40 lines
1.2 KiB
Plaintext
40 lines
1.2 KiB
Plaintext
Dataset: mnist
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Model: basiccnn
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Number of classes: 10
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Train samples: 42000
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Validation samples: 18000
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Test samples: 10000
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Validation accuracy: 0.9895
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Test accuracy: 0.9901
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Github repository: ....
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Data Loading Instructions:
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The extracted features, predictions, targets, and logits are saved in .npz files.
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To load the data in Python:
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import numpy as np
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# Load training data
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train_data = np.load('mnist_basiccnn_train_out.npz')
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train_features = train_data['features']
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train_predictions = train_data['predictions']
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train_targets = train_data['targets']
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train_logits = train_data['logits']
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# Load validation data
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val_data = np.load('mnist_basiccnn_val_out.npz')
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val_features = val_data['features']
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val_predictions = val_data['predictions']
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val_targets = val_data['targets']
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val_logits = val_data['logits']
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# Load test data
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test_data = np.load('mnist_basiccnn_test_out.npz')
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test_features = test_data['features']
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test_predictions = test_data['predictions']
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test_targets = test_data['targets']
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test_logits = test_data['logits']
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Note: features are the output of the feature extractor (before the final classification layer),
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predictions are softmax probabilities, targets are the true labels, and logits are the raw model outputs.
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