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224ebad 1b272e7 224ebad 1b272e7 224ebad | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 | #!/usr/bin/env python3
"""
Local Inference SCript (NumPy Version)
=====================================
Carica i pesi .json scaricati ed esegue inferenza usando SOLO NumPy (no PyTorch necessario).
"""
import json
import numpy as np
import os
import sys
def relu(x):
return np.maximum(0, x)
def sigmoid(x):
return 1 / (1 + np.exp(-x))
def linear_forward(x, W, b):
# W shape: (out_features, in_features)
# x shape: (in_features) or (batch, in_features)
return x @ W.T + b
def predict(model_weights, X):
# Layer 1
w1 = np.array(model_weights['net.0.weight'])
b1 = np.array(model_weights['net.0.bias'])
z1 = linear_forward(X, w1, b1)
a1 = relu(z1)
# Layer 2
w2 = np.array(model_weights['net.2.weight'])
b2 = np.array(model_weights['net.2.bias'])
z2 = linear_forward(a1, w2, b2)
a2 = relu(z2)
# Layer 3
w3 = np.array(model_weights['net.4.weight'])
b3 = np.array(model_weights['net.4.bias'])
z3 = linear_forward(a2, w3, b3)
a3 = sigmoid(z3)
return a3
def main():
model_path = "simple_nn_weights.json"
# Check current dir or ../output/
if not os.path.exists(model_path):
alt_path = os.path.join("..", "output", "simple_nn_weights.json")
if os.path.exists(alt_path):
model_path = alt_path
print(f"📂 Loading weights from {model_path}...")
if not os.path.exists(model_path):
print("❌ Model weights not found! Run 'python examples/train_nn.py' first.")
sys.exit(1)
try:
with open(model_path) as f:
weights = json.load(f)
print("✅ Weights loaded successfully!")
except Exception as e:
print(f"❌ Error loading JSON: {e}")
sys.exit(1)
# Generate Test Data
print("\n🔮 Running Inference (NumPy Engine)...")
test_points = np.array([
[0.0, 0.0], # Center (Should be 1)
[0.8, 0.8], # Outside (Should be 0)
[-0.5, 0.5], # Inside (Should be 1)
[2.0, 2.0] # Far outside (Should be 0)
], dtype=np.float32)
predictions = predict(weights, test_points)
print("-" * 40)
print(f"{'X':<10} {'Y':<10} | {'Prob':<10} {'Class':<10}")
print("-" * 40)
for i, point in enumerate(test_points):
prob = predictions[i][0]
cls = 1 if prob > 0.5 else 0
print(f"{point[0]:<10.2f} {point[1]:<10.2f} | {prob:<10.4f} {cls:<10}")
print("-" * 40)
print("✅ Inference Pipeline Verified (No PyTorch needed locally).")
if __name__ == "__main__":
main()
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