.3696 1.429 1.403 1.2241 1.2071 1.2705 1.4021 1.3768 1.5232 1.3703 1.4823 1.4306 1.3736 1.4323 1.327 1.3533 1.1737
1.9103 1.7056 1.502 1.4693 1.4481 1.4165 1.4431 1.4105 1.361 1.2686 1.2807 1.4197 1.5343 1.6833 1.6565 1.8251 1.6653 1.6455 1.6639 1.6478 1.8038 1.8146 1.7666 1.7347 1.6891 1.7931 1.9111 1.9509 2.0506 1.9691 1.8714 2.1053 2.0647 1.8841 1.724 1.4351 1.3397 1.3401 1.4137 1.4634 1.5271 1.5521 1.5281 1.5178 1.4179 1.3325 1.169 1.1582 1.2362 1.3173 1.3584 1.6034 1.437 1.4806 1.4429 1.3808 1.4554 1.3467 1.3654 1.1865
1.9231 1.6812 1.4807 1.4611 1.4436 1.4232 1.4032 1.3356 1.354 1.3416 1.4123 1.6242 1.6804 1.7247 1.6637 1.7495 1.6645 1.684 1.736 1.7478 1.8746 1.9516 1.9328 1.8947 1.8401 1.8172 1.7645 1.8217 2.0136 2.0211 1.9185 2.127 2.1218 1.8971 1.7537 1.4736 1.3799 1.3909 1.4073 1.4277 1.427 1.4334 1.4605 1.3883 1.3191 1.3073 1.1948 1.1671 1.2345 1.2458 1.2297 1.5086 1.3748 1.495 1.4765 1.3735 1.4362 1.3382 1.355 1.1839
1.8695 1.6299 1.5097 1.4913 1.4648 1.4524 1.4721 1.4284 1.4835 1.4603 1.3511 1.3678 1.3622 1.542 1.6949 1.855 1.7538 1.7208 1.8011 1.8303 1.8867 1.9381 1.9406 1.9504 1.9553 1.9074 1.8483 1.7588 1.7497 1.7509 1.8142 2.1863 2.1279 1.9691 1.7445 1.4757 1.4045 1.406 1.4282 1.4392 1.4262 1.4713 1.4265 1.3656 1.3709 1.3624 1.3051 1.21 1.2503 1.301 1.2612 1.5512 1.408 1.5298 1.5097 1.3912 1.4461 1.3292 1.3467 1.1941
1.8465 1.5249 1.4449 1.4574 1.4517 1.4365 1.4279 1.3915 1.3972 1.3525 1.2258 1.2626 1.3992 1.6116 1.6969 1.7624 1.7105 1.7247 1.8078 1.8826 1.9047 1.9302 1.9296 1.9295 1.9342 1.9255 1.9144 1.9123 1.8786 1.9235 1.9113 2.2338 2.1659 1.9695 1.7209 1.4585 1.3895 1.3974 1.4386 1.4521 1.4767 1.5761 1.5174 1.4431 1.3946 1.3157 1.2964 1.2021 1.2552 1.299 1.2587 1.5627 1.4416 1.5677 1.4999 1.3822 1.4379 1.3408 1.369 1.2174
1.7377 1.5489 1.4504 1.4446 1.4367 1.4228 1.4265 1.3825 1.3843 1.4169 1.4132 1.3282 1.3744 1.5934 1.5911 1.6822 1.6843 1.6815 1.824 1.9042 1.9291 1.9368 1.9399 1.9441 1.9455 1.9449 1.9505 1.9556 1.9357 1.9197 1.9164 2.1712 2.1087 1.9507 1.7137 1.4651 1.4093 1.3977 1.4274 1.4366 1.4733 1.5804 1.5107 1.4452 1.3746 1.2658 1.2767 1.1839 1.2489 1.2899 1.2645 1.572 1.4355 1.5509 1.4864 1.3827 1.4361 1.3451 1.3582 1.2274
1.835 1.5695 1.4477 1.4526 1.4546 1.428 1.4175 1.3743 1.3918 1.4189 1.6381 1.629 1.5785 1.669 1.559 1.6069 1.6874 1.637 1.7786 1.9045 1.9246 1.9358 1.9365 1.9334 1.9388 1.9415 1.9449 1.9499 1.9517 1.9239 1.9489 2.1836 2.1034 1.9469 1.7214 1.4814 1.4113 1.392 1.4083 1.3994 1.4523 1.5153 1.4687 1.4469 1.4104 1.3645 1.2729 1.1313 1.2329 1.2901 1.3093 1.5923 1.4521 1.5554 1.4863 1.3859 1.4469 1.3667 1.3758 1.244
1.7588 1.5494 1.4475 1.4609 1.4635 1.4513 1.4295 1.3558 1.3891 1.5029 1.6669 1.4604 1.4571 1.6332 1.6155 1.6249 1.7237 1.676 1.7661 1.9062 1.9303 1.9299 1.9343 1.9339 1.9356 1.9357
1.9399 1.9368 1.9324 1.9323 1.9707 2.2249 2.1534 1.9684 1.7185 1.4677 1.4016 1.3907 1.4002 1.3971 1.4361 1.482 1.4965 1.4895 1.4345 1.3786 1.2325 1.1348 1.2729 1.3491 1.3939 1.6103
1.4569 1.5429 1.4794 1.3751 1.4531 1.3761 1.3766 1.2879
1.5836 1.3986 1.4101 1.4534 1.4514 1.4344 1.4165 1.3953 1.3479 1.3297 1.5189 1.5189 1.4064 1.4503 1.5792 1.616 1.7529 1.7369 1.7696 1.8899 1.8913 1.8967 1.8953 1.8923 1.9045 1.9088
1.9262 1.9272 1.9171 1.922 1.9503 2.1737 2.0751 1.8658 1.6478 1.4495 1.387 1.3941 1.4059 1.4269 1.464 1.4615 1.4784 1.497 1.4684 1.3926 1.1966 1.1657 1.3153 1.3835 1.4016 1.6064 1.4501
1.5318 1.4767 1.3676 1.4452 1.3772 1.378 1.2887
1.5176 1.3642 1.4154 1.4471 1.4366 1.4456 1.4233 1.3498 1.2585 1.2528 1.303 1.3377 1.2891 1.4669 1.5983 1.5876 1.7537 1.7384 1.7287 1.8458 1.8231 1.8255 1.8189 1.8348 1.8422 1.8289
1.8588 1.8466 1.8144 1.8405 1.8499 2.0068 1.9354 1.7628 1.5565 1.4097 1.3783 1.3899 1.4072 1.4185 1.4202 1.4261 1.439 1.4635 1.4589 1.4363 1.2733 1.244 1.3186 1.3545 1.3677 1.5871
1.4237 1.5007 1.4599 1.3782 1.4479 1.3584 1.3603 1.2931
1.4933 1.3545 1.4086 1.4475 1.4412 1.4261 1.4332 1.4151 1.4321 1.4915 1.6696 1.789 1.4317 1.4522 1.5846 1.5284 1.751 1.7222 1.6742 1.76 1.7323 1.7329 1.7217 1.7293 1.7304 1.7289 1.744
1.7348 1.7231 1.7148 1.7376 1.9066 1.8411 1.722 1.5431 1.4019 1.3994 1.3894 1.4037 1.4292 1.4243 1.4265 1.4424 1.4696 1.4248 1.3648 1.2347 1.2003 1.2879 1.3947 1.4568 1.611 1.4322
