def main():
h5f = h5py.File('vac/KFold_image_data.h5', 'r')
test_images = h5f['test_images'][:]
test_labels = h5f['test_labels'][:]
h5f.close()
print(test_images[0].shape)
shuffled_images, shuffled_labels = Function.shuffle_data(test_images, test_labels, random_state=2)
for i in range(shuffled_images.shape[0]):
#for c in range(5):
shuffled_images, shuffled_labels = Function.shuffle_data(test_images, test_labels, random_state=2)
model = build_model(6)
model2 = resnet.resnet50(6)
#model.summary()
weight_location = 'Weight/BS32_AUG_KF10_eardrum_inceptionv3_013/KF'+str(i)+'Weight000120_Aug_inceptionV3_ji.h5'
weight_location2 = 'Weight/BS32_AUG_KF10_eardrum_resnet50_013/KF'+str(i)+'Weight000120_train50_Aug_ji.h5'
print(weight_location)
model.load_weights(weight_location)
model2.load_weights(weight_location2)
prediction = model.predict(shuffled_images[i], verbose=1)
prediction2 = model2.predict(shuffled_images[i], verbose=1)
predict_labels = []
for j in range(prediction.shape[0]):
Max = 0
idx = -1
#print('[{0}] inception : {1} {2} {3} {4} {5}'.format(str(j), str(prediction[j][0]), str(prediction[j][1], str(prediction[j][2]),str(prediction[j][3]), str(prediction[j][4]))))
#print('[{0}] resnet : {1} {2} {3} {4} {5}'.format(str(j), str(prediction2[j][0]), str(prediction2[j][1], str(prediction2[j][2]),str(prediction2[j][3]), str(prediction2[j][4]))))
for k in range(prediction.shape[1]):
#print('inception : {0} /// : resnet : {1}'.format(str(prediction[j][k]), str(prediction2[j][k])))
if (k == 0):
Max = prediction[j][0]+prediction2[j][0]
idx = 0
else:
if (prediction[j][k]+prediction2[j][k] > Max):
Max = prediction[j][k]+prediction2[j][k]
idx = k
predict_labels.append(idx)
'''
print('')
print('Normal, Traumatic_Perforation, AOM, COM, Congenital_cholesteatoma, OME')
print('Prediction probability: {0}, True Label: {1}'.format(prediction[j], shuffled_labels[i][j])) #To see probability and real label
Display.Im3D(shuffled_images[i][j]) #To see Test Image predicted
'''
Accuracy = Function.display_accuracy_rate(predict_labels, shuffled_labels[i])
print('KF{0}: lr=1e-{1}: Test accuracy is {2}%.'.format(str(i), str(3), Accuracy))
print(confusion_matrix(shuffled_labels[i], predict_labels))
print(classification_report(shuffled_labels[i], predict_labels, target_names=['Normal', 'Traumatic_Perforation', 'AOM', 'COM', 'Congenital_cholesteatoma', 'OME']))
'''
for i in range((len(shuffled_images[0]))//4):
Display._4img3D(shuffled_images[0][i*4], predict_labels[i*4], shuffled_labels[0][i*4], shuffled_images[0][i*4+1], predict_labels[i*4+1], shuffled_labels[0][i*4+1],
shuffled_images[0][i*4+2], predict_labels[i*4+2], shuffled_labels[0][i*4+2], shuffled_images[0][i*4+3], predict_labels[i*4+3], shuffled_labels[0][i*4+3])
'''
'''
def main():
# Get Hyperparameters
batch_size16, batch_size32, epochs = Parameters.train_parameters()
image_path = Parameters.get_image_path()
train_test_set_name = 'train_test_sets.h5'
if not os.path.isfile(train_test_set_name):
ImgGen = DataGenerator(image_path)
images, labels = ImgGen.__getimage__() # Load images
ImgGen.__save__(images, labels, FileName=train_test_set_name)
KFold_train_test_set_name = 'KFold_image_data.h5'
if not os.path.isfile(KFold_train_test_set_name):
# Read all images by theirs labels
images, labels = Function.load_data(train_test_set_name)
print(images.shape)
# Split to disease or normal types
NR, Traumatic_preforation, AOM, COM, Congenital_cholesteatoma, OME = Function.split_types(images, labels)
