Deep Learning Models -- A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.
- Author: Sebastian Raschka
- GitHub Repository: https://github.com/rasbt/deeplearning-models
In [1]:
%load_ext watermark
%watermark -a 'Sebastian Raschka' -v -p torch
Sebastian Raschka CPython 3.7.3 IPython 7.6.1 torch 1.2.0
- Runs on CPU or GPU (if available)
Most Basic Graph Neural Network with Gaussian Filter on MNIST¶
Implementing a very basic graph neural network (GNN) using a Gaussian filter.
Here, the 28x28 image of a digit in MNIST represents the graph, where each pixel (i.e., cell in the grid) represents a particular node. The feature of that node is simply the pixel intensity in range [0, 1].
Here, the adjacency matrix of the pixels is basically just determined by their neighborhood pixels. Using a Gaussian filter, we connect pixels based on their Euclidean distance in the grid.
Using this adjacency matrix A, we can compute the output of a layer as
X(l+1)=AX(l)W(l).Here, A is the N×N adjacency matrix, and X is the N×C feature matrix (a 2D coordinate array, where N is the total number of pixels -- 28×28=784 in MNIST). W is the weight matrix of shape N×P, where P would represent the number of classes if we have only a single hidden layer.
- Inspired by and based on Boris Knyazev's tutorial at https://medium.com/@BorisAKnyazev/tutorial-on-graph-neural-networks-for-computer-vision-and-beyond-part-1-3d9fada3b80d.
Imports¶
In [2]:
import time
import numpy as np
from scipy.spatial.distance import cdist
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import datasets
from torchvision import transforms
from torch.utils.data import DataLoader
from torch.utils.data.dataset import Subset
if torch.cuda.is_available():
torch.backends.cudnn.deterministic = True
In [3]:
%matplotlib inline
import matplotlib.pyplot as plt
Settings and Dataset¶
In [4]:
##########################
### SETTINGS
##########################
# Device
DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# Hyperparameters
RANDOM_SEED = 1
LEARNING_RATE = 0.05
NUM_EPOCHS = 20
BATCH_SIZE = 128
IMG_SIZE = 28
# Architecture
NUM_CLASSES = 10
MNIST Dataset¶
In [5]:
train_indices = torch.arange(0, 59000)
valid_indices = torch.arange(59000, 60000)
custom_transform = transforms.Compose([transforms.ToTensor()])
train_and_valid = datasets.MNIST(root='data',
train=True,
transform=custom_transform,
download=True)
test_dataset = datasets.MNIST(root='data',
train=False,
transform=custom_transform,
download=True)
train_dataset = Subset(train_and_valid, train_indices)
valid_dataset = Subset(train_and_valid, valid_indices)
train_loader = DataLoader(dataset=train_dataset,
batch_size=BATCH_SIZE,
num_workers=4,
shuffle=True)
valid_loader = DataLoader(dataset=valid_dataset,
batch_size=BATCH_SIZE,
num_workers=4,
shuffle=False)
test_loader = DataLoader(dataset=test_dataset,
batch_size=BATCH_SIZE,
num_workers=4,
shuffle=False)
# Checking the dataset
for images, labels in train_loader:
print('Image batch dimensions:', images.shape)
print('Image label dimensions:', labels.shape)
break
Image batch dimensions: torch.Size([128, 1, 28, 28]) Image label dimensions: torch.Size([128])
Model¶
In [6]:
def precompute_adjacency_matrix(img_size):
