KERAS “sparse_categorical_crossentropy” question
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As an input a have a float 1.0 or 0.0. When I try to predict with my model and the sparse_categorical_crossentropy loss I get something like:
[[0.4846592 0.5153408]].
How do I know what category it predicts?
python tensorflow machine-learning keras loss
edited Nov 23 '18 at 12:57
today
11.7k22441
asked Nov 23 '18 at 12:40
user9468014user9468014
5110
add a comment |
As an input a have a float 1.0 or 0.0. When I try to predict with my model and the sparse_categorical_crossentropy loss I get something like:
[[0.4846592 0.5153408]].
How do I know what category it predicts?
python tensorflow machine-learning keras loss
edited Nov 23 '18 at 12:57
today
11.7k22441
asked Nov 23 '18 at 12:40
user9468014user9468014
5110
add a comment |
As an input a have a float 1.0 or 0.0. When I try to predict with my model and the sparse_categorical_crossentropy loss I get something like:
[[0.4846592 0.5153408]].
How do I know what category it predicts?
python tensorflow machine-learning keras loss
edited Nov 23 '18 at 12:57
today
11.7k22441
asked Nov 23 '18 at 12:40
user9468014user9468014
5110
As an input a have a float 1.0 or 0.0. When I try to predict with my model and the sparse_categorical_crossentropy loss I get something like:
[[0.4846592 0.5153408]].
How do I know what category it predicts?
python tensorflow machine-learning keras loss
python tensorflow machine-learning keras loss
edited Nov 23 '18 at 12:57
today
11.7k22441
asked Nov 23 '18 at 12:40
user9468014user9468014
5110
edited Nov 23 '18 at 12:57
today
11.7k22441
asked Nov 23 '18 at 12:40
user9468014user9468014
5110
edited Nov 23 '18 at 12:57
today
11.7k22441
edited Nov 23 '18 at 12:57
today
11.7k22441
edited Nov 23 '18 at 12:57
today
11.7k22441
11.7k22441
asked Nov 23 '18 at 12:40
user9468014user9468014
5110
asked Nov 23 '18 at 12:40
user9468014user9468014
5110
asked Nov 23 '18 at 12:40
user9468014user9468014
5110
5110
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
These numbers you see are the probability of each class for the given input sample. For example, [[0.4846592 0.5153408]] means that the given sample belongs to class 0 with probability of around 0.48 and it belongs to class 1 with probability of around 0.51. So you want to take the class with the highest probability and therefore you can use np.argmax to find which index (i.e. 0 or 1) is the maximum one:
import numpy as np
pred_class = np.argmax(probs, axis=-1)
Further, this has nothing to do with the loss function of the model. These probabilities are given by the last layer in your model which is very likely that it uses softmax as the activation function to normalize the output as a probability distribution.
answered Nov 23 '18 at 12:55
todaytoday
11.7k22441
cool thanks for the perfect explanation!
– user9468014
Nov 23 '18 at 13:16
add a comment |
Your Answer
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
These numbers you see are the probability of each class for the given input sample. For example, [[0.4846592 0.5153408]] means that the given sample belongs to class 0 with probability of around 0.48 and it belongs to class 1 with probability of around 0.51. So you want to take the class with the highest probability and therefore you can use np.argmax to find which index (i.e. 0 or 1) is the maximum one:
import numpy as np
pred_class = np.argmax(probs, axis=-1)
Further, this has nothing to do with the loss function of the model. These probabilities are given by the last layer in your model which is very likely that it uses softmax as the activation function to normalize the output as a probability distribution.
answered Nov 23 '18 at 12:55
todaytoday
11.7k22441
cool thanks for the perfect explanation!
– user9468014
Nov 23 '18 at 13:16
add a comment |
These numbers you see are the probability of each class for the given input sample. For example, [[0.4846592 0.5153408]] means that the given sample belongs to class 0 with probability of around 0.48 and it belongs to class 1 with probability of around 0.51. So you want to take the class with the highest probability and therefore you can use np.argmax to find which index (i.e. 0 or 1) is the maximum one:
import numpy as np
pred_class = np.argmax(probs, axis=-1)
Further, this has nothing to do with the loss function of the model. These probabilities are given by the last layer in your model which is very likely that it uses softmax as the activation function to normalize the output as a probability distribution.
answered Nov 23 '18 at 12:55
todaytoday
11.7k22441
cool thanks for the perfect explanation!
– user9468014
Nov 23 '18 at 13:16
add a comment |
These numbers you see are the probability of each class for the given input sample. For example, [[0.4846592 0.5153408]] means that the given sample belongs to class 0 with probability of around 0.48 and it belongs to class 1 with probability of around 0.51. So you want to take the class with the highest probability and therefore you can use np.argmax to find which index (i.e. 0 or 1) is the maximum one:
import numpy as np
pred_class = np.argmax(probs, axis=-1)
Further, this has nothing to do with the loss function of the model. These probabilities are given by the last layer in your model which is very likely that it uses softmax as the activation function to normalize the output as a probability distribution.
answered Nov 23 '18 at 12:55
todaytoday
11.7k22441
These numbers you see are the probability of each class for the given input sample. For example, [[0.4846592 0.5153408]] means that the given sample belongs to class 0 with probability of around 0.48 and it belongs to class 1 with probability of around 0.51. So you want to take the class with the highest probability and therefore you can use np.argmax to find which index (i.e. 0 or 1) is the maximum one:
import numpy as np
pred_class = np.argmax(probs, axis=-1)
Further, this has nothing to do with the loss function of the model. These probabilities are given by the last layer in your model which is very likely that it uses softmax as the activation function to normalize the output as a probability distribution.
answered Nov 23 '18 at 12:55
todaytoday
11.7k22441
answered Nov 23 '18 at 12:55
todaytoday
11.7k22441
answered Nov 23 '18 at 12:55
todaytoday
11.7k22441
answered Nov 23 '18 at 12:55
todaytoday
11.7k22441
11.7k22441
cool thanks for the perfect explanation!
– user9468014
Nov 23 '18 at 13:16
add a comment |
cool thanks for the perfect explanation!
– user9468014
Nov 23 '18 at 13:16
cool thanks for the perfect explanation!
– user9468014
Nov 23 '18 at 13:16
cool thanks for the perfect explanation!
– user9468014
Nov 23 '18 at 13:16
add a comment |
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