def train(model, data, args):
"""
Training a CapsuleNet
Args:
model: the CapsuleNet model
data: a tuple containing training and testing data, like `((x_train, y_train), (x_test, y_test))`
args: arguments
Returns:
The trained model
"""
# unpacking the data
(x_train, y_train), (x_test, y_test) = data
# callbacks
log = callbacks.CSVLogger(args.save_dir + '/log.csv', separator=',', append=True)
tb = callbacks.TensorBoard(log_dir=args.save_dir + '/tensorboard-logs',
batch_size=args.batch_size, histogram_freq=int(args.debug))
checkpoint = callbacks.ModelCheckpoint(args.save_dir + '/weights-{epoch:02d}.h5', monitor='val_capsnet_acc',
save_best_only=True, save_weights_only=True, verbose=1)
lr_decay = callbacks.LearningRateScheduler(schedule=lambda epoch: args.lr * (args.lr_decay ** epoch))
# compile the model
model.compile(optimizer=optimizers.Adam(lr=args.lr),
loss=[margin_loss, 'mse'],
loss_weights=[1., args.lam_recon],
metrics={'capsnet': 'accuracy'})
"""
# Training without data augmentation:
model.fit([x_train, y_train], [y_train, x_train], batch_size=args.batch_size, epochs=args.epochs,
validation_data=[[x_test, y_test], [y_test, x_test]], callbacks=[log, tb, checkpoint, lr_decay])
"""
# Begin: Training with data augmentation ---------------------------------------------------------------------#
def train_generator(x, y, batch_size, shift_fraction=0.):
train_datagen = ImageDataGenerator(width_shift_range=shift_fraction,
height_shift_range=shift_fraction) # shift up to 2 pixel for MNIST
generator = train_datagen.flow(x, y, batch_size=batch_size)
while 1:
x_batch, y_batch = generator.next()
yield ([x_batch, y_batch], [y_batch, x_batch])
# Training with data augmentation. If shift_fraction=0., also no augmentation.
model.fit_generator(generator=train_generator(x_train, y_train, args.batch_size, args.shift_fraction),
steps_per_epoch=int(y_train.shape[0] / args.batch_size),
initial_epoch=args.initial_epoch,
epochs=args.epochs,
validation_data=[[x_test, y_test], [y_test, x_test]],
callbacks=[log, tb, checkpoint, lr_decay])
# End: Training with data augmentation -----------------------------------------------------------------------#
model.save_weights(args.save_dir + '/trained_model.h5')
print('Trained model saved to \'%s/trained_model.h5\'' % args.save_dir)
from utils import plot_log
plot_log(args.save_dir + '/log.csv', show=True)
return model
def train(model, train_gen, val_gen, args):
log = callbacks.CSVLogger(args.save_dir + '/log.csv')
tb = callbacks.TensorBoard(log_dir=args.save_dir + '/tensorboard-logs',
batch_size=args.batch_size,
histogram_freq=int(args.debug))
checkpoint = callbacks.ModelCheckpoint(args.save_dir +
'/weights-{epoch:02d}.h5',
monitor='val_capsnet_acc',
save_best_only=True,
save_weights_only=True,
verbose=1)
lr_decay = callbacks.LearningRateScheduler(
schedule=lambda epoch: args.lr * (args.lr_decay**epoch))
# compile the model
model.compile(optimizer=optimizers.Adam(lr=args.lr),
loss=[margin_loss, 'mse'],
loss_weights=[1., args.lam_recon],
metrics={'capsnet': 'accuracy'})
model.fit_generator(generator=train_gen,
epochs=args.epochs,
validation_data=val_gen,
callbacks=[log, tb, checkpoint, lr_decay],
use_multiprocessing=True,
workers=6,
verbose=1)
model.save_weights(args.save_dir + '/trained_model.h5')
print('Trained model saved to \'%s/trained_model.h5\'' % args.save_dir)
plot_log(args.save_dir + '/log.csv', show=True)
return model
def train(model, data, args):
"""
Training a 3-level DCNet
:param model: the 3-level DCNet model
:param data: a tuple containing training and testing data, like `((x_train, y_train), (x_test, y_test))`
:param args: arguments
:return: The trained model
"""
# unpacking the data
(x_train, y_train), (x_test, y_test) = data
row = x_train.shape[1]
col = x_train.shape[2]
channel = x_train.shape[3]
# callbacks
log = callbacks.CSVLogger(args.save_dir + '/log.csv')
tb = callbacks.TensorBoard(log_dir=args.save_dir + '/tensorboard-logs', histogram_freq=int(args.debug))
checkpoint = callbacks.ModelCheckpoint(args.save_dir + '/weights-{epoch:02d}.h5', monitor='val_capsnet_acc',
verbose=1)
lr_decay = callbacks.LearningRateScheduler(schedule=lambda epoch: args.lr * (args.lr_decay ** epoch))
# compile the model
# Notice the four separate losses (for separate backpropagations)
model.compile(optimizer=optimizers.Adam(lr=args.lr),
loss=[margin_loss, margin_loss, margin_loss, margin_loss, 'mse'],
loss_weights=[1., 1., 1., 1., args.lam_recon],
metrics={'capsnet': 'accuracy'})
#model.load_weights('result/weights.h5')
"""
# Training without data augmentation:
model.fit([x_train, y_train], [y_train, y_train, y_train, y_train, x_train], batch_size=args.batch_size, epochs=args.epochs,
validation_data=[[x_test, y_test], [y_test, y_test, y_test, y_test, x_test]], callbacks=[log, tb, checkpoint, lr_decay])
"""
# Training with data augmentation
def train_generator(x, y, batch_size, shift_fraction=0.):
train_datagen = ImageDataGenerator(width_shift_range=shift_fraction,
height_shift_range=shift_fraction) # shift up to 2 pixel for MNIST
generator = train_datagen.flow(x, y, batch_size=batch_size)
while 1:
x_batch, y_batch = generator.next()
yield ([x_batch, y_batch], [y_batch, y_batch, y_batch, y_batch, x_batch[:,:,:,0:1]])
# Training with data augmentation. If shift_fraction=0., also no augmentation.
model.fit_generator(generator=train_generator(x_train, y_train, args.batch_size, args.shift_fraction),
steps_per_epoch=int(y_train.shape[0] / args.batch_size),
epochs=args.epochs,
validation_data=[[x_test, y_test], [y_test, y_test, y_test, y_test, x_test[:,:,:,0:1]]],
callbacks=[log, tb, checkpoint, lr_decay])
# Save model weights
model.save_weights(args.save_dir + '/trained_model.h5')
print('Trained model saved to \'%s/trained_model.h5\'' % args.save_dir)
plot_log(args.save_dir + '/log.csv', show=True)
return model
def train_model(model, args):
print('Loading train data!')
images_train, images_mask_train = load_data()
# callbacks
log = callbacks.CSVLogger(args.save_dir + '/log.csv')
# 查看tensorboard:
# methond1:./ python -m tensorboard.main --logdir=./
# method22: ./ tensorboard --logdir=./
tb = callbacks.TensorBoard(log_dir=args.save_dir + '/tensorboard-logs',
batch_size=args.batch_size,
histogram_freq=args.debug)
checkpoint = callbacks.ModelCheckpoint(
args.save_dir + '/multi-trained_model.h5',
monitor='val_loss',
save_best_only=True,
save_weights_only=True,
verbose=1,
mode='min',
)
lr_decay = callbacks.LearningRateScheduler(
schedule=lambda epoch: args.lr * (0.99**epoch))
early_stopping = keras.callbacks.EarlyStopping(
monitor='val_loss',
patience=args.patience,
verbose=0,
mode='min',
)
model = ParallelModel(model, args.gpus)
# 断点续存
# model = keras.models.load_model(args.save_dir + '/trained_model_old.h5',
# custom_objects={'bce_dice_loss': bce_dice_loss, 'mean_iou': mean_iou})
# compile the model
model.compile(optimizer=optimizers.Adam(lr=args.lr),
loss=bce_dice_loss,
metrics=["accuracy", mean_iou])
# Fitting model
model.fit(images_train,
images_mask_train,
batch_size=args.batch_size,
nb_epoch=args.epochs,
verbose=1,
shuffle=True,
validation_split=0.2,
callbacks=[log, tb, lr_decay, checkpoint, early_stopping])
model.save_weights(args.save_dir + '/trained_model.h5')
print('Trained model saved to \'%s/trained_model.h5\'' % args.save_dir)
plot_log(args.save_dir + '/log.csv', show=True)
return model
def train(save_dir, batch_size, lr, shift_fraction, epochs, model, data, running_time):
# unpacking the data
(x_train, y_train), (x_test, y_test) = data
class_weights_array = class_weight.compute_class_weight(
#None
'balanced'
,np.unique(np.argmax(y_train, axis=1))
,np.argmax(y_train, axis=1))
class_weights={0:class_weights_array[1],1:class_weights_array[0]}
# callbacks
log_dir = save_dir + '\\tensorboard-logs-dd' + '\\' + running_time
if not os.path.exists(log_dir):
os.makedirs(log_dir)
log = callbacks.CSVLogger(save_dir + '\\log-dd.csv')
tb = callbacks.TensorBoard(log_dir=log_dir,
batch_size=batch_size)
checkpoint = callbacks.ModelCheckpoint(save_dir + '\\weights-dd-{epoch:02d}.h5', monitor='val_acc',
save_best_only=True, save_weights_only=True, verbose=1)
# compile the model
model.compile(optimizer=optimizers.Adam(lr=lr),
loss='categorical_crossentropy',
metrics=['accuracy'])
# Begin: Training with data augmentation---------------------------------------------------------------------#
def train_generator(x, y, batch_size, savedir, shift_fraction=0.):
if not os.path.exists(savedir):
os.makedirs(savedir)
train_datagen = ImageDataGenerator(width_shift_range=shift_fraction,
samplewise_std_normalization = False,
height_shift_range=shift_fraction) # shift up to 2 pixel for MNIST
generator = train_datagen.flow(x, y, batch_size=batch_size, shuffle=False)
while 1:
x_batch, y_batch = generator.next()
yield (x_batch, y_batch)
