added time logging

examples/alexnet.py

@@ -28,6 +28,11 @@
     et = timeit.default_timer()
     print(f"Predicted in {et-st:.3f} seconds. Idx: {idx}, Logit: {value:.3f}, Category: {cls}")
 
+    import matplotlib.pyplot as plt
+
+    plt.plot(logits.transpose().data)
+    plt.show()
+
     if args.test:
         expected_idx = 36
         expected_value = 24.387

examples/mnist.py

@@ -1,3 +1,4 @@
+import timeit
 import numpy as np
 from gradipy.tensor import Tensor
 import gradipy.nn as nn
@@ -51,6 +52,7 @@ def forward(self, X):
 optimizer = optim.Adam([model.W1, model.W2], lr=lr)
 
 
+st = timeit.default_timer()
 # training loop
 for epoch in range(epochs + 1):
     optimizer.zero_grad()
@@ -67,6 +69,7 @@ def forward(self, X):
             f"Epoch [{epoch+1}/{epochs}], Train Accuracy: {train_accuracy:.4f}, Val Accuracy: {val_accuracy:.4f}"
         )
 
-
+et = timeit.default_timer()
+print(f"Trained in {et-st:.3f} seconds")
 print(evaluate("train"))
 print(evaluate("val"))

gradipy/nn/modules.py

@@ -60,7 +60,6 @@ def __init__(self, in_features: int, out_features: int, bias: bool = True) -> No
             self.bias = Tensor(np.zeros(out_features, dtype=np.float32))
 
     def forward(self, x: Tensor) -> Tensor:
-        # TODO: implement bias
         if self.bias_ is True:
             return x.matmul(self.weight) + self.bias
         return x.matmul(self.weight) + self.bias