165 KiB
165 KiB
from torchvision import datasets
import torch
data_folder = '/content/' # This can be any directory you want to download FMNIST to
fmnist = datasets.FashionMNIST(data_folder, download=True, train=True)tr_images = fmnist.data
tr_targets = fmnist.targetsval_fmnist = datasets.FashionMNIST(data_folder, download=True, train=False)
val_images = val_fmnist.data
val_targets = val_fmnist.targetsimport matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
from torch.utils.data import Dataset, DataLoader
import torch
import torch.nn as nn
device = 'cuda' if torch.cuda.is_available() else 'cpu'class FMNISTDataset(Dataset):
def __init__(self, x, y):
x = x.float()/255
x = x.view(-1,1,28,28)
self.x, self.y = x, y
def __getitem__(self, ix):
x, y = self.x[ix], self.y[ix]
return x.to(device), y.to(device)
def __len__(self):
return len(self.x)
from torch.optim import SGD, Adam
def get_model():
model = nn.Sequential(
nn.Conv2d(1, 64, kernel_size=3),
nn.MaxPool2d(2),
nn.ReLU(),
nn.Conv2d(64, 128, kernel_size=3),
nn.MaxPool2d(2),
nn.ReLU(),
nn.Flatten(),
nn.Linear(3200, 256),
nn.ReLU(),
nn.Linear(256, 10)
).to(device)
loss_fn = nn.CrossEntropyLoss()
optimizer = Adam(model.parameters(), lr=1e-3)
return model, loss_fn, optimizer
def train_batch(x, y, model, opt, loss_fn):
prediction = model(x)
batch_loss = loss_fn(prediction, y)
batch_loss.backward()
optimizer.step()
optimizer.zero_grad()
return batch_loss.item()
@torch.no_grad()
def accuracy(x, y, model):
model.eval()
prediction = model(x)
max_values, argmaxes = prediction.max(-1)
is_correct = argmaxes == y
return is_correct.cpu().numpy().tolist()
def get_data():
train = FMNISTDataset(tr_images, tr_targets)
trn_dl = DataLoader(train, batch_size=32, shuffle=True)
val = FMNISTDataset(val_images, val_targets)
val_dl = DataLoader(val, batch_size=len(val_images), shuffle=True)
return trn_dl, val_dl@torch.no_grad()
def val_loss(x, y, model):
model.eval()
prediction = model(x)
val_loss = loss_fn(prediction, y)
return val_loss.item()trn_dl, val_dl = get_data()
model, loss_fn, optimizer = get_model()!pip install torch_summary
from torchsummary import summary
model, loss_fn, optimizer = get_model()
summary(model, torch.zeros(1,1,28,28));Requirement already satisfied: torch_summary in /usr/local/lib/python3.6/dist-packages (1.4.3) ========================================================================================== Layer (type:depth-idx) Output Shape Param # ========================================================================================== ├─Conv2d: 1-1 [-1, 64, 26, 26] 640 ├─MaxPool2d: 1-2 [-1, 64, 13, 13] -- ├─ReLU: 1-3 [-1, 64, 13, 13] -- ├─Conv2d: 1-4 [-1, 128, 11, 11] 73,856 ├─MaxPool2d: 1-5 [-1, 128, 5, 5] -- ├─ReLU: 1-6 [-1, 128, 5, 5] -- ├─Flatten: 1-7 [-1, 3200] -- ├─Linear: 1-8 [-1, 256] 819,456 ├─ReLU: 1-9 [-1, 256] -- ├─Linear: 1-10 [-1, 10] 2,570 ========================================================================================== Total params: 896,522 Trainable params: 896,522 Non-trainable params: 0 Total mult-adds (M): 10.13 ========================================================================================== Input size (MB): 0.00 Forward/backward pass size (MB): 0.45 Params size (MB): 3.42 Estimated Total Size (MB): 3.87 ==========================================================================================
train_losses, train_accuracies = [], []
val_losses, val_accuracies = [], []
for epoch in range(5):
print(epoch)
train_epoch_losses, train_epoch_accuracies = [], []
for ix, batch in enumerate(iter(trn_dl)):
x, y = batch
batch_loss = train_batch(x, y, model, optimizer, loss_fn)
train_epoch_losses.append(batch_loss)
train_epoch_loss = np.array(train_epoch_losses).mean()
for ix, batch in enumerate(iter(trn_dl)):
x, y = batch
is_correct = accuracy(x, y, model)
train_epoch_accuracies.extend(is_correct)
train_epoch_accuracy = np.mean(train_epoch_accuracies)
for ix, batch in enumerate(iter(val_dl)):
x, y = batch
val_is_correct = accuracy(x, y, model)
validation_loss = val_loss(x, y, model)
val_epoch_accuracy = np.mean(val_is_correct)
train_losses.append(train_epoch_loss)
train_accuracies.append(train_epoch_accuracy)
val_losses.append(validation_loss)
val_accuracies.append(val_epoch_accuracy)0 1 2 3 4
epochs = np.arange(5)+1
import matplotlib.ticker as mtick
import matplotlib.pyplot as plt
import matplotlib.ticker as mticker
%matplotlib inline
plt.subplot(211)
plt.plot(epochs, train_losses, 'bo', label='Training loss')
plt.plot(epochs, val_losses, 'r', label='Validation loss')
plt.gca().xaxis.set_major_locator(mticker.MultipleLocator(1))
plt.title('Training and validation loss with CNN')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()
plt.grid('off')
plt.show()
plt.subplot(212)
plt.plot(epochs, train_accuracies, 'bo', label='Training accuracy')
plt.plot(epochs, val_accuracies, 'r', label='Validation accuracy')
plt.gca().xaxis.set_major_locator(mticker.MultipleLocator(1))
plt.title('Training and validation accuracy with CNN')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
#plt.ylim(0.8,1)
plt.gca().set_yticklabels(['{:.0f}%'.format(x*100) for x in plt.gca().get_yticks()])
plt.legend()
plt.grid('off')
plt.show()preds = []
ix = 24300
for px in range(-5,6):
img = tr_images[ix]/255.
img = img.view(28, 28)
img2 = np.roll(img, px, axis=1)
img3 = torch.Tensor(img2).view(-1,1,28,28).to(device)
np_output = model(img3).cpu().detach().numpy()
pred = np.exp(np_output)/np.sum(np.exp(np_output))
preds.append(pred)
plt.imshow(img2)
plt.title(fmnist.classes[pred[0].argmax()])
plt.show()np.array(preds).shape(11, 1, 10)
import seaborn as sns
fig, ax = plt.subplots(1,1, figsize=(12,10))
plt.title('Probability of each class for various translations')
sns.heatmap(np.array(preds).reshape(11,10), annot=True, ax=ax, fmt='.2f', xticklabels=fmnist.classes, yticklabels=[str(i)+str(' pixels') for i in range(-5,6)], cmap='gray')<matplotlib.axes._subplots.AxesSubplot at 0x7f6f58fa99b0>