# Training with data augmentation. If shift_fraction=0., also no augmentation.
print(class_weights)
model.fit(x_train, y_train, batch_size=batch_size,
epochs=epochs,
validation_data=(x_test, y_test),
callbacks=[log, tb, checkpoint],
class_weight= class_weights,
shuffle=True)
# End: Training with data augmentation -----------------------------------------------------------------------#
model.save_weights(save_dir + 'trained_model_dd_toxo.h5')
print('Trained model saved to \'%s \\trained_mode_dd_toxo.h5\'' % save_dir)
plot_log(os.path.join(save_dir, 'log-dd.csv'), show=True)
return model
def train(model, args):
# output callback func
log = callbacks.CSVLogger(args.save_dir + '/log.csv')
tb = callbacks.TensorBoard(log_dir=args.save_dir + '/tensorboard-logs',
batch_size=args.batch_size,
histogram_freq=int(args.debug))
checkpoint = callbacks.ModelCheckpoint(args.save_dir +
'/weights-{epoch:02d}.h5',
monitor='val_capsnet_acc',
save_best_only=False,
save_weights_only=False,
verbose=1)
lr_decay = callbacks.LearningRateScheduler(
schedule=lambda epoch: args.lr * (args.lr_decay**epoch))
model.compile(optimizer=optimizers.Adam(lr=args.lr),
loss=[margin_loss, 'mse'],
loss_weights=[1., args.lam_recon],
metrics={'capsnet': 'accuracy'})
# x = np.load('sNORB_img_train_azimuths30_32_34_0_2_4.npy')
# y = np.load('sNORB_lbl_train_azimuths30_32_34_0_2_4.npy')
x = np.load('luna-images-merged08.npy')
y = np.load('luna-labels-merged08.npy')
x = np.array([resizeIMG(i) for i in np.array(x)])
# pos_cnt = 0
# for lab in y:
# if(np.argmax(lab) == 1):
# pos_cnt += 1
# # print(str(lab))
# print("Positive: {0}, negative {1}".format(str(pos_cnt), str(len(y)-pos_cnt)))
(train_images, val_images, train_labels,
val_labels) = train_test_split(x,
y,
train_size=0.9,
test_size=0.1,
random_state=45)
print("Using {0} images for training and {1} for validation.".format(
str(len(train_images)), str(len(val_images))))
gen_b = batch_generator(train_images, train_labels)
gen_v = validation_generator(val_images, val_labels)
model.fit_generator(generator=gen_b,
steps_per_epoch=conf.STEPS_PER_EPOCH,
epochs=args.epochs,
validation_data=gen_v,
validation_steps=conf.VALIDATION_STEPS,
callbacks=[log, tb, checkpoint, lr_decay])
model.save_weights(args.save_dir + '/trained_model.h5')
print('Trained model saved to \'%s/trained_model.h5\'' % args.save_dir)
from utils import plot_log
plot_log(args.save_dir + '/log.csv', show=False)
return model
def train(model, data, args, dirs):
"""
The function which defines the training loop of the model
Parameters
----------
model : `keras.models.Model`
The structure of the model which is to be trained
data : `tuple`
The training and validation data
args : `dict`
The argument dictionary which defines other parameters at training time
dirs : `string`
Filepath to store the logs
"""
# Extract the data
(x_train, y_train), (x_val, y_val) = data
# callbacks
log = callbacks.CSVLogger(dirs + '/log.csv')
tb = callbacks.TensorBoard(log_dir=dirs + '/tensorboard-logs',
batch_size=args.batch_size,
histogram_freq=int(args.debug))
checkpoint = callbacks.ModelCheckpoint(dirs + '/model.h5',
monitor='val_acc',
save_best_only=True,
save_weights_only=False,
verbose=1)
lr_decay = callbacks.LearningRateScheduler(
schedule=lambda epoch: args.lr * (args.lr_decay**epoch))
# compile the model
model.compile(optimizer=optimizers.Adam(lr=args.lr),
loss='binary_crossentropy',
metrics=['acc'])
# Training without data augmentation:
model.fit(x_train,
y_train,
batch_size=args.batch_size,
epochs=args.epochs,
verbose=1,
validation_data=(x_val, y_val),
callbacks=[
log, tb, checkpoint, lr_decay
]) #, roc_auc_callback((x_train, y_train), (x_val, y_val))])
# Save the trained model
model.save(dirs + '/trained_model.h5')
# Plot the training results
plot_log(dirs, show=False)
return model
def train(model, data, args):
"""
训练胶囊网络
## model: 胶囊网络模型
## data: 包含训练和测试数据，形如((x_train, y_train), (x_test, y_test))
## args: 输入的训练参数
:return: 训练好的模型
"""
# 解包数据
(x_train, y_train), (x_test, y_test) = data
# 定义回调函数
log = callbacks.CSVLogger(args.save_dir + '/log.csv')
tb = callbacks.TensorBoard(log_dir=args.save_dir + '/tensorboard-logs',
batch_size=args.batch_size,
histogram_freq=int(args.debug))
checkpoint = callbacks.ModelCheckpoint(args.save_dir +
'/weights-{epoch:02d}.h5',
monitor='val_capsnet_acc',
save_best_only=True,
save_weights_only=True,
verbose=1)
lr_decay = callbacks.LearningRateScheduler(
schedule=lambda epoch: args.lr * (args.lr_decay**epoch))
# 编译模型
model.compile(
optimizer=optimizers.Adam(lr=args.lr),
loss=[margin_loss, 'mse'], #有两个损失函数, 编码器损失函数和解码器损失函数
loss_weights=[1., args.lam_recon], #两个损失函数占的权重
metrics={'capsnet': 'accuracy'})
# Begin: 开始训练 ---------------------------------------------------------------------#
def train_generator(x, y, batch_size, shift_fraction=0.):
train_datagen = ImageDataGenerator(width_shift_range=shift_fraction,
height_shift_range=shift_fraction)
generator = train_datagen.flow(x, y, batch_size=batch_size)
while 1:
x_batch, y_batch = generator.next()
yield ([x_batch, y_batch], [y_batch, x_batch])
model.fit_generator(
generator=train_generator(x_train, y_train, args.batch_size,
args.shift_fraction),
steps_per_epoch=int(y_train.shape[0] / args.batch_size),
epochs=args.epochs,
validation_data=[[x_test, y_test], [y_test, x_test]],
callbacks=[log, tb, checkpoint, lr_decay])
# End: 结束训练 -----------------------------------------------------------------------#
model.save_weights(args.save_dir + '/trained_model.h5')
print('Trained model saved to \'%s/trained_model.h5\'' % args.save_dir)
from utils import plot_log
plot_log(args.save_dir + '/log.csv', show=True)
return model
def train(model, data, args):
# unpacking the data
(x_train, y_train), (x_test, y_test) = data
# callbacks
log = callbacks.CSVLogger(args.save_dir + '/log.csv')
tb = callbacks.TensorBoard(log_dir=args.save_dir + '/tensorboard-logs',
batch_size=args.batch_size,
histogram_freq=int(args.debug))
checkpoint = callbacks.ModelCheckpoint(args.save_dir +
'/weights-{epoch:02d}.hdf5',
monitor='val_capsnet_acc',
save_best_only=True,
save_weights_only=True,
verbose=1)
lr_decay = callbacks.LearningRateScheduler(
schedule=lambda epoch: args.lr * (args.lr_decay**epoch))
# compile the model
model.compile(
optimizer=optimizers.Adam(lr=args.lr),
loss=[margin_loss, reconstruction_loss
], # We scale down this reconstruction loss by 0.0005 so that
loss_weights=[
1., args.scale_reconstruction_loss
], # ...it does not dominate the margin loss during training.
