I'd like to call 'on_epoch_end()' at each end of epoch. [keras custom generator]
0
I use keras and tried to define custom generator.
In generator, I expect that the function "on_epoch_end()" is called at each end of epoch, But "on_epoch_end()" never be called anytime.
Could you tell me why? Please.
from pathlib import Path
import math
from tensorflow.keras.utils import Sequence
from keras.utils import np_utils
class ImageSequence(Sequence):
def __init__(self, x, batch_size=512):
self.x_positive = x[0]
self.x_negative = x[1]
self.batch_size = batch_size
def __getitem__(self, idx):
hbs = self.batch_size//2
idx_p = np.random.randint(0, self.x_positive.shape[0], hbs)
batch_x_positive = self.x_positive[idx_p]
#
idx_n = np.random.randint(0, self.x_negative.shape[0], hbs)
batch_x_negative = self.x_negative[idx_n]
#batch_x_negative = self.x_negative[idx*hbs : (idx+1)*hbs]
#
batch_x = np.r_[batch_x_positive, batch_x_negative]
#
batch_y = np.r_[np.ones(len(batch_x_positive)), np.zeros(len(batch_x_negative))]
return batch_x, batch_y
def __len__(self):
return math.ceil(2 * len(self.x_negative) / self.batch_size)
def _shuffle(self):
self.x_negative = shuffle(self.x_negative)
def on_epoch_end(self):
self._shuffle()
data_gen = ImageSequence([train_positive, train_negative], batch_size=BATCH_SIZE)
history = model.fit_generator(
generator=data_gen,
use_multiprocessing=True,
validation_data=(x_valid, y_valid),
steps_per_epoch=2 * len(train_positive) / BATCH_SIZE,
epochs=30,
verbose=2,
callbacks=)
I wrote my environment(version info )
import tensorflow.keras
print(tensorflow.keras.__version__)
2.1.6-tf
tensorflow keras
asked Nov 13 '18 at 9:41
RoadRollerRoadRoller
1
add a comment |
0
I use keras and tried to define custom generator.
In generator, I expect that the function "on_epoch_end()" is called at each end of epoch, But "on_epoch_end()" never be called anytime.
Could you tell me why? Please.
from pathlib import Path
import math
from tensorflow.keras.utils import Sequence
from keras.utils import np_utils
class ImageSequence(Sequence):
def __init__(self, x, batch_size=512):
self.x_positive = x[0]
self.x_negative = x[1]
self.batch_size = batch_size
def __getitem__(self, idx):
hbs = self.batch_size//2
idx_p = np.random.randint(0, self.x_positive.shape[0], hbs)
batch_x_positive = self.x_positive[idx_p]
#
idx_n = np.random.randint(0, self.x_negative.shape[0], hbs)
batch_x_negative = self.x_negative[idx_n]
#batch_x_negative = self.x_negative[idx*hbs : (idx+1)*hbs]
#
batch_x = np.r_[batch_x_positive, batch_x_negative]
#
batch_y = np.r_[np.ones(len(batch_x_positive)), np.zeros(len(batch_x_negative))]
return batch_x, batch_y
def __len__(self):
return math.ceil(2 * len(self.x_negative) / self.batch_size)
def _shuffle(self):
self.x_negative = shuffle(self.x_negative)
def on_epoch_end(self):
self._shuffle()
data_gen = ImageSequence([train_positive, train_negative], batch_size=BATCH_SIZE)
history = model.fit_generator(
generator=data_gen,
use_multiprocessing=True,
validation_data=(x_valid, y_valid),
steps_per_epoch=2 * len(train_positive) / BATCH_SIZE,
epochs=30,
verbose=2,
callbacks=)
I wrote my environment(version info )
import tensorflow.keras
print(tensorflow.keras.__version__)
2.1.6-tf
tensorflow keras
asked Nov 13 '18 at 9:41
RoadRollerRoadRoller
1
you are picking your batch using random indexes with the lineidx_p = np.random.randint(0, self.x_positive.shape[0], hbs)what are you trying to shuffle with on_epoch_end() ?
– Mete Han Kahraman
Nov 13 '18 at 10:17
Thank you for your reply. and I'm sorry for confusing you. At first I tried to shuffle at "on_epoch_end()", but it was not called. so then I define "idx_n = np.random.randint(0, self.x_negative.shape[0], hbs)". I would like to shuffle x.negative at the end of epoch if I can, in order to get completeness of train data in each epoch.
– RoadRoller
Nov 13 '18 at 13:09
This might help: github.com/keras-team/keras/issues/11122
– Mete Han Kahraman
Nov 14 '18 at 5:09
Perhaps you should change math.ceil to math.floor in the__len__function.
– Mete Han Kahraman
Nov 14 '18 at 5:15
Thank you very much!! the problem has benn solved. I replaced math.ceil to math.floor. And I found that there was a difference of definition betweensteps_per_epochand__len__.
