Loading docs/models.rst +64 −4 Original line number Diff line number Diff line Model Classes ============= DeepChem maintains an extensive collection of models for scientific applications. DeepChem maintains an extensive collection of models for scientific applications. DeepChem's focus is on facilitating scientific applications, so we support a broad range of different machine learning frameworks (currently scikit-learn, xgboost, TensorFlow, and PyTorch) since different frameworks are more and less suited for different scientific applications. Model Cheatsheet ---------------- Loading Loading @@ -125,6 +129,12 @@ read off what's needed to train the model from the table below. +----------------------------------------+------------+----------------------+------------------------+----------------------------------------------------------------+----------------------+ | :code:`WGAN` | Adversarial| Pair | | | :code:`fit_gan` | +----------------------------------------+------------+----------------------+------------------------+----------------------------------------------------------------+----------------------+ | :code:`CGCNNModel` | Classifier/| :code:`GraphData` | | :code:`CGCNNFeaturizer` | :code:`fit` | | | Regressor | | | | | +----------------------------------------+------------+----------------------+------------------------+----------------------------------------------------------------+----------------------+ | :code:`GATModel` | Classifier/| :code:`GraphData` | | :code:`MolGraphConvFeaturizer` | :code:`fit` | | | Regressor | | | | | +----------------------------------------+------------+----------------------+------------------------+----------------------------------------------------------------+----------------------+ Model ----- Loading @@ -132,12 +142,24 @@ Model .. autoclass:: deepchem.models.Model :members: Scikit-Learn Models =================== Scikit-learn's models can be wrapped so that they can interact conveniently with DeepChem. Oftentimes scikit-learn models are more robust and easier to train and are a nice first model to train. SklearnModel ------------ .. autoclass:: deepchem.models.SklearnModel :members: Xgboost Models ============== Xgboost models can be wrapped so they can interact with DeepChem. XGBoostModel ------------ Loading @@ -145,9 +167,13 @@ XGBoostModel :members: Keras Models ============ DeepChem extensively uses `Keras`_ to build powerful machine learning models. Deep Learning Infrastructure ============================ DeepChem maintains a lightweight layer of common deep learning model infrastructure that can be used for models built with different underlying frameworks. The losses and optimizers can be used for both TensorFlow and PyTorch models. Losses ------ Loading Loading @@ -216,6 +242,12 @@ Optimizers :members: Keras Models ============ DeepChem extensively uses `Keras`_ to build deep learning models. KerasModel ---------- Loading Loading @@ -373,3 +405,31 @@ ChemCeption .. autoclass:: deepchem.models.ChemCeption :members: PyTorch Models ============== DeepChem supports the use of `PyTorch`_ to build deep learning models. .. _`PyTorch`: https://pytorch.org/ TorchModel ---------- You can wrap an arbitrary :code:`torch.nn.Module` in a :code:`TorchModel` object. .. autoclass:: deepchem.models.TorchModel :members: CGCNNModel ---------- .. autoclass:: deepchem.models.CGCNNModel :members: GATModel -------- .. autoclass:: deepchem.models.GATModel :members: docs/requirements.txt +2 −0 Original line number Diff line number Diff line Loading @@ -3,3 +3,5 @@ scikit-learn sphinx_rtd_theme tensorflow==2.2.0 transformers xgboost torch==1.6.0 Loading
docs/models.rst +64 −4 Original line number Diff line number Diff line Model Classes ============= DeepChem maintains an extensive collection of models for scientific applications. DeepChem maintains an extensive collection of models for scientific applications. DeepChem's focus is on facilitating scientific applications, so we support a broad range of different machine learning frameworks (currently scikit-learn, xgboost, TensorFlow, and PyTorch) since different frameworks are more and less suited for different scientific applications. Model Cheatsheet ---------------- Loading Loading @@ -125,6 +129,12 @@ read off what's needed to train the model from the table below. +----------------------------------------+------------+----------------------+------------------------+----------------------------------------------------------------+----------------------+ | :code:`WGAN` | Adversarial| Pair | | | :code:`fit_gan` | +----------------------------------------+------------+----------------------+------------------------+----------------------------------------------------------------+----------------------+ | :code:`CGCNNModel` | Classifier/| :code:`GraphData` | | :code:`CGCNNFeaturizer` | :code:`fit` | | | Regressor | | | | | +----------------------------------------+------------+----------------------+------------------------+----------------------------------------------------------------+----------------------+ | :code:`GATModel` | Classifier/| :code:`GraphData` | | :code:`MolGraphConvFeaturizer` | :code:`fit` | | | Regressor | | | | | +----------------------------------------+------------+----------------------+------------------------+----------------------------------------------------------------+----------------------+ Model ----- Loading @@ -132,12 +142,24 @@ Model .. autoclass:: deepchem.models.Model :members: Scikit-Learn Models =================== Scikit-learn's models can be wrapped so that they can interact conveniently with DeepChem. Oftentimes scikit-learn models are more robust and easier to train and are a nice first model to train. SklearnModel ------------ .. autoclass:: deepchem.models.SklearnModel :members: Xgboost Models ============== Xgboost models can be wrapped so they can interact with DeepChem. XGBoostModel ------------ Loading @@ -145,9 +167,13 @@ XGBoostModel :members: Keras Models ============ DeepChem extensively uses `Keras`_ to build powerful machine learning models. Deep Learning Infrastructure ============================ DeepChem maintains a lightweight layer of common deep learning model infrastructure that can be used for models built with different underlying frameworks. The losses and optimizers can be used for both TensorFlow and PyTorch models. Losses ------ Loading Loading @@ -216,6 +242,12 @@ Optimizers :members: Keras Models ============ DeepChem extensively uses `Keras`_ to build deep learning models. KerasModel ---------- Loading Loading @@ -373,3 +405,31 @@ ChemCeption .. autoclass:: deepchem.models.ChemCeption :members: PyTorch Models ============== DeepChem supports the use of `PyTorch`_ to build deep learning models. .. _`PyTorch`: https://pytorch.org/ TorchModel ---------- You can wrap an arbitrary :code:`torch.nn.Module` in a :code:`TorchModel` object. .. autoclass:: deepchem.models.TorchModel :members: CGCNNModel ---------- .. autoclass:: deepchem.models.CGCNNModel :members: GATModel -------- .. autoclass:: deepchem.models.GATModel :members:
docs/requirements.txt +2 −0 Original line number Diff line number Diff line Loading @@ -3,3 +3,5 @@ scikit-learn sphinx_rtd_theme tensorflow==2.2.0 transformers xgboost torch==1.6.0
