Dynamic batch size in tensorflow
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I have built a classifier using tesnorflow. I generate proposal regions from images and those proposals are individually classified by my classifier.
My problem is that I do not have a constant batch size when evaluating my model. Because every image has a different number of proposals, the number of proposals to be evaluated for every image is not constant.
Right now I have set the batch size to 1, but this is inefficient and limits the processing speed of my classifier.
Below is the placeholder for the input to the model
self.image_op = tf.placeholder(tf.float32, shape=[batch_size, 48, 48, 3], name='input_image')
And this is how I feed the input to the model
def predict(self,image):
cls_prob = self.sess.run([self.cls_prob], feed_dict={self.image_op: image})
return cls_prob
Is there any way of setting the batch size to a dynamic value without having to restore the model for every image?
python tensorflow deep-learning classification
asked Nov 22 '18 at 0:29
user10664643user10664643
297
add a comment |
I have built a classifier using tesnorflow. I generate proposal regions from images and those proposals are individually classified by my classifier.
My problem is that I do not have a constant batch size when evaluating my model. Because every image has a different number of proposals, the number of proposals to be evaluated for every image is not constant.
Right now I have set the batch size to 1, but this is inefficient and limits the processing speed of my classifier.
Below is the placeholder for the input to the model
self.image_op = tf.placeholder(tf.float32, shape=[batch_size, 48, 48, 3], name='input_image')
And this is how I feed the input to the model
def predict(self,image):
cls_prob = self.sess.run([self.cls_prob], feed_dict={self.image_op: image})
return cls_prob
Is there any way of setting the batch size to a dynamic value without having to restore the model for every image?
python tensorflow deep-learning classification
asked Nov 22 '18 at 0:29
user10664643user10664643
297
try with self.image_op = tf.placeholder(tf.float32, shape=[None, 48, 48, 3], name='input_image'). It should take variable batch sizes
– Biswadip Mandal
Nov 22 '18 at 4:29
That works. Thanks!
– user10664643
Nov 27 '18 at 1:51
add a comment |
I have built a classifier using tesnorflow. I generate proposal regions from images and those proposals are individually classified by my classifier.
My problem is that I do not have a constant batch size when evaluating my model. Because every image has a different number of proposals, the number of proposals to be evaluated for every image is not constant.
Right now I have set the batch size to 1, but this is inefficient and limits the processing speed of my classifier.
Below is the placeholder for the input to the model
self.image_op = tf.placeholder(tf.float32, shape=[batch_size, 48, 48, 3], name='input_image')
And this is how I feed the input to the model
def predict(self,image):
cls_prob = self.sess.run([self.cls_prob], feed_dict={self.image_op: image})
return cls_prob
Is there any way of setting the batch size to a dynamic value without having to restore the model for every image?
python tensorflow deep-learning classification
asked Nov 22 '18 at 0:29
user10664643user10664643
297
I have built a classifier using tesnorflow. I generate proposal regions from images and those proposals are individually classified by my classifier.
My problem is that I do not have a constant batch size when evaluating my model. Because every image has a different number of proposals, the number of proposals to be evaluated for every image is not constant.
Right now I have set the batch size to 1, but this is inefficient and limits the processing speed of my classifier.
Below is the placeholder for the input to the model
self.image_op = tf.placeholder(tf.float32, shape=[batch_size, 48, 48, 3], name='input_image')
And this is how I feed the input to the model
def predict(self,image):
cls_prob = self.sess.run([self.cls_prob], feed_dict={self.image_op: image})
return cls_prob
Is there any way of setting the batch size to a dynamic value without having to restore the model for every image?
python tensorflow deep-learning classification
python tensorflow deep-learning classification
asked Nov 22 '18 at 0:29
user10664643user10664643
297
asked Nov 22 '18 at 0:29
user10664643user10664643
297
asked Nov 22 '18 at 0:29
user10664643user10664643
297
asked Nov 22 '18 at 0:29
user10664643user10664643
297
asked Nov 22 '18 at 0:29
user10664643user10664643
297
297
try with self.image_op = tf.placeholder(tf.float32, shape=[None, 48, 48, 3], name='input_image'). It should take variable batch sizes
– Biswadip Mandal
Nov 22 '18 at 4:29
That works. Thanks!
– user10664643
Nov 27 '18 at 1:51
add a comment |
try with self.image_op = tf.placeholder(tf.float32, shape=[None, 48, 48, 3], name='input_image'). It should take variable batch sizes
– Biswadip Mandal
Nov 22 '18 at 4:29
That works. Thanks!
– user10664643
Nov 27 '18 at 1:51
try with self.image_op = tf.placeholder(tf.float32, shape=[None, 48, 48, 3], name='input_image'). It should take variable batch sizes
– Biswadip Mandal
Nov 22 '18 at 4:29
try with self.image_op = tf.placeholder(tf.float32, shape=[None, 48, 48, 3], name='input_image'). It should take variable batch sizes
– Biswadip Mandal
Nov 22 '18 at 4:29
That works. Thanks!
– user10664643
Nov 27 '18 at 1:51
That works. Thanks!
– user10664643
Nov 27 '18 at 1:51
add a comment |
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try with self.image_op = tf.placeholder(tf.float32, shape=[None, 48, 48, 3], name='input_image'). It should take variable batch sizes
– Biswadip Mandal
Nov 22 '18 at 4:29
That works. Thanks!
– user10664643
Nov 27 '18 at 1:51