diff --git a/ml_pytorch.py b/ml_pytorch.py
index 9f0b449..9d59a7b 100644
--- a/ml_pytorch.py
+++ b/ml_pytorch.py
@@ -1,159 +1,159 @@
-#!/usr/bin/env python
-# coding: utf-8
-
-# In[1]:
-
-
-import torch
-import jovian
-import torchvision
-import matplotlib
-import torch.nn as nn
-import pandas as pd
-import matplotlib.pyplot as plt
-import seaborn as sns
-import torch.nn.functional as F
-from torchvision.datasets.utils import download_url
-from torch.utils.data import DataLoader, TensorDataset, random_split
-import random
-import os
-import sys
-
-
-# In[2]:
-
-
-#load data
-dataframe = pd.read_csv("understat.csv")
-
-#choose columns
-input_cols=list(dataframe.columns)[4:11]
-output_cols = ['position']
-input_cols, output_cols
-
-
-# In[4]:
-
-
-def dataframe_to_arrays(dataframe):
-    dataframe_loc = dataframe.copy(deep=True)
-    inputs_array = dataframe_loc[input_cols].to_numpy()
-    targets_array = dataframe_loc[output_cols].to_numpy()
-    return inputs_array, targets_array
-
-inputs_array, targets_array = dataframe_to_arrays(dataframe)
-
-inputs = torch.from_numpy(inputs_array).type(torch.float)
-targets = torch.from_numpy(targets_array).type(torch.float)
-
-dataset = TensorDataset(inputs, targets)
-
-
-# In[7]:
-
-
-train_ds, val_ds = random_split(dataset, [548, 136])
-batch_size=50
-train_loader = DataLoader(train_ds, batch_size, shuffle=True)
-val_loader = DataLoader(val_ds, batch_size)
-
-
-# In[8]:
-
-
-class Model_xPosition(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.linear = nn.Linear(input_size,output_size) 
-        
-    def forward(self, xb): 
-        out = self.linear(xb)
-        return out
-    
-    def training_step(self, batch):
-        inputs, targets = batch 
-        # Generate predictions
-        out = self(inputs)          
-        # Calcuate loss
-        loss = F.l1_loss(out,targets) 
-        return loss
-    
-    def validation_step(self, batch):
-        inputs, targets = batch
-        out = self(inputs)
-        loss = F.l1_loss(out,targets)   
-        return {'val_loss': loss.detach()}
-        
-    def validation_epoch_end(self, outputs):
-        batch_losses = [x['val_loss'] for x in outputs]
-        epoch_loss = torch.stack(batch_losses).mean() 
-        return {'val_loss': epoch_loss.item()}
-    
-    def epoch_end(self, epoch, result, num_epochs):
-        if (epoch+1) % 100 == 0 or epoch == num_epochs-1:
-            print("Epoch {} loss: {:.4f}".format(epoch+1, result['val_loss']))
-            
-            
-def evaluate(model, val_loader):
-    outputs = [model.validation_step(batch) for batch in val_loader]
-    return model.validation_epoch_end(outputs)
-
-def fit(epochs, lr, model, train_loader, val_loader, opt_func=torch.optim.SGD):
-    history = []
-    optimizer = opt_func(model.parameters(), lr)
-    for epoch in range(epochs):
-        for batch in train_loader:
-            loss = model.training_step(batch)
-            loss.backward()
-            optimizer.step()
-            optimizer.zero_grad()
-        result = evaluate(model, val_loader)
-        model.epoch_end(epoch, result, epochs)
-        history.append(result)
-    return history
-
-
-# In[9]:
-
-
-input_size = len(input_cols)
-output_size = len(output_cols)
-model=Model_xPosition()
-
-
-# In[11]:
-
-
-epochs = 2000
-lr = 1e-5
-learning_proccess = fit(epochs, lr, model, train_loader, val_loader)
-
-
-# In[13]:
-
-
-def predict_single(input, target, model):
-    inputs = input.unsqueeze(0)
-    predictions = model(inputs)
-    prediction = predictions[0].detach()
-
-    return "Target: "+str(target)+"          Predicted: "+str(prediction)+"\n"
-
-
-# In[14]:
-
-
-for i in random.sample(range(0, len(val_ds)), 10):
-    input_, target = val_ds[i]
-    print(predict_single(input_, target, model),end="")
-
-
-# In[15]:
-
-
-with open("result.txt", "w+") as file:
-    for i in range(0, len(val_ds), 1):
-        input_, target = val_ds[i]
