Keras concatenate LSTM model with non-LSTM model
I have two data. One is time series and the other contains features such as Sex, education, etc. and I want to concatenate output of LSTM model and a dense model. However, I got an error message (look at the end).
This is what the data looks like:
And this is the code:
# PAY_data net
input1 = Input(shape=(6,1))
pay = LSTM(10)(input1)
pay = Dense(10, activation='relu')(pay)
# DEMO_data net
input2 = Input(shape=(5,1))
demo = Dense(10, activation='relu')(input2)
demo = Dense(10, activation='relu')(demo)
merge = concatenate([pay, demo])
hidden1 = Dense(10, activation='relu')(merge)
output = Dense(1, activation='sigmoid')(merge)
model = Model(inputs=[input1, input2], outputs=output)
print(model.summary())
model.compile(loss='binary_crossentropy', optimizer='adam', metrics= ['accuracy'])
model.fit([PAY_data, DEMO_data], y,nb_epoch=20, batch_size=50, verbose=2, validation_split=0.2)
and this is the error I get:
python machine-learning keras concatenation lstm
edited Nov 13 '18 at 7:22
today
10.6k21737
asked Nov 13 '18 at 5:05
MinJaeMinJae
11
add a comment |
I have two data. One is time series and the other contains features such as Sex, education, etc. and I want to concatenate output of LSTM model and a dense model. However, I got an error message (look at the end).
This is what the data looks like:
And this is the code:
# PAY_data net
input1 = Input(shape=(6,1))
pay = LSTM(10)(input1)
pay = Dense(10, activation='relu')(pay)
# DEMO_data net
input2 = Input(shape=(5,1))
demo = Dense(10, activation='relu')(input2)
demo = Dense(10, activation='relu')(demo)
merge = concatenate([pay, demo])
hidden1 = Dense(10, activation='relu')(merge)
output = Dense(1, activation='sigmoid')(merge)
model = Model(inputs=[input1, input2], outputs=output)
print(model.summary())
model.compile(loss='binary_crossentropy', optimizer='adam', metrics= ['accuracy'])
model.fit([PAY_data, DEMO_data], y,nb_epoch=20, batch_size=50, verbose=2, validation_split=0.2)
and this is the error I get:
python machine-learning keras concatenation lstm
edited Nov 13 '18 at 7:22
today
10.6k21737
asked Nov 13 '18 at 5:05
MinJaeMinJae
11
If the answer resolved your issue, kindly accept it by clicking on the checkmark next to the answer to mark it as "answered" - see What should I do when someone answers my question?
– today
Nov 26 '18 at 15:53
add a comment |
I have two data. One is time series and the other contains features such as Sex, education, etc. and I want to concatenate output of LSTM model and a dense model. However, I got an error message (look at the end).
This is what the data looks like:
And this is the code:
# PAY_data net
input1 = Input(shape=(6,1))
pay = LSTM(10)(input1)
pay = Dense(10, activation='relu')(pay)
# DEMO_data net
input2 = Input(shape=(5,1))
demo = Dense(10, activation='relu')(input2)
demo = Dense(10, activation='relu')(demo)
merge = concatenate([pay, demo])
hidden1 = Dense(10, activation='relu')(merge)
output = Dense(1, activation='sigmoid')(merge)
model = Model(inputs=[input1, input2], outputs=output)
print(model.summary())
model.compile(loss='binary_crossentropy', optimizer='adam', metrics= ['accuracy'])
model.fit([PAY_data, DEMO_data], y,nb_epoch=20, batch_size=50, verbose=2, validation_split=0.2)
and this is the error I get:
python machine-learning keras concatenation lstm
edited Nov 13 '18 at 7:22
today
10.6k21737
asked Nov 13 '18 at 5:05
MinJaeMinJae
11
I have two data. One is time series and the other contains features such as Sex, education, etc. and I want to concatenate output of LSTM model and a dense model. However, I got an error message (look at the end).
This is what the data looks like:
And this is the code:
# PAY_data net
input1 = Input(shape=(6,1))
pay = LSTM(10)(input1)
pay = Dense(10, activation='relu')(pay)
# DEMO_data net
input2 = Input(shape=(5,1))
demo = Dense(10, activation='relu')(input2)
demo = Dense(10, activation='relu')(demo)
merge = concatenate([pay, demo])
hidden1 = Dense(10, activation='relu')(merge)
output = Dense(1, activation='sigmoid')(merge)
model = Model(inputs=[input1, input2], outputs=output)
print(model.summary())
model.compile(loss='binary_crossentropy', optimizer='adam', metrics= ['accuracy'])
model.fit([PAY_data, DEMO_data], y,nb_epoch=20, batch_size=50, verbose=2, validation_split=0.2)
and this is the error I get:
python machine-learning keras concatenation lstm
python machine-learning keras concatenation lstm
edited Nov 13 '18 at 7:22
today
10.6k21737
asked Nov 13 '18 at 5:05
MinJaeMinJae
11
edited Nov 13 '18 at 7:22
today
10.6k21737
asked Nov 13 '18 at 5:05
MinJaeMinJae
11
edited Nov 13 '18 at 7:22
today
10.6k21737
edited Nov 13 '18 at 7:22
today
10.6k21737
edited Nov 13 '18 at 7:22
today
10.6k21737
10.6k21737
asked Nov 13 '18 at 5:05
MinJaeMinJae
11
asked Nov 13 '18 at 5:05
MinJaeMinJae
11
asked Nov 13 '18 at 5:05
MinJaeMinJae
11
11
If the answer resolved your issue, kindly accept it by clicking on the checkmark next to the answer to mark it as "answered" - see What should I do when someone answers my question?
