'Iris-versicolor': [0, 1, 0],
'Iris-virginica': [1, 0, 0]
}
learning_data = []
with open('../data/training_data/iris.data', newline='') as csvfile:
reader = csv.reader(csvfile)
for row in reader:
learning_data.append({
'inputs': [float(x) for x in row[0:4]],
'outputs': iris_map[row[4]]
})
map = load_som("../data/trained_networks/som.yaml")
inputs = InputLayer([1, 1, 1, 1], name='NormalInput')
inputs_som = InputSom([1, 1, 1, 1], map)
inputs_som.set_network_name('som')
middle = Layer(3)
output = OutputLayer([1, 1, 1])
network = Network([inputs, middle, output], 'som')
network.connect()
network.load_learning_data(learning_data)
middle.connect_layer(inputs_som, 'left')
network.dump_cypher('../data/cypher/som.cypher',
additional_neurons=inputs_som.neurons)
"""
#network.learn(times=5000) #input_layers=[inputs_som])
folds = 4
results = network.learn_kfolds(folds, times=1000)
from pprint import pprint
from random import uniform
from time import time
from core.layer import Layer, InputLayer, OutputLayer
from core.network import Network
with open('snek-learning.yaml', 'r') as file:
data = yaml.load(file)
#data = data[:1000]
learning_data = [{'inputs': list(row[:5]), 'outputs': [row[5]]} for row in data]
inputs = InputLayer([1, 1, 1, 1, 1])
middle = Layer(5)
middle3 = Layer(3)
output = OutputLayer([1])
network = Network([inputs, middle, middle3, output], 'snek')
network.connect()
network.load_learning_data(learning_data)
network.print_data()
folds = 4
result = [0]
i = 0
start_time = time()
while sum(result) / folds < 90:
results = network.learn_kfolds(folds, times=1)
from core.layer import Layer, InputLayer, OutputLayer
from core.network import Network
learning_data = []
tic_tac_map = {'positive': 1, 'negative': 0, 'x': 1, 'o': 2, 'b': 3}
with open('data/training_data/tic-tac-toe.data', newline='') as csvfile:
reader = csv.reader(csvfile)
for row in reader:
learning_data.append({
'inputs': [tic_tac_map[x] for x in row[0:9]],
'outputs': [tic_tac_map[row[9]]]
})
inputs = InputLayer([1] * 9)
middle = Layer(5)
middle2 = Layer(7)
middle3 = Layer(5)
middle4 = Layer(2)
output = OutputLayer([1])
network = Network([inputs, middle, middle2, middle3, middle4, output])
network.connect()
network.load_learning_data(learning_data)
#network.normalize_learning_data()
print("------- learn -------")
network.print_data()