Metadata-Version: 2.1
Name: dnnpy
Version: 0.0.1
Summary: Pytorch-like Neural Network framework
Home-page: UNKNOWN
Author: Urchade Zaratiana
Author-email: urchade.zaratiana@gmail.com
License: UNKNOWN
Description: # dnnpy Framework
        
        ## Installation
        ````bash
        pip install dnnpy
        ````
        ## Example usage
        ```python
        from dnnpy.activations import ReLU
        from dnnpy.data import make_regression_data
        from dnnpy.layers import Sequential, Dense, Dropout
        from dnnpy.loss_functions import MAELoss
        from dnnpy.optimizers import Adam
        from dnnpy.train import train
        from dnnpy.utils import split_data
        import matplotlib.pyplot as plt
        
        n_inputs = 10
        hidden_units = 32
        n_outputs = 1
        
        x, y = make_regression_data(n_samples=1000, n_features=n_inputs, n_labels=1)
        
        (x_train, y_train), (x_test, y_test) = split_data(x, y, ratio=0.7)
        
        model = Sequential(Dense(in_features=n_inputs, out_features=hidden_units, activation=ReLU()),
                           Dropout(0.3),
                           Dense(in_features=hidden_units, out_features=n_outputs))
        
        opt = Adam(model.parameters(), lr=1e-3)
        loss_func = MAELoss()
        
        train_loss, valid_loss = train(data=(x_train, y_train), network=model, loss=loss_func, optimiser=opt, epochs=30,
                                       batch_size=16)
        
        plt.plot(train_loss, label='train')
        plt.plot(valid_loss, label='val')
        plt.legend()
        plt.show()
        ```
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
