added mnsit and cifar tests

bird_cnn/train_cifar.py

@@ -0,0 +1,41 @@
+import torch
+import torch.nn as nn
+from torchvision import datasets, transforms
+from torch.utils.data import DataLoader, random_split
+
+from bird_cnn import Bird_CNN, sample, trainCNN
+
+SAVE_PATH = "./saved_models"
+
+transform = transforms.Compose([
+    transforms.ToTensor(),
+    transforms.Normalize((0.5, 0.5, 0.5),
+                         (0.5, 0.5, 0.5))
+])
+
+train_dataset = datasets.CIFAR10(
+    root="./data/cifar10",
+    train=True,
+    download=True,
+    transform=transform
+)
+
+val_dataset = datasets.CIFAR10(
+    root="./data/cifar10",
+    train=False,
+    download=True,
+    transform=transform
+)
+
+train_loader = DataLoader(train_dataset, batch_size=4, shuffle=True)
+val_loader = DataLoader(val_dataset, batch_size=4, shuffle=False)
+
+device = torch.device("cpu") if not torch.cuda.is_available() else torch.device("cuda:0")
+print("Using device", device)
+
+model = Bird_CNN(c_in=3, c_hidden=16, c_out=10)
+model.to(device)
+optimizer = torch.optim.Adam(model.parameters(), lr=1e-3, weight_decay=1e-4)
+loss_module = nn.CrossEntropyLoss()
+
+trainCNN(model, optimizer, loss_module, train_loader, val_loader, device, 50, SAVE_PATH=SAVE_PATH, save=False)

bird_cnn/train_mnist.py

@@ -0,0 +1,41 @@
+import torch
+import torch.nn as nn
+from torchvision import datasets, transforms
+from torch.utils.data import DataLoader, random_split
+
+from bird_cnn import Bird_CNN, sample, trainCNN
+
+SAVE_PATH = "./saved_models"
+
+transform = transforms.Compose([
+    transforms.ToTensor(),
+    transforms.Normalize((0.5),
+                         (0.5))
+])
+
+train_dataset = datasets.MNIST(
+    root="./data/mnist",
+    train=True,
+    download=True,
+    transform=transform
+)
+
+val_dataset = datasets.MNIST(
+    root="./data/mnist",
+    train=False,
+    download=True,
+    transform=transform
+)
+
+train_loader = DataLoader(train_dataset, batch_size=4, shuffle=True)
+val_loader = DataLoader(val_dataset, batch_size=4, shuffle=False)
+
+device = torch.device("cpu") if not torch.cuda.is_available() else torch.device("cuda:0")
+print("Using device", device)
+
+model = Bird_CNN(c_in=1, c_hidden=8, c_out=10)
+model.to(device)
+optimizer = torch.optim.Adam(model.parameters(), lr=1e-3, weight_decay=1e-4)
+loss_module = nn.CrossEntropyLoss()
+
+trainCNN(model, optimizer, loss_module, train_loader, val_loader, device, 50, SAVE_PATH=SAVE_PATH, save=False)