def test_add_method():
model = Sequential()
model.add(Dense(n_nodes=32, n_inputs=45))
model.build()
with pytest.raises(Exception):
model.add(Dense(n_nodes=32, n_inputs=45))
from torchvision import datasets, transforms
# Create a Sequential model Instance
model = Sequential()
#Build your network
model.add(Conv2D(input_shape=(1, 28, 28), filters=128, kernel_size=3))
model.add(ReLU())
model.add(Conv2D(filters=64, kernel_size=3))
model.add(ReLU())
model.add(Conv2D(filters=32, kernel_size=3))
model.add(ReLU())
model.add(Flatten())
model.add(Dense(n_nodes=10))
model.build()
model.compile(optimizer=SGD(),
loss_function=CrossEntropyLoss(),
metrics=["accuracy"])
print(model.summary())
#Get the MNIST dataset
train_set = torchvision.datasets.MNIST(root='./data',
train=True,
download=True,
transform=transforms.Compose(
[transforms.ToTensor()]))
# Load the dataset from pytorch's Dataloader function
train_loader = torch.utils.data.DataLoader(train_set, batch_size=1000)
#Get the data