Python minmax 예제들

예제 #1

파일: neural.py 프로젝트: arjunbalussery/proj

print l

inp = np.reshape(inp,(-1,l))


print tar[1]
print inp

#target = np.reshape(target,(-1,1))

# Create network with 2 layers and random initialized
#norm = Norm(input)
#input = norm(input)


net = nl.net.newff(minmax(inp), [10, l], transf = [nl.trans.TanSig(), nl.trans.LogSig()])
net.trainf = nl.train.train_bfgs

error = net.train(inp, tar, epochs=10, show=5, goal=0.01)
"""
test = [0,1]

test = np.reshape(test,(-1,2))

out = net.sim(test)

print out


"""

예제 #2

파일: neural.py 프로젝트: cliveverghese/proj

l = len(bag_of_words)

print l

inp = np.reshape(inp, (-1, l))

print tar[1]
print inp

#target = np.reshape(target,(-1,1))

# Create network with 2 layers and random initialized
#norm = Norm(input)
#input = norm(input)

net = nl.net.newff(minmax(inp), [10, l],
                   transf=[nl.trans.TanSig(),
                           nl.trans.LogSig()])
net.trainf = nl.train.train_bfgs

error = net.train(inp, tar, epochs=10, show=5, goal=0.01)
"""
test = [0,1]

test = np.reshape(test,(-1,2))

out = net.sim(test)

print out

예제 #3

파일: train.py 프로젝트: sarathsp06/gesture_recognizer

import numpy as np
import input
import target
from neurolab.tool import minmax

# Create train samples
input = np.array(input.data)
target = np.asfarray(target.data)
input = input[: target.shape[0]]

# Create network with 2 layers and random initialized
#norm = Norm(input)
#input = norm(input)
print input.shape
print target.shape
print '----------',minmax(input)
net = nl.net.newff(minmax(input), [12, 4], transf = [nl.trans.TanSig(), nl.trans.LogSig()])
net.trainf = nl.train.train_bfgs
error = net.train(input, target, epochs=1000, show=10, goal=0.02)
print '-------',error[-1]
net.save('net.net')
#Simulate network
print '\nprinting the simulated output';
net=nl.load('net.net')
output = net.sim(input)
out = output
for i in range(len(output)):
	m=max(output[i])
	print '[',m,']',
	for j in range(4):
		if output[i,j] == m:

예제 #4

파일: train.py 프로젝트: lodestone/gesture_recognizer

import numpy as np
import input
import target
from neurolab.tool import minmax

# Create train samples
input = np.array(input.data)
target = np.asfarray(target.data)
input = input[:target.shape[0]]

# Create network with 2 layers and random initialized
#norm = Norm(input)
#input = norm(input)
print input.shape
print target.shape
print '----------', minmax(input)
net = nl.net.newff(minmax(input), [12, 4],
                   transf=[nl.trans.TanSig(),
                           nl.trans.LogSig()])
net.trainf = nl.train.train_bfgs
error = net.train(input, target, epochs=1000, show=10, goal=0.02)
print '-------', error[-1]
net.save('net.net')
#Simulate network
print '\nprinting the simulated output'
net = nl.load('net.net')
output = net.sim(input)
out = output
for i in range(len(output)):
    m = max(output[i])
    print '[', m, ']',