diff --git a/model_training_nnn_tpu/quick_test_fixes.py b/model_training_nnn_tpu/quick_test_fixes.py
deleted file mode 100644
index 00405b7..0000000
--- a/model_training_nnn_tpu/quick_test_fixes.py
+++ /dev/null
@@ -1,161 +0,0 @@
-#!/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())
\ No newline at end of file