rpotter6298
51 lines
1.6 KiB
Python
51 lines
1.6 KiB
Python
#!/usr/bin/env python3
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"""
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verify_feature_dims.py
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Quick check: what does TF/Keras actually output for each model
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with include_top=False? Compare against manuscript claims.
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"""
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import numpy as np
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from tensorflow.keras.applications import (
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VGG16, DenseNet121, EfficientNetB1, MobileNetV2, ResNet50
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)
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# Match the manuscript's input sizes
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models = {
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"VGG16": (VGG16, 224),
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"DenseNet121": (DenseNet121, 224),
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"EfficientNetB1":(EfficientNetB1, 240),
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"MobileNetV2": (MobileNetV2, 224),
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"ResNet50": (ResNet50, 224),
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}
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manuscript_claims = {
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"VGG16": 25088,
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"DenseNet121": 50176,
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"EfficientNetB1":62720,
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"MobileNetV2": 62720,
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"ResNet50": 100352,
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}
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print(f"{'Model':<18s} {'Input':>5s} {'Spatial':>10s} {'Flattened':>10s} {'Manuscript':>12s} {'Match?':>7s}")
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print("-" * 70)
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for name, (model_fn, input_size) in models.items():
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model = model_fn(weights="imagenet", include_top=False,
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input_shape=(input_size, input_size, 3))
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# Pass a dummy batch through
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dummy = np.random.randn(1, input_size, input_size, 3)
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# Need to preprocess correctly — but shape doesn't depend on values
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output = model.predict(dummy, verbose=0)
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spatial = output.shape[1:4] # (H, W, C) for channels_last
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flattened = int(np.prod(spatial))
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claimed = manuscript_claims[name]
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match = "✅" if flattened == claimed else "❌"
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print(f"{name:<18s} {input_size:>4}d {str(spatial):>10s} {flattened:>10d} {claimed:>12d} {match:>7s}")
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print("\nNote: TF uses channels_last (NHWC) format.")
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