– RoadRoller
Nov 15 '18 at 1:17
add a comment |
0
0
0
I use keras and tried to define custom generator.
In generator, I expect that the function "on_epoch_end()" is called at each end of epoch, But "on_epoch_end()" never be called anytime.
Could you tell me why? Please.
from pathlib import Path
import math
from tensorflow.keras.utils import Sequence
from keras.utils import np_utils
class ImageSequence(Sequence):
def __init__(self, x, batch_size=512):
self.x_positive = x[0]
self.x_negative = x[1]
self.batch_size = batch_size
def __getitem__(self, idx):
hbs = self.batch_size//2
idx_p = np.random.randint(0, self.x_positive.shape[0], hbs)
batch_x_positive = self.x_positive[idx_p]
#
idx_n = np.random.randint(0, self.x_negative.shape[0], hbs)
batch_x_negative = self.x_negative[idx_n]
#batch_x_negative = self.x_negative[idx*hbs : (idx+1)*hbs]
#
batch_x = np.r_[batch_x_positive, batch_x_negative]
#
batch_y = np.r_[np.ones(len(batch_x_positive)), np.zeros(len(batch_x_negative))]
return batch_x, batch_y
def __len__(self):
return math.ceil(2 * len(self.x_negative) / self.batch_size)
def _shuffle(self):
self.x_negative = shuffle(self.x_negative)
def on_epoch_end(self):
self._shuffle()
data_gen = ImageSequence([train_positive, train_negative], batch_size=BATCH_SIZE)
history = model.fit_generator(
generator=data_gen,
use_multiprocessing=True,
validation_data=(x_valid, y_valid),
steps_per_epoch=2 * len(train_positive) / BATCH_SIZE,
epochs=30,
verbose=2,
callbacks=)
I wrote my environment(version info )
import tensorflow.keras
print(tensorflow.keras.__version__)
2.1.6-tf
tensorflow keras
asked Nov 13 '18 at 9:41
RoadRollerRoadRoller
1
I use keras and tried to define custom generator.
In generator, I expect that the function "on_epoch_end()" is called at each end of epoch, But "on_epoch_end()" never be called anytime.
Could you tell me why? Please.
from pathlib import Path
import math
from tensorflow.keras.utils import Sequence
from keras.utils import np_utils
class ImageSequence(Sequence):
def __init__(self, x, batch_size=512):
self.x_positive = x[0]
self.x_negative = x[1]
self.batch_size = batch_size
def __getitem__(self, idx):
hbs = self.batch_size//2
idx_p = np.random.randint(0, self.x_positive.shape[0], hbs)
batch_x_positive = self.x_positive[idx_p]
#
idx_n = np.random.randint(0, self.x_negative.shape[0], hbs)
batch_x_negative = self.x_negative[idx_n]
#batch_x_negative = self.x_negative[idx*hbs : (idx+1)*hbs]
#
batch_x = np.r_[batch_x_positive, batch_x_negative]
#
batch_y = np.r_[np.ones(len(batch_x_positive)), np.zeros(len(batch_x_negative))]
return batch_x, batch_y
def __len__(self):
return math.ceil(2 * len(self.x_negative) / self.batch_size)
def _shuffle(self):
self.x_negative = shuffle(self.x_negative)
def on_epoch_end(self):
self._shuffle()
data_gen = ImageSequence([train_positive, train_negative], batch_size=BATCH_SIZE)
history = model.fit_generator(
generator=data_gen,
use_multiprocessing=True,
validation_data=(x_valid, y_valid),
steps_per_epoch=2 * len(train_positive) / BATCH_SIZE,
epochs=30,
verbose=2,
callbacks=)
I wrote my environment(version info )
import tensorflow.keras
print(tensorflow.keras.__version__)
2.1.6-tf
tensorflow keras
tensorflow keras
asked Nov 13 '18 at 9:41
RoadRollerRoadRoller
1
asked Nov 13 '18 at 9:41
RoadRollerRoadRoller
1
asked Nov 13 '18 at 9:41
RoadRollerRoadRoller
1
asked Nov 13 '18 at 9:41
RoadRollerRoadRoller
1
asked Nov 13 '18 at 9:41
RoadRollerRoadRoller
1
1
you are picking your batch using random indexes with the lineidx_p = np.random.randint(0, self.x_positive.shape[0], hbs)what are you trying to shuffle with on_epoch_end() ?
– Mete Han Kahraman
Nov 13 '18 at 10:17
Thank you for your reply. and I'm sorry for confusing you. At first I tried to shuffle at "on_epoch_end()", but it was not called. so then I define "idx_n = np.random.randint(0, self.x_negative.shape[0], hbs)". I would like to shuffle x.negative at the end of epoch if I can, in order to get completeness of train data in each epoch.
