ium_z487186/my_runs/_sources/train_0709a5bf0f2b010b364bcdd65e92524d.py
Natalia Szymczyk 1bef5ec2d7 sacred final
2023-06-30 17:37:38 +02:00

104 lines
3.0 KiB
Python

import pandas as pd
import torch
import torch.nn as nn
from sklearn.preprocessing import MinMaxScaler
from torch.utils.data import TensorDataset
from torch.utils.data import DataLoader
from sacred import Experiment
from sacred.observers import FileStorageObserver, MongoObserver
exint = Experiment("ium_z487186", interactive=True)
exint.observers.append(FileStorageObserver('my_runs'))
# exint.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
@exint.config
def my_config():
batch_size = 64
learning_rate = 0.001
epochs = 100
@exint.capture
def prepare_message(msg):
return msg
@exint.main
def my_main(batch_size, learning_rate, epochs):
with exint.open_resource('train.data') as f:
train_file = pd.read_csv(f)
train_file = train_file.drop('Unnamed: 0', axis=1)
df_pandas = train_file.dropna()
X_train = df_pandas.drop('class', axis=1)
Y_train = df_pandas['class']
scaler = MinMaxScaler()
X_train = scaler.fit_transform(X_train)
x_tensor = torch.tensor(X_train).float()
y_tensor = torch.tensor(Y_train.values).float()
train_ds = TensorDataset(x_tensor, y_tensor.unsqueeze(1))
train_dl = DataLoader(train_ds, batch_size=batch_size)
class ClassificationModel(nn.Module):
def __init__(self, n_input_dim):
super(ClassificationModel, self).__init__()
self.layer_1 = nn.Linear(n_input_dim, 256)
self.layer_2 = nn.Linear(256, 128)
self.layer_out = nn.Linear(128, 1)
self.relu = nn.ReLU()
self.sigmoid = nn.Sigmoid()
self.dropout = nn.Dropout(p=0.1)
self.batchnorm1 = nn.BatchNorm1d(256)
self.batchnorm2 = nn.BatchNorm1d(128)
def forward(self, inputs):
x = self.relu(self.layer_1(inputs))
x = self.batchnorm1(x)
x = self.relu(self.layer_2(x))
x = self.batchnorm2(x)
x = self.dropout(x)
x = self.sigmoid(self.layer_out(x))
return x
model = ClassificationModel(X_train.shape[1])
print(model)
loss_func = nn.BCEWithLogitsLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
model.train()
train_loss = []
for epoch in range(epochs):
for xb, yb in train_dl:
y_pred = model(xb) # Forward Propagation
loss = loss_func(y_pred, yb) # Loss Computation
optimizer.zero_grad() # Clearing all previous gradients, setting to zero
loss.backward() # Back Propagation
optimizer.step() # Updating the parameters
if epoch % 10 == 0:
print(f"Loss in {epoch}. iteration: {loss.item()}")
train_loss.append(loss.item())
print('Last iteration loss value: '+str(loss.item()))
model_scripted = torch.jit.script(model) # Export to TorchScript
model_scripted.save('model_scripted.pt') # Save
exint.add_artifact('model_scripted.pt')
exint.add_source_file("train.py")
exint.run()