# Kala_NuroNetwork
Kala_NuroNetwork is a hybrid quantum-classical neural network framework that integrates Kala_Quantum and Kala_Torch for advanced machine learning tasks.
## Features
- **Quantum Layer**: Leverage quantum circuits with Hadamard and CNOT gates for preprocessing.
- **Classical Neural Network**: Includes fully connected layers for classical computation.
- **Trainer Class**: Train and evaluate models with ease.
- **Large Dataset Support**: Handle big data with efficient batching and parallelism.
## Installation
```bash
pip install Kala_Quantum Kala_Torch torch
```
## Usage
```python
from KalaNeroNetwork import KalaNuroNetwork, KalaNuroTrainer
import torch
import torch.nn as nn
import torch.optim as optim
# Define hyperparameters
input_size = 2
n_qubits = 2
hidden_size = 128
output_size = 2
batch_size = 128
epochs = 10
# Generate synthetic dataset
def generate_large_data(num_samples, input_size):
data = torch.rand(num_samples, input_size)
labels = (data.sum(axis=1) > 1.0).long() # Binary classification based on sum threshold
return data, labels
num_samples = 10000
data, labels = generate_large_data(num_samples, input_size)
dataset = torch.utils.data.TensorDataset(data, labels)
data_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True)
# Initialize model, optimizer, and criterion
model = KalaNuroNetwork(input_size, n_qubits, hidden_size, output_size)
optimizer = optim.Adam(model.parameters(), lr=0.001)
criterion = nn.CrossEntropyLoss()
# Train and evaluate
trainer = KalaNuroTrainer(model, optimizer, criterion, device="cpu")
print("Starting training...")
trainer.train(data_loader, epochs)
print("Evaluating model...")
trainer.evaluate(data_loader)
```
## License
This project is licensed under the MIT License. See the LICENSE file for details.
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"description": "\r\n# Kala_NuroNetwork\r\n\r\nKala_NuroNetwork is a hybrid quantum-classical neural network framework that integrates Kala_Quantum and Kala_Torch for advanced machine learning tasks.\r\n\r\n## Features\r\n\r\n- **Quantum Layer**: Leverage quantum circuits with Hadamard and CNOT gates for preprocessing.\r\n- **Classical Neural Network**: Includes fully connected layers for classical computation.\r\n- **Trainer Class**: Train and evaluate models with ease.\r\n- **Large Dataset Support**: Handle big data with efficient batching and parallelism.\r\n\r\n## Installation\r\n\r\n```bash\r\npip install Kala_Quantum Kala_Torch torch\r\n```\r\n\r\n## Usage\r\n\r\n```python\r\nfrom KalaNeroNetwork import KalaNuroNetwork, KalaNuroTrainer\r\nimport torch\r\nimport torch.nn as nn\r\nimport torch.optim as optim\r\n\r\n# Define hyperparameters\r\ninput_size = 2\r\nn_qubits = 2\r\nhidden_size = 128\r\noutput_size = 2\r\nbatch_size = 128\r\nepochs = 10\r\n\r\n# Generate synthetic dataset\r\ndef generate_large_data(num_samples, input_size):\r\n data = torch.rand(num_samples, input_size)\r\n labels = (data.sum(axis=1) > 1.0).long() # Binary classification based on sum threshold\r\n return data, labels\r\n\r\nnum_samples = 10000\r\ndata, labels = generate_large_data(num_samples, input_size)\r\ndataset = torch.utils.data.TensorDataset(data, labels)\r\ndata_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True)\r\n\r\n# Initialize model, optimizer, and criterion\r\nmodel = KalaNuroNetwork(input_size, n_qubits, hidden_size, output_size)\r\noptimizer = optim.Adam(model.parameters(), lr=0.001)\r\ncriterion = nn.CrossEntropyLoss()\r\n\r\n# Train and evaluate\r\ntrainer = KalaNuroTrainer(model, optimizer, criterion, device=\"cpu\")\r\n\r\nprint(\"Starting training...\")\r\ntrainer.train(data_loader, epochs)\r\n\r\nprint(\"Evaluating model...\")\r\ntrainer.evaluate(data_loader)\r\n```\r\n\r\n## License\r\n\r\nThis project is licensed under the MIT License. See the LICENSE file for details.\r\n",
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