col, row = np.meshgrid(np.arange(img_size), np.arange(img_size))
# N = img_size^2
# construct 2D coordinate array (shape N x 2) and normalize
# in range [0, 1]
coord = np.stack((col, row), axis=2).reshape(-1, 2) / img_size
# compute pairwise distance matrix (N x N)
dist = cdist(coord, coord, metric='euclidean')
# Apply Gaussian filter
sigma = 0.05 * np.pi
A = np.exp(- dist / sigma ** 2)
A[A < 0.01] = 0
A = torch.from_numpy(A).float()
# Normalization as per (Kipf & Welling, ICLR 2017)
D = A.sum(1) # nodes degree (N,)
D_hat = (D + 1e-5) ** (-0.5)
A_hat = D_hat.view(-1, 1) * A * D_hat.view(1, -1) # N,N
return A_hat
In [7]:
plt.imshow(precompute_adjacency_matrix(28));
In [8]:
##########################
### MODEL
##########################
class GraphNet(nn.Module):
def __init__(self, img_size=28, num_classes=10):
super(GraphNet, self).__init__()
n_rows = img_size**2
self.fc = nn.Linear(n_rows, num_classes, bias=False)
A = precompute_adjacency_matrix(img_size)
self.register_buffer('A', A)
def forward(self, x):
B = x.size(0) # Batch size
### Reshape Adjacency Matrix
# [N, N] Adj. matrix -> [1, N, N] Adj tensor where N = HxW
A_tensor = self.A.unsqueeze(0)
# [1, N, N] Adj tensor -> [B, N, N] tensor
A_tensor = self.A.expand(B, -1, -1)
### Reshape inputs
# [B, C, H, W] => [B, H*W, 1]
x_reshape = x.view(B, -1, 1)
# bmm = batch matrix product to sum the neighbor features
# Input: [B, N, N] x [B, N, 1]
# Output: [B, N]
avg_neighbor_features = (torch.bmm(A_tensor, x_reshape).view(B, -1))
logits = self.fc(avg_neighbor_features)
probas = F.softmax(logits, dim=1)
return logits, probas
In [9]:
torch.manual_seed(RANDOM_SEED)
model = GraphNet(img_size=IMG_SIZE, num_classes=NUM_CLASSES)
model = model.to(DEVICE)
optimizer = torch.optim.SGD(model.parameters(), lr=LEARNING_RATE)
Training¶
In [10]:
def compute_acc(model, data_loader, device):
correct_pred, num_examples = 0, 0
for features, targets in data_loader:
features = features.to(device)
targets = targets.to(device)
logits, probas = model(features)
_, predicted_labels = torch.max(probas, 1)
num_examples += targets.size(0)
correct_pred += (predicted_labels == targets).sum()
return correct_pred.float()/num_examples * 100
start_time = time.time()
cost_list = []
train_acc_list, valid_acc_list = [], []
for epoch in range(NUM_EPOCHS):
model.train()
for batch_idx, (features, targets) in enumerate(train_loader):
features = features.to(DEVICE)
targets = targets.to(DEVICE)
### FORWARD AND BACK PROP
logits, probas = model(features)
cost = F.cross_entropy(logits, targets)
optimizer.zero_grad()
cost.backward()
### UPDATE MODEL PARAMETERS
optimizer.step()
#################################################
### CODE ONLY FOR LOGGING BEYOND THIS POINT
################################################
cost_list.append(cost.item())
if not batch_idx % 150:
print (f'Epoch: {epoch+1:03d}/{NUM_EPOCHS:03d} | '
f'Batch {batch_idx:03d}/{len(train_loader):03d} |'
f' Cost: {cost:.4f}')
model.eval()
with torch.set_grad_enabled(False): # save memory during inference
train_acc = compute_acc(model, train_loader, device=DEVICE)
valid_acc = compute_acc(model, valid_loader, device=DEVICE)
print(f'Epoch: {epoch+1:03d}/{NUM_EPOCHS:03d}\n'