metrics={'capsnet': 'accuracy'})
# Generator with data augmentation as used in [1]
def train_generator_with_augmentation(x, y, batch_size, shift_fraction=0.):
train_datagen = ImageDataGenerator(
width_shift_range=shift_fraction,
height_shift_range=shift_fraction) # shift up to 2 pixel for MNIST
generator = train_datagen.flow(x, y, batch_size=batch_size)
while 1:
x_batch, y_batch = generator.next()
yield ([x_batch, y_batch], [y_batch, x_batch])
generator = train_generator_with_augmentation(x_train, y_train,
args.batch_size,
args.shift_fraction)
model.fit_generator(
generator=generator,
steps_per_epoch=int(y_train.shape[0] / args.batch_size),
epochs=args.epochs,
validation_data=[
[x_test, y_test], [y_test, x_test]
], # Note: For the decoder the input is the label and the output the image
callbacks=[log, tb, checkpoint, lr_decay])
model.save_weights(args.save_dir + '/trained_model.hdf5')
print('Trained model saved to \'%s/trained_model.hdf5\'' % args.save_dir)
utils.plot_log(args.save_dir + '/log.csv', show=True)
return model
def train(model, data, args):
"""
Training a CapsuleNet
:param model: the CapsuleNet model
:param data: a tuple containing training and testing data, like `((x_train, y_train), (x_test, y_test))`
:param args: arguments
:return: The trained model
"""
# unpacking the data
(x_train, y_train), (x_test, y_test) = data
# callbacks
log = callbacks.CSVLogger(args.save_dir + '/log.csv')
tb = callbacks.TensorBoard(log_dir=args.save_dir + '/tensorboard-logs',
batch_size=args.batch_size, histogram_freq=args.debug)
checkpoint = callbacks.ModelCheckpoint(args.save_dir + '/weights-{epoch:02d}.h5',
save_best_only=True, save_weights_only=True, verbose=1)
lr_decay = callbacks.LearningRateScheduler(schedule=lambda epoch: 0.001 * np.exp(-epoch / 10.))
# compile the model
model.compile(optimizer='adam',
loss=[margin_loss, 'mse'],
loss_weights=[1., args.lam_recon],
metrics={'out_caps': 'accuracy'})
"""
# Training without data augmentation:
model.fit([x_train, y_train], [y_train, x_train], batch_size=args.batch_size, epochs=args.epochs,
validation_data=[[x_test, y_test], [y_test, x_test]], callbacks=[log, tb, checkpoint])
"""
# -----------------------------------Begin: Training with data augmentation -----------------------------------#
def train_generator(x, y, batch_size, shift_fraction=0.):
train_datagen = ImageDataGenerator(width_shift_range=shift_fraction,
height_shift_range=shift_fraction) # shift up to 2 pixel for MNIST
generator = train_datagen.flow(x, y, batch_size=batch_size)
while 1:
x_batch, y_batch = generator.next()
yield ([x_batch, y_batch], [y_batch, x_batch])
# Training with data augmentation. If shift_fraction=0., also no augmentation.
model.fit_generator(generator=train_generator(x_train, y_train, args.batch_size, args.shift_fraction),
steps_per_epoch=int(y_train.shape[0] / args.batch_size),
epochs=args.epochs,
validation_data=[[x_test, y_test], [y_test, x_test]],
callbacks=[log, tb, checkpoint, lr_decay])
# -----------------------------------End: Training with data augmentation -----------------------------------#
model.save_weights(args.save_dir + '/trained_model.h5')
print('Trained model saved to \'%s/trained_model.h5\'' % args.save_dir)
from utils import plot_log
plot_log(args.save_dir + '/log.csv', show=True)
return model
def train(model, args):
# callbacks
log = callbacks.CSVLogger(args.save_dir + '/log.csv')
checkpoint = callbacks.ModelCheckpoint(args.save_dir +
'/weights-{epoch:02d}.h5',
monitor='val_capsnet_acc',
save_best_only=True,
save_weights_only=True,
verbose=1)
lr_decay = callbacks.LearningRateScheduler(
schedule=lambda epoch: args.lr * (args.lr_decay**epoch))
# compile the model
model.compile(optimizer=optimizers.Adam(lr=args.lr),
loss=[margin_loss, 'mse'],
loss_weights=[1., args.lam_recon],
metrics={'capsnet': 'accuracy'})
##my block----------------------------------------------------------------------------------------------------#
train_datagen = ImageDataGenerator(validation_split=val_split,
rescale=1. / 255)
train_generator = train_datagen.flow_from_directory(
train_data_dir,
shuffle=False,
target_size=(img_height, img_width),
batch_size=args.batch_size,
class_mode='categorical',
subset='training')
validation_generator = train_datagen.flow_from_directory(
train_data_dir,
shuffle=False,
target_size=(img_height, img_width),
batch_size=args.batch_size,
class_mode='categorical',
subset='validation')
k = TestCallback(validation_generator)
model.fit(train_generator,
steps_per_epoch=(totalpics * (1 - val_split)) // args.batch_size,
epochs=args.epochs,
callbacks=[log, checkpoint, lr_decay, k],
validation_data=validation_generator,
validation_steps=(totalpics * (val_split)) // args.batch_size)
##------------------------------------------------------------------------------------------------------------#
model.save_weights(args.save_dir + '/trained_model.h5')
print('Trained model saved to \'%s/trained_model.h5\'' % args.save_dir)
from utils import plot_log
plot_log(args.save_dir + '/log.csv', show=True)
return model
def train(model, data, args):
"""
Training a CapsuleNet
:param model: the CapsuleNet model
:param data: a tuple containing training and testing data, like `((x_train, y_train), (x_test, y_test))`
:param args: arguments
:return: The trained model
"""
# wandb.init(name=RUN_NAME, project="6_class", notes=COMMENTS)
# unpacking the data
([x_train_channelb, x_train_channelc, x_train_channeld],
y_train), ([x_test_channelb, x_test_channelc,
x_test_channeld], y_test) = data
# callbacks
log = callbacks.CSVLogger(args.save_dir + '/log.csv')
tb = callbacks.TensorBoard(log_dir=args.save_dir + '/tensorboard-logs',
batch_size=args.batch_size,
histogram_freq=int(args.debug))
checkpoint = callbacks.ModelCheckpoint(args.save_dir +
'/weights-{epoch:02d}.h5',
monitor='val_acc',
save_best_only=True,
save_weights_only=True,
verbose=1)
lr_decay = callbacks.LearningRateScheduler(
schedule=lambda epoch: args.lr * (args.lr_decay**epoch))
# compile the model
model.compile(optimizer=optimizers.Adam(lr=args.lr),
loss=[margin_loss],
loss_weights=[1.],
metrics={'capsnet': 'accuracy'})
model.fit(
[x_train_channelb, x_train_channelc, x_train_channeld],
y_train,
batch_size=args.batch_size,
epochs=args.epochs,
validation_data=[[x_test_channelb, x_test_channelc, x_test_channeld],
y_test],
callbacks=[log, tb, checkpoint, lr_decay])
# model.fit([x_train_channelb, x_train_channelc, x_train_channeld], y_train, batch_size=args.batch_size, epochs=args.epochs,
# validation_data=[[x_test_channelb, x_test_channelc, x_test_channeld], y_test], callbacks=[log, tb, checkpoint, lr_decay, WandbCallback()])
model.save_weights(args.save_dir + '/trained_model.h5')
# model.save(os.path.join(wandb.run.dir, "model.h5"))
print('Trained model saved to \'%s/trained_model.h5\'' % args.save_dir)
from utils import plot_log
plot_log(args.save_dir + '/log.csv', show=True)
return model
def train(model, data, args):
"""
Training a CapsuleNet
:param model: the CapsuleNet model
:param data: a tuple containing training and testing data, like `((x_train, y_train), (x_test, y_test))`
:param args: arguments
:return: The trained model
"""
# unpacking the data
(x_train, y_train), (x_test, y_test) = data
# callbacks
log = callbacks.CSVLogger(args.save_dir + '/log.csv')
tb = callbacks.TensorBoard(log_dir=args.save_dir + '/tensorboard-logs',
batch_size=args.batch_size,
histogram_freq=args.debug)
lr_decay = callbacks.LearningRateScheduler(
schedule=lambda epoch: args.lr * (0.9**epoch))
# compile the model
model.compile(optimizer=optimizers.Adam(lr=args.lr),
loss=[margin_loss, 'mse'],
loss_weights=[1., args.lam_recon])
"""
# Training without data augmentation:
model.fit([x_train, y_train], [y_train, x_train], batch_size=args.batch_size, epochs=args.epochs,
validation_data=[[x_test, y_test], [y_test, x_test]], callbacks=[log, tb, checkpoint, lr_decay])
"""
# Begin: Training with data augmentation ---------------------------------------------------------------------#
def train_generator(x, y, batch_size, shift_fraction=0.):
train_datagen = ImageDataGenerator(
width_shift_range=shift_fraction,
height_shift_range=shift_fraction) # shift up to 2 pixel for MNIST
generator = train_datagen.flow(x, y, batch_size=batch_size)
while 1:
x_batch, y_batch = generator.next()
yield ([x_batch, y_batch], [y_batch, x_batch])
