x = self.relu(self.conv1_2(x))
x = self.pool(x)
# Apply block 2followed by max pool
x = self.relu(self.conv2_1(x))
x = self.relu(self.conv2_2(x))
x = self.pool(x)
# Prepare the image for the fully connected layer
x = x.view(-1, int(64 * 32 / 2 / 2 * 32 / 2 / 2))
# Apply the fully connected layer and return the result
return self.fc(x)
img_shape = train_data.image_shape()
num_classes = train_data.num_classes()
net = Net(img_shape, num_classes)
clf = CnnClassifier(net, (0, *img_shape), num_classes, 0.01, 0.01)
not_improved_since = 0
best_accuracy = 0
stop_epoch = 0
for epoch in range(100):
losses = []
for batch in train_batches:
loss = clf.train(batch.data, batch.label)
losses.append(loss)
losses = np.array(losses)
mean = round(np.mean(losses), 3)
def forward(self, x):
# run the steps ...
return self.layers.forward(x)
# Learning rate to use
lr = 0.0001
# weight decay to use
wd = 0.0
# Step 4: wrap into CnnClassifier
net = CatsDogsModel()
# check whether GPU support is available
if torch.cuda.is_available():
net.cuda()
clf = CnnClassifier(net, (0, 3, 32, 32), train_data.num_classes(), lr, wd)
netSimple = CatsDogsModelSimple()
# check whether GPU support is available
if torch.cuda.is_available():
netSimple.cuda()
clfSimple = CnnClassifier(netSimple, (0, 3, 32, 32), train_data.num_classes(),
lr, wd)
netComplex = CatsDogsModelComplex()
# check whether GPU support is available
if torch.cuda.is_available():
netComplex.cuda()
clfComplex = CnnClassifier(netComplex, (0, 3, 32, 32),
train_data.num_classes(), lr, wd)
# transfer to CUDA (if available)
if torch.cuda.is_available():
pretrainedModel.cuda()
netTransfer = CatsDogsModelTransfer(pretrainedModel, 512)
# Learning rate to use
lr = 0.005
# weight decay to use
wd = 0.001
# Step 4: wrap into CnnClassifier
net = CatsDogsModel()
# check whether GPU support is available
if torch.cuda.is_available():
net.cuda()
clf = CnnClassifier(net, (0, 3, 32, 32), train_data.num_classes(), lr, wd)
# check whether GPU support is available
if torch.cuda.is_available():
netTransfer.cuda()
clfTransfer = CnnClassifier(
netTransfer,
(0, 3, 32, 32),
train_data.num_classes(),
lr,
wd
)
# Step 5: train in 100 epochs
def train_model(clf: CnnClassifier, results_file: TextIO) -> TrainedModel:
from dlvc.batches import BatchGenerator
from dlvc.test import Accuracy
from dlvc.dataset import Subset
import dlvc.ops as ops
np.random.seed(0)
pets_train = PetsDataset("../cifar-10-batches-py/", Subset.TRAINING)
pets_val = PetsDataset("../cifar-10-batches-py/", Subset.VALIDATION)
random_accuracy = Accuracy()
validation_accuracy = Accuracy()
train_accuracy = Accuracy()
print('Number of Classes = {}'.format(pets_train.num_classes()))
print('Number of Images = {}'.format(pets_train.__len__()))
print('First 10 Classes >>> {}'.format(pets_train.labels[:10]))
op = ops.chain([
ops.vectorize(),
ops.type_cast(np.float32),
ops.add(-127.5),
ops.mul(1 / 127.5),
])
train_batches = BatchGenerator(pets_train, 100, False, op)
validation_batches = BatchGenerator(pets_val, 100, False, op)
print('Number of Batches = {}'.format(train_batches.__len__()))
