Loading deepchem/models/torch_models/cgcnn.py +17 −18 Original line number Diff line number Diff line Loading @@ -270,25 +270,24 @@ class CGCNNModel(TorchModel): loss: dc.models.losses.Loss or function A Loss or function defining how to compute the training loss for each batch, please confirm the details from `TorchModel` docstring. output_types: List[str], default None The type of each output from the model, please confirm the details from `TorchModel` docstring. batch_size: int, default 100 Default batch size for training and evaluating. model_dir: str, default None The directory on disk where the model will be stored. If this is None, output_types: list of strings, optional (default None) the type of each output from the model, as described above batch_size: int, optional (default 100) default batch size for training and evaluating model_dir: str, optional (default None) the directory on disk where the model will be stored. If this is None, a temporary directory is created. learning_rate: float or LearningRateSchedule, default 0.001 The learning rate to use for fitting. If optimizer is specified, this is learning_rate: float or LearningRateSchedule, optional (default 0.001) the learning rate to use for fitting. If optimizer is specified, this is ignored. optimizer: Optimizer, default None The optimizer to use for fitting. If this is specified, learning_rate is optimizer: Optimizer, optional (default None) the optimizer to use for fitting. If this is specified, learning_rate is ignored. tensorboard: bool, default False Whether to log progress to TensorBoard during training wandb: bool, default False Whether to log progress to Weights & Biases during training log_frequency: int, default 100 tensorboard: bool, optional (default False) whether to log progress to TensorBoard during training wandb: bool, optional (default False) whether to log progress to Weights & Biases during training log_frequency: int, optional (default 100) The frequency at which to log data. Data is logged using `logging` by default. If `tensorboard` is set, data is also logged to TensorBoard. If `wandb` is set, data is also logged Loading @@ -296,8 +295,8 @@ class CGCNNModel(TorchModel): a global step corresponds to one batch of training. If you'd like a printout every 10 batch steps, you'd set `log_frequency=10` for example. device: torch.device, default None The device on which to run computations. If None, a device is device: torch.device, optional (default None) the device on which to run computations. If None, a device is chosen automatically. """ model = CGCNN(in_node_dim, hidden_node_dim, in_edge_dim, num_conv, Loading deepchem/models/torch_models/torch_model.py +9 −9 Original line number Diff line number Diff line Loading @@ -132,24 +132,24 @@ class TorchModel(Model): loss: dc.models.losses.Loss or function a Loss or function defining how to compute the training loss for each batch, as described above output_types: List[str] output_types: list of strings, optional (default None) the type of each output from the model, as described above batch_size: int batch_size: int, optional (default 100) default batch size for training and evaluating model_dir: str model_dir: str, optional (default None) the directory on disk where the model will be stored. If this is None, a temporary directory is created. learning_rate: float or LearningRateSchedule learning_rate: float or LearningRateSchedule, optional (default 0.001) the learning rate to use for fitting. If optimizer is specified, this is ignored. optimizer: Optimizer optimizer: Optimizer, optional (default None) the optimizer to use for fitting. If this is specified, learning_rate is ignored. tensorboard: bool tensorboard: bool, optional (default False) whether to log progress to TensorBoard during training wandb: bool wandb: bool, optional (default False) whether to log progress to Weights & Biases during training log_frequency: int log_frequency: int, optional (default 100) The frequency at which to log data. Data is logged using `logging` by default. If `tensorboard` is set, data is also logged to TensorBoard. If `wandb` is set, data is also logged Loading @@ -157,7 +157,7 @@ class TorchModel(Model): a global step corresponds to one batch of training. If you'd like a printout every 10 batch steps, you'd set `log_frequency=10` for example. device: torch.device device: torch.device, optional (default None) the device on which to run computations. If None, a device is chosen automatically. """ Loading Loading
deepchem/models/torch_models/cgcnn.py +17 −18 Original line number Diff line number Diff line Loading @@ -270,25 +270,24 @@ class CGCNNModel(TorchModel): loss: dc.models.losses.Loss or function A Loss or function defining how to compute the training loss for each batch, please confirm the details from `TorchModel` docstring. output_types: List[str], default None The type of each output from the model, please confirm the details from `TorchModel` docstring. batch_size: int, default 100 Default batch size for training and evaluating. model_dir: str, default None The directory on disk where the model will be stored. If this is None, output_types: list of strings, optional (default None) the type of each output from the model, as described above batch_size: int, optional (default 100) default batch size for training and evaluating model_dir: str, optional (default None) the directory on disk where the model will be stored. If this is None, a temporary directory is created. learning_rate: float or LearningRateSchedule, default 0.001 The learning rate to use for fitting. If optimizer is specified, this is learning_rate: float or LearningRateSchedule, optional (default 0.001) the learning rate to use for fitting. If optimizer is specified, this is ignored. optimizer: Optimizer, default None The optimizer to use for fitting. If this is specified, learning_rate is optimizer: Optimizer, optional (default None) the optimizer to use for fitting. If this is specified, learning_rate is ignored. tensorboard: bool, default False Whether to log progress to TensorBoard during training wandb: bool, default False Whether to log progress to Weights & Biases during training log_frequency: int, default 100 tensorboard: bool, optional (default False) whether to log progress to TensorBoard during training wandb: bool, optional (default False) whether to log progress to Weights & Biases during training log_frequency: int, optional (default 100) The frequency at which to log data. Data is logged using `logging` by default. If `tensorboard` is set, data is also logged to TensorBoard. If `wandb` is set, data is also logged Loading @@ -296,8 +295,8 @@ class CGCNNModel(TorchModel): a global step corresponds to one batch of training. If you'd like a printout every 10 batch steps, you'd set `log_frequency=10` for example. device: torch.device, default None The device on which to run computations. If None, a device is device: torch.device, optional (default None) the device on which to run computations. If None, a device is chosen automatically. """ model = CGCNN(in_node_dim, hidden_node_dim, in_edge_dim, num_conv, Loading
deepchem/models/torch_models/torch_model.py +9 −9 Original line number Diff line number Diff line Loading @@ -132,24 +132,24 @@ class TorchModel(Model): loss: dc.models.losses.Loss or function a Loss or function defining how to compute the training loss for each batch, as described above output_types: List[str] output_types: list of strings, optional (default None) the type of each output from the model, as described above batch_size: int batch_size: int, optional (default 100) default batch size for training and evaluating model_dir: str model_dir: str, optional (default None) the directory on disk where the model will be stored. If this is None, a temporary directory is created. learning_rate: float or LearningRateSchedule learning_rate: float or LearningRateSchedule, optional (default 0.001) the learning rate to use for fitting. If optimizer is specified, this is ignored. optimizer: Optimizer optimizer: Optimizer, optional (default None) the optimizer to use for fitting. If this is specified, learning_rate is ignored. tensorboard: bool tensorboard: bool, optional (default False) whether to log progress to TensorBoard during training wandb: bool wandb: bool, optional (default False) whether to log progress to Weights & Biases during training log_frequency: int log_frequency: int, optional (default 100) The frequency at which to log data. Data is logged using `logging` by default. If `tensorboard` is set, data is also logged to TensorBoard. If `wandb` is set, data is also logged Loading @@ -157,7 +157,7 @@ class TorchModel(Model): a global step corresponds to one batch of training. If you'd like a printout every 10 batch steps, you'd set `log_frequency=10` for example. device: torch.device device: torch.device, optional (default None) the device on which to run computations. If None, a device is chosen automatically. """ Loading