1.5084 1.4414 1.3938 1.4273 1.3333 1.3401 1.2984
1.406 1.3352 1.4189 1.4425 1.4034 1.3979 1.4044 1.4398 1.427 1.4651 1.6181 1.6343 1.5264 1.468 1.5141 1.3926 1.7048 1.7776 1.7597 1.8364 1.811 1.8233 1.8346 1.8316 1.7893 1.757 1.7871
1.8116 1.8102 1.8128 1.8374 2.0004 1.9116 1.7458 1.5484 1.3866 1.3737 1.3657 1.382 1.4222 1.4728 1.4908 1.4475 1.4284 1.4021 1.4143 1.2765 1.2462 1.3751 1.4415 1.4438 1.4996 1.3425
1.4662 1.4079 1.3756 1.4284 1.34 1.3338 1.3145
1.3506 1.3617 1.4272 1.4416 1.3626 1.3924 1.3784 1.4804 1.5483 1.3818 1.5988 1.6499 1.5001 1.4675 1.4646 1.4366 1.7301 1.8121 1.7733 1.787 1.7593 1.7877 1.8322 1.8007 1.7189 1.7028
1.7249 1.7974 1.8375 1.7895 1.7981 1.9527 1.8411 1.7007 1.5118 1.3762 1.3869 1.379 1.4294 1.5141 1.5055 1.4442 1.3755 1.3507 1.3255 1.3458 1.3192 1.3882 1.438 1.4114 1.3698 1.3901
1.2829 1.412 1.4051 1.3898 1.4233 1.3665 1.3399 1.3208
1.29 1.3615 1.4355 1.4536 1.3713 1.3913 1.3988 1.4288 1.5733 1.5996 1.6342 1.7101 1.5936 1.4901 1.4577 1.4596 1.6846 1.7203 1.6543 1.637 1.5995 1.6168 1.6186 1.5123 1.4852 1.5351 1.562
1.5953 1.5742 1.54 1.5799 1.7707 1.7015 1.6134 1.4702 1.3689 1.39 1.3824 1.4791 1.477 1.3885 1.3239 1.2492 1.3093 1.3422 1.3215 1.3075 1.3158 1.2721 1.2648 1.2568 1.3707 1.3066 1.3723
1.3849 1.3989 1.4286 1.3612 1.3186 1.3237
1.293 1.3605 1.4206 1.4463 1.3775 1.3013 1.2175 1.4001 1.374 1.5206 1.7591 1.768 1.7559 1.5582 1.5098 1.4427 1.6027 1.7292 1.7976 1.7319 1.701 1.7088 1.7158 1.6992 1.6767 1.6547 1.6825
1.708 1.6488 1.628 1.6959 1.8196 1.7508 1.6657 1.4944 1.3967 1.4063 1.4014 1.4991 1.4302 1.403 1.4104 1.3672 1.3457 1.3169 1.3536 1.3386 1.306 1.3196 1.3715 1.3735 1.3909 1.2745 1.3422
1.3216 1.3584 1.4052 1.3531 1.3048 1.3255
1.2984 1.3291 1.3194 1.3404 1.3238 1.364 1.3912 1.5294 1.7886 1.6827 1.5945 1.7274 1.8407 1.6951 1.5234 1.4298 1.4601 1.6215 1.8291 1.8619 1.8299 1.8461 1.8495 1.8182 1.8312 1.8059
1.7614 1.7445 1.7447 1.7434 1.7856 1.9286 1.8029 1.6821 1.4948 1.3896 1.397 1.4126 1.5138 1.422 1.4487 1.4084 1.4198 1.4176 1.3395 1.3608 1.3154 1.2897 1.4036 1.4524 1.4959 1.4375
1.2573 1.3371 1.3145 1.3298 1.3803 1.338 1.3021 1.3243
1.2636 1.2937 1.4037 1.5047 1.5303 1.5459 1.5306 1.5606 1.5171 1.784 1.8303 1.7271 1.8789 1.782 1.4572 1.369 1.3765 1.4856 1.5858 1.6035 1.6166 1.6288 1.6177 1.6258 1.629 1.6334 1.6987
1.7559 1.7402 1.7461 1.8272 1.955 1.8542 1.7267 1.4981 1.3942 1.385 1.4282 1.5129 1.383 1.3778 1.2271 1.2235 1.2627 1.284 1.3542 1.2798 1.2461 1.2852 1.2056 1.2631 1.2957 1.2426 1.3355
1.3046 1.3224 1.3873 1.3261 1.2785 1.3079
1.2924 1.3908 1.4097 1.5466 1.5438 1.5577 1.5388 1.5454 1.4808 1.3675 1.488 1.9377 1.99 1.8046 1.5995 1.4093 1.3539 1.4663 1.5985 1.6197 1.661 1.6639 1.6441 1.6387 1.6335 1.6316 1.6302
1.6141 1.6245 1.6185 1.7098 1.9183 1.8128 1.7086 1.5 1.3789 1.3625 1.4083 1.4785 1.3909 1.424 1.3107 1.336 1.2945 1.2551 1.3183 1.2696 1.2849 1.3317 1.2422 1.3216 1.3758 1.2647 1.3332
1.3207 1.3492 1.384 1.3178 1.2836 1.3152
1.2371 1.3343 1.3481 1.4262 1.3651 1.2012 1.2024 1.3867 1.637 1.8075 1.7165 1.8494 1.9335 1.816 1.6097 1.5736 1.5107 1.51 1.5931 1.6028 1.6937 1.7082 1.7103 1.7296 1.7178 1.7008 1.704
1.7086 1.705 1.6943 1.7871 1.9431 1.8133 1.7114 1.4702 1.3585 1.3408 1.3759 1.4479 1.4133 1.4244 1.399 1.3612 1.2255 1.22 1.293 1.3091 1.3244 1.351 1.3845 1.352 1.2881 1.2138 1.2831
1.3208 1.3576 1.3835 1.3241 1.2848 1.3198
1.1889 1.3314 1.4525 1.6464 1.7956 1.895 1.8581 1.6671 1.6832 1.7111 1.8917 2.1598 1.9369 1.806 1.6363 1.5799 1.4728 1.5177 1.5794 1.5957 1.7141 1.7373 1.7368 1.7387 1.7218 1.7201
1.7079 1.6979 1.7028 1.6982 1.7795 1.9332 1.8242 1.7096 1.4462 1.3515 1.335 1.3613 1.4151 1.4115 1.4051 1.3187 1.2453 1.2242 1.2756 1.3349 1.3283 1.3103 1.3098 1.301 1.2819 1.2635 1.21
1.3042 1.3371 1.3463 1.3624 1.3162 1.2831 1.3255
1.144 1.2604 1.4147 1.6842 1.7683 1.73 1.8963 1.9365 1.8705 1.7615 1.775 1.9814 1.9005 1.8483 1.6051 1.5223 1.385 1.4353 1.5271 1.5952 1.711 1.7211 1.7271 1.7436 1.7476 1.7538 1.7565
1.759 1.7403 1.7142 1.8003 1.9298 1.8237 1.708 1.4564 1.3654 1.3455 1.3666 1.3921 1.4207 1.4088 1.3846 1.3046 1.3159 1.3504 1.3538 1.297 1.2869 1.3334 1.3431 1.3019 1.3634 1.3204 1.3502
1.3553 1.3257 1.3585 1.3306 1.2879 1.3203

