Binary Classification of Numeric Sequences with Keras and LSTMs
0
$begingroup$
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. The ratio of 1s to 0s is around 1:3. There are approximately 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 yesterday
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 4 more comments
0
$begingroup$
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. The ratio of 1s to 0s is around 1:3. There are approximately 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 yesterday
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
yesterday
$begingroup$
I have around 100,000 instances
$endgroup$
– George Lee
yesterday
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
yesterday
1
$begingroup$
Can you give us a snippet of the data please?
$endgroup$
– JahKnows
yesterday
$begingroup$
(1) 1:4 (2) Loss actually flattens out after around 3-4 epochs, at around 0.5870, 0.5805, 0.5804
$endgroup$
– George Lee
yesterday
|
show 4 more comments
0
0
0
1
$begingroup$
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. The ratio of 1s to 0s is around 1:3. There are approximately 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 yesterday
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. The ratio of 1s to 0s is around 1:3. There are approximately 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 yesterday
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 yesterday
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 18 hours ago
George Lee
asked yesterday
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 yesterday
George LeeGeorge Lee
11
asked yesterday
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
yesterday
$begingroup$
I have around 100,000 instances
$endgroup$
– George Lee
yesterday
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
yesterday
1
$begingroup$
Can you give us a snippet of the data please?
$endgroup$
– JahKnows
yesterday
$begingroup$
(1) 1:4 (2) Loss actually flattens out after around 3-4 epochs, at around 0.5870, 0.5805, 0.5804
$endgroup$
– George Lee
yesterday
|
show 4 more comments
$begingroup$
How many training instances do you have?
$endgroup$
– JahKnows
yesterday
$begingroup$
I have around 100,000 instances
$endgroup$
– George Lee
yesterday
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
yesterday
1
$begingroup$
Can you give us a snippet of the data please?
$endgroup$
– JahKnows
yesterday
$begingroup$
(1) 1:4 (2) Loss actually flattens out after around 3-4 epochs, at around 0.5870, 0.5805, 0.5804
$endgroup$
– George Lee
yesterday
$begingroup$
How many training instances do you have?
$endgroup$
– JahKnows
yesterday
$begingroup$
How many training instances do you have?
$endgroup$
– JahKnows
yesterday
$begingroup$
I have around 100,000 instances
$endgroup$
– George Lee
yesterday
$begingroup$
I have around 100,000 instances
$endgroup$
– George Lee
yesterday
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
yesterday
$begingroup$
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
yesterday
1
1
$begingroup$
Can you give us a snippet of the data please?
$endgroup$
– JahKnows
yesterday
$begingroup$
Can you give us a snippet of the data please?
$endgroup$
– JahKnows
yesterday
$begingroup$
(1) 1:4 (2) Loss actually flattens out after around 3-4 epochs, at around 0.5870, 0.5805, 0.5804
$endgroup$
– George Lee
yesterday
$begingroup$
(1) 1:4 (2) Loss actually flattens out after around 3-4 epochs, at around 0.5870, 0.5805, 0.5804
$endgroup$
– George Lee
yesterday
|
show 4 more comments
0
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$begingroup$
How many training instances do you have?
$endgroup$
– JahKnows
yesterday
$begingroup$
I have around 100,000 instances
$endgroup$
– George Lee
yesterday
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
yesterday
1
$begingroup$
Can you give us a snippet of the data please?
$endgroup$
– JahKnows
yesterday
$begingroup$
(1) 1:4 (2) Loss actually flattens out after around 3-4 epochs, at around 0.5870, 0.5805, 0.5804
$endgroup$
– George Lee
yesterday