Binary Classification of Numeric Sequences with Keras and LSTMs2019 Community Moderator ElectionLSTM neural network for music generationDot Product between two Keras intermediate variablesWhy does my model accuracy rise and then drop, with the loss sharing similar characteristics?Best model for Machine LearningKeras LSTM model for binary classification with sequencesHow to set input for proper fit with lstm?Neural network outputting same result for all inputsPython - Predicting data based on multidimensional array with KerasHow to reshape data for LSTM training in multivariate sequence predictionIN CIFAR 10 DATASET
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Binary Classification of Numeric Sequences with Keras and LSTMs
2019 Community Moderator ElectionLSTM neural network for music generationDot Product between two Keras intermediate variablesWhy does my model accuracy rise and then drop, with the loss sharing similar characteristics?Best model for Machine LearningKeras LSTM model for binary classification with sequencesHow to set input for proper fit with lstm?Neural network outputting same result for all inputsPython - Predicting data based on multidimensional array with KerasHow to reshape data for LSTM training in multivariate sequence predictionIN CIFAR 10 DATASET
$begingroup$
I'm attempting to use a sequence of numbers (of fixed length) in order to predict a binary output (either 1 or 0) using Keras and a recurrent neural network.
Each training example/sequence has 10 timesteps, each containing a vector of 5 numbers, and each training output consists of either a 1 or 0. There are around 100,000 training examples.
I have tried implementing this using Keras, but the loss stops decreasing after the first epoch of training. I've also attempted modifying the hyper-parameters, but to no avail. Is there something I'm missing here?
The training inputs are as follows: (zero padded)
array([[[0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. ],
...,
[1.24829336, 0.96461449, 3.35142857, 0.74675 , 0.776075 ],
[1.248303 , 0.96427925, 0. , 1.317225 , 1.317225 ],
[1.24831488, 0.96409169, 2.74857143, 1.353775 , 1.377825 ]],
[[0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. ],
...,
[1.24969672, 0.96336315, 0. , 1.319725 , 1.319725 ],
[1.24968077, 0.96331624, 0. , 1.33535 , 1.33535 ],
[1.24969598, 0.96330252, 5.01714286, 1.3508 , 1.3947 ]],
[[0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. ],
...,
[0. , 0. , 0. , 0. , 0. ],
[1.25715364, 0.95520672, 2.57714286, 1.04565 , 1.0682 ],
[1.25291274, 0.96879701, 7.76 , 1.311875 , 1.379775 ]],
...,
[[0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. ],
...,
[1.24791079, 0.96561021, 4.44 , 0.7199 , 0.75875 ],
[1.25265263, 0.96117379, 2.09714286, 0.7636 , 0.78195 ],
[1.25868651, 0.96001674, 3.01142857, 1.35235 , 1.3787 ]]])
The training outputs are as follows:
array([[0.],
[0.],
[0.],
...,
[1.],
[0.],
[0.]])
This is the model I have attempted to train:
#Model
model = Sequential()
model.add(LSTM(100, input_shape= (10, 5)))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
print(model.summary())
model.fit(X_train, y_train, validation_data = (X_test, y_test), epochs = 100, batch_size = 1000)
classification keras lstm binary neural
asked 5 hours ago
George LeeGeorge Lee
11
New contributor
George Lee is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
|
show 3 more comments
$begingroup$
I'm attempting to use a sequence of numbers (of fixed length) in order to predict a binary output (either 1 or 0) using Keras and a recurrent neural network.
Each training example/sequence has 10 timesteps, each containing a vector of 5 numbers, and each training output consists of either a 1 or 0. There are around 100,000 training examples.
I have tried implementing this using Keras, but the loss stops decreasing after the first epoch of training. I've also attempted modifying the hyper-parameters, but to no avail. Is there something I'm missing here?
The training inputs are as follows: (zero padded)
array([[[0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. ],
...,
[1.24829336, 0.96461449, 3.35142857, 0.74675 , 0.776075 ],
[1.248303 , 0.96427925, 0. , 1.317225 , 1.317225 ],
[1.24831488, 0.96409169, 2.74857143, 1.353775 , 1.377825 ]],
[[0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. ],
...,
[1.24969672, 0.96336315, 0. , 1.319725 , 1.319725 ],
[1.24968077, 0.96331624, 0. , 1.33535 , 1.33535 ],
[1.24969598, 0.96330252, 5.01714286, 1.3508 , 1.3947 ]],
[[0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. ],
...,
[0. , 0. , 0. , 0. , 0. ],
[1.25715364, 0.95520672, 2.57714286, 1.04565 , 1.0682 ],
[1.25291274, 0.96879701, 7.76 , 1.311875 , 1.379775 ]],
...,
[[0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. ],
...,
[1.24791079, 0.96561021, 4.44 , 0.7199 , 0.75875 ],
[1.25265263, 0.96117379, 2.09714286, 0.7636 , 0.78195 ],
[1.25868651, 0.96001674, 3.01142857, 1.35235 , 1.3787 ]]])
The training outputs are as follows:
array([[0.],
[0.],
[0.],
...,
[1.],
[0.],
[0.]])
