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All codes and slides are based on the online bookneuralnetworkanddeeplearning.com.
From example1.py to example8.py is implemented via only numpy and use the same architecture of a simple network called multilayer perceptrons (MLP) with one hidden layer.
n is the number of unit in a hidden layer in following results.
| n=30 | n=100 |
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| n=30 | n=100 |
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| n=30 | n=100 |
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| n=30 | n=100 |
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There are also good resources for numpy-only-implementation and laucher for each recourse is provided.
| Resource | Launcher |
|---|---|
| neuralnetworkanddeeplearning.com | launcher_package1.py |
| Stanford CS231 lectures | launcher_package2.py |
Code in tf_code_mnist folder is for CNN implmentation.
ch6_summary.pdf is related slide.
| Command | Description | MNIST acc. |
|---|---|---|
train --model v0 | model v0 : BASE LINE + Softmax Layer + Cross Entropy Loss | 97.80% |
train --model v1 | model v1 : model v0 + 1 Convolutional/Pooling Layers | 98.78% |
train --model v2 | model v2 : model v1 + 1 Convolutional/Pooling Layers | 99.06% |
train --model v3 | model v3 : model v2 + ReLU | 99.23% |
train --model v4 | model v4 : model v3 + Data Augmentation | 99.37% |
train --model v5 | model v5 : model v4 + 1 Fully-Connected Layer | 99.43% |
train --model v6 | model v6 : model v5 + Dropout | 99.60% |
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Implementation of key concepts of neuralnetwork via numpy
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