tensorboard steps limit to 40?
I'm using keras 2.24 + tensorflow (1.12.0) to evaluate my learning experiments. I have a custom Tensorboard class inherited from keras.callbacks.TensorBoard to add in summary images to tensorboard. However, it seems that I can only visualize up to 40 epochs when I have already trained to 90~ epochs? Is this a limitation of tensorboard itself (do I have to change some settings to see data of latter epochs?) or is there something wrong with my Tensorboard code?
class MaskImageTensorBoard(TensorBoard):
def __init__(self, target_size, image_every_x_epochs, output_name, log_dir, data_vis_dir, **kwargs):
self.save_freq = image_every_x_epochs
self.output_name = output_name
self.data_vis_dir = data_vis_dir
self.target_size = target_size
super(MaskImageTensorBoard, self).__init__(log_dir, **kwargs)
def _load_images(self, image_list):
image_arr =
for f in image_list:
img = Image.open(f).resize((self.target_size[1], self.target_size[0]), Image.ANTIALIAS)
image_arr.append(np.array(img))
return np.array(image_arr)
def on_epoch_end(self, epoch, logs=None):
limit = 5
threshold = 0.5
color = [0, 255, 0]
if epoch % self.save_freq == 0:
image_list = [os.path.join(self.data_vis_dir, f) for f in random.sample(os.listdir(self.data_vis_dir), limit )]
images = self._load_images(image_list)
images2 = images / 255.
predictions = K.get_session().run(self.model.output, feed_dict = {self.model.input : images2})
predictions[predictions >= threshold] = 1
predictions[predictions < threshold] = 0 #shape [5, h, w, 1]
mask = np.apply_along_axis(lambda channel: np.concatenate([channel, channel, channel], axis=-1), 3, predictions)
mask *= color
masked_img = 0.5 * images + 0.5 * mask
masked_tensor = tf.convert_to_tensor(masked_img)
summ = tf.summary.image("Validation epoch {}".format(epoch), masked_tensor)
s = self.sess.run(summ)
self.writer.add_summary(s)
super(MaskImageTensorBoard, self).on_epoch_end(epoch, logs)
tensorflow keras tensorboard
asked Dec 28 '18 at 7:33
avpavp
163
add a comment |
I'm using keras 2.24 + tensorflow (1.12.0) to evaluate my learning experiments. I have a custom Tensorboard class inherited from keras.callbacks.TensorBoard to add in summary images to tensorboard. However, it seems that I can only visualize up to 40 epochs when I have already trained to 90~ epochs? Is this a limitation of tensorboard itself (do I have to change some settings to see data of latter epochs?) or is there something wrong with my Tensorboard code?
class MaskImageTensorBoard(TensorBoard):
def __init__(self, target_size, image_every_x_epochs, output_name, log_dir, data_vis_dir, **kwargs):
self.save_freq = image_every_x_epochs
self.output_name = output_name
self.data_vis_dir = data_vis_dir
self.target_size = target_size
super(MaskImageTensorBoard, self).__init__(log_dir, **kwargs)
def _load_images(self, image_list):
image_arr =
for f in image_list:
img = Image.open(f).resize((self.target_size[1], self.target_size[0]), Image.ANTIALIAS)
image_arr.append(np.array(img))
return np.array(image_arr)
def on_epoch_end(self, epoch, logs=None):
limit = 5
threshold = 0.5
color = [0, 255, 0]
if epoch % self.save_freq == 0:
image_list = [os.path.join(self.data_vis_dir, f) for f in random.sample(os.listdir(self.data_vis_dir), limit )]
images = self._load_images(image_list)
images2 = images / 255.
predictions = K.get_session().run(self.model.output, feed_dict = {self.model.input : images2})
predictions[predictions >= threshold] = 1
predictions[predictions < threshold] = 0 #shape [5, h, w, 1]
mask = np.apply_along_axis(lambda channel: np.concatenate([channel, channel, channel], axis=-1), 3, predictions)
mask *= color
masked_img = 0.5 * images + 0.5 * mask
masked_tensor = tf.convert_to_tensor(masked_img)
summ = tf.summary.image("Validation epoch {}".format(epoch), masked_tensor)
s = self.sess.run(summ)
self.writer.add_summary(s)
super(MaskImageTensorBoard, self).on_epoch_end(epoch, logs)
tensorflow keras tensorboard
asked Dec 28 '18 at 7:33
avpavp
163
add a comment |
I'm using keras 2.24 + tensorflow (1.12.0) to evaluate my learning experiments. I have a custom Tensorboard class inherited from keras.callbacks.TensorBoard to add in summary images to tensorboard. However, it seems that I can only visualize up to 40 epochs when I have already trained to 90~ epochs? Is this a limitation of tensorboard itself (do I have to change some settings to see data of latter epochs?) or is there something wrong with my Tensorboard code?
