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output.txt
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output.txt
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Evaluation Metrics:
Accuracy: 0.899
Classification Report:
precision recall f1-score support
0 0.89 0.81 0.85 1000
1 0.99 0.98 0.99 1000
2 0.88 0.82 0.85 1000
3 0.87 0.93 0.90 1000
4 0.82 0.86 0.84 1000
5 0.96 0.96 0.96 1000
6 0.71 0.76 0.73 1000
7 0.93 0.95 0.94 1000
8 0.99 0.99 0.99 1000
9 0.98 0.94 0.96 1000
accuracy 0.90 10000
macro avg 0.90 0.90 0.90 10000
weighted avg 0.90 0.90 0.90 10000
Model Architecture Summary:
Sequential (Input shape: 32 x 3 x 28 x 28)
============================================================================
Layer (type) Output Shape Param # Trainable
============================================================================
32 x 64 x 14 x 14
Conv2d 9408 False
BatchNorm2d 128 True
ReLU
____________________________________________________________________________
32 x 64 x 7 x 7
MaxPool2d
Conv2d 36864 False
BatchNorm2d 128 True
ReLU
Conv2d 36864 False
BatchNorm2d 128 True
Conv2d 36864 False
BatchNorm2d 128 True
ReLU
Conv2d 36864 False
BatchNorm2d 128 True
Conv2d 36864 False
BatchNorm2d 128 True
ReLU
Conv2d 36864 False
BatchNorm2d 128 True
____________________________________________________________________________
32 x 128 x 4 x 4
Conv2d 73728 False
BatchNorm2d 256 True
ReLU
Conv2d 147456 False
BatchNorm2d 256 True
Conv2d 8192 False
BatchNorm2d 256 True
Conv2d 147456 False
BatchNorm2d 256 True
ReLU
Conv2d 147456 False
BatchNorm2d 256 True
Conv2d 147456 False
BatchNorm2d 256 True
ReLU
Conv2d 147456 False
BatchNorm2d 256 True
Conv2d 147456 False
BatchNorm2d 256 True
ReLU
Conv2d 147456 False
BatchNorm2d 256 True
____________________________________________________________________________
32 x 256 x 2 x 2
Conv2d 294912 False
BatchNorm2d 512 True
ReLU
Conv2d 589824 False
BatchNorm2d 512 True
Conv2d 32768 False
BatchNorm2d 512 True
Conv2d 589824 False
BatchNorm2d 512 True
ReLU
Conv2d 589824 False
BatchNorm2d 512 True
Conv2d 589824 False
BatchNorm2d 512 True
ReLU
Conv2d 589824 False
BatchNorm2d 512 True
Conv2d 589824 False
BatchNorm2d 512 True
ReLU
Conv2d 589824 False
BatchNorm2d 512 True
Conv2d 589824 False
BatchNorm2d 512 True
ReLU
Conv2d 589824 False
BatchNorm2d 512 True
Conv2d 589824 False
BatchNorm2d 512 True
ReLU
Conv2d 589824 False
BatchNorm2d 512 True
____________________________________________________________________________
32 x 512 x 1 x 1
Conv2d 1179648 False
BatchNorm2d 1024 True
ReLU
Conv2d 2359296 False
BatchNorm2d 1024 True
Conv2d 131072 False
BatchNorm2d 1024 True
Conv2d 2359296 False
BatchNorm2d 1024 True
ReLU
Conv2d 2359296 False
BatchNorm2d 1024 True
Conv2d 2359296 False
BatchNorm2d 1024 True
ReLU
Conv2d 2359296 False
BatchNorm2d 1024 True
AdaptiveAvgPool2d
____________________________________________________________________________
32 x 512
Flatten
____________________________________________________________________________
32 x 10
Linear 5130 True
____________________________________________________________________________
Total params: 21,289,802
Total trainable params: 22,154
Total non-trainable params: 21,267,648
Optimizer used: Adam
Loss function: FlattenedLoss of CrossEntropyLoss()
Model frozen up to parameter group #2
Callbacks:
- TrainEvalCallback
- CastToTensor
- Recorder
- ProgressCallback