Max Pooling 30x30 With Flatten 900 :-
==============

1.5531 1.5577 1.5714 1.5883 1.5913 1.5986 1.6036 1.6113 1.6222 1.6285 1.6206 1.629 1.6283 1.6293 1.6325 1.6351 1.636 1.6329 1.6373 1.6409 1.6467 1.6551 1.662 1.663 1.668 1.6766 1.6826
1.6857 1.6932 1.7024 1.5612 1.5762 1.5949 1.6099 1.6169 1.6229 1.6282 1.6263 1.631 1.6325 1.6372 1.6452 1.6459 1.6407 1.6465 1.6472 1.6423 1.645 1.6493 1.6475 1.6562 1.6597 1.6705
1.6772 1.6807 1.6821 1.6942 1.6977 1.7019 1.708 1.5855 1.6003 1.6143 1.6234 1.6287 1.6376 1.6447 1.6497 1.6533 1.6522 1.652 1.6559 1.7308 1.658 1.6417 1.7086 1.6541 1.6582 1.6583 1.6624
1.6738 1.6758 1.679 1.6875 1.6945 1.6948 1.7022 1.703 1.7086 1.7161 1.6051 1.6183 1.6308 1.6457 1.6532 1.6497 1.6952 1.6893 1.6665 1.6696 1.6722 1.6739 1.736 1.6775 1.6438 1.7535 1.7383
1.6742 1.672 1.6729 1.6822 1.6839 1.692 1.6956 1.6954 1.7026 1.7057 1.7121 1.7232 1.7263 1.62 1.6336 1.6516 1.6635 1.7253 1.6487 1.7652 1.7942 1.6873 1.6754 1.6875 1.6905 1.7262 1.7358
1.7314 1.7654 1.7119 1.6875 1.688 1.6916 1.6971 1.6965 1.6985 1.7045 1.7105 1.72 1.721 1.719 1.7274 1.7315 1.6454 1.6549 1.6704 1.7404 1.8715 1.578 1.5953 1.6542 1.7157 1.791 1.6987
1.6986 1.7251 1.7221 1.7327 1.7567 1.7077 1.7051 1.7027 1.7031 1.7044 1.7069 1.7128 1.7189 1.7225 1.7222 1.7303 1.7342 1.7367 1.7381 1.6621 1.6725 1.6882 1.8636 1.8916 1.6562 1.5554
1.5547 1.694 1.7984 1.7479 1.7088 1.7409 1.7259 1.7428 1.7433 1.7101 1.7176 1.7222 1.7204 1.7218 1.7276 1.7317 1.7334 1.7364 1.7395 1.7411 1.7432 1.7457 1.7479 1.6821 1.6959 1.7011
1.9202 1.8304 1.5314 1.4303 1.485 1.4637 1.6636 1.7611 1.72 1.7716 1.7208 1.7705 1.7334 1.7219 1.7307 1.7358 1.7356 1.7395 1.7381 1.7393 1.7435 1.7469 1.7545 1.7588 1.7584 1.763 1.7663
1.6965 1.7105 1.7523 1.9239 1.7501 1.4631 1.4797 1.5862 1.4253 1.4987 1.7195 1.7384 1.8044 1.7583 1.7963 1.7412 1.7397 1.7462 1.7454 1.7467 1.754 1.7556 1.7559 1.7579 1.7644 1.7653
1.7697 1.77 1.7668 1.7704 1.7182 1.7259 1.9123 1.9647 1.6969 1.4192 1.6629 1.6037 1.2724 1.512 1.7373 1.7494 1.8051 1.7214 1.7444 1.7492 1.7488 1.756 1.7633 1.7607 1.7632 1.7692 1.7704
1.7719 1.7772 1.7763 1.7704 1.7784 1.7826 1.7802 1.7307 1.768 1.9978 1.8889 1.5161 1.4157 1.7181 1.6038 1.2916 1.5824 1.7635 1.7583 1.7812 1.8133 1.781 1.761 1.7635 1.766 1.7659 1.7747
1.7736 1.773 1.7779 1.7944 1.7937 1.7918 1.7938 1.8085 1.8064 1.813 1.7502 1.9001 1.9505 1.6068 1.6498 1.6877 1.7677 1.646 1.6234 1.7537 1.8111 1.7742 1.7744 1.7749 1.7755 1.7744 1.7804
1.7658 1.8042 1.8052 1.8018 1.8351 1.8298 1.8158 1.7944 1.7994 1.8262 1.9479 2.0656 1.9799 1.8973 1.8891 1.682 1.7331 1.723 1.6007 1.6886 1.8202 1.8248 1.8793 1.8887 1.8721 1.7823
1.7823 1.7791 1.878 1.8846 1.7582 1.7336 1.8025 1.8471 1.8424 1.8314 1.8088 1.8141 1.8719 1.9639 2.0306 1.9129 1.5656 1.9542 1.8142 1.6484 1.6657 1.7499 1.6803 1.6244 1.8743 1.9066
1.7278 1.8665 1.87 1.8115 1.79 1.7824 1.9814 1.9986 1.8112 1.6884 1.7575 1.6932 1.6728 1.6834 1.5647 1.5408 1.5998 1.5805 1.6998 1.7398 1.6157 1.9895 1.7486 1.5554 1.5452 1.7102 1.7275
1.652 1.992 2.0591 1.8612 1.6011 1.7723 1.8539 1.8492 1.8355 1.992 1.9645 1.7234 1.8041 1.7585 1.7286 1.76 1.7486 1.6148 1.7875 1.8953 1.7774 1.6862 1.5662 1.3675 1.9658 1.7158 1.5273
