raphael
fbc6ee8187
- worse performance than with initial design
- vgg16 pretrained weights are used for the base
network, which is then piped into a custom head
model, which
- flattens the layer (previously done in the base model)
+ Dense Layer
+ Normalization
+ Activation
- training split with 360 fruits used, same as previous mode
- maximum prediction level around 0.95 after ca 60 epochs
14 lines
499 B
Python
14 lines
499 B
Python
import tensorflow.keras as keras
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from PIL import Image
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import numpy as np
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import pdb
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model = keras.models.load_model('./siamese_checkpoint')
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image1 = np.asarray(Image.open('../towards/data/fruits-360/Training/Avocado/r_254_100.jpg').convert('RGB').resize((100,
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100))) / 255
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image2 = np.asarray(Image.open('../towards/data/fruits-360/Training/Avocado/r_250_100.jpg').convert('RGB').resize((100,
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100))) / 255
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output = model.predict([np.array([image2]), np.array([image1])])
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pdb.set_trace()
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