Skip to content

Commit

Permalink
update
Browse files Browse the repository at this point in the history
  • Loading branch information
@Yazhou-Z
Yazhou-Z committed Oct 23, 2021
1 parent 8474bb7 commit db8268f
Show file tree
Hide file tree
Showing 2 changed files with 35 additions and 38 deletions.
12 changes: 7 additions & 5 deletions CNN_multichannel.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -132,7 +132,7 @@ def forward(self, x):
return logit

batch_size = 2
learning_rate = 1e-4
learning_rate = 1e-5
num_epoches = 20000

model = Cnn1d(7)
Expand All @@ -144,7 +144,7 @@ def forward(self, x):
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=learning_rate)

import random
# import random

loss_curve = []
tr_acc = []
Expand All @@ -168,7 +168,7 @@ def forward(self, x):
label = torch.tensor(label, dtype=torch.long)
# print("label: ", label.shape) # torch.Size([1, 1])
# loss = criterion(out, label)
loss = torch.nn.CrossEntropyLoss()(out[0], label) # target 必须是1D
loss = torch.nn.CrossEntropyLoss()(out[0], label) # target 必须是1D
data = [datas, label]
print_loss = loss.data.item()
optimizer.zero_grad()
Expand All @@ -178,10 +178,9 @@ def forward(self, x):
assert torch.all(out > 0), f"not all in {out} > 0"

_, pred = torch.max(out, 2)

print(label)
# if random.uniform(0, 1) < 0.01:
# print(out, '->', pred, ':', label - 1, loss)

# print(pred)
train_acc += (pred + 1 == label).float().mean()

Expand All @@ -198,12 +197,15 @@ def forward(self, x):

if epoch % 10 == 0:
print('epoch: {}, loss: {:.4}, acc: {:.4}'.format(epoch, print_loss, acc))
print(out, '->', pred, ':', label - 1, loss)

plt.plot(loss_curve)
plt.show()
plt.savefig('loss_20000.png')

plt.plot(tr_acc)
plt.show()
plt.savefig('accuracy_20000.png')

# test
model.eval()
Expand Down
61 changes: 28 additions & 33 deletions test_CNN_multichannel.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -8,6 +8,7 @@
from torch.autograd import Variable
import matplotlib.pyplot as plt


def read_data(data="kaggle_data.xlsx"):
resArray = []
data = xlrd.open_workbook(data)
Expand All @@ -21,7 +22,7 @@ def read_data(data="kaggle_data.xlsx"):
i = 0
yy = 0
print(len(resArray))
while i < 300:
while i < len(resArray):
bool = 0
onedata = []
for n in range(10):
Expand All @@ -31,7 +32,7 @@ def read_data(data="kaggle_data.xlsx"):
elif i == 0:
yy += 1
onedata.append(list(resArray[i][:-1]))
elif resArray[i][-1] != resArray[i-1][-1]:
elif resArray[i][-1] != resArray[i - 1][-1]:
bool = 1
else:
onedata.append(list(resArray[i][:-1]))
Expand All @@ -43,9 +44,9 @@ def read_data(data="kaggle_data.xlsx"):
if bool == 0:
onedata = np.array(onedata)
# print(onedata.shape)
onedata = np.transpose(onedata) # reshape (630, 10, 80): (N, L, C) to (N, C, L)
onedata = np.transpose(onedata) # reshape (630, 10, 80): (N, L, C) to (N, C, L)
X.append(onedata)
y.append(resArray[i-1][-1] - 1)
y.append(resArray[i - 1][-1] - 1)

# print(yy)
X = np.array(X)
Expand All @@ -58,29 +59,20 @@ def read_data(data="kaggle_data.xlsx"):

X, y = read_data()

'''
Minimize the training size
'''

# X = X[30]

'''
Minimize the training size
'''

