def __init__(self):
super(Cifar10ConvNet, self).__init__()
self.conv1 = pytk.Conv2d(3, 32, 3, padding=1)
self.conv2 = pytk.Conv2d(32, 64, 3, padding=1)
self.conv3 = pytk.Conv2d(64, 128, 3, padding=1)
self.fc1 = pytk.Linear(4 * 4 * 128, 512)
self.out = pytk.Linear(512, NUM_CLASSES)
def __init__(self):
super(MNISTConvNet2, self).__init__()
self.convNet = nn.Sequential(
pytk.Conv2d(1, 128, kernel_size=3),
nn.ReLU(),
nn.MaxPool2d(2),
nn.Dropout(p=0.20),
pytk.Conv2d(128, 64, kernel_size=3),
nn.ReLU(),
nn.MaxPool2d(2),
nn.Dropout(p=0.10),
nn.Flatten(),
pytk.Linear(7 * 7 * 64, 512),
nn.ReLU(),
nn.Dropout(p=0.20),
pytk.Linear(512, NUM_CLASSES)
)
def __init__(self, lr):
super(MNISTModel, self).__init__()
self.convNet = nn.Sequential(pytk.Conv2d(1, 128, kernel_size=3),
nn.ReLU(), nn.MaxPool2d(2),
nn.Dropout(p=0.20),
pytk.Conv2d(128, 64, kernel_size=3),
nn.ReLU(), nn.MaxPool2d(2),
nn.Dropout(p=0.10), nn.Flatten(),
pytk.Linear(7 * 7 * 64, 512), nn.ReLU(),
nn.Dropout(p=0.20),
pytk.Linear(512, NUM_CLASSES))
self.lr = lr
self.loss_fn = nn.CrossEntropyLoss()
self.train_acc = tm.Accuracy()
self.val_acc = tm.Accuracy()
self.train_batch_losses = []
self.val_batch_losses = []
self.train_batch_accs = []
self.val_batch_accs = []
self.history = {"loss": [], "acc": [], "val_loss": [], "val_acc": []}
self.log_file = open(os.path.join(os.getcwd(), 'mnist_log.txt'), 'w')
def __init__(self):
super(MNISTConvNet, self).__init__()
self.conv1 = pytk.Conv2d(1, 128, kernel_size=3)
self.conv2 = pytk.Conv2d(128, 64, kernel_size=3)
self.fc1 = pytk.Linear(7 * 7 * 64, 512)
self.out = pytk.Linear(512, NUM_CLASSES)