print(NR.shape, Traumatic_preforation.shape,AOM.shape,COM.shape,Congenital_cholesteatoma.shape,OME.shape)
NR, Traumatic_preforation, AOM, COM, Congenital_cholesteatoma, OME = Function.enlarge_data(NR, Traumatic_preforation, AOM, COM, Congenital_cholesteatoma, OME, times=3)
n_splits = 5
NR, Traumatic_preforation, AOM, COM, Congenital_cholesteatoma, OME = Function.data_ratio_equalization(NR, Traumatic_preforation, AOM, COM, Congenital_cholesteatoma, OME, n_splits=n_splits)
print(NR.shape, Traumatic_preforation.shape,AOM.shape,COM.shape,Congenital_cholesteatoma.shape)
NR_train, NR_validation, NR_test = Function.kfold_split(NR, n_splits=n_splits)
Traumatic_preforation_train, Traumatic_preforation_validation, Traumatic_preforation_test = Function.kfold_split(Traumatic_preforation, n_splits=n_splits)
AOM_train, AOM_validation, AOM_test = Function.kfold_split(AOM, n_splits=n_splits)
COM_train, COM_validation, COM_test = Function.kfold_split(COM, n_splits=n_splits)
Congenital_cholesteatoma_train, Congenital_cholesteatoma_validation, Congenital_cholesteatoma_test = Function.kfold_split(Congenital_cholesteatoma, n_splits=n_splits)
OME_train, OME_validation, OME_test = Function.kfold_split(OME, n_splits=n_splits)
train_images, train_labels = Function.combine_images(NR_train, Traumatic_preforation_train,AOM_train,COM_train,Congenital_cholesteatoma_train,OME_train, n_splits=n_splits)
validation_images, validation_labels = Function.combine_images(NR_validation, Traumatic_preforation_validation,AOM_validation,COM_validation,Congenital_cholesteatoma_validation,OME_validation, n_splits=n_splits)
test_images, test_labels = Function.combine_images(NR_test, Traumatic_preforation_test,AOM_test,COM_test,Congenital_cholesteatoma_test,OME_test, n_splits=n_splits)
Function.save_kfold_data(train_images, train_labels,
validation_images, validation_labels,
test_images, test_labels, FileName=KFold_train_test_set_name)
shuffled_train_validation_set_name = 'shuffled_train_validation_set.h5'
if not os.path.isfile(shuffled_train_validation_set_name):
# Load train images and labels
h5f = h5py.File(KFold_train_test_set_name, 'r')
train_images = h5f['train_images'][:]
train_labels = h5f['train_labels'][:]
validation_images = h5f['validation_images'][:]
validation_labels = h5f['validation_labels'][:]
h5f.close()
shuffled_train_images, shuffled_train_labels = Function.shuffle_data(train_images, train_labels, random_state=5)
shuffled_validation_images, shuffled_validation_labels = Function.shuffle_data(validation_images, validation_labels, random_state=5)
# Save train images and labels
h5f = h5py.File(shuffled_train_validation_set_name, 'a')
h5f.create_dataset('shuffled_train_images', data=shuffled_train_images)
h5f.create_dataset('shuffled_train_labels', data=shuffled_train_labels)
h5f.create_dataset('shuffled_validation_images', data=shuffled_validation_images)
h5f.create_dataset('shuffled_validation_labels', data=shuffled_validation_labels)
h5f.close()
# Load train images and labels
h5f = h5py.File(shuffled_train_validation_set_name, 'r')
shuffled_train_images = h5f['shuffled_train_images'][:]
shuffled_train_labels = h5f['shuffled_train_labels'][:]
shuffled_validation_images = h5f['shuffled_validation_images'][:]
shuffled_validation_labels = h5f['shuffled_validation_labels'][:]
h5f.close()
for i in range(shuffled_train_images.shape[0]):
#input_tensor = Input(shape=(384, 384, 3))