# Training with data augmentation. If shift_fraction=0., also no augmentation.
model.fit_generator(
generator=train_generator(x_train, y_train, args.batch_size,
args.shift_fraction),
steps_per_epoch=int(y_train.shape[0] / args.batch_size),
epochs=args.epochs,
validation_data=[[x_test, y_test], [y_test, x_test]],
callbacks=[log, tb, lr_decay])
# End: Training with data augmentation -----------------------------------------------------------------------#
from utils import plot_log
plot_log(args.save_dir + '/log.csv', show=True)
return model
def train(model, data, args):
"""
Training a CapsuleNet
:param model: the CapsuleNet model
:param data: a tuple containing training and testing data, like `((x_train, y_train), (x_test, y_test))`
:param args: arguments
:return: The trained model
"""
# unpacking the data
(x_train, y_train), (x_test, y_test) = data
# callbacks
log = callbacks.CSVLogger(args.save_dir + '/log.csv')
tb = callbacks.TensorBoard(log_dir=args.save_dir + '/tensorboard-logs',
batch_size=args.batch_size, histogram_freq=args.debug)
checkpoint = callbacks.ModelCheckpoint(args.save_dir + '/weights-{epoch:02d}.h5',
save_best_only=True, save_weights_only=True, verbose=1)
lr_decay = callbacks.LearningRateScheduler(schedule=lambda epoch: args.lr * (0.9 ** epoch))
# compile the model
model.compile(optimizer=optimizers.Adam(lr=args.lr),
loss=[margin_loss, 'mse'],
loss_weights=[1., args.lam_recon],
metrics={'out_caps': 'accuracy'})
# Training without data augmentation:
if len(x_test) == 0:
model.fit([x_train, y_train], [y_train, x_train],
batch_size=args.batch_size,
epochs=args.epochs,
validation_split=0.2,
callbacks=[log, tb, checkpoint, lr_decay])
else:
model.fit([x_train, y_train], [y_train, x_train],
batch_size=args.batch_size,
epochs=args.epochs,
validation_data=[[x_test, y_test], [y_test, x_test]],
callbacks=[log, tb, checkpoint, lr_decay])
weights_file = os.path.join(args.save_dir,args.save_weight)
model.save_weights(weights_file)
print('Trained model saved to {}'.format( weights_file))
from utils import plot_log
plot_log(args.save_dir + '/log.csv', show=True)
return model
def train(model, data, args):
"""
Training a CapsuleNet
:param model: the CapsuleNet model
:param data: a tuple containing training and testing data, like `((x_train, y_train), (x_test, y_test))`
:param args: arguments
:return: The trained model
"""
# unpacking the data
(x_train, y_train), (x_test, y_test) = data
# callbacks
log = callbacks.CSVLogger(args.save_dir + '/log.csv')
tb = callbacks.TensorBoard(log_dir=args.save_dir + '/tensorboard-logs',
batch_size=args.batch_size, histogram_freq=args.debug)
lr_decay = callbacks.LearningRateScheduler(schedule=lambda epoch: args.lr * (0.9 ** epoch))
# compile the model
model.compile(optimizer=optimizers.Adam(lr=args.lr),
loss=[margin_loss, 'mse'],
loss_weights=[1., args.lam_recon])
"""
# Training without data augmentation:
model.fit([x_train, y_train], [y_train, x_train], batch_size=args.batch_size, epochs=args.epochs,
validation_data=[[x_test, y_test], [y_test, x_test]], callbacks=[log, tb, checkpoint, lr_decay])
"""
# Begin: Training with data augmentation ---------------------------------------------------------------------#
def train_generator(x, y, batch_size, shift_fraction=0.):
train_datagen = ImageDataGenerator(width_shift_range=shift_fraction,
height_shift_range=shift_fraction) # shift up to 2 pixel for MNIST
generator = train_datagen.flow(x, y, batch_size=batch_size)
while 1:
x_batch, y_batch = generator.next()
yield ([x_batch, y_batch], [y_batch, x_batch])
# Training with data augmentation. If shift_fraction=0., also no augmentation.
model.fit_generator(generator=train_generator(x_train, y_train, args.batch_size, args.shift_fraction),
steps_per_epoch=int(y_train.shape[0] / args.batch_size),
epochs=args.epochs,
validation_data=[[x_test, y_test], [y_test, x_test]],
callbacks=[log, tb, lr_decay])
# End: Training with data augmentation -----------------------------------------------------------------------#
from utils import plot_log
plot_log(args.save_dir + '/log.csv', show=True)
return model
def train(model, train_seq, validation_seq, args):
"""
Training a CapsuleNet
:param model: the CapsuleNet model
:param data: a tuple containing training and testing data, like `((x_train, y_train), (x_test, y_test))`
:param args: arguments
:return: The trained model
"""
# callbacks
log = callbacks.CSVLogger(args.save_dir + '/log.csv')
tb = callbacks.TensorBoard(
log_dir=args.save_dir + '/tensorboard-logs',
batch_size=args.batch_size,
histogram_freq=int(args.debug))
checkpoint = callbacks.ModelCheckpoint(
args.save_dir + '/weights-{epoch:02d}.h5',
monitor='val_acc',
save_best_only=True,
save_weights_only=True,
verbose=1)
lr_decay = callbacks.LearningRateScheduler(
schedule=lambda epoch: args.lr * (args.lr_decay**epoch))
# compile the model
model.compile(
optimizer=optimizers.Adam(lr=args.lr),
loss=margin_loss,
metrics={'capsnet': 'accuracy'})
"""
# Training without data augmentation:
"""
model.fit_generator(
generator=train_seq,
validation_data=validation_seq,
epochs=args.epochs,
class_weight={
0: 1,
1: 1.8669997421
},
callbacks=[log, tb, checkpoint, lr_decay])
model.save_weights(args.save_dir + '/trained_model.h5')
print('Trained model saved to \'%s/trained_model.h5\'' % args.save_dir)
from utils import plot_log
plot_log(args.save_dir + '/log.csv', show=True)
return model
def train(model, train_list, val_list):
(x_train, y_train) = train_list
(x_test, y_test) = val_list
# Compile the loaded model
opt = Adam(lr=args.lr, beta_1=0.99, beta_2=0.999, decay=1e-6)
metrics = {'out_seg': [dice_hard, dice_hard_intersection, dice_hard_union]}
loss = {'out_seg': 'binary_crossentropy', 'out_recon': 'mse'}
loss_weighting = {'out_seg': 1., 'out_recon': args.recon_wei}
training_model.compile(optimizer=opt, loss=loss, loss_weights=loss_weighting, metrics=metrics)
#######################################
# CallBacks #
#######################################
monitor_name = 'val_out_seg_dice_hard_intersection'
csv_logger = CSVLogger(join(args.logs_dir, 'logs_'+str(args.time)+'.csv'))
tb = TensorBoard(join(args.logs_dir, 'tensorflow_logs'), batch_size=args.batch_size, histogram_freq=0)
model_checkpoint = ModelCheckpoint(join(args.weights_dir, 'weights_{epoch:04d}.h5'),
monitor=monitor_name, save_weights_only=True,
verbose=1)
lr_reducer = ReduceLROnPlateau(monitor=monitor_name, factor=0.05, cooldown=0, patience=5, verbose=1, mode='max')
early_stopper = EarlyStopping(monitor=monitor_name, min_delta=0, patience=25, verbose=0, mode='max')
# Begin: Training with data augmentation ---------------------------------------------------------------------#
def train_generator(x, y, batch_size):
train_datagen = ImageDataGenerator()
generator = train_datagen.flow(x, y, batch_size=batch_size)
while 1:
x_batch, y_batch = generator.next()
yield ([x_batch, y_batch], [y_batch, x_batch])
