def predict(X):
nn = NN([6, 8, 4, 1])
nn.load_state_dict(torch.load('model_state/state'))
nn.eval()
result = nn.forward(X).detach().numpy().reshape(-1, 1)
result_transformed = scaler.inverse_transform(result)
return result_transformed
def train(self, epochs=10):
Model = NN(batch_size=30)
opt = optim.Adam(Model.parameters(), lr=0.005)
criterion = nn.BCELoss()
softmax = nn.Softmax(dim=0)
loss = 0
print(self.labels.shape)
for i in range(epochs):
item_loss = 0
for i, (feat, lab) in enumerate(self.dataloader):
feat = feat[0][:, :1, :, :]
output = Model.forward(feat)
loss = criterion(output, lab.view((30)))
loss.backward()
opt.step()
item_loss += loss.item()
print("LOSS ---->", item_loss / len(self.dataloader))
torch.save(Model.state_dict(), "1")
import cv2
from model import NN
import numpy as np
import torch
cap = cv2.VideoCapture(0)
i = 0
classify = 1
labels =[]
Model = NN(batch_size = 1)
Model.load_state_dict(torch.load("1"))
Model.eval()
tardict = {1 : 'Face Detected' , 0 : 'Undetected' }
while True:
i += 1
ret , frame = cap.read()
gray = cv2.cvtColor(frame , cv2.COLOR_RGB2GRAY)
gray = cv2.GaussianBlur(gray, (15,15), 0)
cv2.imshow('feed' , frame)
gray = torch.from_numpy(gray).view(1 , 1, 480 , 640).float()
output = torch.round(Model.forward(gray))
output = output.item()
print (tardict[output])
if output != 0:
input()
if cv2.waitKey(1) & 0xFF == ord('q') :
break
print('Early stopping')
break
last_loss_test = loss_test
print('epoch {}, loss - {}'.format(epoch, loss))
print('test loss - {}'.format(loss_test))
optimizer.zero_grad()
loss.backward()
optimizer.step()
if __name__ == '__main__':
data = pd.read_csv('datasets/anchor-weed.csv')
scaler.fit_transform(data[['price']])
np.random.seed(100)
nr_classes = 6
hidden_size = 12
data = pd.read_csv('datasets/anchor-weed-prepared.csv')
data = data.drop('Unnamed: 0', axis=1)
y = data['price+1'].values
X = data.drop('price+1', axis=1).values
model = NN([nr_classes, 8, 4, 1])
train(model, X, y, 30000, 0.01)
torch.save(model.state_dict(), 'model_state/state')
y_pred = model.forward(torch.from_numpy(X[275, :]).float())
y_pred = np.exp(y_pred.detach().numpy().reshape(-1, 1))
y_ = np.exp(y[275])
plot_results(model, X, y)