#384, 384, 3)
#model=InceptionV3(include_top=False,input_tensor=None,input_shape=None, weights=None, include_top=True, classes=6)
#model=InceptionV3(include_top=False,input_tensor=None,input_shape=None, weights=None, include_top=True, classes=6)
model=build_model(6)
Adadelta0 = optimizers.Adadelta(lr=0.13)
monitor = EarlyStopping(monitor='val_loss', min_delta=1e-3, patience=30, verbose=1,
mode='auto', restore_best_weights=True)
model.compile( optimizer=Adadelta0,
loss='sparse_categorical_crossentropy',
metrics=['sparse_categorical_accuracy'])
history0 = model.fit(shuffled_train_images[i], shuffled_train_labels[i],
validation_data=(shuffled_validation_images[i], shuffled_validation_labels[i]),
callbacks=[monitor], batch_size=batch_size16, epochs=epochs, verbose=1)
model.save_weights('Weight/BS32_AUG_KF5_proeardrum_inceptionv3_013/KF'+str(i)+'Weight000120_Aug_inceptionV3_ji.h5')
'''
model.compile( optimizer=Adadelta1,
loss='sparse_categorical_crossentropy',
metrics=['sparse_categorical_accuracy'])
history1 = model.fit(shuffled_train_images[i], shuffled_train_labels[i],
validation_data=(shuffled_validation_images[i], shuffled_validation_labels[i]),
callbacks=[monitor], batch_size=batch_size, epochs=epochs, verbose=1)
model.save_weights('Weight/HE_patience20_BS64_20190829/KF'+str(i)+'Weight1e1.h5')
model.compile( optimizer=Adadelta2,
loss='sparse_categorical_crossentropy',
metrics=['sparse_categorical_accuracy'])
history2 = model.fit(shuffled_train_images[i], shuffled_train_labels[i],
validation_data=(shuffled_validation_images[i], shuffled_validation_labels[i]),
callbacks=[monitor], batch_size=batch_size, epochs=epochs, verbose=1)
model.save_weights('Weight/HE_patience20_BS64_20190829/KF'+str(i)+'Weight1e2.h5')
model.compile( optimizer=Adadelta3,
loss='sparse_categorical_crossentropy',
metrics=['sparse_categorical_accuracy'])
history3 = model.fit(shuffled_train_images[i], shuffled_train_labels[i],
validation_data=(shuffled_validation_images[i], shuffled_validation_labels[i]),
callbacks=[monitor], batch_size=batch_size, epochs=epochs, verbose=1)
model.save_weights('Weight/HE_patience20_BS64_20190829/KF'+str(i)+'Weight1e3.h5')
model.compile( optimizer=Adadelta4,
loss='sparse_categorical_crossentropy',
metrics=['sparse_categorical_accuracy'])
history4 = model.fit(shuffled_train_images[i], shuffled_train_labels[i],
validation_data=(shuffled_validation_images[i], shuffled_validation_labels[i]),
callbacks=[monitor], batch_size=batch_size, epochs=epochs, verbose=1)
model.save_weights('Weight/HE_patience20_BS64_20190829/KF'+str(i)+'Weight1e4.h5')
'''
# Save cross validation train history
history_location = 'history/BS32_AUG_KF5_proeardrum_inceptionv3_013/KF'+str(i)+'_cv_history000120_train50_Aug_inceptionV3_ji.h5'
#print(history_location)
h5f = h5py.File(history_location, 'w')
h5f.create_dataset('history0_train_accuracy', data=history0.history['sparse_categorical_accuracy'])
#h5f.create_dataset('history1_train_accuracy', data=history1.history['sparse_categorical_accuracy'])
#h5f.create_dataset('history2_train_accuracy', data=history2.history['sparse_categorical_accuracy'])
#h5f.create_dataset('history3_train_accuracy', data=history3.history['sparse_categorical_accuracy'])
#h5f.create_dataset('history4_train_accuracy', data=history4.history['sparse_categorical_accuracy'])
h5f.create_dataset('history0_train_loss', data=history0.history['loss'])
#h5f.create_dataset('history1_train_loss', data=history1.history['loss'])