# Training with data augmentation. If shift_fraction=0., also no augmentation.
model.fit_generator(generator=train_generator(x_train, y_train, args.batch_size),
steps_per_epoch=int(y_train.shape[0] / args.batch_size),
epochs=args.epochs,
validation_data=[[x_test, y_test], [y_test, x_test]],
callbacks=[model_checkpoint, csv_logger, lr_reducer, early_stopper, tb], verbose=1)
model.save_weights(join(args.models_dir, "model_"+str(args.time)+".h5"))
print("=" * 50)
print("Model saved to --> " + str(join(args.models_dir, "model_"+str(args.time)+".h5")))
print("=" * 50)
plot_log(args.logs_dir,'logs_'+str(args.time)+'.csv', save=True, verbose=args.debug)
def train(model, data, args):
"""
Training a CapsuleNet
:param model: the CapsuleNet model
:param data: a tuple containing training and testing data, like `((x_train, y_train), (x_test, y_test))`
:param args: arguments
:return: The trained model
"""
# unpacking the data
(x_train, y_train), (x_test, y_test) = data
# callbacks
log = callbacks.CSVLogger(args['save_dir'] + '/log.csv')
tb = callbacks.TensorBoard(log_dir=args['save_dir'] + '/tensorboard-logs',
batch_size=args['batch_size'],
histogram_freq=int(args['debug']))
checkpoint = callbacks.ModelCheckpoint(args['save_dir'] +
'/weights-{epoch:02d}.h5',
monitor='val_capsnet_acc',
save_best_only=True,
save_weights_only=True,
verbose=1)
lr_decay = callbacks.LearningRateScheduler(
schedule=lambda epoch: args['lr'] * (args['lr_decay']**epoch))
early_stop = callbacks.EarlyStopping(monitor='val_loss',
patience=3,
verbose=1)
# compile the model
model.compile(optimizer=optimizers.Adam(lr=args['lr']),
loss=[margin_loss, 'mse'],
loss_weights=[1., args['lam_recon']],
metrics={'capsnet': 'accuracy'})
# Training
model.fit([x_train, y_train], [y_train, x_train],
batch_size=args['batch_size'],
epochs=args['epochs'],
validation_data=[[x_test, y_test], [y_test, x_test]],
callbacks=[log, tb, checkpoint, lr_decay, early_stop])
model.save_weights(args['save_dir'] + '/trained_model.h5')
print('Trained model saved to \'%s/trained_model.h5\'' % args['save_dir'])
plot_log(args['save_dir'] + '/log.csv', show=True)
return model
def train(model, data, args):
"""
Training a CapsuleNet
:param model: the CapsuleNet model
:param data: a tuple containing training and testing data, like `((x_train, y_train), (x_test, y_test))`
:param args: arguments
:return: The trained model
"""
# unpacking the data
(x_train, y_train), (x_test, y_test) = data
# callbacks
log = callbacks.CSVLogger(args.save_dir + '/log.csv')
tb = callbacks.TensorBoard(log_dir=args.save_dir + '/tensorboard-logs',
batch_size=args.batch_size,
histogram_freq=args.debug)
checkpoint = callbacks.ModelCheckpoint(args.save_dir +
'/weights-{epoch:02d}.h5',
save_best_only=True,
save_weights_only=True,
verbose=1)
lr_decay = callbacks.LearningRateScheduler(
schedule=lambda epoch: 0.001 * np.exp(-epoch / 10.))
# compile the model
model.compile(optimizer='adam',
loss=[margin_loss, 'mse'],
loss_weights=[1., args.lam_recon],
metrics={'out_caps': 'accuracy'})
model.fit([x_train, y_train], [y_train, x_train],
batch_size=args.batch_size,
epochs=args.epochs,
validation_data=[[x_test, y_test], [y_test, x_test]],
callbacks=[log, tb, checkpoint],
verbose=1)
model.save_weights(args.save_dir + '/trained_model.h5')
print('Trained model saved to \'%s/trained_model.h5\'' % args.save_dir)
from utils import plot_log
plot_log(args.save_dir + '/log.csv', show=True)
return model
def train(model, data, args):
(x_train, y_train), (x_test, y_test) = data
log = callbacks.CSVLogger(args.save_dir + '/log.csv')
tb = callbacks.TensorBoard(log_dir=args.save_dir + '/tensorboard-logs',
batch_size=args.batch_size,
histogram_freq=args.debug)
checkpoint = callbacks.ModelCheckpoint(args.save_dir + '/best-weight.h5',
monitor='val_capsnet_acc',
save_best_only=True,
save_weights_only=True,
verbose=1)
lr_decay = callbacks.LearningRateScheduler(
schedule=lambda epoch: args.lr * (0.9**epoch))
model.compile(optimizer=optimizers.Adam(lr=args.lr),
loss=[margin_loss, 'mse'],
loss_weights=[1., args.lam_recon],
metrics={'capsnet': 'accuracy'})
def train_generator(x, y, batch_size):
train_datagen = ImageDataGenerator(width_shift_range=0.1,
height_shift_range=0.1)
generator = train_datagen.flow(x, y, batch_size=batch_size)
while 1:
x_batch, y_batch = generator.next()
yield ([x_batch, y_batch], [y_batch, x_batch])
model.fit_generator(
generator=train_generator(x_train, y_train, args.batch_size),
steps_per_epoch=int(y_train.shape[0] / args.batch_size),
epochs=args.epochs,
validation_data=[[x_test, y_test], [y_test, x_test]],
callbacks=[log, tb, checkpoint, lr_decay])
model.save_weights(args.save_dir + '/trained_model.h5')
print('Trained model saved to \'%s/trained_model.h5\'' % args.save_dir)
from utils import plot_log
plot_log(args.save_dir + '/log.csv', show=True)
return model
def train(model, data, args):
# unpacking the data
(x_train, y_train), (x_test, y_test) = data
# callbacks
log = callbacks.CSVLogger(args.save_dir + '/log.csv')
tb = callbacks.TensorBoard(log_dir=args.save_dir + '/tensorboard-logs',
batch_size=args.batch_size, histogram_freq=int(args.debug))
checkpoint = callbacks.ModelCheckpoint(args.save_dir + '/weights-{epoch:02d}.hdf5', monitor='val_acc',
save_best_only=True, save_weights_only=True, verbose=1)
lr_decay = callbacks.LearningRateScheduler(schedule=lambda epoch: args.lr * (args.lr_decay ** epoch))
# compile the model
model.compile(optimizer=optimizers.Adam(lr=args.lr),
loss=categorical_crossentropy,
metrics=['accuracy'])
# Generator with data augmentation as used in [1] ([...] also trained on 2-pixel shifted MNIST)
def train_generator_with_augmentation(x, y, batch_size, shift_fraction=0.):
train_datagen = ImageDataGenerator(width_shift_range=shift_fraction,
height_shift_range=shift_fraction) # shift up to 2 pixel for MNIST
generator = train_datagen.flow(x, y, batch_size=batch_size)
while 1:
x_batch, y_batch = generator.next()
if args.crop_x is not None and args.crop_y is not None:
x_batch = utils.random_crop(x_batch, [args.crop_x, args.crop_y])
yield (x_batch, y_batch)
generator = train_generator_with_augmentation(x_train, y_train, args.batch_size, args.shift_fraction)
model.fit_generator(generator=generator,
steps_per_epoch=int(y_train.shape[0] / args.batch_size),
epochs=args.epochs,
validation_data=[x_test, y_test],
callbacks=[log, tb, checkpoint, lr_decay])
model.save_weights(args.save_dir + '/trained_model.hdf5')
print('Trained model saved to \'%s/trained_model.hdf5\'' % args.save_dir)
utils.plot_log(args.save_dir + '/log.csv', show=True)
return model
def train(model, train_seq, validation_seq, args):
# callbacks
log = callbacks.CSVLogger(args.save_dir + '/log.csv')
tb = callbacks.TensorBoard(log_dir=args.save_dir + '/tensorboard-logs',
batch_size=args.batch_size,
histogram_freq=int(args.debug))
checkpoint = callbacks.ModelCheckpoint(args.save_dir +
'/weights-{epoch:02d}.h5',
monitor='val_acc',
save_best_only=True,
save_weights_only=True,
verbose=1)
# compile the model
optimizer, losses, loss_weights, reduce_lr = ResNet101HyperParameters(
args.lr)
model.compile(optimizer=optimizer,
loss=losses,
loss_weights=loss_weights,
metrics=['accuracy'])
# Training without data augmentation:
model.fit_generator(generator=train_seq,
validation_data=validation_seq,
epochs=args.epochs,
class_weight={
0: 1,
1: 1.8669997421
},
callbacks=[log, tb, checkpoint, reduce_lr])
model.save_weights(args.save_dir + '/trained_model.h5')
print('Trained model saved to \'%s/trained_model.h5\'' % args.save_dir)
plot_log(args.save_dir + '/log.csv', show=True)
return model
def train(model, data, args):
X, Y = data
# Callbacks
log = callbacks.CSVLogger(filename=args.save_dir + '/log.csv')
tb = callbacks.TensorBoard(log_dir=args.save_dir + '/tensorboard-logs',
batch_size=args.batch_size,
histogram_freq=args.debug)
checkpoint = callbacks.ModelCheckpoint(filepath=args.save_dir + '/weights-improvement-{epoch:02d}.hdf5',
save_best_only=True,
save_weights_only=True,
verbose=1)
lr_decay = callbacks.LearningRateScheduler(schedule=lambda epoch: 0.001 * np.exp(-epoch / 10.))