1.5008 1.689 1.67 1.4331 1.6324 1.8846 1.8525 1.7611 1.6889 1.8583 1.896 1.8531 2.0245 2.0579 1.7322 1.6427 1.5812 1.5367 1.4595 1.4204 1.2933 1.4903 1.5196 1.432 1.4927 1.5216 1.3615
1.917 1.6915 1.4242 1.5425 1.6032 1.5671 1.45 1.5919 1.7563 1.7541 1.5816 1.5947 1.7679 1.8928 1.8858 2.0951 2.1373 1.8156 1.5672 1.5913 1.5284 1.3568 1.404 1.3778 1.3672 1.4032 1.4183
1.4206 1.4779 1.3586 1.9876 1.683 1.4429 1.488 1.5286 1.4643 1.4145 1.7207 1.6724 1.6643 1.6865 1.4888 1.6021 1.8029 1.875 2.1534 2.1012 1.8857 1.552 1.5063 1.4677 1.4172 1.3475 1.3541
1.4182 1.546 1.4069 1.4438 1.4483 1.3143 1.9352 1.5938 1.4589 1.4685 1.4132 1.54 1.5431 1.7343 1.5698 1.5822 1.8832 1.87 1.8718 1.6905 1.8203 2.1694 2.1621 1.8496 1.4676 1.4591 1.4792
1.3013 1.3366 1.3599 1.3186 1.4325 1.3579 1.3815 1.4505 1.3445 1.9853 1.5774 1.4692 1.4354 1.4773 1.5334 1.5411 1.8619 1.6913 1.6101 1.9008 1.967 2.0786 1.9709 1.7443 2.044 2.1517 1.8
1.4121 1.4949 1.475 1.3696 1.429 1.301 1.4021 1.5232 1.4823 1.4306 1.4323 1.3533 1.9231 1.502 1.4481 1.4431 1.361 1.6242 1.7247 1.8251 1.684 1.7478 1.9516 1.9328 1.8401 1.9509 2.0506
2.127 2.1218 1.7537 1.3909 1.4634 1.5521 1.5281 1.4179 1.1948 1.3173 1.6034 1.495 1.4765 1.4554 1.3654 1.8695 1.5097 1.4648 1.4721 1.4835 1.3678 1.6116 1.855 1.7538 1.8826 1.9381 1.9504
1.9553 1.9144 1.9235 2.2338 2.1659 1.7445 1.406 1.4521 1.5761 1.5174 1.3946 1.3051 1.301 1.5627 1.5677 1.5097 1.4461 1.369 1.835 1.4526 1.4546 1.4265 1.4189 1.6381 1.669 1.6822 1.6874
1.9045 1.9368 1.9441 1.9455 1.9556 1.9517 2.1836 2.1087 1.7214 1.4113 1.4366 1.5804 1.5107 1.4104 1.2767 1.2901 1.5923 1.5554 1.4864 1.4469 1.3758 1.7588 1.4609 1.4635 1.4295 1.5029
1.6669 1.6332 1.6249 1.7529 1.9062 1.9303 1.9343 1.9357 1.9399 1.9324 2.2249 2.1534 1.7185 1.4016 1.4269 1.482 1.497 1.4684 1.2325 1.3835 1.6103 1.5429 1.4794 1.4531 1.378 1.5176 1.4475
1.4456 1.4332 1.4915 1.789 1.4669 1.5983 1.7537 1.8458 1.8255 1.8348 1.8422 1.8588 1.8405 2.0068 1.9354 1.5565 1.3994 1.4292 1.4265 1.4696 1.4589 1.2733 1.3947 1.611 1.5084 1.4599
1.4479 1.3603 1.406 1.4425 1.4034 1.4804 1.5483 1.6499 1.5264 1.5141 1.8121 1.8364 1.8233 1.8346 1.7893 1.8116 1.8375 2.0004 1.9116 1.5484 1.3869 1.5141 1.5055 1.4475 1.4143 1.3882
1.4415 1.4996 1.4662 1.4079 1.4284 1.3399 1.3615 1.4536 1.3913 1.4288 1.5996 1.768 1.7559 1.5098 1.7292 1.7976 1.7088 1.7158 1.6767 1.708 1.6488 1.8196 1.7508 1.4944 1.4063 1.4991
1.4104 1.3672 1.3536 1.3386 1.3715 1.3909 1.3723 1.3989 1.4286 1.3255 1.3291 1.5047 1.5459 1.5606 1.7886 1.8303 1.8789 1.5234 1.6215 1.8619 1.8461 1.8495 1.8312 1.7614 1.7461 1.955
1.8542 1.4981 1.4282 1.5138 1.4487 1.4198 1.3608 1.3154 1.4524 1.4959 1.3371 1.3298 1.3873 1.3243 1.3908 1.5466 1.5577 1.5454 1.8075 1.9377 1.99 1.6097 1.5107 1.6197 1.7082 1.7296
1.7178 1.7086 1.705 1.9431 1.8133 1.5 1.4083 1.4785 1.4244 1.3612 1.3183 1.3244 1.3845 1.3758 1.3332 1.3576 1.384 1.3198 1.3314 1.6842 1.895 1.9365 1.8705 2.1598 1.9369 1.6363 1.5177
1.5957 1.7373 1.7436 1.7538 1.759 1.7403 1.9332 1.8242 1.4564 1.3666 1.4207 1.4088 1.3159 1.3538 1.3283 1.3431 1.3634 1.3502 1.3553 1.3624 1.3255

