Stage-2A CIFAR-10 (Subset)
the Confusion Matrix is the most honest look at our model's "brain." While accuracy gives us a single percentage, the confusion matrix tells us exactly where the model is confident and where it is confused.
Here is a breakdown of what your specific matrix is explaining:
1. The "Vertical Line" Problem (Automobile Bias)
What it shows: You have a very dark vertical column under the "automobile" category.
The Understanding: This means the model is "defaulting" to guessing automobile for almost every image, regardless of what it actually is. For example, it predicted 174 airplanes, 159 birds, and 176 frogs as "automobiles".
The Reason: This usually happens when the model finds a "shortcut" feature (like a certain color of asphalt or a specific shape) that it associates too strongly with cars, or it hasn't learned enough distinct features to tell the classes apart.
2. Successful Identifications (The Diagonal)
What it shows: In a perfect model, you would see a dark diagonal line from the top-left to the bottom-right.
The Understanding: Your model is most successful at identifying "horse" (121 correct) and "automobile" (160 correct).
The Gap: It is completely failing to identify "bird," "cat," "deer," "dog," "frog," and "ship" (almost all 0 correct).
3. Class Confusion Pairs
What it shows: High numbers outside the diagonal.
The Understanding: The model is frequently confusing "airplane" with "horse" (94 times) and "truck" with "automobile" (130 times).
Research Insight: The confusion between truck and automobile is logical because they share similar shapes and textures. However, confusing an airplane with a horse suggests the model might just be looking at the background (e.g., blue sky vs. green grass) rather than the animal or machine itself.
What we can understand for our Report
By looking at this "perspective," we can conclude that:
Technical Rigor: The model is not actually "learning" 10 classes; it is mostly learning to distinguish between "Large Metal Objects" and "Everything Else".
Impact of Small Data: With only 7,000 images, the model didn't have enough variety to learn the subtle differences between a cat and a dog, leading it to give up on those classes entirely.
Future Direction: To fix this, your report should mention that Data Augmentation (flipping or zooming) or a balanced loss function is needed to force the model to pay attention to the classes it is currently ignoring.
To finalize your Comprehensive Research Report, we will use the specific data from your images to show the mentors you didn't just run code—you performed a deep "Forensic Analysis" of your model's behavior.
Here is the drafted Analysis of Confusion section for your report:
### Phase 4: Deep Evaluation & Error Analysis
#### 1. The "Default Class" Phenomenon
Looking at the Confusion Matrix for the Baseline Configuration, there is a distinct vertical bias toward the 'automobile' class. While the model achieved a 59.4% validation accuracy in the training logs, the matrix reveals that it "cheated" by defaulting to 'automobile' for a wide variety of unrelated images, such as 174 airplanes and 176 frogs.
* Insight: This suggests that the model identified a specific high-frequency feature (likely a color or texture common in the 7,000-sample training set) and over-associated it with cars.
#### 2. Performance "Dead Zones"
The detailed classification report shows Zero Recall for several classes, including 'bird', 'cat', 'deer', 'dog', 'frog', and 'ship'.
* Insight: In a restricted data environment, the model struggled to find "discriminative features"—the unique things that make a cat look different from a dog. Instead of learning subtle differences, the model simply ignored these classes to minimize its overall Loss function.
#### 3. Comparative Perspective (Baseline vs. Dropout)
Based on the Training & Validation Curves:
* Configuration A (Baseline): Showed extreme overfitting, reaching 91.8% Train Acc but only 59.4% Val Acc. The gap of 32.4% indicates the model was memorizing the training subset rather than learning general patterns.
* Configuration B (Dropout): The gap was significantly narrowed to 13.6% (74.3% Train vs. 60.7% Val).
* Conclusion: Dropout acted as a "regularizer," preventing the model from becoming too confident in its training data and resulting in a slightly higher ability to handle the 3,000 unseen validation images.
### Final Pro-Tip for your Submission
In the Google Form, when they ask for "failed experiments," mention the Confusion Matrix error.
> "While my accuracy reached ~60%, the confusion matrix revealed a failure to distinguish between biological and mechanical classes. I identified an 'Automobile bias' where the model over-predicted cars. This taught me that accuracy is a misleading metric in small datasets and that I need to use Data Augmentation to force the model to see class-specific features."