This is the model I have attempted to train:
#Model
model = Sequential()
model.add(LSTM(100, input_shape= (10, 5)))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
print(model.summary())
model.fit(X_train, y_train, validation_data = (X_test, y_test), epochs = 100, batch_size = 1000)
classification keras lstm binary neural
asked 5 hours ago
George LeeGeorge Lee
11
New contributor
George Lee is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
$begingroup$
How many training instances do you have?
$endgroup$
– JahKnows
5 hours ago
$begingroup$
I have around 100,000 instances
$endgroup$
– George Lee
5 hours ago
1
$begingroup$
Welcome to SE.DataScience! Please provide these two: (1) ratio of 1s to all instances, and (2) value of loss for first, second, and third epochs. I may have an answer.
$endgroup$
– Esmailian
5 hours ago
1
$begingroup$
Can you give us a snippet of the data please?
$endgroup$
– JahKnows
5 hours ago
$begingroup$
(1) 1:4 (2) Loss actually flattens out after around 3-4 epochs, at around 0.5870, 0.5805, 0.5804
$endgroup$
– George Lee
4 hours ago
|
show 3 more comments
$begingroup$
I'm attempting to use a sequence of numbers (of fixed length) in order to predict a binary output (either 1 or 0) using Keras and a recurrent neural network.
Each training example/sequence has 10 timesteps, each containing a vector of 5 numbers, and each training output consists of either a 1 or 0. There are around 100,000 training examples.
I have tried implementing this using Keras, but the loss stops decreasing after the first epoch of training. I've also attempted modifying the hyper-parameters, but to no avail. Is there something I'm missing here?
The training inputs are as follows: (zero padded)
array([[[0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. ],
...,
[1.24829336, 0.96461449, 3.35142857, 0.74675 , 0.776075 ],
[1.248303 , 0.96427925, 0. , 1.317225 , 1.317225 ],
[1.24831488, 0.96409169, 2.74857143, 1.353775 , 1.377825 ]],
[[0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. ],
...,
[1.24969672, 0.96336315, 0. , 1.319725 , 1.319725 ],
[1.24968077, 0.96331624, 0. , 1.33535 , 1.33535 ],
[1.24969598, 0.96330252, 5.01714286, 1.3508 , 1.3947 ]],
[[0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. ],
...,
[0. , 0. , 0. , 0. , 0. ],
[1.25715364, 0.95520672, 2.57714286, 1.04565 , 1.0682 ],
[1.25291274, 0.96879701, 7.76 , 1.311875 , 1.379775 ]],
...,
[[0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. ],
...,
[1.24791079, 0.96561021, 4.44 , 0.7199 , 0.75875 ],
[1.25265263, 0.96117379, 2.09714286, 0.7636 , 0.78195 ],
[1.25868651, 0.96001674, 3.01142857, 1.35235 , 1.3787 ]]])
The training outputs are as follows:
array([[0.],
[0.],
[0.],
...,
[1.],
[0.],
[0.]])
This is the model I have attempted to train:
#Model
model = Sequential()
model.add(LSTM(100, input_shape= (10, 5)))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
print(model.summary())
model.fit(X_train, y_train, validation_data = (X_test, y_test), epochs = 100, batch_size = 1000)
classification keras lstm binary neural
asked 5 hours ago
George LeeGeorge Lee
11
New contributor
George Lee is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I'm attempting to use a sequence of numbers (of fixed length) in order to predict a binary output (either 1 or 0) using Keras and a recurrent neural network.
Each training example/sequence has 10 timesteps, each containing a vector of 5 numbers, and each training output consists of either a 1 or 0. There are around 100,000 training examples.
I have tried implementing this using Keras, but the loss stops decreasing after the first epoch of training. I've also attempted modifying the hyper-parameters, but to no avail. Is there something I'm missing here?
The training inputs are as follows: (zero padded)
array([[[0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. ],
...,
[1.24829336, 0.96461449, 3.35142857, 0.74675 , 0.776075 ],
[1.248303 , 0.96427925, 0. , 1.317225 , 1.317225 ],
[1.24831488, 0.96409169, 2.74857143, 1.353775 , 1.377825 ]],
[[0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. ],
...,
[1.24969672, 0.96336315, 0. , 1.319725 , 1.319725 ],
[1.24968077, 0.96331624, 0. , 1.33535 , 1.33535 ],
[1.24969598, 0.96330252, 5.01714286, 1.3508 , 1.3947 ]],
[[0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. ],
...,
[0. , 0. , 0. , 0. , 0. ],
[1.25715364, 0.95520672, 2.57714286, 1.04565 , 1.0682 ],
[1.25291274, 0.96879701, 7.76 , 1.311875 , 1.379775 ]],
...,
[[0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. ],
...,
[1.24791079, 0.96561021, 4.44 , 0.7199 , 0.75875 ],
[1.25265263, 0.96117379, 2.09714286, 0.7636 , 0.78195 ],
[1.25868651, 0.96001674, 3.01142857, 1.35235 , 1.3787 ]]])
The training outputs are as follows:
array([[0.],
[0.],
[0.],
...,
[1.],
[0.],
[0.]])
