#if update:
# stop = raw_input("Loading from folder "+folder+" : Hit enter to proceed or ctrl+C to cancel")
#else:
# print "Initializing in folder "+folder
"""Load the train/test split information if update, else split and write out which images are in which dataset"""
trainFs, testFs = helperFuncs.getTrainTestSplit(False, folder, numEx,
trainTestSplit, ld)
trainL = len(trainFs)
testL = len(testFs)
print "number of examples: ", numEx
print "training examples : ", trainL
print "test examples : ", testL
features, labels = helperFuncs.getTargets(
"justbonds") #get the target vector for each CID
outsize = helperFuncs.getOutSize(features)
"""DEFINE THE MODEL HERE"""
model = Sequential()
model.add(Convolution2D(8, 8, 8, input_shape=(1, size, size)))
model.add(Activation('relu'))
model.add(Convolution2D(8, 5, 5))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(3, 3)))
model.add(Dropout(0.25))
model.add(Convolution2D(8, 5, 5))
ydim = image.shape[0] - wSize + 1
xdim = image.shape[1] - wSize + 1
output = []
xran = np.linspace(0,xdim,(xdim/stride))
yran = np.linspace(0,ydim,(ydim/stride))
for y in yran:
for x in xran:
output.append(canvas[y:y+wSize,x:x+wSize])
return np.reshape(np.array(output),(len(output),1,wSize,wSize))
#Process the arguments given (see helperFuncs.py for details)
size, indir, binarize, blur, padding, targetType = helperFuncs.dataProcessorArgs(sys.argv[1:])
targets, labels = helperFuncs.getTargets(targetType)
outsize = helperFuncs.getOutSize(targets)
#wait until the folder exists
while not isdir(indir+"tempTrain/"):
time.sleep(10.)
print "I'm sleeping", isdir(indir), indir, " \r",
if not isdir(indir+"tempTrainNP/"):
mkdir(indir+"tempTrainNP/")
mkdir(indir+"tempTestNP/")
#define folders
trainFolder = indir+"tempTrain/"
trainNPfolder = indir+"tempTrainNP/"
"""Define the folder where the model will be stored based on the input arguments"""
folder = helperFuncs.defineFolder(True,outType,size,run)
print folder
trainDirect = folder+"tempTrain/"
trainNP = folder+"tempTrainNP/"
testDirect = folder+"tempTest/"
testNP = folder+"tempTestNP/"
"""Load the train/test split information"""
trainFs, testFs = helperFuncs.getTrainTestSplit(True,folder)
trainL = len(trainFs)
testL = len(testFs)
features,labels = helperFuncs.getTargets(outType) #get the OCR vector for each CID
outsize = len(features[features.keys()[0]]) #this it the size of the target (# of OCRfeatures)
means,stds = helperFuncs.getMeansStds(features)
"""load model"""
model = helperFuncs.loadModel(folder+"wholeModel")
while not isfile(testNP+"Xtest.h5"):
print "sleeping because Test folder empty \r",
time.sleep(1.)
print ""
print "Loading np test arrays"
#if update:
# stop = raw_input("Loading from folder "+folder+" : Hit enter to proceed or ctrl+C to cancel")
#else:
# print "Initializing in folder "+folder
"""Load the train/test split information if update, else split and write out which images are in which dataset"""
trainFs, testFs = helperFuncs.getTrainTestSplit(False, folder, numEx,
trainTestSplit, ld)
trainL = len(trainFs)
testL = len(testFs)
print "number of examples: ", numEx
print "training examples : ", trainL
print "test examples : ", testL
features, labels = helperFuncs.getTargets(
"ocr") #get the target vector for each CID
outsize = len(features[
features.keys()[0]]) #this it the size of the target (# of OCRfeatures)
"""DEFINE THE MODEL HERE"""
model = Sequential()
model.add(Convolution2D(16, 8, 8, input_shape=(1, size, size)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Convolution2D(32, 5, 5))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
"""Load the train/test split information if update, else split and write out which images are in which dataset"""
trainFs, testFs = helperFuncs.getTrainTestSplit(False,folder,numEx,trainTestSplit,ld)
trainL = len(trainFs)
testL = len(testFs)
print "number of examples: ", numEx
print "training examples : ", trainL
print "test examples : ", testL
features,labels = helperFuncs.getTargets("ocr") #get the target vector for each CID
outsize = len(features[features.keys()[0]]) #this it the size of the target (# of OCRfeatures)
"""DEFINE THE MODEL HERE"""
model = Sequential()
model.add(Convolution2D(8, 1, 5, 5, border_mode='full'))
model.add(Activation('relu'))
model.add(MaxPooling2D(poolsize=(2,2)))
lastLayerSize = getLastSize(model)
print lastLayerSize
print model.layers[-1]
model.add(BatchNormalization())
from os import mkdir
from os.path import isfile
from random import shuffle
import sys
import time
import numpy as np
import subprocess
import cPickle
import helperFuncs
import h5py
"""Processes images and pickles numpy arrays of them for training/testing"""
size, indir, binarize, blur, padding, targetType = helperFuncs.dataProcessorArgs(sys.argv[1:])
targets, labels = helperFuncs.getTargets(targetType)
outsize = len(targets[targets.keys()[0]])
while not isdir(indir+"tempTrain/"):
time.sleep(10.)
print "I'm sleeping", isdir(indir), indir, " \r",
if not isdir(indir+"tempTrainNP/"):
mkdir(indir+"tempTrainNP/")
mkdir(indir+"tempTestNP/")
#print "reading Train/Test files"
#train = [x for x in file(indir+"traindata.csv").read().split("\n") if x != '']
#test = [x for x in file(indir+"testdata.csv").read().split("\n") if x != '']
trainFolder = indir+"tempTrain/"
"""Define the folder where the model will be stored based on the input arguments"""
folder = helperFuncs.defineFolder(True,outType,size,run)
print folder
trainDirect = folder+"tempTrain/"
trainNP = folder+"tempTrainNP/"
testDirect = folder+"tempTest/"
testNP = folder+"tempTestNP/"
"""Load the train/test split information"""
trainFs, testFs = helperFuncs.getTrainTestSplit(True,folder)
trainL = len(trainFs)
testL = len(testFs)
features,labels = helperFuncs.getTargets(outType) #get the OCR vector for each CID
outsize = len(features[features.keys()[0]]) #this it the size of the target (# of OCRfeatures)
means,stds = helperFuncs.getMeansStds()
"""load model"""
#with open(folder+"bestModel.pickle",'rb') as f:
# model = cPickle.load(f)
model = helperFuncs.loadModel(folder+"wholeModel")
while not isfile(testNP+"Xtest.h5"):
print "sleeping because Test folder empty \r",
time.sleep(1.)
print ""
