Measuring uncertainty in an LSTM network using dropout in keras/tensorflow
$begingroup$
I've created a simple LSTM network for testing
model = tf.keras.Sequential()
model.add(layers.LSTM(32, input_shape = (timesteps, data_dim), recurrent_dropout = 0.2))
model.add(layers.Dense(1))
model.compile(loss = 'mae', metrics = ['accuracy'], optimizer = tf.train.AdamOptimizer())
I am using dropout method as researched by Yarin Gal here and Lingxue Zhu here
and my function for dropout looks like this:
f = K.function([model.layers[0].input, K.learning_phase()],
[model.layers[-1].output])
after training I'm using the function above in the "predict_with_dropout"
def predict_with_dropout(x, f=f, n_iter=100):
result = np.zeros((n_iter,))
#print(f([x,1]))
for iter in range(n_iter):
result[iter] = f([x, 1])[0]
return result
results =
for point in test_X:
results+= [predict_with_dropout([point])]
results_avg = np.apply_along_axis(np.mean, 1, results)
variance = np.apply_along_axis(np.var, 1, results)
This code is working as expected and as I understand it the "predict_with_dropout" function is using the f-function to re-train the LSTM model 100 times and within those 100 times it is dropping out certain cells of the model.
Is this the correct implementation of the papers or am I missing something?
If it is correct - is there any way to speed this up?
python neural-network tensorflow lstm dropout
asked 5 mins ago
LMPLMP
61
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LMP is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
I've created a simple LSTM network for testing
model = tf.keras.Sequential()
model.add(layers.LSTM(32, input_shape = (timesteps, data_dim), recurrent_dropout = 0.2))
model.add(layers.Dense(1))
model.compile(loss = 'mae', metrics = ['accuracy'], optimizer = tf.train.AdamOptimizer())
I am using dropout method as researched by Yarin Gal here and Lingxue Zhu here
and my function for dropout looks like this:
f = K.function([model.layers[0].input, K.learning_phase()],
[model.layers[-1].output])
after training I'm using the function above in the "predict_with_dropout"
def predict_with_dropout(x, f=f, n_iter=100):
result = np.zeros((n_iter,))
#print(f([x,1]))
for iter in range(n_iter):
result[iter] = f([x, 1])[0]
return result
results =
for point in test_X:
results+= [predict_with_dropout([point])]
results_avg = np.apply_along_axis(np.mean, 1, results)
variance = np.apply_along_axis(np.var, 1, results)
This code is working as expected and as I understand it the "predict_with_dropout" function is using the f-function to re-train the LSTM model 100 times and within those 100 times it is dropping out certain cells of the model.
Is this the correct implementation of the papers or am I missing something?
If it is correct - is there any way to speed this up?
python neural-network tensorflow lstm dropout
asked 5 mins ago
LMPLMP
61
New contributor
LMP is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
I've created a simple LSTM network for testing
model = tf.keras.Sequential()
model.add(layers.LSTM(32, input_shape = (timesteps, data_dim), recurrent_dropout = 0.2))
model.add(layers.Dense(1))
model.compile(loss = 'mae', metrics = ['accuracy'], optimizer = tf.train.AdamOptimizer())
I am using dropout method as researched by Yarin Gal here and Lingxue Zhu here
and my function for dropout looks like this:
f = K.function([model.layers[0].input, K.learning_phase()],
[model.layers[-1].output])
after training I'm using the function above in the "predict_with_dropout"
def predict_with_dropout(x, f=f, n_iter=100):
result = np.zeros((n_iter,))
#print(f([x,1]))
for iter in range(n_iter):
result[iter] = f([x, 1])[0]
return result
results =
for point in test_X:
results+= [predict_with_dropout([point])]
results_avg = np.apply_along_axis(np.mean, 1, results)
variance = np.apply_along_axis(np.var, 1, results)
This code is working as expected and as I understand it the "predict_with_dropout" function is using the f-function to re-train the LSTM model 100 times and within those 100 times it is dropping out certain cells of the model.
Is this the correct implementation of the papers or am I missing something?
If it is correct - is there any way to speed this up?
python neural-network tensorflow lstm dropout
asked 5 mins ago
LMPLMP
61
New contributor
LMP is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I've created a simple LSTM network for testing
model = tf.keras.Sequential()
model.add(layers.LSTM(32, input_shape = (timesteps, data_dim), recurrent_dropout = 0.2))
model.add(layers.Dense(1))
model.compile(loss = 'mae', metrics = ['accuracy'], optimizer = tf.train.AdamOptimizer())
I am using dropout method as researched by Yarin Gal here and Lingxue Zhu here
and my function for dropout looks like this:
f = K.function([model.layers[0].input, K.learning_phase()],
[model.layers[-1].output])
after training I'm using the function above in the "predict_with_dropout"
def predict_with_dropout(x, f=f, n_iter=100):
result = np.zeros((n_iter,))
#print(f([x,1]))
for iter in range(n_iter):
result[iter] = f([x, 1])[0]
return result
results =
for point in test_X:
results+= [predict_with_dropout([point])]
results_avg = np.apply_along_axis(np.mean, 1, results)
variance = np.apply_along_axis(np.var, 1, results)
This code is working as expected and as I understand it the "predict_with_dropout" function is using the f-function to re-train the LSTM model 100 times and within those 100 times it is dropping out certain cells of the model.
Is this the correct implementation of the papers or am I missing something?
If it is correct - is there any way to speed this up?
python neural-network tensorflow lstm dropout
python neural-network tensorflow lstm dropout
asked 5 mins ago
LMPLMP
61
New contributor
LMP is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 5 mins ago
LMPLMP
61
New contributor
LMP is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 5 mins ago
LMPLMP
61
New contributor
LMP is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 5 mins ago
LMPLMP
61
asked 5 mins ago
LMPLMP
61
61
New contributor
LMP is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
LMP is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
LMP is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
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