# -*- coding: utf-8 -*-

# Convolutional Neural Network
MODEL_PATH = './trained_models/'
IMAGE_PATH = './data/'
EXT = ('.png', '.csv')
IMAGE_SHAPE = (128, 128, 4)
BATCH_SIZE = 128
# roughly 10 epochs of training data
MAX_STEPS = 5000
EPOCHS = 5
ALPHA = 5e-4
BETA = 1e-2
VALID_SIZE = .10

TAGS = ['agriculture', 'artisinal_mine', 'bare_ground', 'blooming',
        'blow_down', 'clear', 'cloudy', 'conventional_mine', 'cultivation',
        'habitation', 'haze', 'partly_cloudy', 'primary', 'road',
        'selective_logging', 'slash_burn', 'water']

TAGS_WEIGHTINGS = [0.8941, 0.9971, 0.9926, 0.9971, 0.9991, 0.7579, 0.98,
                   0.9991, 0.961, 0.9686, 0.9769, 0.9378, 0.6751, 0.9307,
                   0.9971, 0.9982, 0.9377]

# TAGS_THRESHOLDS = [0.3, 0.2, 0.2, 0.2, 0.1, 0.4, 0.2,
#                    0.1, 0.2, 0.2, 0.2, 0.2, 0.5, 0.2,
#                    0.2, 0.1, 0.2]

TAGS_THRESHOLDS = [0.245, 0.1375, 0.2225, 0.19, 0.0475, 0.2375, 0.12,
                   0.0875, 0.265, 0.2175, 0.1925, 0.1625, 0.2625, 0.21,
                   0.14, 0.085, 0.205]

@@ -147,16 +147,14 @@
  = (
-128, 128
+64, 64
 , 4)

128 to 64