numpy Pillow)1import numpy as np
2from PIL import Image
3
4def predict(model, img):
5 pil_image = img
6 pil_image = pil_image.resize((64, 64))
7
8 image_array = np.array(pil_image) / 255.0
9
10 image_array = np.expand_dims(image_array, axis=0)
11
12 input_shape = (64, 64, pil_image.mode == 'RGB' and 3 or 1)
13
14 decimal_prediction = model.predict(image_array)[0][0]
15 return decimal_prediction| Hyperparameters | Value |
|---|---|
| name | Adam |
| weight_decay | None |
| clipnorm | None |
| global_clipnorm | None |
| clipvalue | None |
| use_ema | False |
| ema_momentum | 0.99 |
| ema_overwrite_frequency | None |
| jit_compile | False |
| is_legacy_optimizer | False |
| learning_rate | 0.0010000000474974513 |
| beta_1 | 0.9 |
| beta_2 | 0.999 |
| epsilon | 1e-07 |
| amsgrad | False |
| training_precision | float32 |