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Weights Biases For Fully Connected Layer 🔗 https://github.com/AlwaysDhruv/Image_Classification/blob/main/Kernal.h
==============
Now The weigths and bias are genrated for fully connected layer means dence layer. Here are the flatten value gonna be predict by using linear algebra. But First we need to weigths and bias values.

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![image-3](https://github.com/user-attachments/assets/d41ec49a-b0da-429d-a1b3-72040980037f)

Here you can see i have give the arguments in Kernal class of object kernal of value methos to generate the value in given parameters or shape. Firts 2 arguments are stores to weigths and bias. 3rd parameter are returning their row size that is 900. 4th argument are used for columns that are count of your given datset folders or objects that are 2.
Means 900x2 values for weigths and for bias are 2.

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How It's Look :-

Weigths 900x2 :-

0.03 0.01 0.03 -0.05 0.03 -0.03 -0.03 0.05 0 -0.02 0.02 0.04 -0.01 -0.03 0.03 -0.04 0.04 -0.04 0.02 0.01 -0.02 0.02 -0.01 0.03 0.05 0.03 0.03 0.05 -0.04 0.06 -0.04 0.04 0.03 -0.01 0.06 0.05 -0.04 0.03 0.04 0.05 0.03 0.03 0.05 0.06 0.01 -0.03 0 0.01 0.01 -0.01 0.01 0 -0.05 0.04 0.01 0.02 -0.03 0.06 0.01 0.05 -0.02 0.05 0.01 -0.04 -0.05 -0.04 0.01 -0.02 -0.04 -0.01 0.04 -0.02 -0.04 -0.02 -0.03 0.05 -0.02 -0.02 0.03 -0.02 -0.01 0 -0.03 0.03 0.05 0.02 0.02 0.03 -0.05 0.02 0.03 -0.06 0.01 0.06 -0.05 0.04 -0.01 0 -0.01 -0.02 0.01 -0.03 -0.01 -0.03 0.05 -0.01 -0 0.02 -0.02 -0 0.01 -0.05 -0.05 0.02 0 0 0.06 -0.04 -0.03 0.06 -0.06 -0.03 0.04 -0.05 0.06 -0.03 0.05 0.05 -0 -0.04 0.03 0.03 -0.05 0.05 0.02 -0.04 0.02 0.01 -0.05 0.04 -0.04 -0.03 -0.01 0.02 0.05 -0.01 -0.03 0.02 -0.04 -0.04 -0.02 -0.02 -0 0.05 -0.01 -0.05 -0.04 -0.01 0.04 -0.02 -0.04 0 -0.02 0.03 -0.02 0.02 -0.02 -0.03 -0 -0.01 -0.01 -0.03 0.02 0.05 -0 -0.05 -0.03 -0.02 -0.02 0.02 -0 -0 0.04 0.03 0.01 0.05 -0.04 0.02 -0 -0.02 -0 0.05 0.02 0.03 -0.02 -0.03 -0.05 -0.05 -0.05 0.01 0.04 0 -0 -0.06 -0.05 -0.01 0.05 0.05 -0 0 -0.03 0.01 -0.05 -0.01 0.04 0.05 -0.05 -0.02 0.02 -0.04 -0.01 -0.01 0.03 -0 0.05 0.01 0.01 -0.04 -0.05 0.04 0.01 0.01 0.04 0.03 -0.01 -0 0.01 0.02 0 0.02 -0.03 0.04 0.03 0 -0.02 0.03 -0.05 0.05 0.05 -0.04 0 -0.05 -0.03 -0.04 -0.04 -0.04 0.03 -0.06 0.05 0.04 -0.04 0.02 -0.03 -0 0 -0.04 -0 -0.03 -0.03 -0.02 -0.03 0.02 -0.01 -0.01 -0.05 0.01 0.03 0.01 0.01 0.01 -0.02 0.03 -0.01 0.03 0.05 -0.02 0.03 0.05 0.02 -0.06 -0.05 0.05 -0.05 -0.05 -0.06 -0 0.04 -0.05 -0.03 0.06 -0.03 -0.01 0.03 0.02 0.01 0.01 -0.04 0.02 0.02 -0.01 0.04 0.04 -0.02 -0.02 -0.04 -0.05 -0.01 -0.01 0.01 0.02 -0.04 0.06 -0.05 0.02 0.04 0.04 -0.02 0.02 0.02 0.01 -0.01 0.02 -0.01 0.05 0.05 -0.05 0.04 0.01 -0.03 -0.02 -0.04 0.01 0.02 -0 0.05 0 0.03 