Sequential ( AdditiveGausNoise (), View ( - 1, 1, 28, 28 ), nn. from_dict ( results )ĭnauto_encoder_conv_big = nn. val_loader - Optional PyTorch DataLoader to evaluate on after every epoch score_funcs - A dictionary of scoring functions to use to evalue the performance of the model epochs - the number of training epochs to perform device - the compute lodation to perform training """ if score_funcs = None : score_funcs =, checkpoint_file ) if del_opt : del optimizer return pd. to ( device ) else : return x def train_network ( model, loss_func, train_loader, val_loader = None, score_funcs = None, epochs = 50, device = "cpu", checkpoint_file = None, lr_schedule = None, optimizer = None, disable_tqdm = False ): """Train simple neural networks Keyword arguments: model - the PyTorch model / "Module" to train loss_func - the loss function that takes in batch in two arguments, the model outputs and the labels, and returns a score train_loader - PyTorch DataLoader object that returns tuples of (input, label) pairs. shape ) def moveTo ( obj, device ): if isinstance ( obj, tuple ): return tuple () elif isinstance ( obj, list ): return elif isinstance ( obj, torch. shape = shape def forward ( self, input ): return input. Module ): def _init_ ( self, * shape ): super ( View, self ). Module ): def forward ( self, input ): return input.
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