#h5f.create_dataset('history2_train_loss', data=history2.history['loss'])
#h5f.create_dataset('history3_train_loss', data=history3.history['loss'])
#h5f.create_dataset('history4_train_loss', data=history4.history['loss'])
h5f.create_dataset('history0_val_accuracy', data=history0.history['val_sparse_categorical_accuracy'])
#h5f.create_dataset('history1_val_accuracy', data=history1.history['val_sparse_categorical_accuracy'])
#h5f.create_dataset('history2_val_accuracy', data=history2.history['val_sparse_categorical_accuracy'])
#h5f.create_dataset('history3_val_accuracy', data=history3.history['val_sparse_categorical_accuracy'])
#h5f.create_dataset('history4_val_accuracy', data=history4.history['val_sparse_categorical_accuracy'])
h5f.create_dataset('history0_val_loss', data=history0.history['val_loss'])
#h5f.create_dataset('history1_val_loss', data=history1.history['val_loss'])
#h5f.create_dataset('history2_val_loss', data=history2.history['val_loss'])
#h5f.create_dataset('history3_val_loss', data=history3.history['val_loss'])
#h5f.create_dataset('history4_val_loss', data=history4.history['val_loss'])
h5f.close()
def main():
# Get Hyperparameters
os.environ['CUDA_DEVICE_ORDER'] = "PCI_BUS_ID"
os.environ['CUDA_VISIBLE_DEVICES'] = "1"
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
batch_size16, batch_size32, epochs = Parameters.train_parameters()
image_path = Parameters.get_image_path()
train_test_set_name = 'train_test_sets.h5'
if not os.path.isfile(train_test_set_name):
ImgGen = DataGenerator(image_path)
images, labels = ImgGen.__getimage__() # Load images
ImgGen.__save__(images, labels, FileName=train_test_set_name)
KFold_train_test_set_name = 'KFold_image_data.h5'
if not os.path.isfile(KFold_train_test_set_name):
# Read all images by theirs labels
images, labels = Function.load_data(train_test_set_name)
# Split to disease or normal types
NR, Traumatic_preforation, AOM, COM, Congenital_cholesteatoma, OME = Function.split_types(
images, labels)
print(NR.shape, Traumatic_preforation.shape, AOM.shape, COM.shape,
Congenital_cholesteatoma.shape)
NR, Traumatic_preforation, AOM, COM, Congenital_cholesteatoma, OME = Function.enlarge_data(
NR,
Traumatic_preforation,
AOM,
COM,
Congenital_cholesteatoma,
OME,
times=3)
n_splits = 5
NR, Traumatic_preforation, AOM, COM, Congenital_cholesteatoma, OME = Function.data_ratio_equalization(
NR,
Traumatic_preforation,
AOM,
COM,
Congenital_cholesteatoma,
OME,
n_splits=n_splits)
print(NR.shape, Traumatic_preforation.shape, AOM.shape, COM.shape,
Congenital_cholesteatoma.shape)
NR_train, NR_validation, NR_test = Function.kfold_split(
NR, n_splits=n_splits)
Traumatic_preforation_train, Traumatic_preforation_validation, Traumatic_preforation_test = Function.kfold_split(
Traumatic_preforation, n_splits=n_splits)
AOM_train, AOM_validation, AOM_test = Function.kfold_split(
AOM, n_splits=n_splits)
COM_train, COM_validation, COM_test = Function.kfold_split(
COM, n_splits=n_splits)
Congenital_cholesteatoma_train, Congenital_cholesteatoma_validation, Congenital_cholesteatoma_test = Function.kfold_split(
Congenital_cholesteatoma, n_splits=n_splits)
OME_train, OME_validation, OME_test = Function.kfold_split(
OME, n_splits=n_splits)
train_images, train_labels = Function.combine_images(
NR_train,
Traumatic_preforation_train,
AOM_train,
COM_train,
Congenital_cholesteatoma_train,
OME_train,
n_splits=n_splits)
