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AI: 0.75 → 0.5?

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Starbeamrainbowlabs 2022-09-02 18:12:51 +01:00
parent b0e1aeac35
commit 23bcd294c4
Signed by: sbrl
GPG key ID: 1BE5172E637709C2
2 changed files with 2 additions and 2 deletions
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2
aimodel/src/lib/dataset/dataset.py
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@ -31,7 +31,7 @@ def parse_item(metadata):
rainfall = tf.image.resize(rainfall, tf.cast(tf.constant(metadata["waterdepth"]) / 2, dtype=tf.int32)) rainfall = tf.image.resize(rainfall, tf.cast(tf.constant(metadata["waterdepth"]) / 2, dtype=tf.int32))
# [width, height] → [width, height, channels] # [width, height] → [width, height, channels]
water = tf.expand_dims(water, axis=-1) water = tf.expand_dims(water, axis=-1)
water = tf.image.central_crop(water, 0.75) # Predict for only the centre 75% of the water data water = tf.image.central_crop(water, 0.5) # Predict for only the centre 75% of the water data
# TODO: The shape of the resulting tensor can't be statically determined, so we need to reshape here # TODO: The shape of the resulting tensor can't be statically determined, so we need to reshape here
print("DEBUG:dataset ITEM rainfall:shape", rainfall.shape, "water:shape", water.shape) print("DEBUG:dataset ITEM rainfall:shape", rainfall.shape, "water:shape", water.shape)

2
aimodel/src/subcommands/pretrain.py
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@ -53,7 +53,7 @@ def run(args):
feature_dim=args.feature_dim, feature_dim=args.feature_dim,
shape_rainfall=dataset_metadata["rainfallradar"], shape_rainfall=dataset_metadata["rainfallradar"],
shape_water=[ math.ceil(value * 0.75) for value in dataset_metadata["waterdepth"] ] shape_water=[ math.ceil(value * 0.5) for value in dataset_metadata["waterdepth"] ]
) )
ai.train(dataset_train, dataset_validate) ai.train(dataset_train, dataset_validate)