f'Train ACC: {train_acc:.2f} | Validation ACC: {valid_acc:.2f}')
train_acc_list.append(train_acc)
valid_acc_list.append(valid_acc)
elapsed = (time.time() - start_time)/60
print(f'Time elapsed: {elapsed:.2f} min')
elapsed = (time.time() - start_time)/60
print(f'Total Training Time: {elapsed:.2f} min')
Epoch: 001/020 | Batch 000/461 | Cost: 2.2677 Epoch: 001/020 | Batch 150/461 | Cost: 0.8999 Epoch: 001/020 | Batch 300/461 | Cost: 0.6701 Epoch: 001/020 | Batch 450/461 | Cost: 0.4905 Epoch: 001/020 Train ACC: 87.02 | Validation ACC: 92.40 Time elapsed: 0.08 min Epoch: 002/020 | Batch 000/461 | Cost: 0.5868 Epoch: 002/020 | Batch 150/461 | Cost: 0.4526 Epoch: 002/020 | Batch 300/461 | Cost: 0.4192 Epoch: 002/020 | Batch 450/461 | Cost: 0.3647 Epoch: 002/020 Train ACC: 88.25 | Validation ACC: 92.60 Time elapsed: 0.14 min Epoch: 003/020 | Batch 000/461 | Cost: 0.4316 Epoch: 003/020 | Batch 150/461 | Cost: 0.4165 Epoch: 003/020 | Batch 300/461 | Cost: 0.4130 Epoch: 003/020 | Batch 450/461 | Cost: 0.3991 Epoch: 003/020 Train ACC: 88.80 | Validation ACC: 93.30 Time elapsed: 0.22 min Epoch: 004/020 | Batch 000/461 | Cost: 0.3537 Epoch: 004/020 | Batch 150/461 | Cost: 0.3460 Epoch: 004/020 | Batch 300/461 | Cost: 0.4011 Epoch: 004/020 | Batch 450/461 | Cost: 0.4666 Epoch: 004/020 Train ACC: 89.34 | Validation ACC: 93.40 Time elapsed: 0.29 min Epoch: 005/020 | Batch 000/461 | Cost: 0.4523 Epoch: 005/020 | Batch 150/461 | Cost: 0.4006 Epoch: 005/020 | Batch 300/461 | Cost: 0.4396 Epoch: 005/020 | Batch 450/461 | Cost: 0.4509 Epoch: 005/020 Train ACC: 89.65 | Validation ACC: 93.40 Time elapsed: 0.36 min Epoch: 006/020 | Batch 000/461 | Cost: 0.3381 Epoch: 006/020 | Batch 150/461 | Cost: 0.3627 Epoch: 006/020 | Batch 300/461 | Cost: 0.2736 Epoch: 006/020 | Batch 450/461 | Cost: 0.3932 Epoch: 006/020 Train ACC: 89.85 | Validation ACC: 93.50 Time elapsed: 0.42 min Epoch: 007/020 | Batch 000/461 | Cost: 0.4984 Epoch: 007/020 | Batch 150/461 | Cost: 0.3718 Epoch: 007/020 | Batch 300/461 | Cost: 0.2903 Epoch: 007/020 | Batch 450/461 | Cost: 0.4040 Epoch: 007/020 Train ACC: 90.02 | Validation ACC: 93.50 Time elapsed: 0.50 min Epoch: 008/020 | Batch 000/461 | Cost: 0.5250 Epoch: 008/020 | Batch 150/461 | Cost: 0.3481 Epoch: 008/020 | Batch 300/461 | Cost: 0.3838 Epoch: 008/020 | Batch 450/461 | Cost: 0.4789 Epoch: 008/020 Train ACC: 90.14 | Validation ACC: 93.90 Time elapsed: 0.57 min Epoch: 009/020 | Batch 000/461 | Cost: 0.3028 Epoch: 009/020 | Batch 150/461 | Cost: 0.3982 Epoch: 009/020 | Batch 300/461 | Cost: 0.4042 Epoch: 009/020 | Batch 450/461 | Cost: 0.5471 Epoch: 009/020 Train ACC: 90.26 | Validation ACC: 93.90 Time elapsed: 0.64 min Epoch: 010/020 | Batch 000/461 | Cost: 0.2279 Epoch: 010/020 | Batch 150/461 | Cost: 0.2992 Epoch: 010/020 | Batch 300/461 | Cost: 0.4507 Epoch: 010/020 | Batch 450/461 | Cost: 0.2165 Epoch: 010/020 Train ACC: 90.40 | Validation ACC: 93.90 Time elapsed: 0.71 min Epoch: 011/020 | Batch 000/461 | Cost: 0.5089 Epoch: 011/020 | Batch 150/461 | Cost: 0.2480 Epoch: 011/020 | Batch 300/461 | Cost: 0.3782 Epoch: 011/020 | Batch 450/461 | Cost: 0.3228 Epoch: 011/020 Train ACC: 90.47 | Validation ACC: 93.40 Time elapsed: 0.78 min Epoch: 012/020 | Batch 000/461 | Cost: 0.2597 