Keras LSTM model data reshapping
I have some Y-axis values of sine wave as features and I labeled it as pass or fail and used linear regression to train it and got 98% (since it is a synthetic data)
Now I tried to feed the data to a LSTM model and want to see the accuracy. But I don't know how to specify a LSTM model using my data.
I have Y = label =
array([[1, 0],[1, 0],[1, 0],[1, 0],[1, 0],[0, 1],[0, 1],[0, 1],[0, 1],[0, 1],[0, 1],[0, 1],[0, 1],[0, 1],[0, 1],[0, 1],[0, 1],[0, 1],[0, 1],[0, 1],...] with a shape(11564, 2).
and I have a feature = X =
array([[ 0., 0.03140919, 0.06278424, ..., -0.08864117,-0.0591398 , -0.02958302],[ 0., 0.03140762, 0.06277796, ..., -0.08349163,-0.05570133, -0.02786163],[ 0., 0.03140605, 0.06277169, ..., -0.07864125,-0.05246279, -0.02624041],...,[ 0. , 0.96491418, -0.5409955 , ..., 0. , 0. , 0. ],[ 0., 0.96496242, -0.5410496 , ..., 0. , 0. , 0. ],[ 0. , 0.96501067, -0.54110371, ..., 0. ,0. , 0.]]) with a shape of (11564, 1200))
Now how to I choose the values for the LSTM code:
model = Sequential()
model.add(keras.layers.LSTM(hidden_nodes, input_shape=(window, num_features)))
model.add(Dropout(0.2))
model.add(keras.layers.Dense(num_features, activation='sigmoid'))
optimizer = keras.optimizers.SGD(lr=learning_rate, decay=1e-6, momentum=0.9, nesterov=True)
lstm
asked Nov 26 '18 at 2:26
T.MouT.Mou
1
add a comment |
I have some Y-axis values of sine wave as features and I labeled it as pass or fail and used linear regression to train it and got 98% (since it is a synthetic data)
Now I tried to feed the data to a LSTM model and want to see the accuracy. But I don't know how to specify a LSTM model using my data.
I have Y = label =
array([[1, 0],[1, 0],[1, 0],[1, 0],[1, 0],[0, 1],[0, 1],[0, 1],[0, 1],[0, 1],[0, 1],[0, 1],[0, 1],[0, 1],[0, 1],[0, 1],[0, 1],[0, 1],[0, 1],[0, 1],...] with a shape(11564, 2).
and I have a feature = X =
array([[ 0., 0.03140919, 0.06278424, ..., -0.08864117,-0.0591398 , -0.02958302],[ 0., 0.03140762, 0.06277796, ..., -0.08349163,-0.05570133, -0.02786163],[ 0., 0.03140605, 0.06277169, ..., -0.07864125,-0.05246279, -0.02624041],...,[ 0. , 0.96491418, -0.5409955 , ..., 0. , 0. , 0. ],[ 0., 0.96496242, -0.5410496 , ..., 0. , 0. , 0. ],[ 0. , 0.96501067, -0.54110371, ..., 0. ,0. , 0.]]) with a shape of (11564, 1200))
Now how to I choose the values for the LSTM code:
model = Sequential()
model.add(keras.layers.LSTM(hidden_nodes, input_shape=(window, num_features)))
model.add(Dropout(0.2))
model.add(keras.layers.Dense(num_features, activation='sigmoid'))
optimizer = keras.optimizers.SGD(lr=learning_rate, decay=1e-6, momentum=0.9, nesterov=True)
lstm
asked Nov 26 '18 at 2:26
T.MouT.Mou
1
add a comment |
I have some Y-axis values of sine wave as features and I labeled it as pass or fail and used linear regression to train it and got 98% (since it is a synthetic data)
Now I tried to feed the data to a LSTM model and want to see the accuracy. But I don't know how to specify a LSTM model using my data.