# compile the model
model.compile(optimizer='adam',
loss=[margin_loss],
metrics=['accuracy'])
model.fit(x=X,
y=Y,
validation_split=0.2,
batch_size=args.batch_size,
epochs=args.epochs,
callbacks=[log, tb, checkpoint, lr_decay],
shuffle=True,
verbose=1)
model.save_weights(args.save_dir + '/trained_model.h5')
print('Trained model saved to \'%s/trained_model.h5\'' % args.save_dir)
plot_log(args.save_dir + '/log.csv', show=True)
return model
def train(model, eval_model, data, args):
"""
Training a CapsuleNet
:param model: the CapsuleNet model
:param data: a tuple containing training and testing data, like `((x_train, y_train), (x_test, y_test))`
:param args: arguments
:return: The trained model
"""
# unpacking the data
(x_train, y_train), (x_test, y_test), classes = data
print("x_train {}, y_train {}, x_test {}, y_test {}".format(
x_train.shape, y_train.shape, x_test.shape, y_test.shape))
# callbacks
log = callbacks.CSVLogger(args.save_dir + '/log.csv')
tb = callbacks.TensorBoard(log_dir=args.save_dir + '/tensorboard-logs',
batch_size=args.batch_size,
histogram_freq=int(args.debug))
checkpoint = callbacks.ModelCheckpoint(args.save_dir +
'/weights-{epoch:02d}.h5',
monitor='val_rec_macro',
mode='max',
save_best_only=True,
save_weights_only=True,
verbose=1)
lr_decay = callbacks.LearningRateScheduler(
schedule=lambda epoch: args.lr * (args.lr_decay**epoch))
if os.path.isfile(args.save_dir + '/trained_model.h5'):
model.load_weights(args.save_dir + '/trained_model.h5')
# compile the model
model.compile(optimizer=optimizers.Adam(lr=args.lr),
loss=[margin_loss, 'mse'],
loss_weights=[1., args.lam_recon],
metrics={'capsnet': 'accuracy'})
mc = MetricCallback(validation_data=((x_test, y_test), (y_test, x_test)),
labels=classes,
batch_size=args.batch_size)
model.fit([x_train, y_train], [y_train, x_train],
batch_size=args.batch_size,
epochs=args.epochs,
validation_data=[[x_test, y_test], [y_test, x_test]],
callbacks=[mc, log, tb, checkpoint, lr_decay],
shuffle=True)
model.save_weights(args.save_dir + '/trained_model.h5')
print('Trained model saved to \'%s/trained_model.h5\'' % args.save_dir)
plot_log(args.save_dir + '/log.csv', show=True)
y_pred = eval_model.predict(
x_test, batch_size=args.batch_size)[0].astype("float32")
acc = accuracy_score(y_test, y_pred)
cm = confusion_matrix(y_test, y_pred)
recall = recall_score(y_test, y_pred, average="macro")
print("Accuracy: {:.2f}%".format(acc * 100))
print("Recall score: {:.2f}%".format(recall * 100))
print("Confusion matrix:\n{}".format(cm))
return model
def train(model, data, args):
"""
Kapsül Ağının Eğitimi
: "model" parametresi: CapsNet (Kapsül Ağ) Modeli
:"data" parametresi: Eğitim ve test verisinden bir grup içerir, örneğin; `((x_train, y_train), (x_test, y_test))`
:"args" parametresi: Bağımsız değişkenler
: Fonksiyon çıktısı: Eğitilmiş model
"""
# Verilerin Kullanıma Hazır Hale Getir
(x_train, y_train), (x_test, y_test) = data
# Tutulacak Kayıtlar
log = callbacks.CSVLogger(args.save_dir + '/log.csv')
tb = callbacks.TensorBoard(log_dir=args.save_dir + '/tensorboard-logs',
batch_size=args.batch_size,
histogram_freq=args.debug)
checkpoint = callbacks.ModelCheckpoint(args.save_dir +
'/weights-{epoch:02d}.h5',
monitor='val_capsnet_acc',
save_best_only=True,
save_weights_only=True,
verbose=1)
lr_decay = callbacks.LearningRateScheduler(
schedule=lambda epoch: args.lr * (0.9**epoch))
# Model Derlenir
model.compile(optimizer=optimizers.Adam(lr=args.lr),
loss=[margin_loss, 'mse'],
loss_weights=[1., args.lam_recon],
metrics={'capsnet': 'accuracy'})
# EĞİTİM ÖNCESİ VERİ ARTIRMA (DATA AUGMENTATION) YAPALIM
### VERİ ARTIRMA BAŞLA
def train_generator(x, y, batch_size, shift_fraction=0.):
train_datagen = ImageDataGenerator(width_shift_range=shift_fraction,
height_shift_range=shift_fraction)
generator = train_datagen.flow(x, y, batch_size=batch_size)
while 1:
x_batch, y_batch = generator.next()
yield ([x_batch, y_batch], [y_batch, x_batch])
# Veri Artırma Yaparak Modelin Eğitimi. Eğer shift_fraction=0., Bu durumda da veri artırma olmaz.
model.fit_generator(
generator=train_generator(x_train, y_train, args.batch_size,
args.shift_fraction),
steps_per_epoch=int(y_train.shape[0] / args.batch_size),
epochs=args.epochs,
validation_data=[[x_test, y_test], [y_test, x_test]],
callbacks=[log, tb, checkpoint, lr_decay])
"""
# Veri Artırma (Data Augmentation) Yapmadan Modelin Eğitimi:
model.fit([x_train, y_train], [y_train, x_train], batch_size=args.batch_size, epochs=args.epochs,
validation_data=[[x_test, y_test], [y_test, x_test]], callbacks=[log, tb, checkpoint, lr_decay])
"""
### VERİ ARTIRMA BİTİR
model.save_weights(args.save_dir + '/trained_model.h5')
print('Trained model saved to \'%s/trained_model.h5\'' % args.save_dir)
from utils import plot_log
plot_log(args.save_dir + '/log.csv', show=True)
return model
batch_size=batch_size)
log = callbacks.CSVLogger(saveDir + '/log.csv')
for layer in full_model.layers[1].layers[:-1]:
layer.trainable = False
history = full_model.fit_generator(generator=train_generator(batch_size),
steps_per_epoch=int(520 / batch_size),
epochs=epochs,
verbose=1,
validation_data=val_generator(batch_size),
validation_steps=int(208 / batch_size),
callbacks=[es_cb, tb, log])
from utils import plot_log
plot_log(saveDir + '/log.csv', show=True)
#
test_eval = []
def test_generator(batch_size=batch_size):
test_datagen = ImageDataGenerator(rescale=1. / 255)
generator_test = test_datagen.flow_from_directory(
directory="D:/EURECOM_Kinect_Face_Dataset/RGB/test",
target_size=(224, 224),
color_mode="rgb",
batch_size=1,
class_mode="categorical",
shuffle=True,
seed=42)
def train(model, data, args):
"""
Kapsül Ağının Eğitimi
:param model:CapsNet (Kapsül Ağ) Modeli
:param data:Eğitim ve test verisinden bir grup içerir, örneğin; `((x_train, y_train), (x_test, y_test))`
:param args:Bağımsız değişkenler
:return :Eğitilmiş model
"""
# Verilerin Kullanıma Hazır Hale Gelir
(x_train, y_train), (x_test, y_test) = data
# Geri Çağırmalar
log = callbacks.CSVLogger(args.save_dir + '/log.csv')
tb = callbacks.TensorBoard(log_dir=args.save_dir + '/tensorboard-logs',
batch_size=args.batch_size,
histogram_freq=args.debug)
checkpoint = callbacks.ModelCheckpoint(args.save_dir +
'/weights-{epoch:02d}.h5',
monitor='val_capsnet_acc',
save_best_only=True,
save_weights_only=True,
verbose=1)
lr_decay = callbacks.LearningRateScheduler(
schedule=lambda epoch: args.lr * (0.9**epoch))
# Model Derlenir
model.compile(optimizer=optimizers.Adam(lr=args.lr),
loss=[margin_loss, 'mse'],
loss_weights=[1., args.lam_recon],
metrics={'capsnet': 'accuracy'})
"""
# Veri Büyütme Yapmadan Modelin Eğitimi:
model.fit([x_train, y_train], [y_train, x_train], batch_size=args.batch_size, epochs=args.epochs,
validation_data=[[x_test, y_test], [y_test, x_test]], callbacks=[log, tb, checkpoint, lr_decay])
"""
# Başlangıç: Veri Büyütme Yaparak Modelin Eğitimi ---------------------------------------------------------------------#
def train_generator(x, y, batch_size, shift_fraction=0.):
train_datagen = ImageDataGenerator(
width_shift_range=shift_fraction,
height_shift_range=shift_fraction
) # MNIST veri setini 2 piksel yukarı kaydırır
generator = train_datagen.flow(x, y, batch_size=batch_size)
while 1:
x_batch, y_batch = generator.next()
yield ([x_batch, y_batch], [y_batch, x_batch])
