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how to apply MC dropout to an LSTM network keras
2019 Community Moderator ElectionKeras LSTM: use weights from Keras model to replicate predictions using numpyDropout implies stochastic descent?How to set input for proper fit with lstm?What does SpatialDropout1D() do to output of Embedding() in Keras?Dropout in Deep Neural NetworksWhat is the correct format of the test input for LSTM neural network?Monte Carlo dropoutHow to design a many-to-many LSTM?Split timeline for training LSTM network
$begingroup$
I have a simple LSTM network developped using keras:
model = Sequential()
model.add(LSTM(rnn_size,input_shape=(2,w),dropout = 0.25 , recurrent_dropout=0.25))
model.add(Dense(2))
I would like to apply the MC dropout method. How can I enable dropout in the test phase in order to compute the uncertainty?
Thanks.
deep-learning lstm recurrent-neural-net dropout
edited Mar 27 at 4:10
Ethan
671425
asked Mar 26 at 14:24
khaoulakhaoula
61
$endgroup$
add a comment |
$begingroup$
I have a simple LSTM network developped using keras:
model = Sequential()
model.add(LSTM(rnn_size,input_shape=(2,w),dropout = 0.25 , recurrent_dropout=0.25))
model.add(Dense(2))
I would like to apply the MC dropout method. How can I enable dropout in the test phase in order to compute the uncertainty?
Thanks.
deep-learning lstm recurrent-neural-net dropout
edited Mar 27 at 4:10
Ethan
671425
asked Mar 26 at 14:24
khaoulakhaoula
61
$endgroup$
1
$begingroup$
Welcome! What do you mean by MC? Please consider dropout is used while training not testing.
$endgroup$
– Media
Mar 26 at 14:29
$begingroup$
i mean montecarlo dropout ,which is a bayesien neural network approach for computing the uncertainty in deep learning
$endgroup$
– khaoula
Mar 26 at 14:38
add a comment |
$begingroup$
I have a simple LSTM network developped using keras:
model = Sequential()
model.add(LSTM(rnn_size,input_shape=(2,w),dropout = 0.25 , recurrent_dropout=0.25))
model.add(Dense(2))
I would like to apply the MC dropout method. How can I enable dropout in the test phase in order to compute the uncertainty?
Thanks.
deep-learning lstm recurrent-neural-net dropout
edited Mar 27 at 4:10
Ethan
671425
asked Mar 26 at 14:24
khaoulakhaoula
61
$endgroup$
I have a simple LSTM network developped using keras:
model = Sequential()
model.add(LSTM(rnn_size,input_shape=(2,w),dropout = 0.25 , recurrent_dropout=0.25))
model.add(Dense(2))
I would like to apply the MC dropout method. How can I enable dropout in the test phase in order to compute the uncertainty?
Thanks.
deep-learning lstm recurrent-neural-net dropout
deep-learning lstm recurrent-neural-net dropout
edited Mar 27 at 4:10
Ethan
671425
asked Mar 26 at 14:24
khaoulakhaoula
61
edited Mar 27 at 4:10
Ethan
671425
asked Mar 26 at 14:24
khaoulakhaoula
61
edited Mar 27 at 4:10
Ethan
671425
edited Mar 27 at 4:10
Ethan
671425
edited Mar 27 at 4:10
Ethan
671425
671425
asked Mar 26 at 14:24
khaoulakhaoula
61
asked Mar 26 at 14:24
khaoulakhaoula
61
asked Mar 26 at 14:24
khaoulakhaoula
61
61
1
$begingroup$
Welcome! What do you mean by MC? Please consider dropout is used while training not testing.
$endgroup$
– Media
Mar 26 at 14:29
$begingroup$
i mean montecarlo dropout ,which is a bayesien neural network approach for computing the uncertainty in deep learning
$endgroup$
– khaoula
Mar 26 at 14:38
add a comment |
1
$begingroup$
Welcome! What do you mean by MC? Please consider dropout is used while training not testing.
$endgroup$
– Media
Mar 26 at 14:29
$begingroup$
i mean montecarlo dropout ,which is a bayesien neural network approach for computing the uncertainty in deep learning
$endgroup$
– khaoula
Mar 26 at 14:38
1
1
$begingroup$
Welcome! What do you mean by MC? Please consider dropout is used while training not testing.
$endgroup$
– Media
Mar 26 at 14:29
$begingroup$
Welcome! What do you mean by MC? Please consider dropout is used while training not testing.
$endgroup$
– Media
Mar 26 at 14:29
$begingroup$
i mean montecarlo dropout ,which is a bayesien neural network approach for computing the uncertainty in deep learning
$endgroup$
– khaoula
Mar 26 at 14:38
$begingroup$
i mean montecarlo dropout ,which is a bayesien neural network approach for computing the uncertainty in deep learning
$endgroup$
– khaoula
Mar 26 at 14:38
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
Well, in order to enable dropout during test phase you can do something like this:
keras.layers.Dropout(0.5)(x, training=True)
Then you'll probably want to run it multiple times. If you don't care about the inference time just run forward pass multiple times and at the end calculate mean and variance of your output.
answered Mar 27 at 10:01
Marat SeroglazovMarat Seroglazov
112
$endgroup$
add a comment |
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$begingroup$
Well, in order to enable dropout during test phase you can do something like this:
keras.layers.Dropout(0.5)(x, training=True)
Then you'll probably want to run it multiple times. If you don't care about the inference time just run forward pass multiple times and at the end calculate mean and variance of your output.
answered Mar 27 at 10:01
Marat SeroglazovMarat Seroglazov
112
$endgroup$
add a comment |
$begingroup$
Well, in order to enable dropout during test phase you can do something like this:
keras.layers.Dropout(0.5)(x, training=True)
Then you'll probably want to run it multiple times. If you don't care about the inference time just run forward pass multiple times and at the end calculate mean and variance of your output.
answered Mar 27 at 10:01
Marat SeroglazovMarat Seroglazov
112
$endgroup$
add a comment |
$begingroup$
Well, in order to enable dropout during test phase you can do something like this:
keras.layers.Dropout(0.5)(x, training=True)
Then you'll probably want to run it multiple times. If you don't care about the inference time just run forward pass multiple times and at the end calculate mean and variance of your output.
answered Mar 27 at 10:01
Marat SeroglazovMarat Seroglazov
112
$endgroup$
Well, in order to enable dropout during test phase you can do something like this:
keras.layers.Dropout(0.5)(x, training=True)
Then you'll probably want to run it multiple times. If you don't care about the inference time just run forward pass multiple times and at the end calculate mean and variance of your output.
answered Mar 27 at 10:01
Marat SeroglazovMarat Seroglazov
112
edited Mar 27 at 10:07
answered Mar 27 at 10:01
Marat SeroglazovMarat Seroglazov
112
answered Mar 27 at 10:01
Marat SeroglazovMarat Seroglazov
112
answered Mar 27 at 10:01
Marat SeroglazovMarat Seroglazov
112
112
add a comment |
add a comment |
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1
$begingroup$
Welcome! What do you mean by MC? Please consider dropout is used while training not testing.
$endgroup$
– Media
Mar 26 at 14:29
$begingroup$
i mean montecarlo dropout ,which is a bayesien neural network approach for computing the uncertainty in deep learning
$endgroup$
– khaoula
Mar 26 at 14:38