add coder.py
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shaaqu
parent
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@ -19,12 +19,12 @@
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58
coder/coder.py
Normal file
58
coder/coder.py
Normal file
@ -0,0 +1,58 @@
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import numpy as np
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import torch
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import torchvision
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import matplotlib.pyplot as plt
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from time import time
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from torchvision import datasets, transforms
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from torch import nn, optim, nn, optim
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import cv2
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def view_classify(img, ps):
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''' Function for viewing an image and it's predicted classes.
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'''
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ps = ps.data.numpy().squeeze()
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fig, (ax1, ax2) = plt.subplots(figsize=(6,9), ncols=2)
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ax1.imshow(img.resize_(1, 28, 28).numpy().squeeze())
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ax1.axis('off')
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ax2.barh(np.arange(10), ps)
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ax2.set_aspect(0.1)
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ax2.set_yticks(np.arange(10))
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ax2.set_yticklabels(np.arange(10))
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ax2.set_title('Class Probability')
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ax2.set_xlim(0, 1.1)
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plt.tight_layout()
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# load nn model
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model = torch.load('digit_reco_model2.pt')
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if model is None:
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print("Model is not loaded.")
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else:
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print("Model is loaded.")
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# image
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img = cv2.cvtColor(cv2.imread('test3.png'), cv2.COLOR_BGR2GRAY)
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img = cv2.blur(img, (9, 9)) # poprawia jakosc
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img = cv2.resize(img, (28, 28), interpolation=cv2.INTER_AREA)
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img = img.reshape((len(img), -1))
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print(type(img))
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# print(img.shape)
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# plt.imshow(img ,cmap='binary')
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# plt.show()
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img = np.array(img, dtype=np.float32)
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img = torch.from_numpy(img)
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img = img.view(1, 784)
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# recognizing
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with torch.no_grad():
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logps = model(img)
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ps = torch.exp(logps)
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probab = list(ps.numpy()[0])
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print("Predicted Digit =", probab.index(max(probab)))
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view_classify(img.view(1, 28, 28), ps)
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BIN
coder/digit_reco_model.pt
Normal file
BIN
coder/digit_reco_model.pt
Normal file
Binary file not shown.
BIN
coder/digit_reco_model2.pt
Normal file
BIN
coder/digit_reco_model2.pt
Normal file
Binary file not shown.
@ -6,17 +6,19 @@ from time import time
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from torchvision import datasets, transforms
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from torch import nn, optim
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# IMG transform
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transform = transforms.Compose([transforms.ToTensor(),
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transforms.Normalize((0.5,), (0.5,)),
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])
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trainset = datasets.MNIST('PATH_TO_STORE_TRAINSET', download=True, train=True, transform=transform)
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valset = datasets.MNIST('PATH_TO_STORE_TESTSET', download=True, train=False, transform=transform)
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trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)
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valloader = torch.utils.data.DataLoader(valset, batch_size=64, shuffle=True)
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# dataset download
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train_set = datasets.MNIST('PATH_TO_STORE_TRAINSET', download=True, train=True, transform=transform)
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val_set = datasets.MNIST('PATH_TO_STORE_TESTSET', download=True, train=False, transform=transform)
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train_loader = torch.utils.data.DataLoader(train_set, batch_size=64, shuffle=True)
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val_loader = torch.utils.data.DataLoader(val_set, batch_size=64, shuffle=True)
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dataiter = iter(trainloader)
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images, labels = dataiter.next()
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data_iter = iter(train_loader)
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images, labels = data_iter.next()
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print(images.shape)
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print(labels.shape)
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@ -25,15 +27,93 @@ plt.imshow(images[0].numpy().squeeze(), cmap='gray_r')
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plt.show()
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# building nn model
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input_size = 784
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hidden_sizes = [128, 64]
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input_size = 784 # = 28*28
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hidden_sizes = [128, 128, 64]
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output_size = 10
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model = nn.Sequential(nn.Linear(input_size, hidden_sizes[0]),
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nn.ReLU(),
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nn.Linear(hidden_sizes[0], hidden_sizes[1]),
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nn.ReLU(),
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nn.Linear(hidden_sizes[1], output_size),
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nn.LogSoftmax(dim=1))
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print(model)
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nn.Linear(hidden_sizes[1], hidden_sizes[2]),
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nn.ReLU(),
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nn.Linear(hidden_sizes[2], output_size),
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nn.LogSoftmax(dim=-1))
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# print(model)
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criterion = nn.NLLLoss()
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images, labels = next(iter(train_loader))
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images = images.view(images.shape[0], -1)
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logps = model(images) # log probabilities
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loss = criterion(logps, labels) # calculate the NLL loss
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# print('Before backward pass: \n', model[0].weight.grad)
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loss.backward()
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# print('After backward pass: \n', model[0].weight.grad)
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# training
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optimizer = optim.SGD(model.parameters(), lr=0.003, momentum=0.9)
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time0 = time()
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epochs = 100
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for e in range(epochs):
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running_loss = 0
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for images, labels in train_loader:
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# Flatten MNIST images into a 784 long vector
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images = images.view(images.shape[0], -1)
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# Training pass
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optimizer.zero_grad()
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output = model(images)
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loss = criterion(output, labels)
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# This is where the model learns by backpropagating
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loss.backward()
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# And optimizes its weights here
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optimizer.step()
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running_loss += loss.item()
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else:
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print("Epoch {} - Training loss: {}".format(e + 1, running_loss / len(train_loader)))
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print("\nTraining Time (in minutes) =", (time() - time0) / 60)
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# testing
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images, labels = next(iter(val_loader))
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print(type(images))
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img = images[0].view(1, 784)
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with torch.no_grad():
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logps = model(img)
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ps = torch.exp(logps)
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probab = list(ps.numpy()[0])
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print("Predicted Digit =", probab.index(max(probab)))
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# view_classify(img.view(1, 28, 28), ps)
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# accuracy
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correct_count, all_count = 0, 0
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for images, labels in val_loader:
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for i in range(len(labels)):
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img = images[i].view(1, 784)
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with torch.no_grad():
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logps = model(img)
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ps = torch.exp(logps)
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probab = list(ps.numpy()[0])
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pred_label = probab.index(max(probab))
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true_label = labels.numpy()[i]
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if true_label == pred_label:
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correct_count += 1
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all_count += 1
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print("Number Of Images Tested =", all_count)
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print("\nModel Accuracy =", (correct_count / all_count))
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# saving model
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# torch.save(model, './digit_reco_model.pt')
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torch.save(model, './digit_reco_model2.pt')
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