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sacred3

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s434766 2021-05-11 22:16:03 +02:00
parent 34e2139417
commit b3d4c74ecb
2 changed files with 46 additions and 47 deletions
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1
pytorch-training-eval/Dockerfile
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@ -2,7 +2,6 @@ FROM ubuntu:latest
RUN apt-get update && apt-get install -y python3-pip && pip3 install setuptools && pip3 install numpy && pip3 install pandas && pip3 install wget && pip3 install scikit-learn && pip3 install matplotlib && rm -rf /var/lib/apt/lists/*
RUN pip3 install torch torchvision torchaudio
RUN pip3 install sacred
WORKDIR /app
COPY ./../create.py ./

70
stroke-pytorch.py
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@ -13,10 +13,10 @@ from sacred import Experiment
from sacred.observers import FileStorageObserver
np.set_printoptions(suppress=False)
ex = Experiment("stroke-pytorch", interactive=True)
ex.observers.append(FileStorageObserver('ium_s434766O_files'))
ex.observers.append(MongoObserver(url='mongodb://mongo_user:mongo_password_IUM_2021@localhost:27017',
db_name='sacred'))
# ex = Experiment("stroke-pytorch", interactive=True)
# ex.observers.append(FileStorageObserver('ium_s434766O_files'))
# ex.observers.append(MongoObserver(url='mongodb://mongo_user:mongo_password_IUM_2021@localhost:27017',
# db_name='sacred'))
class LogisticRegressionModel(nn.Module):
def __init__(self, input_dim, output_dim):
super(LogisticRegressionModel, self).__init__()
@ -26,38 +26,38 @@ class LogisticRegressionModel(nn.Module):
out = self.linear(x)
return self.sigmoid(out)
@ex.main
def my_main(_log):
data_train = pd.read_csv("data_train.csv")
data_test = pd.read_csv("data_test.csv")
data_val = pd.read_csv("data_val.csv")
FEATURES = ['age','hypertension','heart_disease','ever_married', 'avg_glucose_level', 'bmi']
# @ex.main
# def my_main(_log):
data_train = pd.read_csv("data_train.csv")
data_test = pd.read_csv("data_test.csv")
data_val = pd.read_csv("data_val.csv")
FEATURES = ['age','hypertension','heart_disease','ever_married', 'avg_glucose_level', 'bmi']
x_train = data_train[FEATURES].astype(np.float32)
y_train = data_train['stroke'].astype(np.float32)
x_train = data_train[FEATURES].astype(np.float32)
y_train = data_train['stroke'].astype(np.float32)
x_test = data_test[FEATURES].astype(np.float32)
y_test = data_test['stroke'].astype(np.float32)
x_test = data_test[FEATURES].astype(np.float32)
y_test = data_test['stroke'].astype(np.float32)
fTrain = torch.from_numpy(x_train.values)
tTrain = torch.from_numpy(y_train.values.reshape(2945,1))
fTrain = torch.from_numpy(x_train.values)
tTrain = torch.from_numpy(y_train.values.reshape(2945,1))
fTest= torch.from_numpy(x_test.values)
tTest = torch.from_numpy(y_test.values)
fTest= torch.from_numpy(x_test.values)
tTest = torch.from_numpy(y_test.values)
batch_size = int(sys.argv[1]) if len(sys.argv) > 1 else 16
num_epochs = int(sys.argv[2]) if len(sys.argv) > 2 else 5
learning_rate = 0.001
input_dim = 6
output_dim = 1
info_params = "Batch size = " + str(batch_size) + " Epochs = " + str(num_epochs)
_log.info(info_params)
model = LogisticRegressionModel(input_dim, output_dim)
batch_size = int(sys.argv[1]) if len(sys.argv) > 1 else 16
num_epochs = int(sys.argv[2]) if len(sys.argv) > 2 else 5
learning_rate = 0.001
input_dim = 6
output_dim = 1
info_params = "Batch size = " + str(batch_size) + " Epochs = " + str(num_epochs)
# _log.info(info_params)
model = LogisticRegressionModel(input_dim, output_dim)
criterion = torch.nn.BCELoss(reduction='mean')
optimizer = torch.optim.SGD(model.parameters(), lr = learning_rate)
criterion = torch.nn.BCELoss(reduction='mean')
optimizer = torch.optim.SGD(model.parameters(), lr = learning_rate)
for epoch in range(num_epochs):
for epoch in range(num_epochs):
# print ("Epoch #",epoch)
model.train()
optimizer.zero_grad()
@ -69,11 +69,11 @@ def my_main(_log):
# Backward pass
loss.backward()
optimizer.step()
info_loss = "Last loss = " + str(loss.item())
_log.info(info_loss)
y_pred = model(fTest)
# print("predicted Y value: ", y_pred.data)
info_loss = "Last loss = " + str(loss.item())
# _log.info(info_loss)
y_pred = model(fTest)
print("predicted Y value: ", y_pred.data)
torch.save(model.state_dict(), 'stroke.pth')
torch.save(model.state_dict(), 'stroke.pth')
ex.run()
# ex.run()