b2txt25/model_training_nnn_tpu/quick_test_fixes.py

#!/usr/bin/env python3
"""
Quick test to verify TensorFlow implementation fixes
This tests the core fixes without requiring external dependencies
"""

try:
    import tensorflow as tf
    print("✅ TensorFlow imported successfully")
except ImportError as e:
    print(f"❌ TensorFlow import failed: {e}")
    exit(1)

def test_gradient_reversal():
    """Test gradient reversal layer fix"""
    print("\n=== Testing Gradient Reversal Fix ===")
    try:
        # Import our fixed gradient reversal function
        import sys
        import os
        sys.path.append(os.path.dirname(os.path.abspath(__file__)))

        from rnn_model_tf import gradient_reverse

        x = tf.constant([[1.0, 2.0], [3.0, 4.0]])

        # Test forward pass (should be identity)
        y = gradient_reverse(x, lambd=0.5)

        # Check forward pass
        if tf.reduce_all(tf.equal(x, y)):
            print("✅ Gradient reversal forward pass works")

            # Test gradient computation
            with tf.GradientTape() as tape:
                tape.watch(x)
                y = gradient_reverse(x, lambd=0.5)
                loss = tf.reduce_sum(y)

            grad = tape.gradient(loss, x)
            expected_grad = -0.5 * tf.ones_like(x)

            if tf.reduce_all(tf.abs(grad - expected_grad) < 1e-6):
                print("✅ Gradient reversal gradients work correctly")
                return True
            else:
                print(f"❌ Gradient reversal gradients incorrect: got {grad}, expected {expected_grad}")
                return False
        else:
            print("❌ Gradient reversal forward pass failed")
            return False

    except Exception as e:
        print(f"❌ Gradient reversal test failed: {e}")
        return False

def test_ctc_loss():
    """Test CTC loss fix"""
    print("\n=== Testing CTC Loss Fix ===")
    try:
        from rnn_model_tf import CTCLoss

        ctc_loss = CTCLoss(blank_index=0, reduction='none')

        # Create simple test data
        batch_size = 2
        time_steps = 5
        n_classes = 4

        logits = tf.random.normal((batch_size, time_steps, n_classes))
        labels = tf.constant([[1, 2, 0, 0], [3, 1, 2, 0]], dtype=tf.int32)
        input_lengths = tf.constant([time_steps, time_steps], dtype=tf.int32)
        label_lengths = tf.constant([2, 3], dtype=tf.int32)

        loss_input = {
            'labels': labels,
            'input_lengths': input_lengths,
            'label_lengths': label_lengths
        }

        loss = ctc_loss(loss_input, logits)

        if tf.reduce_all(tf.math.is_finite(loss)) and loss.shape == (batch_size,):
            print("✅ CTC loss computation works")
            return True
        else:
            print(f"❌ CTC loss failed: shape {loss.shape}, finite: {tf.reduce_all(tf.math.is_finite(loss))}")
            return False

    except Exception as e:
        print(f"❌ CTC loss test failed: {e}")
        return False

def test_basic_model():
    """Test basic model creation"""
    print("\n=== Testing Basic Model Creation ===")
    try:
        from rnn_model_tf import TripleGRUDecoder

        model = TripleGRUDecoder(
            neural_dim=64,  # Smaller for testing
            n_units=32,
            n_days=2,
            n_classes=10,
            rnn_dropout=0.1,
            input_dropout=0.1,
            patch_size=2,
            patch_stride=1
        )

        # Test forward pass
        batch_size = 2
        time_steps = 10
        x = tf.random.normal((batch_size, time_steps, 64))
        day_idx = tf.constant([0, 1], dtype=tf.int32)

        # Test inference mode
        logits = model(x, day_idx, mode='inference', training=False)
        expected_time_steps = (time_steps - 2) // 1 + 1

        if logits.shape == (batch_size, expected_time_steps, 10):
            print("✅ Basic model inference works")
            return True
        else:
            print(f"❌ Model output shape incorrect: {logits.shape}")
            return False

    except Exception as e:
        print(f"❌ Basic model test failed: {e}")
        return False

def main():
    """Run all tests"""
    print("🧪 Testing TensorFlow Implementation Fixes")
    print("=" * 50)

    tests = [
        test_gradient_reversal,
        test_ctc_loss,
        test_basic_model
    ]

    passed = 0
    total = len(tests)

    for test in tests:
        if test():
            passed += 1

    print("\n" + "=" * 50)
    print(f"📊 Test Results: {passed}/{total} tests passed")

    if passed == total:
        print("🎉 All fixes working correctly!")
        return 0
    else:
        print("❌ Some fixes still need work")
        return 1

if __name__ == "__main__":
    exit(main())