– RoadRoller
Nov 13 '18 at 13:09
This might help: github.com/keras-team/keras/issues/11122
– Mete Han Kahraman
Nov 14 '18 at 5:09
Perhaps you should change math.ceil to math.floor in the__len__function.
– Mete Han Kahraman
Nov 14 '18 at 5:15
Thank you very much!! the problem has benn solved. I replaced math.ceil to math.floor. And I found that there was a difference of definition betweensteps_per_epochand__len__.
– RoadRoller
Nov 15 '18 at 1:17
add a comment |
you are picking your batch using random indexes with the lineidx_p = np.random.randint(0, self.x_positive.shape[0], hbs)what are you trying to shuffle with on_epoch_end() ?
– Mete Han Kahraman
Nov 13 '18 at 10:17
Thank you for your reply. and I'm sorry for confusing you. At first I tried to shuffle at "on_epoch_end()", but it was not called. so then I define "idx_n = np.random.randint(0, self.x_negative.shape[0], hbs)". I would like to shuffle x.negative at the end of epoch if I can, in order to get completeness of train data in each epoch.
– RoadRoller
Nov 13 '18 at 13:09
This might help: github.com/keras-team/keras/issues/11122
– Mete Han Kahraman
Nov 14 '18 at 5:09
Perhaps you should change math.ceil to math.floor in the__len__function.
– Mete Han Kahraman
Nov 14 '18 at 5:15
Thank you very much!! the problem has benn solved. I replaced math.ceil to math.floor. And I found that there was a difference of definition betweensteps_per_epochand__len__.
– RoadRoller
Nov 15 '18 at 1:17
you are picking your batch using random indexes with the line
idx_p = np.random.randint(0, self.x_positive.shape[0], hbs) what are you trying to shuffle with on_epoch_end() ?– Mete Han Kahraman
Nov 13 '18 at 10:17
you are picking your batch using random indexes with the line
idx_p = np.random.randint(0, self.x_positive.shape[0], hbs) what are you trying to shuffle with on_epoch_end() ?– Mete Han Kahraman
Nov 13 '18 at 10:17
Thank you for your reply. and I'm sorry for confusing you. At first I tried to shuffle at "on_epoch_end()", but it was not called. so then I define "idx_n = np.random.randint(0, self.x_negative.shape[0], hbs)". I would like to shuffle x.negative at the end of epoch if I can, in order to get completeness of train data in each epoch.
– RoadRoller
Nov 13 '18 at 13:09
Thank you for your reply. and I'm sorry for confusing you. At first I tried to shuffle at "on_epoch_end()", but it was not called. so then I define "idx_n = np.random.randint(0, self.x_negative.shape[0], hbs)". I would like to shuffle x.negative at the end of epoch if I can, in order to get completeness of train data in each epoch.
– RoadRoller
Nov 13 '18 at 13:09
This might help: github.com/keras-team/keras/issues/11122
– Mete Han Kahraman
Nov 14 '18 at 5:09
This might help: github.com/keras-team/keras/issues/11122
– Mete Han Kahraman
Nov 14 '18 at 5:09
Perhaps you should change math.ceil to math.floor in the
__len__function.– Mete Han Kahraman
Nov 14 '18 at 5:15
Perhaps you should change math.ceil to math.floor in the
__len__function.– Mete Han Kahraman
Nov 14 '18 at 5:15
Thank you very much!! the problem has benn solved. I replaced math.ceil to math.floor. And I found that there was a difference of definition between
steps_per_epoch and __len__.– RoadRoller
Nov 15 '18 at 1:17
Thank you very much!! the problem has benn solved. I replaced math.ceil to math.floor. And I found that there was a difference of definition between
steps_per_epoch and __len__.– RoadRoller
Nov 15 '18 at 1:17
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you are picking your batch using random indexes with the line
idx_p = np.random.randint(0, self.x_positive.shape[0], hbs)what are you trying to shuffle with on_epoch_end() ?– Mete Han Kahraman
Nov 13 '18 at 10:17
Thank you for your reply. and I'm sorry for confusing you. At first I tried to shuffle at "on_epoch_end()", but it was not called. so then I define "idx_n = np.random.randint(0, self.x_negative.shape[0], hbs)". I would like to shuffle x.negative at the end of epoch if I can, in order to get completeness of train data in each epoch.
– RoadRoller
Nov 13 '18 at 13:09
This might help: github.com/keras-team/keras/issues/11122
– Mete Han Kahraman
Nov 14 '18 at 5:09
Perhaps you should change math.ceil to math.floor in the
__len__function.– Mete Han Kahraman
Nov 14 '18 at 5:15
Thank you very much!! the problem has benn solved. I replaced math.ceil to math.floor. And I found that there was a difference of definition between
steps_per_epochand__len__.– RoadRoller
Nov 15 '18 at 1:17