Would you like me to help you draft the response for Stage 2B (the reasoning task about real-world impact) now that we've finished the technical analysis?
confusion matrix or improved CNN
now we'll try Data_augmentation and white decay and might increase epochs as it will take longer for model to learn with Data Augmentation
introduced data augmentation adn let's see after trainign what happens to loss and accuracy of moth the models baseline and improved cnn ,
🚀 Starting Training: Configuration A (Baseline)
Epoch 1: Train Acc 28.1% | Val Acc 39.0%
Epoch 2: Train Acc 40.3% | Val Acc 46.0%
Epoch 3: Train Acc 45.3% | Val Acc 47.7%
Epoch 4: Train Acc 48.6% | Val Acc 50.9%
Epoch 5: Train Acc 50.3% | Val Acc 52.1%
Epoch 6: Train Acc 53.4% | Val Acc 55.4%
Epoch 7: Train Acc 54.0% | Val Acc 56.7%
Epoch 8: Train Acc 56.2% | Val Acc 56.5%
Epoch 9: Train Acc 57.7% | Val Acc 58.4%
Epoch 10: Train Acc 60.1% | Val Acc 59.0%
🚀 Starting Training: Configuration B (Dropout)
Epoch 1: Train Acc 26.5% | Val Acc 35.1%
Epoch 2: Train Acc 36.6% | Val Acc 42.1%
Epoch 3: Train Acc 39.2% | Val Acc 47.2%
Epoch 4: Train Acc 42.8% | Val Acc 49.7%
Epoch 5: Train Acc 44.6% | Val Acc 50.0%
Epoch 6: Train Acc 46.4% | Val Acc 51.4%
Epoch 7: Train Acc 47.5% | Val Acc 49.9%
Epoch 8: Train Acc 49.2% | Val Acc 52.4%
Epoch 9: Train Acc 50.6% | Val Acc 54.9%
Epoch 10: Train Acc 51.6% | Val Acc 55.6%
before when we are checking, the confusion matrix of untrained model because of error in our code.
# mistake from our side that shouldn't have been there.
new start !!!
Let's See what happens now.........................................
here, we have trained our baseline model and improved CNN with dropout , which hopefully reduces the noise if model is learning noise ,
below we have results and the graph of loss and accuracy of both the models.
but before that i am facing one problem it takes too much time. even when i am just having , 10 epochs
let's find a solution for this , otherwise how i will test for higher number of epochs .
implementation 01
one improved CNN (using dropout)
time series of loss and accuracy of both the model in training and testing \
confusion matrix for both the matrix ,
let's see what will happen if we will increase no of
epoch =33
Now we will introduce , data augmentation
we have changed from cpu to t4 gpu let's see , from notebook settings / we might use parallel processing to decrease time of training.
# we have introduced it and changed from cpu to t4 gpu to increase speed of training.
# num_workers=2, # <--- ADD THIS: Uses 2 CPU cores to load data
# pin_memory=True # <--- ADD THIS: Speeds up transfer from RAM to GPU
# now we will try weight decay and penalises and reduce dropout to see what will happen
let's see the results bro
even after doing weight decay our results didn't improved well, as we assumed for.
so, now i think we should not use weight decay, as it is not improved
and we. can see clearly right now our baseline model is predicting difference between dog and cat far better .
now we have tried to increase one more layer convolutional
let's see the results ,
3 layers of convolutional neural network
5 convolutional neral layers and dropout =0.5 epoch=33 , learning_rate=0.001
increased kernel size =5 and padding =2 in convo_1. and removed weight decay. let's see and dropout 0.3
have reason what you thought
we got good result , but still model facing error in recognising cats dogs horses and all,
so now after recognizing , sudden changes after 25th epoch each iteration we did by changign differen tvariables i guess after 25th epoch we should make our learning_rate =0.0001
let's see it's result
we have got somehtign far better till now ,
let's see by increasing epoch to 50 , because i felt like it cna go better
let's seee.
i kno wi am giving model to gor mor memorizing but let;s ee validation
results are not as expected but we got conformity of
that our prevoipus values for epoch learnirng rate and all are pretty good
let's analyze everyhtign cpmingin gemini whole conversation collab and this
