def train(self):
batch_size = 32
epochs = 50
xTrain, yTrain = self.trainData
valData = self.valData
#Set up generator for data augmentation.
dataGen = augmentor(rotation_range=359,horizontal_flip=True,vertical_flip=True)
dataGen.fit(xTrain)
#Set up callbacks tuple for checkpoints. This will save the model every time validation loss improves.
callbacks = [ModelCheckpoint(self.model_file, monitor='val_loss', verbose=1, save_best_only=True)]
#Train the model and save its training progress in a history object.
history = self.model.fit_generator(dataGen.flow(xTrain, yTrain,batch_size=batch_size),
steps_per_epoch=len(xTrain)/batch_size,epochs=epochs,
verbose=2,validation_data=valData,callbacks=callbacks)
val_acc = open("val_acc_history.txt",'w')
acc = open("acc_history.txt",'w')
loss = open("loss_history.txt",'w')
val_loss = open("val_loss_history.txt",'w')
a = history.history['acc']
va = history.history['val_acc']
l = history.history['loss']
vl = history.history['val_loss']
self.modelAccuracy = max(a)
for accVal,vacc,lossVal,vloss in zip(a,va,l,vl):
acc.write("{}\n".format(accVal))
val_acc.write("{}\n".format(vacc))
loss.write("{}\n".format(lossVal))
val_loss.write("{}\n".format(vloss))
val_acc.close()
loss.close()
val_loss.close()
acc.close()
#Plot and save training and validation accuracy data.
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='lower right')
plt.savefig("Accuracy.png")
plt.clf()
#Plot and save training and validation loss data.
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper right')
plt.savefig("Loss.png")
#net = filter_CNN()
def train(self):
"""
Function to train a fresh model.
:return: Void.
"""
batch_size = 64
epochs = 150
xTrain, yTrain = self.trainData
valData = self.valData
#Set up generator for data augmentation.
dataGen = augmentor(rotation_range=90,
horizontal_flip=True,
vertical_flip=True)
dataGen.fit(xTrain)
#Set up callbacks tuple for checkpoints. This will save the model every time validation loss improves.
callbacks = [
ModelCheckpoint(self.model_file,
monitor='val_loss',
verbose=1,
save_best_only=True)
]
#Train the model and save its training progress in a history object.
history = self.model.fit_generator(dataGen.flow(xTrain,
yTrain,
batch_size=batch_size),
steps_per_epoch=len(xTrain) /
batch_size,
epochs=epochs,
verbose=2,
validation_data=valData,
callbacks=callbacks)
self.plot_training_metrics(history)
import numpy as np
from keras.callbacks import ModelCheckpoint
from keras import backend as K
from keras.layers import Conv2D, MaxPooling2D, BatchNormalization, AveragePooling2D, Activation
from keras.layers import Dense, Dropout, Flatten, Input, Add, Concatenate, Reshape, Average
from keras.models import Model, load_model
from keras.optimizers import Adam, Adadelta
from keras.preprocessing.image import ImageDataGenerator as augmentor
from util import Data_Loader as dl
import os
matplotlib.use('Agg')
import matplotlib.pyplot as plt
dataGen = augmentor(rotation_range=359,
horizontal_flip=True,
vertical_flip=True)
def multi_input_data_generator(x1, x2, y1, y2, batch_size):
genX1 = dataGen.flow(x1, y1, batch_size=batch_size)
genX2 = dataGen.flow(x2, y2, batch_size=batch_size)
while True:
out1 = genX1.next()
out2 = genX2.next()
yield [out1[0], out2[0]], [out1[1], out2[1]]
class Reconnet(object):
def __init__(self, modelNumber):
if not os.path.exists("data/model_{}".format(modelNumber)):