X = torch.tensor(X)
y = torch.tensor(y)
X_tensor = X.view(30, 80, 10)
y = y.view(30, 1)
X_tensor = X.view(630, 80, 10)
y = y.view(630, 1)
Xtrain, Xtest, Ytrain, Ytest = train_test_split(X, y, test_size=0.3, random_state=420)

print(X.shape) # torch.Size([630, 80, 10]) N, C, L
print(y.shape) # torch.Size([630, 1])
print(X.shape) # torch.Size([630, 80, 10]) N, C, L
print(y.shape) # torch.Size([630, 1])


class Cnn1d(nn.Module):

def __init__(self,
num_class = 7
num_class=7
# in_size, out_channels,
# # n_len_seg,
# n_classes,
Expand All @@ -99,7 +91,7 @@ def __init__(self,

# (630, 80, 10)
self.conv1 = nn.Sequential(
nn.Conv1d(80, 81, kernel_size=2, stride=1, padding=1), # 81, 10
nn.Conv1d(80, 81, kernel_size=2, stride=1, padding=1), # 81, 10
nn.BatchNorm1d(81),
nn.ReLU(),
nn.MaxPool1d(2, stride=2)) # 81, 5
Expand Down Expand Up @@ -141,9 +133,10 @@ def forward(self, x):

return logit


batch_size = 2
learning_rate = 1e-4
num_epoches = 100
learning_rate = 1e-5
num_epoches = 2

model = Cnn1d(7)

Expand All @@ -154,7 +147,7 @@ def forward(self, x):
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=learning_rate)

import random
# import random

loss_curve = []
tr_acc = []
Expand All @@ -178,7 +171,7 @@ def forward(self, x):
label = torch.tensor(label, dtype=torch.long)
# print("label: ", label.shape) # torch.Size([1, 1])
# loss = criterion(out, label)
loss = torch.nn.CrossEntropyLoss()(out[0], label) # target 必须是1D
loss = torch.nn.CrossEntropyLoss()(out[0], label) # target 必须是1D
data = [datas, label]
print_loss = loss.data.item()
optimizer.zero_grad()
Expand All @@ -188,10 +181,9 @@ def forward(self, x):
assert torch.all(out > 0), f"not all in {out} > 0"

_, pred = torch.max(out, 2)

if random.uniform(0, 1) < 0.01:
print(out, '->', pred, ':', label, loss)

print(label)
# if random.uniform(0, 1) < 0.01:
# print(out, '->', pred, ':', label - 1, loss)
# print(pred)
train_acc += (pred + 1 == label).float().mean()

Expand All @@ -208,12 +200,15 @@ def forward(self, x):

if epoch % 10 == 0:
print('epoch: {}, loss: {:.4}, acc: {:.4}'.format(epoch, print_loss, acc))
print(out, '->', pred, ':', label - 1, loss)

plt.plot(loss_curve)
plt.show()
plt.savefig('loss_20000.png')

plt.plot(tr_acc)
plt.show()
plt.savefig('accuracy_20000.png')

# test
model.eval()
Expand Down Expand Up @@ -243,16 +238,16 @@ def forward(self, x):
# loss = criterion(out, label)
loss = torch.nn.CrossEntropyLoss()(out.squeeze(1), label) # target 必须是1D

eval_loss += loss*label.size(0)
eval_loss += loss * label.size(0)

_, pred = torch.max(out, 2)
correct += (pred == label).float().mean()
# print("Current: ", correct)
# correct += torch.sum(out == label)

train_loss = eval_loss/len(Xtest)
accu = correct/len(Xtest)
train_loss = eval_loss / len(Xtest)
accu = correct / len(Xtest)

# train_accu.append(accu)
# train_losses.append(train_loss)
# print('Train Loss: %.3f | Accuracy: %.3f'%(train_loss,accu))
Expand All @@ -261,7 +256,7 @@ def forward(self, x):
# _, pred = torch.max(out)
# num_correct = (pred == label).sum()
# eval_acc += num_correct.item()
print('Train Loss: %.3f | Accuracy: %.3f'%(train_loss, accu))
print('Train Loss: %.3f | Accuracy: %.3f' % (train_loss, accu))

# print('Test Loss: {:.6f}, Acc: {:.6f}'.format(
# eval_loss / (len(Xtest)),
Expand Down

0 comments on commit db8268f

Please sign in to comment.