def __init__(self, in_size, out_size, **kwargs):
self.in_size = in_size
self.out_size = out_size
if "statsHandler" in kwargs:
self.statsHandler = kwargs["statsHandler"]
else:
self.statsHandler = StatsHandler()
def __init__(self,
out_size,
in_out_table: Dict,
null_output=None,
**kwargs):
super().__init__(0, out_size, **kwargs)
self.in_out_table = in_out_table
self.null_output = null_output if null_output is not None else numpy.zeros(
out_size)
if "statsHandler" in kwargs:
self.statsHandler = kwargs["statsHandler"]
else:
self.statsHandler = StatsHandler()
import sys
import numpy
import matplotlib.pyplot as plt
from MLLibrary.Models.MatrixNet import MatrixNet
from MLLibrary.Models.SequenceNet import SequenceNet
from MLLibrary.StatsHandler import StatsHandler
I = 2
O = 1
statsHandler = StatsHandler()
NET = SequenceNet([MatrixNet(I,
I), MatrixNet(I, O)],statsHandler=statsHandler)
MAX_ITER = 1000000
BATCH = 100
LEARNING_RATIO = 0.01
def get_X():
index = 1
while True:
index += 1
numpy.random.seed(index)
yield [[numpy.random.choice([0.0, 1.0]), numpy.random.choice([0.0, 1.0])]]
def get_Y():
index = 1
while True:
import sys
import numpy
import matplotlib.pyplot as plt
from MLLibrary.Models.MatrixNet import MatrixNet
from MLLibrary.Models.SequenceNet import SequenceNet
from MLLibrary.StatsHandler import StatsHandler
I = 2
O = 1
statsHandler = StatsHandler()
NET = SequenceNet([MatrixNet(I, I), MatrixNet(I, O)],
statsHandler=statsHandler)
MAX_ITER = 1000000
BATCH = 50
LEARNING_RATIO = 0.1
def get_X():
index = 1
while True:
index += 1
numpy.random.seed(index)
yield [[
numpy.random.choice([0.0, 1.0]),
numpy.random.choice([0.0, 1.0])
]]