This is the model I have attempted to train:
#Model
model = Sequential()
model.add(LSTM(100, input_shape= (10, 5)))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
print(model.summary())
model.fit(X_train, y_train, validation_data = (X_test, y_test), epochs = 100, batch_size = 1000)
classification keras lstm binary neural
classification keras lstm binary neural
asked 5 hours ago
George LeeGeorge Lee
11
New contributor
George Lee is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 5 hours ago
George LeeGeorge Lee
11
New contributor
George Lee is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
edited 4 hours ago
George Lee
asked 5 hours ago
George LeeGeorge Lee
11
New contributor
George Lee is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 5 hours ago
George LeeGeorge Lee
11
asked 5 hours ago
George LeeGeorge Lee
11
11
New contributor
George Lee is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
George Lee is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
George Lee is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$begingroup$
How many training instances do you have?
$endgroup$
– JahKnows
5 hours ago
$begingroup$
I have around 100,000 instances
$endgroup$
– George Lee
5 hours ago
1
$begingroup$
Welcome to SE.DataScience! Please provide these two: (1) ratio of 1s to all instances, and (2) value of loss for first, second, and third epochs. I may have an answer.
$endgroup$
– Esmailian
5 hours ago
1
$begingroup$
Can you give us a snippet of the data please?
$endgroup$
– JahKnows
5 hours ago
$begingroup$
(1) 1:4 (2) Loss actually flattens out after around 3-4 epochs, at around 0.5870, 0.5805, 0.5804
$endgroup$
– George Lee
4 hours ago
|
show 3 more comments
$begingroup$
How many training instances do you have?
$endgroup$
– JahKnows
5 hours ago
$begingroup$
I have around 100,000 instances
$endgroup$
– George Lee
5 hours ago
1
$begingroup$
Welcome to SE.DataScience! Please provide these two: (1) ratio of 1s to all instances, and (2) value of loss for first, second, and third epochs. I may have an answer.
$endgroup$
– Esmailian
5 hours ago
1
$begingroup$
Can you give us a snippet of the data please?
$endgroup$
– JahKnows
5 hours ago
$begingroup$
(1) 1:4 (2) Loss actually flattens out after around 3-4 epochs, at around 0.5870, 0.5805, 0.5804
$endgroup$
– George Lee
4 hours ago
$begingroup$
How many training instances do you have?
$endgroup$
– JahKnows
5 hours ago
$begingroup$
How many training instances do you have?
$endgroup$
– JahKnows
5 hours ago
$begingroup$
I have around 100,000 instances
$endgroup$
– George Lee
5 hours ago
$begingroup$
I have around 100,000 instances
$endgroup$
– George Lee
5 hours ago
1
1
$begingroup$
Welcome to SE.DataScience! Please provide these two: (1) ratio of 1s to all instances, and (2) value of loss for first, second, and third epochs. I may have an answer.
$endgroup$
– Esmailian
5 hours ago
$begingroup$
Welcome to SE.DataScience! Please provide these two: (1) ratio of 1s to all instances, and (2) value of loss for first, second, and third epochs. I may have an answer.
$endgroup$
– Esmailian
5 hours ago
1
1
$begingroup$
Can you give us a snippet of the data please?
$endgroup$
– JahKnows
5 hours ago
$begingroup$
Can you give us a snippet of the data please?
$endgroup$
– JahKnows
5 hours ago
$begingroup$
(1) 1:4 (2) Loss actually flattens out after around 3-4 epochs, at around 0.5870, 0.5805, 0.5804
$endgroup$
– George Lee
4 hours ago
$begingroup$
(1) 1:4 (2) Loss actually flattens out after around 3-4 epochs, at around 0.5870, 0.5805, 0.5804
$endgroup$
– George Lee
4 hours ago
|
show 3 more comments
0
active
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$begingroup$
How many training instances do you have?
$endgroup$
– JahKnows
5 hours ago
$begingroup$
I have around 100,000 instances
$endgroup$
– George Lee
5 hours ago
1
$begingroup$
Welcome to SE.DataScience! Please provide these two: (1) ratio of 1s to all instances, and (2) value of loss for first, second, and third epochs. I may have an answer.
$endgroup$
– Esmailian
5 hours ago
1
$begingroup$
Can you give us a snippet of the data please?
$endgroup$
– JahKnows
5 hours ago
$begingroup$
(1) 1:4 (2) Loss actually flattens out after around 3-4 epochs, at around 0.5870, 0.5805, 0.5804
$endgroup$
– George Lee
4 hours ago