Python get_biases_needed Examples

Example #1

File: parameters.py Project: mrminy/GomokuPython

DEFAULT_SIZE = (20, 20)
DEFAULT_FIRST_MOVE = (10, 10)
PLAY_WITH_AI = True
AI_VS_AI = True
AI_FIRST = True
RANDOM_START = True

# Static AI parameters
DEFAULT_ALPHA_BETA_DEPTH = 1
DEFAULT_ALPHA_BETA_WIDTH = 10
ANN_ALPHA_BETA_DEPTH = 4
PRINT_EVALUATION = False

# EA parameters
SHOW_PLOT = True
POP_SIZE = 40
NGEN = 30
CXPB = 0.8
MUTPB = 0.5
TOURNAMENT_SIZE = 10
MAX_FIT = 10000.0
ELITISM = 1

# ANN parameters
LAYER_SIZES = [400, 25, 5, 1]
NEEDED_WEIGHTS = new_ann.get_weights_needed(LAYER_SIZES)
NEEDED_BIASES = new_ann.get_biases_needed(LAYER_SIZES)


# Testing...
SIZES = [(10, 10)]

Example #2

File: ann_test.py Project: mrminy/GomokuPython

# # :type filter_shape: tuple or list of length 4
# # :param filter_shape: (number of filters, num input feature maps, filter height, filter width)
#
# # :type image_shape: tuple or list of length 4
# # :param image_shape: (batch size, num input feature maps, image height, image width)
#
# ann = LeNetConvPoolLayer(rng, input1d, filter_shape=(2, 3, 5, 5), image_shape=(4,3,20,20), poolsize=(2,2))
#
# print(ann.output)

import numpy as np
from ann import new_ann

layer_sizes = [5, 10, 1]
print("Weights needed", new_ann.get_weights_needed(layer_sizes))
print("Biases needed", new_ann.get_biases_needed(layer_sizes))


test_weights = [0.1, 0.3, 0.4, 0.1, 0.3, 0.1, 0.3, 0.4, 0.1, 0.3, 0.1, 0.3, 0.4, 0.1, 0.3, 0.1, 0.3, 0.4, 0.1, 0.3,
                0.1, 0.3, 0.4, 0.1, 0.3, 0.1, 0.3, 0.4, 0.1, 0.3, 0.1, 0.3, 0.4, 0.1, 0.3, 0.1, 0.3, 0.4, 0.1, 0.3,
                0.1, 0.3, 0.4, 0.1, 0.3, 0.1, 0.3, 0.4, 0.1, 0.3, 0.1, 0.3, 0.4, 0.1, 0.3, 0.1, 0.3, 0.4, 0.1, 0.3]
test_biases = [0.1, 0.3, 0.4, 0.1, 0.2, 0.1, 0.3, 0.4, 0.1, 0.2, 0.1]

actual_weights = [np.array([[0.1, 0.3, 0.4, 0.1, 0.3, 0.1, 0.3, 0.4, 0.1, 0.3], [0.1, 0.3, 0.4, 0.1, 0.3, 0.1, 0.3, 0.4, 0.1, 0.3],
              [0.1, 0.3, 0.4, 0.1, 0.3, 0.1, 0.3, 0.4, 0.1, 0.3], [0.1, 0.3, 0.4, 0.1, 0.3, 0.1, 0.3, 0.4, 0.1, 0.3],
              [0.1, 0.3, 0.4, 0.1, 0.3, 0.1, 0.3, 0.4, 0.1, 0.3]]),
    np.array([[0.1], [0.3], [0.4], [0.1], [0.3], [0.1], [0.3], [0.4], [0.1], [0.3]])]  # in --> [out]
actual_biases = [np.array([0.1, 0.3, 0.4, 0.1, 0.2, 0.1, 0.3, 0.4, 0.1, 0.2]), np.array([0.1])]  # out


splitted_weights = new_ann.convert_weights_to_arrays(layer_sizes, test_weights)