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(MNISTNet2, self).__init__()
self.flatten = nn.Flatten()
self.linear = nn.Sequential(
pytk.Linear(IMAGE_HEIGHT * IMAGE_WIDTH * NUM_CHANNELS, 128),
nn.ReLU(),
nn.Dropout(0.1),
pytk.Linear(128, 64),
nn.ReLU(),
nn.Dropout(0.1),
# NOTE: we'll be using nn.CrossEntropyLoss(), which includes a
# logsoftmax call that applies a softmax function to outputs.
# So, don't apply one yourself!
pytk.Linear(64, 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, inp_size, hidden1, num_classes):
super(WineNet, self).__init__()
self.fc1 = pytk.Linear(inp_size, hidden1)
self.out = pytk.Linear(hidden1, num_classes)
def __init__(self, features):
super(Net, self).__init__()
self.fc1 = pytk.Linear(features, 10)
self.fc2 = pytk.Linear(10, 5)
self.out = pytk.Linear(5, 1)
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)
def __init__(self):
super(MNISTNet, self).__init__()
self.fc1 = pytk.Linear(IMAGE_HEIGHT * IMAGE_WIDTH * NUM_CHANNELS, 128)
self.fc2 = pytk.Linear(128, 64)
self.out = pytk.Linear(64, NUM_CLASSES)
self.dropout = nn.Dropout(0.10)
def __init__(self, features):
super(Net, self).__init__()
self.fc1 = pytk.Linear(features, 32)
self.fc2 = pytk.Linear(32, 16)
self.fc3 = pytk.Linear(16, 8)
self.out = pytk.Linear(8, 1)
def __init__(self):
super(FMNISTNet, self).__init__()
self.fc1 = pytk.Linear(IMAGE_HEIGHT * IMAGE_WIDTH * NUM_CHANNELS, 256)
self.fc2 = pytk.Linear(256, 128)
self.out = pytk.Linear(128, NUM_CLASSES)
def __init__(self, inp_size, hidden1, num_classes):
super(WineNet, self).__init__()
self.fc1 = pytk.Linear(inp_size, hidden1)
self.relu1 = nn.ReLU()
self.out = pytk.Linear(hidden1, num_classes)
self.dropout = nn.Dropout(0.20)