-        file.write(str(predict_single(input_, target, model)))
-
-
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+import torch
+import jovian
+import torchvision
+import matplotlib
+import torch.nn as nn
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+import torch.nn.functional as F
+from torchvision.datasets.utils import download_url
+from torch.utils.data import DataLoader, TensorDataset, random_split
+import random
+import os
+import sys
+
+
+# In[2]:
+
+
+#load data
+dataframe = pd.read_csv("understat.csv")
+
+#choose columns
+input_cols=list(dataframe.columns)[4:11]
+output_cols = ['position']
+input_cols, output_cols
+
+
+# In[4]:
+
+
+def dataframe_to_arrays(dataframe):
+    dataframe_loc = dataframe.copy(deep=True)
+    inputs_array = dataframe_loc[input_cols].to_numpy()
+    targets_array = dataframe_loc[output_cols].to_numpy()
+    return inputs_array, targets_array
+
+inputs_array, targets_array = dataframe_to_arrays(dataframe)
+
+inputs = torch.from_numpy(inputs_array).type(torch.float)
+targets = torch.from_numpy(targets_array).type(torch.float)
+
+dataset = TensorDataset(inputs, targets)
+
+
+# In[7]:
+
+
+train_ds, val_ds = random_split(dataset, [548, 136])
+batch_size=50
+train_loader = DataLoader(train_ds, batch_size, shuffle=True)
+val_loader = DataLoader(val_ds, batch_size)
+
+
+# In[8]:
+
+
+class Model_xPosition(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.linear = nn.Linear(input_size,output_size) 
+        
+    def forward(self, xb): 
+        out = self.linear(xb)
+        return out
+    
+    def training_step(self, batch):
+        inputs, targets = batch 
+        # Generate predictions
+        out = self(inputs)          
+        # Calcuate loss
+        loss = F.l1_loss(out,targets) 
+        return loss
+    
+    def validation_step(self, batch):
+        inputs, targets = batch
+        out = self(inputs)
+        loss = F.l1_loss(out,targets)   
+        return {'val_loss': loss.detach()}
+        
+    def validation_epoch_end(self, outputs):
+        batch_losses = [x['val_loss'] for x in outputs]
+        epoch_loss = torch.stack(batch_losses).mean() 
+        return {'val_loss': epoch_loss.item()}
+    
+    def epoch_end(self, epoch, result, num_epochs):
+        if (epoch+1) % 100 == 0 or epoch == num_epochs-1:
+            print("Epoch {} loss: {:.4f}".format(epoch+1, result['val_loss']))
+            
+            
+def evaluate(model, val_loader):
+    outputs = [model.validation_step(batch) for batch in val_loader]
+    return model.validation_epoch_end(outputs)
+
+def fit(epochs, lr, model, train_loader, val_loader, opt_func=torch.optim.SGD):
+    history = []
+    optimizer = opt_func(model.parameters(), lr)
+    for epoch in range(epochs):
+        for batch in train_loader:
+            loss = model.training_step(batch)
+            loss.backward()
+            optimizer.step()
+            optimizer.zero_grad()
+        result = evaluate(model, val_loader)
+        model.epoch_end(epoch, result, epochs)
+        history.append(result)
+    return history
+
+
+# In[9]:
+
+
+input_size = len(input_cols)
+output_size = len(output_cols)
+model=Model_xPosition()
+
+
+# In[11]:
+
+
+epochs = 1000
+lr = 1e-5
+learning_proccess = fit(epochs, lr, model, train_loader, val_loader)
+
+
+# In[13]:
+
+
+def predict_single(input, target, model):
+    inputs = input.unsqueeze(0)
+    predictions = model(inputs)
+    prediction = predictions[0].detach()
+
+    return "Target: "+str(target)+"          Predicted: "+str(prediction)+"\n"
+
+
+# In[14]:
+
+
+for i in random.sample(range(0, len(val_ds)), 10):
+    input_, target = val_ds[i]
+    print(predict_single(input_, target, model),end="")
+
+
+# In[15]:
+
+
+with open("result.txt", "w+") as file:
+    for i in range(0, len(val_ds), 1):
+        input_, target = val_ds[i]
+        file.write(str(predict_single(input_, target, model)))
+
+