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angle_prediction.py~
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from keras.preprocessing.image import ImageDataGenerator
import keras.applications as keras_applications
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
from sklearn.metrics import classification_report, confusion_matrix
import pandas as pd
import sys
from timeit import default_timer as timer
from keras import backend as K
from keras.callbacks import CSVLogger
import json
from keras.callbacks import ModelCheckpoint
from keras.optimizers import RMSprop, Adam, Adadelta
from keras.layers.convolutional import Convolution2D
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation, Flatten
from sklearn.model_selection import train_test_split
from keras.utils import plot_model
def create_model():
nb_filters = 8
nb_conv = 5
image_size=200
model = Sequential()
model.add(Convolution2D(nb_filters, nb_conv, nb_conv,
border_mode='valid',
input_shape=( image_size, image_size,1) ) )
model.add(Activation('relu'))
model.add(Convolution2D(nb_filters, nb_conv, nb_conv))
model.add(Activation('relu'))
model.add(Convolution2D(nb_filters, nb_conv, nb_conv))
model.add(Activation('relu'))
model.add(Convolution2D(nb_filters, nb_conv, nb_conv))
model.add(Activation('relu'))
model.add(Dropout(0.25))
model.add(Convolution2D(nb_filters*2, nb_conv, nb_conv))
model.add(Activation('relu'))
model.add(Convolution2D(nb_filters*2, nb_conv, nb_conv))
model.add(Activation('relu'))
model.add(Convolution2D(nb_filters*2, nb_conv, nb_conv))
model.add(Activation('relu'))
model.add(Convolution2D(nb_filters*2, nb_conv, nb_conv))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Flatten())
model.add(Dense(256))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(128))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('linear'))
model.compile(loss='mean_squared_error', optimizer=Adadelta(), metrics=['mean_squared_error'])
return model
start=timer()
input_shape=(200,200,1)
#i=int(sys.argv[1])-1
#classes=int(sys.argv[2])
rootoutput='outputs/'
rootdataset='dataset/'
expprefix="customeregression"
datapath="rotationtraindata"
#classes_name=["nonsym","H"]
timerfile= rootoutput+ expprefix+'/timer.csv'
outdir=rootoutput + expprefix+"/output/"
checkpoint_dir = rootoutput+ expprefix+ "/models/"
validation_data_dir = rootdataset+ datapath+'/valid'
train_path = rootdataset+datapath+'/'
test_path = rootdataset+datapath+'_test/'
modelname= expprefix
weigthfile=checkpoint_dir+modelname+'_model_saved_weight.h5'
weigthfile=checkpoint_dir+modelname+'_checkpoint.best.hdf5'
normalizefactor=1 # or 90
img_width, img_height = 200, 200
train_data_dir = train_path
nb_train_samples = 12000#14000
nb_validation_samples = 1600
epochs = 20
batch_size = 16
model=create_model()
model.load_weights(weigthfile)
#model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy',f1_m,precision_m, recall_m])
#model.summary()
#plot_model(model, to_file='model.png')
#exit()
test_datagen = ImageDataGenerator(rescale = 1. / 255)
test_generator = test_datagen.flow_from_directory(test_path, classes= [""], target_size =(img_width, img_height), batch_size = 10000, class_mode='sparse',color_mode="grayscale")
X,_=test_generator.next()
y=[int(f.split("-")[5].replace(".png",""))/normalizefactor for f in test_generator.filenames]
cv_size = 2000
steps = np.ceil(test_generator.samples/batch_size)
Y_pred = model.predict_generator(test_generator, steps=1)
Y_90=Y_pred * normalizefactor
error= np.array(y)*normalizefactor - Y_pred * normalizefactor
error=np.abs(error)
e=error.mean()
print("+/- error", e)
result=np.array((test_generator.filenames,y,Y_pred.flatten())).T
np.savetxt('pred_angle.csv',result,fmt='%s', delimiter=',')