# Veri Büyütme Yaparak Modelin Eğitimi. Eğer shift_fraction=0., Bu durumda da veri büyütme olmaz.
model.fit_generator(
generator=train_generator(x_train, y_train, args.batch_size,
args.shift_fraction),
steps_per_epoch=int(y_train.shape[0] / args.batch_size),
epochs=args.epochs,
validation_data=[[x_test, y_test], [y_test, x_test]],
callbacks=[log, tb, checkpoint, lr_decay])
# Son: Veri Büyütme Yaparak Modelin Eğitimi -----------------------------------------------------------------------#
model.save_weights(args.save_dir + '/trained_model.h5')
print('Trained model saved to \'%s/trained_model.h5\'' % args.save_dir)
from utils import plot_log
plot_log(args.save_dir + '/log.csv', show=True)
return model
def train(model, fold, args):
"""
Training
:param model: the model
:param args: arguments
:return: The trained model
"""
# unpacking the data
# (x_train, y_train), (x_test, y_test) = data
# callbacks
save_dirc = args.save_dir + str(fold)
log = callbacks.CSVLogger(save_dirc + '/log.csv')
es_cb = callbacks.EarlyStopping(monitor='val_loss',
patience=10,
verbose=1,
mode='auto')
tb = callbacks.TensorBoard(log_dir=save_dirc + '/tensorboard-logs',
batch_size=args.batch_size,
histogram_freq=int(args.debug))
checkpoint = callbacks.ModelCheckpoint(save_dirc + '/weights-best.h5',
monitor='val_acc',
save_best_only=True,
save_weights_only=True,
verbose=1)
lr_decay = callbacks.LearningRateScheduler(
schedule=lambda epoch: args.lr * (args.lr_decay**epoch))
# compile the model
model.compile(optimizer=optimizers.Adam(lr=args.lr),
loss=['categorical_crossentropy'],
metrics=['accuracy'])
# Begin: Training with data augmentation ---------------------------------------------------------------------#
def train_generator(batch_size, val_train):
# train_datagen = ImageDataGenerator(rescale=1./255,shear_range=0.2,zoom_range=0.2,horizontal_flip=True, validation_split=0.2)
#
# train_generator = train_datagen.flow_from_directory("D:/RGB_D_Dataset/train/depth/",target_size=(224, 224), color_mode="rgb",
# batch_size=args.batch_size, class_mode='categorical',subset='training')
#
# validation_generator = train_datagen.flow_from_directory("D:/RGB_D_Dataset/train/depth/",target_size=(224, 224), color_mode="rgb",
# batch_size=args.batch_size,class_mode='categorical',subset='validation')
#
batch_size = int(batch_size / 5)
train_datagen = ImageDataGenerator(rescale=1. / 255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
generator_rgb = train_datagen.flow_from_directory(
directory="D:/RGB_D_Dataset_new/fold{}/train/RGB/".format(fold),
target_size=(224, 224),
color_mode="rgb",
batch_size=batch_size,
class_mode="categorical",
shuffle=True,
seed=42)
generator_depth = train_datagen.flow_from_directory(
directory="D:/RGB_D_Dataset_new/fold{}/train/depth/".format(fold),
target_size=(224, 224),
color_mode="rgb",
batch_size=batch_size,
class_mode="categorical",
shuffle=True,
seed=42)
generator_rgb_val = train_datagen.flow_from_directory(
directory="D:/RGB_D_Dataset_new/fold{}/test/RGB/".format(fold),
target_size=(224, 224),
color_mode="rgb",
batch_size=batch_size,
class_mode="categorical",
shuffle=True,
seed=42)
generator_depth_val = train_datagen.flow_from_directory(
directory="D:/RGB_D_Dataset_new/fold{}/test/depth/".format(fold),
target_size=(224, 224),
color_mode="rgb",
batch_size=batch_size,
class_mode="categorical",
shuffle=True,
seed=42)
if val_train == 'train':
while 1:
#rgb data aug
x_batch_rgb, y_batch_rgb = generator_rgb.next()
flip_img = iaa.Fliplr(1)(images=x_batch_rgb)
rot_img = iaa.Affine(rotate=(-30, 30))(images=x_batch_rgb)
shear_aug = iaa.Affine(shear=(-16, 16))(images=x_batch_rgb)
trans_aug = iaa.Affine(scale={
"x": (0.5, 1.5),
"y": (0.5, 1.5)
})(images=x_batch_rgb)
x_batch_rgb_final = np.concatenate(
[x_batch_rgb, flip_img, rot_img, shear_aug, trans_aug],
axis=0)
y_batch_rgb_final = np.tile(y_batch_rgb, (5, 1))
## depth data aug
x_batch_depth, y_batch_depth = generator_depth.next()
flip_img = iaa.Fliplr(1)(images=x_batch_depth)
rot_img = iaa.Affine(rotate=(-30, 30))(images=x_batch_depth)
shear_aug = iaa.Affine(shear=(-16, 16))(images=x_batch_depth)
trans_aug = iaa.Affine(scale={
"x": (0.5, 1.5),
"y": (0.5, 1.5)
})(images=x_batch_depth)
x_batch_depth_final = np.concatenate(
[x_batch_depth, flip_img, rot_img, shear_aug, trans_aug],
axis=0)
y_batch_depth_final = np.tile(y_batch_rgb, (5, 1))
yield [[x_batch_rgb_final, x_batch_depth_final],
y_batch_rgb_final]
elif val_train == 'val':
while 1:
x_batch_rgb, y_batch_rgb = generator_rgb_val.next()
x_batch_depth, y_batch_depth = generator_depth_val.next()
yield [[x_batch_rgb, x_batch_depth], y_batch_rgb]
# Training with data augmentation. If shift_fraction=0., also no augmentation.
model.fit_generator(
generator=train_generator(args.batch_size, 'train'),
steps_per_epoch=int(
424 /
int(args.batch_size / 5)), ##936 curtin faces###424 fold1 iiitd
epochs=args.epochs,
validation_data=train_generator(args.batch_size, 'val'),
validation_steps=int(
4181 /
int(args.batch_size / 5)), ##4108 curtin faces###4181 fold1 iiitd
callbacks=[log, tb, checkpoint, lr_decay, es_cb])
# End: Training with data augmentation -----------------------------------------------------------------------#
model.save_weights(save_dirc + '/trained_model.h5')
print('Trained model saved to \'%s/trained_model.h5\'' % save_dirc)
from utils import plot_log
plot_log(save_dirc + '/log.csv', show=True)
return model
def train(model, args):
"""
Training
:param model: the model
:param args: arguments
:return: The trained model
"""
# unpacking the data
# (x_train, y_train), (x_test, y_test) = data
# callbacks
log = callbacks.CSVLogger(args.save_dir + '/log.csv')
es_cb = callbacks.EarlyStopping(monitor='loss',
patience=10,
verbose=1,
mode='auto')
tb = callbacks.TensorBoard(log_dir=args.save_dir + '/tensorboard-logs',
batch_size=args.batch_size,
histogram_freq=int(args.debug))
checkpoint = callbacks.ModelCheckpoint(args.save_dir +
'/weights-{epoch:02d}.h5',
monitor='acc',
save_best_only=True,
save_weights_only=True,
verbose=1)
lr_decay = callbacks.LearningRateScheduler(
schedule=lambda epoch: args.lr * (args.lr_decay**epoch))
# compile the model
model.compile(optimizer=optimizers.Adam(lr=args.lr),
loss=['categorical_crossentropy'],
metrics=['accuracy'])
# Begin: Training with data augmentation ---------------------------------------------------------------------#
def train_generator(batch_size, val_train):
#
train_datagen = ImageDataGenerator(
rescale=1. / 255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True) #, validation_split=0.2)
generator_rgb = train_datagen.flow_from_directory(
directory="D:/CurtinFaces_crop/RGB/train/",
target_size=(224, 224),
color_mode="rgb",
batch_size=batch_size,
class_mode="categorical",
shuffle=True,
subset='training',
seed=42)
generator_depth = train_datagen.flow_from_directory(
directory="D:/CurtinFaces_crop/normalized/DEPTH/train/",
target_size=(224, 224),
color_mode="rgb",
batch_size=batch_size,
class_mode="categorical",
shuffle=True,
subset='training',
seed=42)
# generator_rgb_val = train_datagen.flow_from_directory(directory="D:/CurtinFaces_crop/RGB/train/", target_size=(224, 224), color_mode="rgb",
# batch_size=batch_size, class_mode="categorical", shuffle=True,subset='validation', seed=42)
# generator_depth_val = train_datagen.flow_from_directory(directory="D:/CurtinFaces_crop/normalized/DEPTH/train/", target_size=(224, 224), color_mode="rgb",
# batch_size=batch_size, class_mode="categorical", shuffle=True,subset='validation', seed=42)
if val_train == 'train':
while 1:
x_batch_rgb, y_batch_rgb = generator_rgb.next()
x_batch_depth, y_batch_depth = generator_depth.next()
yield [[x_batch_rgb, x_batch_depth], y_batch_rgb]
elif val_train == 'val':
while 1:
x_batch_rgb, y_batch_rgb = generator_rgb_val.next()
x_batch_depth, y_batch_depth = generator_depth_val.next()
yield [[x_batch_rgb, x_batch_depth], y_batch_rgb]