I have Y = label =
array([[1, 0],[1, 0],[1, 0],[1, 0],[1, 0],[0, 1],[0, 1],[0, 1],[0, 1],[0, 1],[0, 1],[0, 1],[0, 1],[0, 1],[0, 1],[0, 1],[0, 1],[0, 1],[0, 1],[0, 1],...] with a shape(11564, 2).
and I have a feature = X =
array([[ 0., 0.03140919, 0.06278424, ..., -0.08864117,-0.0591398 , -0.02958302],[ 0., 0.03140762, 0.06277796, ..., -0.08349163,-0.05570133, -0.02786163],[ 0., 0.03140605, 0.06277169, ..., -0.07864125,-0.05246279, -0.02624041],...,[ 0. , 0.96491418, -0.5409955 , ..., 0. , 0. , 0. ],[ 0., 0.96496242, -0.5410496 , ..., 0. , 0. , 0. ],[ 0. , 0.96501067, -0.54110371, ..., 0. ,0. , 0.]]) with a shape of (11564, 1200))
Now how to I choose the values for the LSTM code:
model = Sequential()
model.add(keras.layers.LSTM(hidden_nodes, input_shape=(window, num_features)))
model.add(Dropout(0.2))
model.add(keras.layers.Dense(num_features, activation='sigmoid'))
optimizer = keras.optimizers.SGD(lr=learning_rate, decay=1e-6, momentum=0.9, nesterov=True)
lstm
asked Nov 26 '18 at 2:26
T.MouT.Mou
1
I have some Y-axis values of sine wave as features and I labeled it as pass or fail and used linear regression to train it and got 98% (since it is a synthetic data)
Now I tried to feed the data to a LSTM model and want to see the accuracy. But I don't know how to specify a LSTM model using my data.
I have Y = label =
array([[1, 0],[1, 0],[1, 0],[1, 0],[1, 0],[0, 1],[0, 1],[0, 1],[0, 1],[0, 1],[0, 1],[0, 1],[0, 1],[0, 1],[0, 1],[0, 1],[0, 1],[0, 1],[0, 1],[0, 1],...] with a shape(11564, 2).
and I have a feature = X =
array([[ 0., 0.03140919, 0.06278424, ..., -0.08864117,-0.0591398 , -0.02958302],[ 0., 0.03140762, 0.06277796, ..., -0.08349163,-0.05570133, -0.02786163],[ 0., 0.03140605, 0.06277169, ..., -0.07864125,-0.05246279, -0.02624041],...,[ 0. , 0.96491418, -0.5409955 , ..., 0. , 0. , 0. ],[ 0., 0.96496242, -0.5410496 , ..., 0. , 0. , 0. ],[ 0. , 0.96501067, -0.54110371, ..., 0. ,0. , 0.]]) with a shape of (11564, 1200))
Now how to I choose the values for the LSTM code:
model = Sequential()
model.add(keras.layers.LSTM(hidden_nodes, input_shape=(window, num_features)))
model.add(Dropout(0.2))
model.add(keras.layers.Dense(num_features, activation='sigmoid'))
optimizer = keras.optimizers.SGD(lr=learning_rate, decay=1e-6, momentum=0.9, nesterov=True)
lstm
lstm
asked Nov 26 '18 at 2:26
T.MouT.Mou
1
asked Nov 26 '18 at 2:26
T.MouT.Mou
1
asked Nov 26 '18 at 2:26
T.MouT.Mou
1
asked Nov 26 '18 at 2:26
T.MouT.Mou
1
asked Nov 26 '18 at 2:26
T.MouT.Mou
1
1
add a comment |
add a comment |
1 Answer
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checklist:
1 make sure your X, input is (11564,n) n is the number of length in each row, make sure n is the same in every row, if they are now ,please consider use the padding func
2 seems u need a embedding layer or stuff like that to let lstm accept your data by, either shrink them to low degree, or reduce somehow
keras.layers.LSTM(units, activation='tanh', recurrent_activation='hard_sigmoid', use_bias=True, kernel_initializer='glorot_uniform', recurrent_initializer='orthogonal', bias_initializer='zeros', unit_forget_bias=True, kernel_regularizer=None, recurrent_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, recurrent_constraint=None, bias_constraint=None, dropout=0.0, recurrent_dropout=0.0, implementation=1, return_sequences=False, return_state=False, go_backwards=False, stateful=False, unroll=False)
here is a example on kaggle:
https://www.kaggle.com/divrikwicky/fast-basic-lstm-with-proper-k-fold-sentimentembed
answered Nov 26 '18 at 2:57
陈海栋陈海栋
504
for X all the n is same 1200. and this is not sentiment analysis, so do I need embedding?