0.04 0.02 -0.01 0.02 0.02 0.05 0.02 0.05 0.05 0.01 0.06 0.02 -0.04 -0.05 0.06 0.02 0.05 0.04 0.02 -0.01 -0.02 -0.02 0.02 0.03 -0.05 0.03 0.04 -0.01 0.06 0.01 0.03 0.05 0.04 0.01 -0.04 0.04 -0.05 -0.03 0.06 -0.01 -0.01 -0.02 -0.05 0.01 -0.04 -0.03 -0.05 0.02 0.02 0.02 0.04 0 0.01 -0.03 0.02 -0.02 -0.03 -0.06 -0.04 -0.02 0.06 0.05 -0.01 0.04 -0.03 0.01 -0.05 -0 0.05 0.05 -0.04 0.02 -0.05 -0.05 -0.05 -0.01 -0.03 -0.05 -0.02 0.03 -0 0.03 -0.03 -0.05 0.04 -0.01 -0.01 0 0.05 0.05 0.06 0.01 -0 -0.03 0.04 -0.05 0.05 0.02 0.03 0.05 0.05 0.03 0.03 -0.02 -0.02 0 -0.03 -0.03 -0.04 -0.03 0.02 0.01 0 -0.03 0.03 0.05 0.06 0.05 -0.05 -0.04 0.05 -0.05 0.01 -0.05 -0.05 0.04 0.04 -0.03 0.04 0.02 -0 -0.03 -0.01 0.05 0.01 -0.06 0.05 0.04 0.03 -0.02 -0.01 0.03 -0.03 0.01 -0.04 0.04 -0.03 -0.01 0.01 -0.02 -0.05 -0.04 -0.02 -0.02 -0.05 -0.03 0.04 0.04 -0.02 0.02 -0.05 0.05 -0.04 0.02 0.06 -0.03 -0.04 0 -0.04 -0.04 0.04 -0.01 0.06 -0 0.01 -0.03 0.03 0.05 0.05 0.04 0.02 -0.03 0.04 -0.01 0.02 0.01 -0.05 0.05 0.04 -0.01 -0.04 0 -0.05 0.03 0.03 0.05 -0.04 -0.01 0.04 0.01 -0.01 -0.06 -0.02 -0.03 0.01 -0.02 -0.01 0.02 -0.02 -0.01 -0.03 -0.02 -0.05 0.01 0.04 0 0.02 -0.04 -0 -0.05 0.05 0.05 -0.03 0.04 -0.04 -0 -0.06 -0.02 0.04 0.05 0.05 -0 -0.04 0.04 0.03 0.03 0.05 -0.01 -0.06 0.02 0.04 0.02 0.05 0 0.05 -0.05 -0.01 0.05 -0.03 0.04 -0.04 0.01 0.03 -0.02 -0.04 0 -0.03 0.03 -0.01 0.01 0.02 0.05 -0.02 -0.01 0.05 -0 0.02 0.01 0.04 -0.04 -0.02 0.03 0.03 0.04 -0.06 0 0.04 0.04 -0.03 0.02 0.03 -0.02 0.04 -0.02 -0.03 -0.03 0.01 -0.06 -0.04 -0.01 0.05 0.04 -0.04 0.03 -0.05 0 -0.05 -0.03 0.03 -0 -0.03 0.01 0.02 -0.02 0.01 -0.06 0.04 -0 0.05 0.02 -0.05 0.04 0.05 0.03 -0 -0.02 -0.02 -0.02 -0.04 -0.05 0.05 -0.02 0.05 -0 -0.03 0.05 -0.05 0.03 -0 -0.04 0.02 0.02 0.01 -0.04 -0.01 0.01 0.03 0.03 0.03 -0.05 0.06 -0.02 0.03 -0.03 -0.06 0.02 0.01 0.01 -0.02 -0.02 0.04 -0.05 -0.04 0.01 0.01 0.04 -0.03 0.02 -0.04 0.05 0.02 -0.02 0.04 -0.04 0.04 -0.01 0.03 -0.04 -0.02 0.03 0 0.03 0.02 -0.04 0.04 0.04 0.03 -0.03 -0.04 0.02 -0.01 0.04 0.05 -0.03 0.03 -0.04 0.04 -0.01 -0.04 -0.03 0.02 -0.01 -0.04 -0.04 -0.05 -0.04 0.05 -0.01 0.02 0.02 0.03 0.02 -0 0.03 -0.02 -0.01 -0.02 0.05 0.03 0.03 0 -0.04 -0.02 0.04 -0.03 -0.01 -0 0 0.05 -0 -0.05 -0.03 0.02 0.03 0.05 -0 -0 -0.05 0.02 -0.03 -0.03 0.03 0.01 -0.04 0.04 -0.04 0.04 -0.01 0.01 -0.03 0.04 -0.02 0.06 0.05 0 0.02 0.06 0.03 0.04 -0.03 0.04 -0.06 -0.01 -0.04 0.01 0.01 -0.02 0.05 0.03 -0.04 0.04 -0.05 -0.05 0.06 0 0.04 0.05 0.04 0.05 -0.06 -0 -0.02 0.04 0.01 -0.01 -0.03 0 0.03 0.04 0.01 0.02 -0.03 0.04 0 -0.05 0.01 0.04 0.06 -0.03 -0.04 -0 0.03 0.01 0.02 0.02 0.04 0.04 0.04 0.02 -0 0.05 0.04 -0.04 -0.04 -0 0.04 0.01 -0 0.01 0.05 0.04 0.04 -0.02 0.02 0.04 0.02 0.02 0.02 0.05 -0.04 0.03 0.05 -0.03 0.04 0.04 -0.03 -0.02 -0.04 0.04 -0.05 -0.03 0.02 0 0.03 -0.02 -0.01 -0.01 -0.03 -0.04 -0.02 -0.04 