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sacred final

This commit is contained in:
Natalia Szymczyk 2023-06-30 17:37:38 +02:00
parent 7029181457
commit 1bef5ec2d7
22 changed files with 1016 additions and 3 deletions
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model_scripted.pt
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INFO - ium_z487186 - Running command 'my_main'
INFO - ium_z487186 - Started run with ID "2"
ClassificationModel(
(layer_1): Linear(in_features=10, out_features=256, bias=True)
(layer_2): Linear(in_features=256, out_features=128, bias=True)
(layer_out): Linear(in_features=128, out_features=1, bias=True)
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(batchnorm2): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
Loss in 0. iteration: 0.7717282772064209
Loss in 10. iteration: 0.6942546963691711
Loss in 20. iteration: 0.6702170372009277
Loss in 30. iteration: 0.6453146934509277
Loss in 40. iteration: 0.6157739162445068
Loss in 50. iteration: 0.6053208708763123
Loss in 60. iteration: 0.5998749732971191
Loss in 70. iteration: 0.6101130247116089
Loss in 80. iteration: 0.6052132248878479
Loss in 90. iteration: 0.5935238003730774
Last iteration loss value: 0.5927996635437012
INFO - ium_z487186 - Completed after 0:00:03

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my_runs/3/config.json Normal file
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my_runs/3/cout.txt Normal file
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INFO - ium_z487186 - Running command 'my_main'
INFO - ium_z487186 - Started run with ID "3"
ClassificationModel(
(layer_1): Linear(in_features=10, out_features=256, bias=True)
(layer_2): Linear(in_features=256, out_features=128, bias=True)
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Loss in 0. iteration: 0.7848939299583435
Loss in 10. iteration: 0.6893437504768372
Loss in 20. iteration: 0.6607671976089478
Loss in 30. iteration: 0.6494117379188538
Loss in 40. iteration: 0.6445630192756653
Loss in 50. iteration: 0.630591094493866
Loss in 60. iteration: 0.622308611869812
Loss in 70. iteration: 0.6175063252449036
Loss in 80. iteration: 0.6140344142913818
Loss in 90. iteration: 0.5995607376098633
Last iteration loss value: 0.5983042120933533
INFO - ium_z487186 - Completed after 0:00:04

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my_runs/4/config.json Normal file
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my_runs/4/cout.txt Normal file
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INFO - ium_z487186 - Running command 'my_main'
INFO - ium_z487186 - Started run with ID "4"
ClassificationModel(
(layer_1): Linear(in_features=10, out_features=256, bias=True)
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Loss in 0. iteration: 0.7733072638511658
Loss in 10. iteration: 0.6968318223953247
Loss in 20. iteration: 0.6751510500907898
Loss in 30. iteration: 0.6422412395477295
Loss in 40. iteration: 0.6177173852920532
Loss in 50. iteration: 0.6049436926841736
Loss in 60. iteration: 0.6044824719429016
Loss in 70. iteration: 0.5992134213447571
Loss in 80. iteration: 0.5993654131889343
Loss in 90. iteration: 0.5977444648742676
Last iteration loss value: 0.5973907113075256
INFO - ium_z487186 - Completed after 0:00:04

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my_runs/4/run.json Normal file
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@ -0,0 +1,103 @@
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()

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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()

103
my_runs/_sources/train_c0ef5b35d55c55bd37a7031fd3f247c2.py Normal file
View File

@ -0,0 +1,103 @@
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.run()

1
predicted_values.txt Normal file
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@ -0,0 +1 @@
1 1 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 1 0 1 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 0 1 1 0 0 1 0 1 0 0 1 1 1 0 0 0 0 1 0 0 0 0 1

9
train.py
View File

@ -11,8 +11,7 @@ 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.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
@exint.config
def my_config():
@ -28,7 +27,10 @@ def prepare_message(msg):
@exint.main
def my_main(batch_size, learning_rate, epochs):
train_file = pd.read_csv('train.data').drop('Unnamed: 0', axis=1)
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)
@ -96,5 +98,6 @@ def my_main(batch_size, learning_rate, epochs):
exint.add_artifact('model_scripted.pt')
exint.add_source_file("train.py")
exint.run()