validation_images, validation_labels = Function.combine_images(
NR_validation,
Traumatic_preforation_validation,
AOM_validation,
COM_validation,
Congenital_cholesteatoma_validation,
OME_validation,
n_splits=n_splits)
test_images, test_labels = Function.combine_images(
NR_test,
Traumatic_preforation_test,
AOM_test,
COM_test,
Congenital_cholesteatoma_test,
OME_test,
n_splits=n_splits)
Function.save_kfold_data(train_images,
train_labels,
validation_images,
validation_labels,
test_images,
test_labels,
FileName=KFold_train_test_set_name)
shuffled_train_validation_set_name = 'shuffled_train_validation_set.h5'
if not os.path.isfile(shuffled_train_validation_set_name):
# Load train images and labels
h5f = h5py.File(KFold_train_test_set_name, 'r')
train_images = h5f['train_images'][:]
train_labels = h5f['train_labels'][:]
validation_images = h5f['validation_images'][:]
validation_labels = h5f['validation_labels'][:]
h5f.close()
shuffled_train_images, shuffled_train_labels = Function.shuffle_data(
train_images, train_labels, random_state=5)
shuffled_validation_images, shuffled_validation_labels = Function.shuffle_data(
validation_images, validation_labels, random_state=5)
# Save train images and labels
h5f = h5py.File(shuffled_train_validation_set_name, 'a')
h5f.create_dataset('shuffled_train_images', data=shuffled_train_images)
h5f.create_dataset('shuffled_train_labels', data=shuffled_train_labels)
h5f.create_dataset('shuffled_validation_images',
data=shuffled_validation_images)
h5f.create_dataset('shuffled_validation_labels',
data=shuffled_validation_labels)
h5f.close()
# Load train images and labels
h5f = h5py.File(shuffled_train_validation_set_name, 'r')
shuffled_train_images = h5f['shuffled_train_images'][:]
shuffled_train_labels = h5f['shuffled_train_labels'][:]
shuffled_validation_images = h5f['shuffled_validation_images'][:]
shuffled_validation_labels = h5f['shuffled_validation_labels'][:]
h5f.close()
print(shuffled_train_images.shape[1])
for i in range(1, 4):
model = resnet.resnet50(6)
#model.summary()
'''dataGenerator = ImageDataGenerator( rotation_range=8,
width_shift_range=0.2,
brightness_range=(1,1.5),
shear_range=0.01,
zoom_range=0.2,
channel_shift_range=0.2,
height_shift_range=0.2,
horizontal_flip=True)
dataGenerator.fit(shuffled_train_images[i])'''
#print(shuffled_train_labels[i])
#for c in range(shuffled_train_images.shape[1]):
#Display.Im3D(shuffled_train_images[i][c])
Adadelta0 = optimizers.Adadelta(lr=0.13)
#Adadelta1 = optimizers.Adadelta(lr=1e-1)
#Adadelta2 = optimizers.Adadelta(lr=1e-2)
#Adadelta3 = optimizers.Adadelta(lr=1e-3)
#Adadelta4 = optimizers.Adadelta(lr=1e-4)
monitor = EarlyStopping(monitor='val_loss',
min_delta=1e-3,
patience=30,
verbose=1,
mode='auto',
restore_best_weights=True)
model.compile(optimizer=Adadelta0,
loss='sparse_categorical_crossentropy',
metrics=['sparse_categorical_accuracy'])
history0 = model.fit(shuffled_train_images[i],
shuffled_train_labels[i],
validation_data=(shuffled_validation_images[i],
shuffled_validation_labels[i]),
callbacks=[monitor],
batch_size=batch_size16,
epochs=epochs,
verbose=1)
model.save_weights('Weight/BS32_AUG_KF5_proeardrum_resnet50_013/KF' +
str(i) + 'Weight000120_train50_Aug_ji.h5')
'''
model.compile( optimizer=Adadelta1,
loss='sparse_categorical_crossentropy',
metrics=['sparse_categorical_accuracy'])
history1 = model.fit(shuffled_train_images[i], shuffled_train_labels[i],
validation_data=(shuffled_validation_images[i], shuffled_validation_labels[i]),
