PyTorch_Generate_CNN_Model Namespace Reference

Classes
class	Reshape

Functions
	fit (model, train_loader, val_loader, num_epochs, batch_size, optimizer, criterion, save_best, scheduler)
	predict (model, test_X, batch_size=100)

Variables
	criterion = nn.BCELoss()
dict	load_model_custom_objects = {"optimizer": optimizer, "criterion": criterion, "train_func": fit, "predict_func": predict}
	m = torch.jit.script(net)
	net
	optimizer = torch.optim.Adam

Function Documentation

fit()

PyTorch_Generate_CNN_Model.fit	(		model,
			train_loader,
			val_loader,
			num_epochs,
			batch_size,
			optimizer,
			criterion,
			save_best,
			scheduler
	)

Definition at line 32 of file PyTorch_Generate_CNN_Model.py.

predict()

PyTorch_Generate_CNN_Model.predict	(		model,
			test_X,
			batch_size = 100
	)

Definition at line 91 of file PyTorch_Generate_CNN_Model.py.

Variable Documentation

criterion

PyTorch_Generate_CNN_Model.criterion = nn.BCELoss()

Definition at line 28 of file PyTorch_Generate_CNN_Model.py.

load_model_custom_objects

dict PyTorch_Generate_CNN_Model.load_model_custom_objects = {"optimizer": optimizer, "criterion": criterion, "train_func": fit, "predict_func": predict}

Definition at line 114 of file PyTorch_Generate_CNN_Model.py.

m

PyTorch_Generate_CNN_Model.m = torch.jit.script(net)

Definition at line 117 of file PyTorch_Generate_CNN_Model.py.

net

PyTorch_Generate_CNN_Model.net

Initial value:

=  torch.nn.Sequential(
    Reshape(),
    nn.Conv2d(1, 10, kernel_size=3, padding=1),
    nn.ReLU(),
    nn.BatchNorm2d(10),
    nn.Conv2d(10, 10, kernel_size=3, padding=1),
    nn.ReLU(),
    nn.MaxPool2d(kernel_size=2),
    nn.Flatten(),
    nn.Linear(10*8*8, 256),
    nn.ReLU(),
    nn.Linear(256, 2),
    nn.Sigmoid()
    )

Definition at line 12 of file PyTorch_Generate_CNN_Model.py.

optimizer

PyTorch_Generate_CNN_Model.optimizer = torch.optim.Adam

Definition at line 29 of file PyTorch_Generate_CNN_Model.py.