ndarray axis

1 files changed, 42 insertions, 0 deletions

diff --git a/learn_torch/basics/nd_grad.py b/learn_torch/basics/nd_grad.py
new file mode 100644
index 0000000..d8af59e
--- /dev/null
+++ b/learn_torch/basics/nd_grad.py
@@ -0,0 +1,42 @@
+# -*- coding: utf-8 -*-
+import numpy as np
+import math
+
+# Create random input and output data
+x = np.linspace(-math.pi, math.pi, 2000)
+y = np.sin(x)
+
+# Randomly initialize weights
+a = np.random.randn()
+b = np.random.randn()
+c = np.random.randn()
+d = np.random.randn()
+
+learning_rate = 1e-3
+for t in range(500):
+    # Forward pass: compute predicted y
+    # y = a + b x + c x^2 + d x^3
+    y_pred = a + b * x + c * x ** 2 + d * x ** 3
+
+    # Compute and print loss
+    loss = np.square(y_pred - y).mean()
+    if t % 10 == 0:
+        print(x.shape, y_pred.shape, y.shape)
+        print(t, loss)
+
+    # Backprop to compute gradients of a, b, c, d with respect to loss
+    grad_y_pred = 2.0 * (y_pred - y)
+    # grad_y_pred = 2.0 * (y - y_pred)
+
+    grad_a = grad_y_pred.mean()
+    grad_b = (grad_y_pred * x).mean()
+    grad_c = (grad_y_pred * x ** 2).mean()
+    grad_d = (grad_y_pred * x ** 3).mean()
+
+    # Update weights
+    a -= learning_rate * grad_a
+    b -= learning_rate * grad_b
+    c -= learning_rate * grad_c
+    d -= learning_rate * grad_d
+
+print(f'Result: y = {a} + {b} x + {c} x^2 + {d} x^3')
\ No newline at end of file