diff --git a/model_scripted.pt b/model_scripted.pt index 1305430..07ff93c 100644 Binary files a/model_scripted.pt and b/model_scripted.pt differ diff --git a/my_runs/2/config.json b/my_runs/2/config.json new file mode 100644 index 0000000..93c3c33 --- /dev/null +++ b/my_runs/2/config.json @@ -0,0 +1,6 @@ +{ + "batch_size": 64, + "epochs": 100, + "learning_rate": 0.001, + "seed": 938956257 +} \ No newline at end of file diff --git a/my_runs/2/cout.txt b/my_runs/2/cout.txt new file mode 100644 index 0000000..08c9394 --- /dev/null +++ b/my_runs/2/cout.txt @@ -0,0 +1,24 @@ +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) + (relu): ReLU() + (sigmoid): Sigmoid() + (dropout): Dropout(p=0.1, inplace=False) + (batchnorm1): 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100644 index 0000000..e8acad9 --- /dev/null +++ b/my_runs/3/config.json @@ -0,0 +1,6 @@ +{ + "batch_size": 64, + "epochs": 100, + "learning_rate": 0.001, + "seed": 509506776 +} \ No newline at end of file diff --git a/my_runs/3/cout.txt b/my_runs/3/cout.txt new file mode 100644 index 0000000..3d26f8a --- /dev/null +++ b/my_runs/3/cout.txt @@ -0,0 +1,24 @@ +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) + (layer_out): Linear(in_features=128, out_features=1, bias=True) + (relu): ReLU() + (sigmoid): Sigmoid() + (dropout): Dropout(p=0.1, inplace=False) + (batchnorm1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (batchnorm2): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) +) +Loss in 0. iteration: 0.7848939299583435 +Loss 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b/my_runs/4/cout.txt new file mode 100644 index 0000000..63caf29 --- /dev/null +++ b/my_runs/4/cout.txt @@ -0,0 +1,24 @@ +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) + (layer_2): Linear(in_features=256, out_features=128, bias=True) + (layer_out): Linear(in_features=128, out_features=1, bias=True) + (relu): ReLU() + (sigmoid): Sigmoid() + (dropout): Dropout(p=0.1, inplace=False) + (batchnorm1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (batchnorm2): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) +) +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: 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+425,0.6666666666666666,True,0.0,0.0,0.0180752320468979,0.03656716417910448,0.02338348922326149,0.23188405797101447,0.3478260869565218,0.43999999999999995,0 diff --git a/my_runs/_sources/train_0709a5bf0f2b010b364bcdd65e92524d.py b/my_runs/_sources/train_0709a5bf0f2b010b364bcdd65e92524d.py new file mode 100644 index 0000000..b685093 --- /dev/null +++ b/my_runs/_sources/train_0709a5bf0f2b010b364bcdd65e92524d.py @@ -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() + diff --git a/my_runs/_sources/train_5cada4f87bfdc80ed58349427ac376ee.py b/my_runs/_sources/train_5cada4f87bfdc80ed58349427ac376ee.py new file mode 100644 index 0000000..2bacd18 --- /dev/null +++ b/my_runs/_sources/train_5cada4f87bfdc80ed58349427ac376ee.py @@ -0,0 +1,104 @@ +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() + diff --git a/my_runs/_sources/train_c0ef5b35d55c55bd37a7031fd3f247c2.py b/my_runs/_sources/train_c0ef5b35d55c55bd37a7031fd3f247c2.py new file mode 100644 index 0000000..a468603 --- /dev/null +++ b/my_runs/_sources/train_c0ef5b35d55c55bd37a7031fd3f247c2.py @@ -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() + diff --git a/predicted_values.txt b/predicted_values.txt new file mode 100644 index 0000000..90ce82c --- /dev/null +++ b/predicted_values.txt @@ -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 \ No newline at end of file diff --git a/train.py b/train.py index d827367..b685093 100644 --- a/train.py +++ b/train.py @@ -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()