– T.Mou
Nov 26 '18 at 21:45
i just mean, you need a way to fit the data format into the model, or u can try use just tensorflow to overwrite the data input format
– 陈海栋
Nov 27 '18 at 1:36
or u can add a pre-process to fit in the data
– 陈海栋
Nov 27 '18 at 1:49
1
Ok I got it. Thanks.
– T.Mou
Nov 27 '18 at 3:00
any chance u can vote or accept the answer?
– 陈海栋
Nov 27 '18 at 7:05
add a comment |
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1 Answer
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checklist:
1 make sure your X, input is (11564,n) n is the number of length in each row, make sure n is the same in every row, if they are now ,please consider use the padding func
2 seems u need a embedding layer or stuff like that to let lstm accept your data by, either shrink them to low degree, or reduce somehow
keras.layers.LSTM(units, activation='tanh', recurrent_activation='hard_sigmoid', use_bias=True, kernel_initializer='glorot_uniform', recurrent_initializer='orthogonal', bias_initializer='zeros', unit_forget_bias=True, kernel_regularizer=None, recurrent_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, recurrent_constraint=None, bias_constraint=None, dropout=0.0, recurrent_dropout=0.0, implementation=1, return_sequences=False, return_state=False, go_backwards=False, stateful=False, unroll=False)
here is a example on kaggle:
https://www.kaggle.com/divrikwicky/fast-basic-lstm-with-proper-k-fold-sentimentembed
answered Nov 26 '18 at 2:57
陈海栋陈海栋
504
for X all the n is same 1200. and this is not sentiment analysis, so do I need embedding?
– T.Mou
Nov 26 '18 at 21:45
i just mean, you need a way to fit the data format into the model, or u can try use just tensorflow to overwrite the data input format
– 陈海栋
Nov 27 '18 at 1:36
or u can add a pre-process to fit in the data
– 陈海栋
Nov 27 '18 at 1:49
1
Ok I got it. Thanks.
– T.Mou
Nov 27 '18 at 3:00
any chance u can vote or accept the answer?
– 陈海栋
Nov 27 '18 at 7:05
add a comment |
checklist:
1 make sure your X, input is (11564,n) n is the number of length in each row, make sure n is the same in every row, if they are now ,please consider use the padding func
2 seems u need a embedding layer or stuff like that to let lstm accept your data by, either shrink them to low degree, or reduce somehow
keras.layers.LSTM(units, activation='tanh', recurrent_activation='hard_sigmoid', use_bias=True, kernel_initializer='glorot_uniform', recurrent_initializer='orthogonal', bias_initializer='zeros', unit_forget_bias=True, kernel_regularizer=None, recurrent_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, recurrent_constraint=None, bias_constraint=None, dropout=0.0, recurrent_dropout=0.0, implementation=1, return_sequences=False, return_state=False, go_backwards=False, stateful=False, unroll=False)
here is a example on kaggle:
https://www.kaggle.com/divrikwicky/fast-basic-lstm-with-proper-k-fold-sentimentembed
answered Nov 26 '18 at 2:57
陈海栋陈海栋
504
for X all the n is same 1200. and this is not sentiment analysis, so do I need embedding?
– T.Mou
Nov 26 '18 at 21:45
i just mean, you need a way to fit the data format into the model, or u can try use just tensorflow to overwrite the data input format
– 陈海栋
Nov 27 '18 at 1:36
or u can add a pre-process to fit in the data
– 陈海栋
Nov 27 '18 at 1:49
1
Ok I got it. Thanks.
– T.Mou
Nov 27 '18 at 3:00
any chance u can vote or accept the answer?
