Metadata-Version: 2.1
Name: mlvtk
Version: 0.0.22a7
Summary: loss surface visualization tool
Home-page: https://github.com/tm-schwartz/mlvtk
Author: tm-schwartz
Author-email: tschwartz@csumb.edu
License: MIT
Description: # MLVTK
        ### A loss surface visualization tool
        
        
        <img alt="Png" src="https://raw.githubusercontent.com/tm-schwartz/mlvtk/dev/visuals/adamax.png" width="80%" />
        
        _Simple DNN trained on MNIST data set, using Adamax optimizer_
        
        ---
        
        <img alt="Gif" src="https://raw.githubusercontent.com/tm-schwartz/mlvtk/dev/visuals/gifs/sgd3.gif" width="80%" />
        
        _Simple DNN trained on MNIST, using SGD optimizer_
        
        ---
        
        <img alt="Gif" src="https://raw.githubusercontent.com/tm-schwartz/mlvtk/dev/visuals/gifs/adam2.gif" width="80%" />
        
        _Simple DNN trained on MNIST, using Adam optimizer_
        
        ---
        
        <img alt="Gif" src="https://raw.githubusercontent.com/tm-schwartz/mlvtk/dev/visuals/gifs/sgd1.gif" width="80%" />
        
        _Simple DNN trained on MNIST, using SGD optimizer_
        
        
        
        
        ## Why?
        
        - :shipit: **Simple**: A single line addition is all thats needed.
        - :question: **Informative**: Gain insight into what your model is seeing.
        - :notebook: **Educational**: *See* how your hyperparameters and architecture impact your
          models perception.
        
        
        ## Quick Start
        
        Requires | version
        -------- | -------
        python | >= 3.6.0 
        tensorflow | 2.3.x
        plotly | 4.9.0
        
        Install locally (Also works in google Colab!):
        ```sh
        pip install mlvtk
        ```
        
        Optionally for use with jupyter notebook/lab:
        
        *Notebook*
        ---
        ```sh
        pip install "notebook>=5.3" "ipywidgets==7.5"
        ```
        
        *Lab*
        ---
        ```sh
        pip install jupyterlab "ipywidgets==7.5"
        
        # Basic JupyterLab renderer support
        jupyter labextension install jupyterlab-plotly@4.10.0
        
        # OPTIONAL: Jupyter widgets extension for FigureWidget support
        jupyter labextension install @jupyter-widgets/jupyterlab-manager plotlywidget@4.10.0
        ```
        
        ### Usage
        
        ```python
        # construct standard 3 layer network
        inputs = tf.keras.layers.Input(shape=(None,784))
        dense_1 = tf.keras.layers.Dense(50, activation='relu')(inputs)
        outputs = tf.keras.layers.Dense(np.unique(label_train, axis=0).size, activation='softmax')(dense_1) # hard coded outputs size
        _model = tf.keras.Model(inputs, outputs)
        
        # create mlvtk model
        model = create_model(_model)
        
        # compile and fit like a standard tensorflow model
        model.compile(optimizer=tf.keras.optimizers.SGD(),
        loss=tf.keras.losses.CategoricCategoricalCrossentropy(), metrics=['accuracy'])
        
        history = model.fit(train_data, validation_data=val_data, epochs=epochs, verbose=0)
        
        # add title to surface plot
        model.surface_plot(title_text=f'Data: {dataname}, Epochs: {epochs}, Optimizer: {model.opt}, LR: {lr}')
        
        model.interp_plot(title=f'Data: {dataname}, Epochs: {epochs}, Optimizer: {model.opt}, LR: {lr}')
        
        ```
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