class MaskImageTensorBoard(TensorBoard):
def __init__(self, target_size, image_every_x_epochs, output_name, log_dir, data_vis_dir, **kwargs):
self.save_freq = image_every_x_epochs
self.output_name = output_name
self.data_vis_dir = data_vis_dir
self.target_size = target_size
super(MaskImageTensorBoard, self).__init__(log_dir, **kwargs)
def _load_images(self, image_list):
image_arr =
for f in image_list:
img = Image.open(f).resize((self.target_size[1], self.target_size[0]), Image.ANTIALIAS)
image_arr.append(np.array(img))
return np.array(image_arr)
def on_epoch_end(self, epoch, logs=None):
limit = 5
threshold = 0.5
color = [0, 255, 0]
if epoch % self.save_freq == 0:
image_list = [os.path.join(self.data_vis_dir, f) for f in random.sample(os.listdir(self.data_vis_dir), limit )]
images = self._load_images(image_list)
images2 = images / 255.
predictions = K.get_session().run(self.model.output, feed_dict = {self.model.input : images2})
predictions[predictions >= threshold] = 1
predictions[predictions < threshold] = 0 #shape [5, h, w, 1]
mask = np.apply_along_axis(lambda channel: np.concatenate([channel, channel, channel], axis=-1), 3, predictions)
mask *= color
masked_img = 0.5 * images + 0.5 * mask
masked_tensor = tf.convert_to_tensor(masked_img)
summ = tf.summary.image("Validation epoch {}".format(epoch), masked_tensor)
s = self.sess.run(summ)
self.writer.add_summary(s)
super(MaskImageTensorBoard, self).on_epoch_end(epoch, logs)
tensorflow keras tensorboard
asked Dec 28 '18 at 7:33
avpavp
163
I'm using keras 2.24 + tensorflow (1.12.0) to evaluate my learning experiments. I have a custom Tensorboard class inherited from keras.callbacks.TensorBoard to add in summary images to tensorboard. However, it seems that I can only visualize up to 40 epochs when I have already trained to 90~ epochs? Is this a limitation of tensorboard itself (do I have to change some settings to see data of latter epochs?) or is there something wrong with my Tensorboard code?
class MaskImageTensorBoard(TensorBoard):
def __init__(self, target_size, image_every_x_epochs, output_name, log_dir, data_vis_dir, **kwargs):
self.save_freq = image_every_x_epochs
self.output_name = output_name
self.data_vis_dir = data_vis_dir
self.target_size = target_size
super(MaskImageTensorBoard, self).__init__(log_dir, **kwargs)
def _load_images(self, image_list):
image_arr =
for f in image_list:
img = Image.open(f).resize((self.target_size[1], self.target_size[0]), Image.ANTIALIAS)
image_arr.append(np.array(img))
return np.array(image_arr)
def on_epoch_end(self, epoch, logs=None):
limit = 5
threshold = 0.5
color = [0, 255, 0]
if epoch % self.save_freq == 0:
image_list = [os.path.join(self.data_vis_dir, f) for f in random.sample(os.listdir(self.data_vis_dir), limit )]
images = self._load_images(image_list)
images2 = images / 255.
predictions = K.get_session().run(self.model.output, feed_dict = {self.model.input : images2})
predictions[predictions >= threshold] = 1
predictions[predictions < threshold] = 0 #shape [5, h, w, 1]
mask = np.apply_along_axis(lambda channel: np.concatenate([channel, channel, channel], axis=-1), 3, predictions)
mask *= color
masked_img = 0.5 * images + 0.5 * mask
masked_tensor = tf.convert_to_tensor(masked_img)
summ = tf.summary.image("Validation epoch {}".format(epoch), masked_tensor)
s = self.sess.run(summ)
self.writer.add_summary(s)
super(MaskImageTensorBoard, self).on_epoch_end(epoch, logs)
tensorflow keras tensorboard
tensorflow keras tensorboard
asked Dec 28 '18 at 7:33
avpavp
163
asked Dec 28 '18 at 7:33
avpavp
163
asked Dec 28 '18 at 7:33
avpavp
163
asked Dec 28 '18 at 7:33
avpavp
163
asked Dec 28 '18 at 7:33
avpavp
163
163
add a comment |
add a comment |
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