# Training with data augmentation. If shift_fraction=0., also no augmentation.
model.fit_generator(
generator=train_generator(args.batch_size, 'train'),
steps_per_epoch=int(
936 / args.batch_size), ##936 curtin faces###424 fold1 iiitd
epochs=args.epochs,
# validation_data=train_generator(args.batch_size,'val'),
# validation_steps = int( 156 / args.batch_size),##4108 curtin faces###4181 fold1 iiitd
callbacks=[log, tb, checkpoint, lr_decay, es_cb])
# End: Training with data augmentation -----------------------------------------------------------------------#
# model.save_weights(args.save_dir + '/trained_model.h5')
# print('Trained model saved to \'%s/trained_model.h5\'' % args.save_dir)
from utils import plot_log
plot_log(args.save_dir + '/log.csv', show=True)
return model
def train(model, args):
"""
Training a CapsuleNet
:param model: the CapsuleNet model
:param data: a tuple containing training and testing data, like `((x_train, y_train), (x_test, y_test))`
:param args: arguments
:return: The trained model
"""
# unpacking the data
# (x_train, y_train), (x_test, y_test) = data
# callbacks
log = callbacks.CSVLogger(args.save_dir + '/log.csv')
tb = callbacks.TensorBoard(log_dir=args.save_dir + '/tensorboard-logs',
batch_size=args.batch_size,
histogram_freq=int(args.debug))
checkpoint = callbacks.ModelCheckpoint(args.save_dir +
'/weights-{epoch:02d}.h5',
monitor='val_acc',
save_best_only=True,
save_weights_only=True,
verbose=1)
lr_decay = callbacks.LearningRateScheduler(
schedule=lambda epoch: args.lr * (args.lr_decay**epoch))
# compile the model
model.compile(optimizer=optimizers.Adam(lr=args.lr),
loss=['categorical_crossentropy'],
metrics=['accuracy'])
# Begin: Training with data augmentation ---------------------------------------------------------------------#
# def train_generator(batch_size):
# train_datagen = ImageDataGenerator(rescale=1./255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True)
# generator_rgb = train_datagen.flow_from_directory(directory="D:/RGB_D_Dataset/train/RGB/", target_size=(224, 224), color_mode="rgb",
# batch_size=batch_size, class_mode="categorical", shuffle=True, seed=42)
# generator_depth = train_datagen.flow_from_directory(directory="D:/RGB_D_Dataset/train/depth/", target_size=(224, 224), color_mode="rgb",
# batch_size=batch_size, class_mode="categorical", shuffle=True, seed=42)
#
# while 1:
# x_batch_rgb, y_batch_rgb = generator_rgb.next()
# x_batch_depth, y_batch_depth = generator_depth.next()
# yield [x_batch_depth, y_batch_depth]
#
# def val_generator(batch_size):
# train_datagen = ImageDataGenerator(rescale=1./255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True)
# generator_rgb = train_datagen.flow_from_directory(directory="D:/RGB_D_Dataset/train/RGB/", target_size=(224, 224), color_mode="rgb",
# batch_size=batch_size, class_mode="categorical", shuffle=True, seed=42)
# generator_depth = train_datagen.flow_from_directory(directory="D:/RGB_D_Dataset/test/depth/", target_size=(224, 224), color_mode="rgb",
# batch_size=batch_size, class_mode="categorical", shuffle=True, seed=42)
#
# while 1:
# x_batch_rgb, y_batch_rgb = generator_rgb.next()
# x_batch_depth, y_batch_depth = generator_depth.next()
# yield [x_batch_depth, y_batch_depth]
train_datagen = ImageDataGenerator(rescale=1. / 255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
validation_split=0.2)
train_generator = train_datagen.flow_from_directory(
"D:/RGB_D_Dataset/train/depth/",
target_size=(224, 224),
color_mode="rgb",
batch_size=args.batch_size,
class_mode='categorical',
subset='training')
validation_generator = train_datagen.flow_from_directory(
"D:/RGB_D_Dataset/train/depth/",
target_size=(224, 224),
color_mode="rgb",
batch_size=args.batch_size,
class_mode='categorical',
subset='validation')
# Training with data augmentation. If shift_fraction=0., also no augmentation.
model.fit_generator(
generator=train_generator,
steps_per_epoch=int(train_generator.samples / args.batch_size),
epochs=args.epochs,
validation_data=validation_generator,
validation_steps=int(validation_generator.samples / args.batch_size),
callbacks=[log, tb, checkpoint, lr_decay])
# End: Training with data augmentation -----------------------------------------------------------------------#
model.save_weights(args.save_dir + '/trained_model.h5')
print('Trained model saved to \'%s/trained_model.h5\'' % args.save_dir)
from utils import plot_log
plot_log(args.save_dir + '/log.csv', show=True)
return model
def train(model, data, args):
"""
カプセルネットの学習
:param model: カプセルネットのモデル
:param data: trainデータもtestデータも含んだタプルデータ, 右のような形式 `((x_train, y_train), (x_test, y_test))`
:param args: arguments
:return: 学習ずみモデル
"""
# データ切り分け
(x_train, y_train), (x_test, y_test) = data
# コールバック
log = callbacks.CSVLogger(args.save_dir + '/log.csv')
tb = callbacks.TensorBoard(log_dir=args.save_dir + '/tensorboard-logs',
batch_size=args.batch_size,
histogram_freq=int(args.debug))
checkpoint = callbacks.ModelCheckpoint(args.save_dir +
'/weights-{epoch:02d}.h5',
monitor='val_capsnet_acc',
save_best_only=True,
save_weights_only=True,
verbose=1)
lr_decay = callbacks.LearningRateScheduler(
schedule=lambda epoch: args.lr * (args.lr_decay**epoch))
# モデルをコンパイル
model.compile(optimizer=optimizers.Adam(lr=args.lr),
loss=[margin_loss, 'mse'],
loss_weights=[1., args.lam_recon],
metrics={'capsnet': 'accuracy'})
"""
データ増強無しの学習をする場合:
model.fit([x_train, y_train], [y_train, x_train], bacth_size=args.batch_size, epochs=args.epochs,
validation_data=[[x_test, y_test], [y_test, x_test]], callbacks=[log, tb, checkpoint, lr_decay])
としてください
"""
# 開始 : データ増強ありの学習 ---------------------------------------------------------------------#
def train_generator(x, y, batch_size, shift_fraction=0.):
train_datagen = ImageDataGenerator(
width_shift_range=shift_fraction,
height_shift_range=shift_fraction) # MNISTの画像を2ピクセルをずらす
generator = train_datagen.flow(x, y, batch_size=batch_size)
while 1:
x_batch, y_batch = generator.next()
yield ([x_batch, y_batch], [y_batch, x_batch])
# shift_fraction=0とすれば, データ増強無しの学習.
model.fit_generator(
generator=train_generator(x_train, y_train, args.batch_size,
args.shift_fraction),
steps_per_epoch=int(y_train.shape[0] / args.batch_size),
epochs=args.epochs,
validation_data=[[x_test, y_test], [y_test, x_test]],
callbacks=[log, tb, checkpoint, lr_decay])
# 終了 : データ増強無しの学習 -----------------------------------------------------------------------#
model.save_weights(args.save_dir + '/trained_model.h5')
print('Trained model saved to \'%s/trained_model.h5\'' % args.save_dir)
from utils import plot_log
plot_log(args.save_dir + '/log.csv', show=True)
return model
checkpoint = tf.keras.callbacks.ModelCheckpoint('/weights-{epoch:02d}.h5',
monitor='val_capsnet_acc',
save_best_only=True,
save_weights_only=True,
verbose=1)
lr_decay = tf.keras.callbacks.LearningRateScheduler(
schedule=lambda epoch: 0.001 * (0.9**epoch))
# compile the model
train_model.compile(optimizer=tf.keras.optimizers.Adam(lr=0.001),
loss=[margin_loss, 'mse'],
loss_weights=[1., 0.392],
metrics={'capsnet': 'accuracy'})
train_model.summary()
train_model.fit([x_train, y_train], [y_train, x_train],
batch_size=32,
epochs=50,
validation_data=[[x_test, y_test], [y_test, x_test]],
callbacks=[log, checkpoint, lr_decay])
model.save_weights('/trained_model.h5')
print('Trained model saved to \'%s/trained_model.h5\'')
from utils import plot_log
plot_log('/log.csv', show=True)