0.04 0.01 -0.05 0.01 -0.06 0.05 0.01 0.02 0.04 0.03 -0.05 0.03 0.05 -0 0.02 -0.01 0.04 0 0 -0 -0.04 -0.05 -0.05 -0.05 -0 -0 0.03 -0.04 -0.04 0.05 -0.01 0.05 0.04 -0.03 -0.03 -0 -0.04 -0.01 0.05 -0.05 0 0 0.02 -0.05 0.05 -0.01 -0.04 0 -0.01 0.05 -0 -0.02 -0.01 -0.01 -0 0.05 0.02 -0.01 -0.04 0.01 0.04 -0.05 0.02 0.01 -0.03 0.05 -0.02 0.03 0.04 -0.02 -0.01 -0.03 0.05 -0.04 0.01 0 -0.02 -0.03 0.03 -0 0.05 -0.04 -0 0.06 0.01 -0.05 0.06 0 -0.01 -0.05 -0.04 -0.04 0.02 -0.03 -0.03 -0.03 0.04 0.01 0.01 -0.04 -0.03 -0.06 0.03 -0.02 0 0.05 -0.01 0.01 0.03 0.03 -0.06 -0.03 0.04 0.04 0.05 -0.03 -0.02 -0.06 -0.01 0.01 -0.05 -0.02 0 0.04 -0.05 0.06 -0.04 0.01 -0.04 0.01 -0.02 0.05 0.01 0.01 0.01 -0.05 -0.02 0 -0.01 0.03 -0.02 -0.02 0 0.02 0.01 -0.05 0.01 0.01 0.01 0.04 0.02 0 -0.01 0.05 0 -0.05 -0.02 0.04 0.02 -0.05 0.04 -0.01 -0.01 0.05 0 -0.05 0.03 -0.01 0 0.04 0.03 -0.05 -0.05 -0.04 -0.03 0.04 0 0.04 0.03 0.05 -0.03 0.01 -0.04 -0.02 0.05 0.06 -0 -0 -0.03 -0.01 -0.04 0.05 -0.04 0.01 -0.04 0.01 -0.04 -0 0.04 -0.03 -0.03 -0.02 -0.04 -0.01 -0 0.01 0.06 -0 -0.06 -0.03 -0.01 -0.01 0.02 -0 -0.04 0.01 -0.02 -0.06 -0.01 -0.03 -0.03 -0.02 0.03 0.04 -0.03 0.02 0 -0.05 -0.04 0.01 0.04 0.02 -0.06 0.04 -0.01 0.04 -0.04 -0.01 -0.05 0.05 -0.01 0.04 -0.04 -0.05 -0.03 -0.03 0.06 0.04 -0.03 0 -0 -0.05 0 0.04 0.02 0.04 0.02 0.01 -0.01 0.01 -0.03 0.06 0 -0.03 0.03 0.04 -0.04 -0.04 -0 -0.02 -0.05 -0.05 0.04 0.05 0.02 -0.04 0.04 0.01 0.05 -0.02 -0.02 0.02 0.03 -0.05 0.06 -0.03 0.05 0.03 0 -0.04 0.04 0 0.04 0.01 -0.05 0.05 0.05 0.02 -0.06 0.04 0 0.03 -0 -0.04 0.05 -0.03 -0 -0.01 -0.05 -0.05 -0.02 -0 0.03 0.02 -0.04 0.02 -0.05 -0.04 -0.04 0.05 0.01 -0.01 0.03 0.01 -0.02 -0.04 -0.01 -0 -0.05 -0.04 0.04 0.05 0.02 -0.02 -0.02 0.02 0.02 -0.05 0.05 -0 0.02 -0.01 -0.04 -0.01 -0.01 -0.02 0 0.05 -0.05 -0 -0.05 -0.02 0 -0.04 0.01 -0 0.02 -0.04 0.03 -0.01 -0.04 -0.03 -0.03 0.04 0.04 0 0.01 0.04 0.05 0.04 -0.01 0 -0.04 -0 -0.04 0.03 -0.05 0.01 0.02 0.05 0.05 0.03 0.05 -0.06 -0.03 0.02 -0.02 -0.05 -0.03 0.02 0 0.03 0.06 -0 -0.01 -0.04 0.05 0.02 0.04 -0.01 -0.02 0.06 0.02 0.03 0.04 -0.01 0.01 0.04 -0.05 -0 -0.02 -0.01 -0 0.03 0.01 0 -0 -0.05 0.04 0.05 -0.01 0.01 -0.01 -0.05 -0.04 0 0.01 0.04 0.03 -0.02 -0.04 -0.01 -0.01 0.03 0.05 -0.04 0.04 -0.04 -0.06 -0.05 0.02 -0.02 -0.03 0.06 -0.04 -0.05 0.01 -0.05 0.05 -0.05 0.02 0.04 0 0.02 0.05 0.05 0.05 -0.02 -0.04 -0.03 0.01 0.01 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-0.05 -0.02 0.05 0.03 -0.03 -0.02 0 0.04 0.05 0 0.01 0.02 0.01 0.05 0.03 -0.05 -0.05 -0.03 -0.02 -0.04 -0.03 0.03 0.04 0.02 -0.05 0.04 -0 -0.03 0.03 -0.05 0.02 -0.03 0.02 -0.02 0.01 -0.03 -0.01 0.04 -0.02 -0.01 0.03 0.05 -0.05 0.05 -0.01 0.03 -0.01 -0.04 0.04 0.02 0.01 0.05 0.01 -0.04 0.06 0.01 0.03 0.02 0.04 -0.01 0.05 -0.03 -0.01 0.05 0.04 0.04 0.01 -0.03 0.06 -0.04 -0.05 -0.01 -0.04 0.01 -0.01 0.05 -0.04

