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Runtime error
Runtime error
Initial commit
Browse files- .gitignore +3 -0
- .vscode/settings.json +6 -0
- README.md +3 -3
- app.py +42 -0
- cnn_model_9907.bin +0 -0
- cnn_model_9961_sm.bin +0 -0
- cnn_model_9973.bin +0 -0
- networktorch.py +50 -0
- requirements.txt +4 -0
.gitignore
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.venv/
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__pycache__/
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.vscode/settings.json
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{
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"[python]": {
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"editor.defaultFormatter": "ms-python.autopep8"
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},
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"python.formatting.provider": "none"
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}
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README.md
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---
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title:
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emoji:
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colorFrom: blue
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colorTo: red
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sdk: gradio
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license: mit
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---
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title: CNN MNIST Classifier
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emoji: 🔢
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colorFrom: blue
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colorTo: red
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sdk: gradio
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license: mit
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---
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The model used is a Convolutional Neural Network made with PyTorch.
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app.py
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import gradio as gr
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import pickle
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import torch
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import numpy as np
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device = 'cuda' if torch.cuda.is_available() else 'cpu'
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with open('cnn_model_9961_sm.bin', 'rb') as f:
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nn = pickle.load(f)
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nn.to(device)
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def predict(input):
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if input is None:
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return 'None'
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x = np.array([[input]])
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x = torch.tensor(x).to(device)
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p = nn(x)
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p = p[0].cpu().detach().numpy()
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return dict(enumerate(p.tolist()))
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demo = gr.Interface(
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fn=predict,
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inputs=[
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gr.Sketchpad(
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shape=(28, 28),
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brush_radius=1.2,
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)
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],
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outputs=[
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gr.Label(
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num_top_classes=3,
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scale=2,
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)
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],
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live=True,
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allow_flagging='never',
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).launch()
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cnn_model_9907.bin
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Binary file (140 kB). View file
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cnn_model_9961_sm.bin
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Binary file (140 kB). View file
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cnn_model_9973.bin
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Binary file (140 kB). View file
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networktorch.py
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from torch import nn
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class NeuralNetworkTorch(nn.Module):
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def __init__(self):
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super().__init__()
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self.stack = nn.Sequential(
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nn.Linear(784, 64),
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nn.Sigmoid(),
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nn.Linear(64, 10),
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nn.Sigmoid()
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)
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def forward(self, x):
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return self.stack(x)
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class ConvNeuralNetworkTorch(nn.Module):
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def __init__(self):
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super().__init__()
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self.conv = nn.Sequential(
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nn.Conv2d(1, 16, kernel_size=3, stride=1, padding=1),
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nn.ReLU(),
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nn.MaxPool2d(kernel_size=2, stride=2),
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nn.Conv2d(16, 16, kernel_size=3, stride=1, padding=1),
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nn.ReLU(),
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# nn.MaxPool2d(kernel_size=2, stride=2),
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)
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self.fc = nn.Sequential(
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nn.Linear(16 * 14 * 14, 10),
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nn.Sigmoid(),
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)
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def forward(self, x):
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# we do some reshaping here simply to avoid making changes to the caller
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# so it continues to work with the fully conected network above
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x = x.reshape(-1, 1, 28, 28) / 255
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conv_output = self.conv(x)
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flat = conv_output.reshape(len(x), -1)
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final_output = self.fc(flat)
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return final_output
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requirements.txt
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numpy==1.25.2
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torch==2.0.1
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torchvision==0.15.2
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gradio==3.47.1
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