def LoadToDevice(f, paramList):
global sp
global scaleList
nameList = []
subNameList = []
tempParamList = []
while True:
name = f.readline()
if name == '':
f.close()
break
name = name[0:len(name) - 1]
info = f.readline()
infolist = info.split(' ')[1:4]
param = p.param(name, int(infolist[0]))
param.private = (infolist[1] == 'True')
subName = []
for i in range(0, int(infolist[2])):
subline = f.readline()
sublist = subline.split(' ')
param.addSubParam(
p.subParam(sublist[1], float(sublist[0]), int(sublist[2]), i))
subName.append(sublist[1])
tempParamList.append(param)
nameList.append(name)
subNameList.append(subName)
for param in paramList:
try:
index = nameList.index(param.name)
for subParam in param.subParams:
try:
subIndex = subNameList[index].index(subParam.name)
if subParam.value != tempParamList[index].subParams[
subIndex].value:
subParam.value = tempParamList[index].subParams[
subIndex].value
sp.sendParam(subParam.value, param.index,
subParam.index)
print 'Updated \"' + subParam.name + '\" of \"' + param.name + "\" to be " + str(
subParam.value)
if subParam.power != tempParamList[index].subParams[
subIndex].power:
subParam.power = tempParamList[index].subParams[
subIndex].power
sp.sendScale(param.index, subParam.index,
subParam.power)
except ValueError:
print 'W: Parameter \"' + param.name + '\" (Num:' + str(
tempParamList[index].index
) + ') missing sub-param in file'
except ValueError:
print 'W: Parameter \"' + param.name + '\" (Num:' + str(
tempParamList[index].index) + ') not found in file'
clearScale()
def param_update(sp):
global paramList
param_val_index = 9 * (sp.param_count)
for i in range(0, sp.param_count):
parameter = p.param(sp.paramName[i], sp.rxBuffer[9 * i + 8])
parameter.private = sp._private_flag[parameter.index]
for j in range(0, sp.rxBuffer[9 * i]):
parameter.addSubParam(p.subParam(sp.subParamName[i][j],\
sp.rxBuffer[param_val_index],sp.rxBuffer[9*i + j + 1],j))
param_val_index = param_val_index + 1
paramList.append(parameter)
sp.clearRxBuffer()
Accuracy: epochs learning rate cv1 size cv2 size cv1 channels cv2channels hidden img resize dropout
0.976308 10000 0.0005 5 5 4 8 4 16 0.5
AUC 0.983739837398 Cost 0.230548 patient 1 con todo
'''
import tensorflow as tf
import numpy as np
from epinn31 import *
import scipy.io
from epinn24 import *
from params import param
from apiepi import read_images_balanced
print "begin"
patient = 1
group = "train_%s_new" % patient
parameters = param(patient)
training_epochs = 4000
#images, labels, names = read_images(group)
#print images.shape, labels.shape, len(names)
images, labels, names = read_images_balanced(group, 2)
print images.shape, labels.shape, len(names)
features, prob, acc, cost = train_tf(images, labels, parameters, training_epochs=training_epochs)
print "Accuracy:", "epochs", "learning rate", "cv1 size", "cv2 size", "cv1 channels", "cv2channels", "hidden", "img resize", "dropout"
print acc, training_epochs, parameters["learning_rate"], parameters["cv1_size"], parameters["cv2_size"], parameters["cv1_channels"], parameters["cv2_channels"], parameters["hidden"], parameters["img_resize"], parameters["dropout"]
print "AUC", auc(labels, prob), "Cost", cost, "patient", patient, "con todo"
scipy.io.savemat("resp_%s_new" % patient, features, do_compression=True)
print "end"
'''
Model Evaluation
@author: botpi
'''
import tensorflow as tf
import numpy as np
from apifish import *
import scipy.io
from params import param
import time
import os
features = scipy.io.loadmat("resp_50_cost_conv5_diff_chan_1")
sub_file = "submission_64_stg1.csv"
parameters = param()
# files = os.listdir("../../data/fish/train-fix/")
files = os.listdir("../../data/fish/test_stg1_fix/")
samples = len(files)
cv1_size = parameters["cv1_size"]
cv2_size = parameters["cv2_size"]
cv1_channels = parameters["cv1_channels"]
cv2_channels = parameters["cv2_channels"]
hidden = parameters["hidden"]
img_width = parameters["img_width"]
img_height = parameters["img_height"]
categories = parameters["categories"]
cv_all_size = 7
cv_all_channels = 1
last_img_size = 7
from params import Params as param
if __name__ == '__main__':
m = param.m1
p = param(77)
n2= p.m2
print("n is {}".format(n2))
print(m)
