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)