Is it possible to make tensorflow print out everything it see in a given image and not just the top five...
$begingroup$
I'm working through the python API tutorials for Tensorflow and I'm seeing the results that are normally displayed, but it's always giving me the top five results.
I'm trying to discern all possibilities within a certain list of basic attributes, like if I'm given a picture of a forest, I want to ask tensorflow if the picture contains oak trees, pine trees, bushes, rivers, etc. I don't need to know if the image is a picture of a forest. Is this possible?
I'm not saying give me results it hasn't been trained to see, I'm saying I'm going to train the model with different types of trees/bushes/etc and I want to know if the given image contains any of those attributes (or the probability it thinks of for a given attribute).
tensorflow image-classification image-recognition
edited Jul 23 '18 at 21:16
Stephen Rauch♦
1,52551330
asked Jul 23 '18 at 20:51
slimslim
1011
$endgroup$
bumped to the homepage by Community♦ 5 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
I'm working through the python API tutorials for Tensorflow and I'm seeing the results that are normally displayed, but it's always giving me the top five results.
I'm trying to discern all possibilities within a certain list of basic attributes, like if I'm given a picture of a forest, I want to ask tensorflow if the picture contains oak trees, pine trees, bushes, rivers, etc. I don't need to know if the image is a picture of a forest. Is this possible?
I'm not saying give me results it hasn't been trained to see, I'm saying I'm going to train the model with different types of trees/bushes/etc and I want to know if the given image contains any of those attributes (or the probability it thinks of for a given attribute).
tensorflow image-classification image-recognition
edited Jul 23 '18 at 21:16
Stephen Rauch♦
1,52551330
asked Jul 23 '18 at 20:51
slimslim
1011
$endgroup$
bumped to the homepage by Community♦ 5 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
$begingroup$
Just take all results that gave larger than some probabilitypthat you pick.
$endgroup$
– kbrose
Jul 23 '18 at 21:02
add a comment |
$begingroup$
I'm working through the python API tutorials for Tensorflow and I'm seeing the results that are normally displayed, but it's always giving me the top five results.
I'm trying to discern all possibilities within a certain list of basic attributes, like if I'm given a picture of a forest, I want to ask tensorflow if the picture contains oak trees, pine trees, bushes, rivers, etc. I don't need to know if the image is a picture of a forest. Is this possible?
I'm not saying give me results it hasn't been trained to see, I'm saying I'm going to train the model with different types of trees/bushes/etc and I want to know if the given image contains any of those attributes (or the probability it thinks of for a given attribute).
tensorflow image-classification image-recognition
edited Jul 23 '18 at 21:16
Stephen Rauch♦
1,52551330
asked Jul 23 '18 at 20:51
slimslim
1011
$endgroup$
I'm working through the python API tutorials for Tensorflow and I'm seeing the results that are normally displayed, but it's always giving me the top five results.
I'm trying to discern all possibilities within a certain list of basic attributes, like if I'm given a picture of a forest, I want to ask tensorflow if the picture contains oak trees, pine trees, bushes, rivers, etc. I don't need to know if the image is a picture of a forest. Is this possible?
I'm not saying give me results it hasn't been trained to see, I'm saying I'm going to train the model with different types of trees/bushes/etc and I want to know if the given image contains any of those attributes (or the probability it thinks of for a given attribute).
tensorflow image-classification image-recognition
tensorflow image-classification image-recognition
edited Jul 23 '18 at 21:16
Stephen Rauch♦
1,52551330
asked Jul 23 '18 at 20:51
slimslim
1011
edited Jul 23 '18 at 21:16
Stephen Rauch♦
1,52551330
asked Jul 23 '18 at 20:51
slimslim
1011
edited Jul 23 '18 at 21:16
Stephen Rauch♦
1,52551330
edited Jul 23 '18 at 21:16
Stephen Rauch♦
1,52551330
edited Jul 23 '18 at 21:16
Stephen Rauch♦
1,52551330
1,52551330
asked Jul 23 '18 at 20:51
slimslim
1011
asked Jul 23 '18 at 20:51
slimslim
1011
asked Jul 23 '18 at 20:51
slimslim
1011
1011
bumped to the homepage by Community♦ 5 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
bumped to the homepage by Community♦ 5 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
$begingroup$
Just take all results that gave larger than some probabilitypthat you pick.
