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--- |
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library_name: keras |
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license: mit |
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--- |
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## Model description |
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A very simple model that converts an image into a number! |
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### the hepler function |
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(requirements: `numpy Pillow`) |
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```python |
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import numpy as np |
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from PIL import Image |
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def predict(model, img): |
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pil_image = img |
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pil_image = pil_image.resize((64, 64)) |
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image_array = np.array(pil_image) / 255.0 |
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image_array = np.expand_dims(image_array, axis=0) |
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input_shape = (64, 64, pil_image.mode == 'RGB' and 3 or 1) |
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decimal_prediction = model.predict(image_array)[0][0] |
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return decimal_prediction |
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``` |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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| Hyperparameters | Value | |
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| :-- | :-- | |
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| name | Adam | |
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| weight_decay | None | |
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| clipnorm | None | |
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| global_clipnorm | None | |
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| clipvalue | None | |
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| use_ema | False | |
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| ema_momentum | 0.99 | |
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| ema_overwrite_frequency | None | |
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| jit_compile | False | |
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| is_legacy_optimizer | False | |
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| learning_rate | 0.0010000000474974513 | |
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| beta_1 | 0.9 | |
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| beta_2 | 0.999 | |
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| epsilon | 1e-07 | |
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| amsgrad | False | |
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| training_precision | float32 | |
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## Model Plot |
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<details> |
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<summary>View Model Plot</summary> |
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</details> |