callbacks=[monitor], batch_size=batch_size, epochs=epochs, verbose=1)
model.save_weights('Weight/HE_patience20_BS64_20190829/KF'+str(i)+'Weight1e1.h5')
model.compile( optimizer=Adadelta2,
loss='sparse_categorical_crossentropy',
metrics=['sparse_categorical_accuracy'])
history2 = model.fit(shuffled_train_images[i], shuffled_train_labels[i],
validation_data=(shuffled_validation_images[i], shuffled_validation_labels[i]),
callbacks=[monitor], batch_size=batch_size, epochs=epochs, verbose=1)
model.save_weights('Weight/HE_patience20_BS64_20190829/KF'+str(i)+'Weight1e2.h5')
model.compile( optimizer=Adadelta3,
loss='sparse_categorical_crossentropy',
metrics=['sparse_categorical_accuracy'])
history3 = model.fit(shuffled_train_images[i], shuffled_train_labels[i],
validation_data=(shuffled_validation_images[i], shuffled_validation_labels[i]),
callbacks=[monitor], batch_size=batch_size, epochs=epochs, verbose=1)
model.save_weights('Weight/HE_patience20_BS64_20190829/KF'+str(i)+'Weight1e3.h5')
model.compile( optimizer=Adadelta4,
loss='sparse_categorical_crossentropy',
metrics=['sparse_categorical_accuracy'])
history4 = model.fit(shuffled_train_images[i], shuffled_train_labels[i],
validation_data=(shuffled_validation_images[i], shuffled_validation_labels[i]),
callbacks=[monitor], batch_size=batch_size, epochs=epochs, verbose=1)
model.save_weights('Weight/HE_patience20_BS64_20190829/KF'+str(i)+'Weight1e4.h5')
'''
# Save cross validation train history
history_location = 'history/BS32_AUG_KF5_proeardrum_resnet50_013/KF' + str(
i) + '_cv_history000120_train50_Aug_ji.h5'
#print(history_location)
h5f = h5py.File(history_location, 'w')
h5f.create_dataset(
'history0_train_accuracy',
data=history0.history['sparse_categorical_accuracy'])
#h5f.create_dataset('history1_train_accuracy', data=history1.history['sparse_categorical_accuracy'])
#h5f.create_dataset('history2_train_accuracy', data=history2.history['sparse_categorical_accuracy'])
#h5f.create_dataset('history3_train_accuracy', data=history3.history['sparse_categorical_accuracy'])
#h5f.create_dataset('history4_train_accuracy', data=history4.history['sparse_categorical_accuracy'])
h5f.create_dataset('history0_train_loss',
data=history0.history['loss'])
#h5f.create_dataset('history1_train_loss', data=history1.history['loss'])
#h5f.create_dataset('history2_train_loss', data=history2.history['loss'])
#h5f.create_dataset('history3_train_loss', data=history3.history['loss'])
#h5f.create_dataset('history4_train_loss', data=history4.history['loss'])
h5f.create_dataset(
'history0_val_accuracy',
data=history0.history['val_sparse_categorical_accuracy'])
#h5f.create_dataset('history1_val_accuracy', data=history1.history['val_sparse_categorical_accuracy'])
#h5f.create_dataset('history2_val_accuracy', data=history2.history['val_sparse_categorical_accuracy'])
#h5f.create_dataset('history3_val_accuracy', data=history3.history['val_sparse_categorical_accuracy'])
#h5f.create_dataset('history4_val_accuracy', data=history4.history['val_sparse_categorical_accuracy'])
h5f.create_dataset('history0_val_loss',
data=history0.history['val_loss'])
#h5f.create_dataset('history1_val_loss', data=history1.history['val_loss'])
#h5f.create_dataset('history2_val_loss', data=history2.history['val_loss'])
#h5f.create_dataset('history3_val_loss', data=history3.history['val_loss'])
#h5f.create_dataset('history4_val_loss', data=history4.history['val_loss'])
h5f.close()