$endgroup$
– kbrose
Jul 23 '18 at 21:02
add a comment |
$begingroup$
Just take all results that gave larger than some probabilitypthat you pick.
$endgroup$
– kbrose
Jul 23 '18 at 21:02
$begingroup$
Just take all results that gave larger than some probability
p that you pick.$endgroup$
– kbrose
Jul 23 '18 at 21:02
$begingroup$
Just take all results that gave larger than some probability
p that you pick.$endgroup$
– kbrose
Jul 23 '18 at 21:02
add a comment |
1 Answer
1
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$begingroup$
Sounds like you want to see the predicted probabilities of a softmax function. You can assign the values to a list during training so you can see probabilities of each epoch if you'd like. As is written below, in_top_k will select the single top prediction of the softmax cross entropy function but if you have multiple targets in each picture you will want to change that "1" to the desired amount.
xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits = logits,
labels = y)
logit = tf.nn.in_top_k(logits, y, 1)
y_one_prob = tf.sigmoid(logit)
answered Jul 23 '18 at 21:21
stephen barterstephen barter
293
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1 Answer
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$begingroup$
Sounds like you want to see the predicted probabilities of a softmax function. You can assign the values to a list during training so you can see probabilities of each epoch if you'd like. As is written below, in_top_k will select the single top prediction of the softmax cross entropy function but if you have multiple targets in each picture you will want to change that "1" to the desired amount.
xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits = logits,
labels = y)
logit = tf.nn.in_top_k(logits, y, 1)
y_one_prob = tf.sigmoid(logit)
answered Jul 23 '18 at 21:21
stephen barterstephen barter
293
$endgroup$
add a comment |
$begingroup$
Sounds like you want to see the predicted probabilities of a softmax function. You can assign the values to a list during training so you can see probabilities of each epoch if you'd like. As is written below, in_top_k will select the single top prediction of the softmax cross entropy function but if you have multiple targets in each picture you will want to change that "1" to the desired amount.
xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits = logits,
labels = y)
logit = tf.nn.in_top_k(logits, y, 1)
y_one_prob = tf.sigmoid(logit)
answered Jul 23 '18 at 21:21
stephen barterstephen barter
293
$endgroup$
add a comment |
$begingroup$
Sounds like you want to see the predicted probabilities of a softmax function. You can assign the values to a list during training so you can see probabilities of each epoch if you'd like. As is written below, in_top_k will select the single top prediction of the softmax cross entropy function but if you have multiple targets in each picture you will want to change that "1" to the desired amount.
xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits = logits,
labels = y)
logit = tf.nn.in_top_k(logits, y, 1)
y_one_prob = tf.sigmoid(logit)
answered Jul 23 '18 at 21:21
stephen barterstephen barter
293
$endgroup$
Sounds like you want to see the predicted probabilities of a softmax function. You can assign the values to a list during training so you can see probabilities of each epoch if you'd like. As is written below, in_top_k will select the single top prediction of the softmax cross entropy function but if you have multiple targets in each picture you will want to change that "1" to the desired amount.
xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits = logits,
labels = y)
logit = tf.nn.in_top_k(logits, y, 1)
y_one_prob = tf.sigmoid(logit)
answered Jul 23 '18 at 21:21
stephen barterstephen barter
293
answered Jul 23 '18 at 21:21
stephen barterstephen barter
293
answered Jul 23 '18 at 21:21
stephen barterstephen barter
293
answered Jul 23 '18 at 21:21
stephen barterstephen barter
293
293
add a comment |
add a comment |
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$begingroup$
Just take all results that gave larger than some probability
pthat you pick.$endgroup$
– kbrose
Jul 23 '18 at 21:02