02

Co-Authored-By: Ranuga <[email protected]>

Author: Programmer-RD-AI

.ipynb_checkpoints/02-checkpoint.ipynb

@@ -2,12 +2,13 @@
 from sklearn.datasets import make_circles
 # Make 100 Samples
 n_samples = 10000
-X,y = make_circles(n_samples,noise=0.069,random_state=42)
+X,y = make_circles(n_samples,noise=0.125,random_state=42)
 
 
 import matplotlib.pyplot as plt
 import numpy as np
 import torch
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
 
 
 X[:5],y[:5]
@@ -37,8 +38,8 @@
 X.dtype
 
 
-X = torch.from_numpy(X).type(torch.float32)
-y = torch.from_numpy(y).type(torch.float32)
+X = torch.from_numpy(X).type(torch.float32).to(device)
+y = torch.from_numpy(y).type(torch.float32).to(device)
 
 
 X[:2],y[:2]
@@ -47,10 +48,51 @@
 from sklearn.model_selection import train_test_split
 
 
-X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.25)
+X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.25,random_state=42)
 
 
+len(X_train),len(y_test)
 
 
+import torch
+from torch import nn
+
+# Make device agnositic code
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+
+device
+
+
+class CircleModelV0(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.layer_1 = nn.Linear(2,1024) # 
+        self.layer_2 = nn.Linear(1024,1)
+    
+    def forward(self,X):
+        return self.layer_2(self.layer_1(X)) # x -> layer_1 -> layer_2
+
+
+model_0 = CircleModelV0().to(device)
+
+
+model_0
+
+
+list(model_0.parameters())
+
+
+model_0 = nn.Sequential(
+    nn.Linear(in_features=2,out_features=64),
+    nn.Linear(64,1)
+).to(device)
+
+
+untrained_preds = model_0(X_test)
+
+
+untrained_preds[0],y_test[0]
+