– today
Nov 26 '18 at 15:53
add a comment |
If the answer resolved your issue, kindly accept it by clicking on the checkmark next to the answer to mark it as "answered" - see What should I do when someone answers my question?
– today
Nov 26 '18 at 15:53
If the answer resolved your issue, kindly accept it by clicking on the checkmark next to the answer to mark it as "answered" - see What should I do when someone answers my question?
– today
Nov 26 '18 at 15:53
If the answer resolved your issue, kindly accept it by clicking on the checkmark next to the answer to mark it as "answered" - see What should I do when someone answers my question?
– today
Nov 26 '18 at 15:53
add a comment |
1 Answer
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Since the Dense layer is applied on the last axis of its input data, and considering that you have specified an input shape of (5,1) for your "Demo_data net", the output shape of this model would be (None, 5, 10) and therefore it cannot be concatenated with the output of the "Pay_data net" which has an output shape of (None, 10). To resolve this, you can remove the redundant last axis from PAY_data using np.squeeze():
PAY_data = np.squeeze(PAY_data)
and also set the input shape accordingly:
input2 = Input(shape=(5,)) # now the input shape is (5,) and not (5,1)
answered Nov 13 '18 at 7:19
todaytoday
10.6k21737
add a comment |
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1 Answer
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1 Answer
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active
oldest
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oldest
votes
Since the Dense layer is applied on the last axis of its input data, and considering that you have specified an input shape of (5,1) for your "Demo_data net", the output shape of this model would be (None, 5, 10) and therefore it cannot be concatenated with the output of the "Pay_data net" which has an output shape of (None, 10). To resolve this, you can remove the redundant last axis from PAY_data using np.squeeze():
PAY_data = np.squeeze(PAY_data)
and also set the input shape accordingly:
input2 = Input(shape=(5,)) # now the input shape is (5,) and not (5,1)
answered Nov 13 '18 at 7:19
todaytoday
10.6k21737
add a comment |
Since the Dense layer is applied on the last axis of its input data, and considering that you have specified an input shape of (5,1) for your "Demo_data net", the output shape of this model would be (None, 5, 10) and therefore it cannot be concatenated with the output of the "Pay_data net" which has an output shape of (None, 10). To resolve this, you can remove the redundant last axis from PAY_data using np.squeeze():
PAY_data = np.squeeze(PAY_data)
and also set the input shape accordingly:
input2 = Input(shape=(5,)) # now the input shape is (5,) and not (5,1)
answered Nov 13 '18 at 7:19
todaytoday
10.6k21737
add a comment |
Since the Dense layer is applied on the last axis of its input data, and considering that you have specified an input shape of (5,1) for your "Demo_data net", the output shape of this model would be (None, 5, 10) and therefore it cannot be concatenated with the output of the "Pay_data net" which has an output shape of (None, 10). To resolve this, you can remove the redundant last axis from PAY_data using np.squeeze():
PAY_data = np.squeeze(PAY_data)
and also set the input shape accordingly:
input2 = Input(shape=(5,)) # now the input shape is (5,) and not (5,1)
answered Nov 13 '18 at 7:19
todaytoday
10.6k21737
Since the Dense layer is applied on the last axis of its input data, and considering that you have specified an input shape of (5,1) for your "Demo_data net", the output shape of this model would be (None, 5, 10) and therefore it cannot be concatenated with the output of the "Pay_data net" which has an output shape of (None, 10). To resolve this, you can remove the redundant last axis from PAY_data using np.squeeze():
PAY_data = np.squeeze(PAY_data)
and also set the input shape accordingly:
input2 = Input(shape=(5,)) # now the input shape is (5,) and not (5,1)
answered Nov 13 '18 at 7:19
todaytoday
10.6k21737
answered Nov 13 '18 at 7:19
todaytoday
10.6k21737
answered Nov 13 '18 at 7:19
todaytoday
10.6k21737
answered Nov 13 '18 at 7:19
todaytoday
10.6k21737
10.6k21737
add a comment |
add a comment |
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If the answer resolved your issue, kindly accept it by clicking on the checkmark next to the answer to mark it as "answered" - see What should I do when someone answers my question?
– today
Nov 26 '18 at 15:53