– 陈海栋
Nov 27 '18 at 7:05
add a comment |
checklist:
1 make sure your X, input is (11564,n) n is the number of length in each row, make sure n is the same in every row, if they are now ,please consider use the padding func
2 seems u need a embedding layer or stuff like that to let lstm accept your data by, either shrink them to low degree, or reduce somehow
keras.layers.LSTM(units, activation='tanh', recurrent_activation='hard_sigmoid', use_bias=True, kernel_initializer='glorot_uniform', recurrent_initializer='orthogonal', bias_initializer='zeros', unit_forget_bias=True, kernel_regularizer=None, recurrent_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, recurrent_constraint=None, bias_constraint=None, dropout=0.0, recurrent_dropout=0.0, implementation=1, return_sequences=False, return_state=False, go_backwards=False, stateful=False, unroll=False)
here is a example on kaggle:
https://www.kaggle.com/divrikwicky/fast-basic-lstm-with-proper-k-fold-sentimentembed
answered Nov 26 '18 at 2:57
陈海栋陈海栋
504
checklist:
1 make sure your X, input is (11564,n) n is the number of length in each row, make sure n is the same in every row, if they are now ,please consider use the padding func
2 seems u need a embedding layer or stuff like that to let lstm accept your data by, either shrink them to low degree, or reduce somehow
keras.layers.LSTM(units, activation='tanh', recurrent_activation='hard_sigmoid', use_bias=True, kernel_initializer='glorot_uniform', recurrent_initializer='orthogonal', bias_initializer='zeros', unit_forget_bias=True, kernel_regularizer=None, recurrent_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, recurrent_constraint=None, bias_constraint=None, dropout=0.0, recurrent_dropout=0.0, implementation=1, return_sequences=False, return_state=False, go_backwards=False, stateful=False, unroll=False)
here is a example on kaggle:
https://www.kaggle.com/divrikwicky/fast-basic-lstm-with-proper-k-fold-sentimentembed
answered Nov 26 '18 at 2:57
陈海栋陈海栋
504
answered Nov 26 '18 at 2:57
陈海栋陈海栋
504
answered Nov 26 '18 at 2:57
陈海栋陈海栋
504
answered Nov 26 '18 at 2:57
陈海栋陈海栋
504
504
for X all the n is same 1200. and this is not sentiment analysis, so do I need embedding?
– T.Mou
Nov 26 '18 at 21:45
i just mean, you need a way to fit the data format into the model, or u can try use just tensorflow to overwrite the data input format
– 陈海栋
Nov 27 '18 at 1:36
or u can add a pre-process to fit in the data
– 陈海栋
Nov 27 '18 at 1:49
1
Ok I got it. Thanks.
– T.Mou
Nov 27 '18 at 3:00
any chance u can vote or accept the answer?
– 陈海栋
Nov 27 '18 at 7:05
add a comment |
for X all the n is same 1200. and this is not sentiment analysis, so do I need embedding?
– T.Mou
Nov 26 '18 at 21:45
i just mean, you need a way to fit the data format into the model, or u can try use just tensorflow to overwrite the data input format
– 陈海栋
Nov 27 '18 at 1:36
or u can add a pre-process to fit in the data
– 陈海栋
Nov 27 '18 at 1:49
1
Ok I got it. Thanks.
– T.Mou
Nov 27 '18 at 3:00
any chance u can vote or accept the answer?
– 陈海栋
Nov 27 '18 at 7:05
for X all the n is same 1200. and this is not sentiment analysis, so do I need embedding?
– T.Mou
Nov 26 '18 at 21:45
for X all the n is same 1200. and this is not sentiment analysis, so do I need embedding?
– T.Mou
Nov 26 '18 at 21:45
i just mean, you need a way to fit the data format into the model, or u can try use just tensorflow to overwrite the data input format
– 陈海栋
Nov 27 '18 at 1:36
i just mean, you need a way to fit the data format into the model, or u can try use just tensorflow to overwrite the data input format
– 陈海栋
Nov 27 '18 at 1:36
or u can add a pre-process to fit in the data
– 陈海栋
Nov 27 '18 at 1:49
or u can add a pre-process to fit in the data
– 陈海栋
Nov 27 '18 at 1:49
1
1
Ok I got it. Thanks.
– T.Mou
Nov 27 '18 at 3:00
Ok I got it. Thanks.
– T.Mou
Nov 27 '18 at 3:00
any chance u can vote or accept the answer?
– 陈海栋
Nov 27 '18 at 7:05
any chance u can vote or accept the answer?
– 陈海栋
Nov 27 '18 at 7:05
add a comment |
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