Epoch: 012/020 | Batch 150/461 | Cost: 0.2955 Epoch: 012/020 | Batch 300/461 | Cost: 0.2243 Epoch: 012/020 | Batch 450/461 | Cost: 0.2967 Epoch: 012/020 Train ACC: 90.58 | Validation ACC: 93.60 Time elapsed: 0.85 min Epoch: 013/020 | Batch 000/461 | Cost: 0.3367 Epoch: 013/020 | Batch 150/461 | Cost: 0.3696 Epoch: 013/020 | Batch 300/461 | Cost: 0.2744 Epoch: 013/020 | Batch 450/461 | Cost: 0.4097 Epoch: 013/020 Train ACC: 90.65 | Validation ACC: 93.80 Time elapsed: 0.92 min Epoch: 014/020 | Batch 000/461 | Cost: 0.2629 Epoch: 014/020 | Batch 150/461 | Cost: 0.3282 Epoch: 014/020 | Batch 300/461 | Cost: 0.2407 Epoch: 014/020 | Batch 450/461 | Cost: 0.2714 Epoch: 014/020 Train ACC: 90.66 | Validation ACC: 93.80 Time elapsed: 0.99 min Epoch: 015/020 | Batch 000/461 | Cost: 0.2497 Epoch: 015/020 | Batch 150/461 | Cost: 0.3774 Epoch: 015/020 | Batch 300/461 | Cost: 0.3405 Epoch: 015/020 | Batch 450/461 | Cost: 0.4727 Epoch: 015/020 Train ACC: 90.81 | Validation ACC: 93.90 Time elapsed: 1.06 min Epoch: 016/020 | Batch 000/461 | Cost: 0.4100 Epoch: 016/020 | Batch 150/461 | Cost: 0.3284 Epoch: 016/020 | Batch 300/461 | Cost: 0.3974 Epoch: 016/020 | Batch 450/461 | Cost: 0.2978 Epoch: 016/020 Train ACC: 90.86 | Validation ACC: 93.90 Time elapsed: 1.13 min Epoch: 017/020 | Batch 000/461 | Cost: 0.2101 Epoch: 017/020 | Batch 150/461 | Cost: 0.3024 Epoch: 017/020 | Batch 300/461 | Cost: 0.2714 Epoch: 017/020 | Batch 450/461 | Cost: 0.2259 Epoch: 017/020 Train ACC: 90.91 | Validation ACC: 93.90 Time elapsed: 1.20 min Epoch: 018/020 | Batch 000/461 | Cost: 0.3154 Epoch: 018/020 | Batch 150/461 | Cost: 0.2534 Epoch: 018/020 | Batch 300/461 | Cost: 0.3008 Epoch: 018/020 | Batch 450/461 | Cost: 0.2815 Epoch: 018/020 Train ACC: 90.98 | Validation ACC: 93.90 Time elapsed: 1.27 min Epoch: 019/020 | Batch 000/461 | Cost: 0.2850 Epoch: 019/020 | Batch 150/461 | Cost: 0.2086 Epoch: 019/020 | Batch 300/461 | Cost: 0.4104 Epoch: 019/020 | Batch 450/461 | Cost: 0.2749 Epoch: 019/020 Train ACC: 90.94 | Validation ACC: 94.00 Time elapsed: 1.35 min Epoch: 020/020 | Batch 000/461 | Cost: 0.4211 Epoch: 020/020 | Batch 150/461 | Cost: 0.2129 Epoch: 020/020 | Batch 300/461 | Cost: 0.2256 Epoch: 020/020 | Batch 450/461 | Cost: 0.5096 Epoch: 020/020 Train ACC: 91.02 | Validation ACC: 94.30 Time elapsed: 1.42 min Total Training Time: 1.42 min
Evaluation¶
In [11]:
plt.plot(cost_list, label='Minibatch cost')
plt.plot(np.convolve(cost_list,
np.ones(200,)/200, mode='valid'),
label='Running average')
plt.ylabel('Cross Entropy')
plt.xlabel('Iteration')
plt.legend()
plt.show()
In [12]:
plt.plot(np.arange(1, NUM_EPOCHS+1), train_acc_list, label='Training')
plt.plot(np.arange(1, NUM_EPOCHS+1), valid_acc_list, label='Validation')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.legend()
plt.show()
In [13]:
with torch.set_grad_enabled(False):
test_acc = compute_acc(model=model,
data_loader=test_loader,
device=DEVICE)
valid_acc = compute_acc(model=model,
data_loader=valid_loader,
device=DEVICE)
print(f'Validation ACC: {valid_acc:.2f}%')
print(f'Test ACC: {test_acc:.2f}%')
Validation ACC: 94.30% Test ACC: 91.63%
In [14]:
%watermark -iv
torchvision 0.4.0a0+6b959ee torch 1.2.0 numpy 1.16.4 matplotlib 3.1.0