Bias 1x2 :-

-0.01 -0
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Fully Connected Layer 🔗 https://github.com/AlwaysDhruv/Image_Classification/blob/main/Model.h
==============

Neural Network :-
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![sddefault](https://github.com/user-attachments/assets/d7ce0b56-3e84-48eb-80c7-2f64406b4674)

The Neural network are insipiration from our brain that have also nn. So the we watch any perticular objetc how we can say that its laptop, its TV, etc how it's possible? the brain are recogniza that object using that characteristic their colour their shape or everything we can noticed it in that object. That why CNNs are used Neural Network to recongnize any objects.

Formula :-

![download](https://github.com/user-attachments/assets/799cb368-3bd7-46a6-bcdf-706c29521500)

Where The,
Xi are Flatten values,
Wi are weigths,
b are bias

Source Code :-

![image](https://github.com/user-attachments/assets/46e4067e-fc7d-4f2f-a5a0-e54329d157b0)

How its look :-

![image](https://github.com/user-attachments/assets/2a53e56d-81bc-4119-9dc6-426bd2f3f508)

In dataset have 2 folder in every foldeer have 5 image so you see in image there are 10 prediction are there

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Softmax :-
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Formula :-

![582a6c51701bb584c1cdd6662cc376b9cadb7160-2048x1152](https://github.com/user-attachments/assets/679b92f1-34ad-4f5d-9d96-f79cd9f33fa6)

Graph :-

![Graphic-representation-of-the-softmax-activation-function](https://github.com/user-attachments/assets/dda3fa1f-534b-4d9b-8d0b-696dd487cdc5)

Softmax function are typically type of activation function so predict the given input.

Source Code :-

![image](https://github.com/user-attachments/assets/34fe50f8-42ae-42a3-954a-28ec5221a8a4)

How It's Look :-

![image](https://github.com/user-attachments/assets/4b1baebd-96cf-43b4-986f-4688562b8868)

Where greatest 1 out of two that value index number return and find object name in folder_name vector using index number.

How Object name are genrated :-

![image](https://github.com/user-attachments/assets/f14f1c26-0584-4497-a5c8-912ec4bad522)

The Model class of predict mothod will generate index number after prediction using model object.

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Gradients Method 🔗 https://github.com/AlwaysDhruv/Image_Classification/blob/main/backpropagation.h
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Formula :-

![conv_bias_eq](https://github.com/user-attachments/assets/3b39c6ab-5175-4b6f-8606-859ae323caf4)

Gradients are typically work for calculate one type value that can update weights and bias to make the suitable for prediction.

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Error Function :-

For Calclating gradient we need to loss for it.

Formula :-

![iSWyZ](https://github.com/user-attachments/assets/b7ce624f-d39f-46b7-add9-6301041ddce9)

Source Code :-

![image](https://github.com/user-attachments/assets/15ad885f-6bb1-40db-9ab3-cbc0c20b91ba)

temp = predicted output, labels(One Hot Values) = real output

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Gradients Source Code :-

![image](https://github.com/user-attachments/assets/41a9d120-bdcb-4e86-843d-f95b723d900e)

Here are four values are will generate for update the convo and fc parameters. Fully Connected Weigths and Bias, Convolution Weigths and Bias.

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Update The Weigths and Bias 🔗 https://github.com/AlwaysDhruv/Image_Classification/blob/main/update.h
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Formula :-

![image](https://github.com/user-attachments/assets/7e6e8e2a-9822-4d96-b131-67cdf5282848)

Source Code :-

![image](https://github.com/user-attachments/assets/7523b791-9e23-4028-8744-ca9bd13a07be)

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So it's Process are repeated when loss will optimized in give n range of training.
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