johnolafenwa

#A resnet block consisting of N number of resnet modules, first layer has is pooled.
def resnet_block(x,filters,num_layers,pool_first_layer=True):
    for i in range(num_layers):
        pool = False
        if i == 0 and pool_first_layer: pool = True
        x = resnet_module(x,filters=filters,pool=pool)
    return x

johnolafenwa

# A single resnet module consisting of 1 x 1 conv - 3 x 3 conv and 1 x 1 conv
def resnet_identity_module(x, filters, pool=False):
    res = x
    stride = 1
    if pool:
        stride = 2
        res = Conv2D(filters, kernel_size=1, strides=2, padding="same")(res)

    x = BatchNormalization()(x)
    x = Activation("relu")(x)

johnolafenwa

# A single resnet module consisting of 1 x 1 conv - 3 x 3 conv and 1 x 1 conv
def resnet_module(x, filters, pool=False):
    res = x
    stride = 1
    if pool:
        stride = 2
        res = Conv2D(filters, kernel_size=1, strides=2, padding="same")(res)
        res = BatchNormalization()(res)

    x = Conv2D(int(filters / 4), kernel_size=1, strides=stride, padding="same")(x)

johnolafenwa

import keras
from keras.layers import *
from keras.models import Model

# A single resnet module consisting of 1 x 1 conv - 3 x 3 conv and 1 x 1 conv
def resnet_module(x, filters, pool=False):
    res = x
    stride = 1
    if pool:
        stride = 2

johnolafenwa

import keras
from keras.layers import *
from keras.models import Model

# A single resnet module consisting of 1 x 1 conv - 3 x 3 conv and 1 x 1 conv
def resnet_identity_module(x, filters, pool=False):
    res = x
    stride = 1
    if pool:
        stride = 2

johnolafenwa

class Unit(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(Unit, self).__init__()

        self.conv = nn.Conv2d(in_channels=in_channels, kernel_size=3, out_channels=out_channels, stride=1, padding=1)
        self.bn = nn.BatchNorm2d(num_features=out_channels)
        self.relu = nn.ReLU()

    def forward(self, input):
        output = self.conv(input)

johnolafenwa

# Define transformations for the test set
test_transformations = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))

])

# Load the test set, note that train is set to False
test_set = CIFAR10(root="./data", train=False, transform=test_transformations, download=True)

johnolafenwa

from torch.optim import Adam


# Check if gpu support is available
cuda_avail = torch.cuda.is_available()

# Create model, optimizer and loss function
model = SimpleNet(num_classes=10)

johnolafenwa

# Create a learning rate adjustment function that divides the learning rate by 10 every 30 epochs
def adjust_learning_rate(epoch):
    lr = 0.001

    if epoch > 180:
        lr = lr / 1000000
    elif epoch > 150:
        lr = lr / 100000
    elif epoch > 120:

johnolafenwa

def save_models(epoch):
    torch.save(model.state_dict(), "cifar10model_{}.model".format(epoch))
    print("Chekcpoint saved")