trying to concatenate two layers in keras with the same shape giving error in shapes matching
I am trying to build a multi-input multi-output model using keras functional api and I am following their code but I got that error:
ValueError: A
Concatenate
layer requires inputs with matching
shapes except for the concat axis. Got inputs shapes: [(None, 50),
(None, 50, 1)]
I have skipped the Embedding layer, here is the code:
def build_model(self):
main_input = Input(shape=(self.seq_len, 1), name='main_input')
print(main_input.shape)
# seq_len = 50
# A LSTM will transform the vector sequence into a single vector,
# containing information about the entire sequence
lstm_out = LSTM(self.seq_len,input_shape=(self.seq_len,1) )(main_input)
self.auxiliary_output = Dense(1, activation='sigmoid', name='aux_output')(lstm_out)
auxiliary_input = Input(shape=(self.seq_len,1), name='aux_input')
print(auxiliary_input.shape)
x = concatenate([lstm_out, auxiliary_input])
# We stack a deep densely-connected network on top
x = Dense(64, activation='relu')(x)
x = Dense(64, activation='relu')(x)
x = Dense(64, activation='relu')(x)
# And finally we add the main logistic regression layer
main_output = Dense(1, activation='sigmoid', name='main_output')(x)
self.model = Model(inputs=[main_input, auxiliary_input], outputs=[main_output, auxiliary_output])
print(self.model.summary())
self.model.compile(optimizer='rmsprop', loss='binary_crossentropy',
loss_weights=[1., 0.2])
I got that error in the concatenation step, although printing the shape of both layers are (?,50,1).
I do not know exactly why I got this, and what is the exact error in the input_shape of the first layer and why it does not give me the same shape as it should be using print(main_input.shape)
, and how to solve it ?
UPDATE:
I found a solution for the error by changing the shape of the second input layer
auxiliary_input = Input(shape=(self.seq_len,), name='aux_input')
so now they can concatenate smoothly, but still not clear to me why ?
python machine-learning keras neural-network lstm
add a comment |
I am trying to build a multi-input multi-output model using keras functional api and I am following their code but I got that error:
ValueError: A
Concatenate
layer requires inputs with matching
shapes except for the concat axis. Got inputs shapes: [(None, 50),
(None, 50, 1)]
I have skipped the Embedding layer, here is the code:
def build_model(self):
main_input = Input(shape=(self.seq_len, 1), name='main_input')
print(main_input.shape)
# seq_len = 50
# A LSTM will transform the vector sequence into a single vector,
# containing information about the entire sequence
lstm_out = LSTM(self.seq_len,input_shape=(self.seq_len,1) )(main_input)
self.auxiliary_output = Dense(1, activation='sigmoid', name='aux_output')(lstm_out)
auxiliary_input = Input(shape=(self.seq_len,1), name='aux_input')
print(auxiliary_input.shape)
x = concatenate([lstm_out, auxiliary_input])
# We stack a deep densely-connected network on top
x = Dense(64, activation='relu')(x)
x = Dense(64, activation='relu')(x)
x = Dense(64, activation='relu')(x)
# And finally we add the main logistic regression layer
main_output = Dense(1, activation='sigmoid', name='main_output')(x)
self.model = Model(inputs=[main_input, auxiliary_input], outputs=[main_output, auxiliary_output])
print(self.model.summary())
self.model.compile(optimizer='rmsprop', loss='binary_crossentropy',
loss_weights=[1., 0.2])
I got that error in the concatenation step, although printing the shape of both layers are (?,50,1).
I do not know exactly why I got this, and what is the exact error in the input_shape of the first layer and why it does not give me the same shape as it should be using print(main_input.shape)
, and how to solve it ?
UPDATE:
I found a solution for the error by changing the shape of the second input layer
auxiliary_input = Input(shape=(self.seq_len,), name='aux_input')
so now they can concatenate smoothly, but still not clear to me why ?
python machine-learning keras neural-network lstm
add a comment |
I am trying to build a multi-input multi-output model using keras functional api and I am following their code but I got that error:
ValueError: A
Concatenate
layer requires inputs with matching
shapes except for the concat axis. Got inputs shapes: [(None, 50),
(None, 50, 1)]
I have skipped the Embedding layer, here is the code:
def build_model(self):
main_input = Input(shape=(self.seq_len, 1), name='main_input')
print(main_input.shape)
# seq_len = 50
# A LSTM will transform the vector sequence into a single vector,
# containing information about the entire sequence
lstm_out = LSTM(self.seq_len,input_shape=(self.seq_len,1) )(main_input)
self.auxiliary_output = Dense(1, activation='sigmoid', name='aux_output')(lstm_out)
auxiliary_input = Input(shape=(self.seq_len,1), name='aux_input')
print(auxiliary_input.shape)
x = concatenate([lstm_out, auxiliary_input])
# We stack a deep densely-connected network on top
x = Dense(64, activation='relu')(x)
x = Dense(64, activation='relu')(x)
x = Dense(64, activation='relu')(x)
# And finally we add the main logistic regression layer
main_output = Dense(1, activation='sigmoid', name='main_output')(x)
self.model = Model(inputs=[main_input, auxiliary_input], outputs=[main_output, auxiliary_output])
print(self.model.summary())
self.model.compile(optimizer='rmsprop', loss='binary_crossentropy',
loss_weights=[1., 0.2])
I got that error in the concatenation step, although printing the shape of both layers are (?,50,1).
I do not know exactly why I got this, and what is the exact error in the input_shape of the first layer and why it does not give me the same shape as it should be using print(main_input.shape)
, and how to solve it ?
UPDATE:
I found a solution for the error by changing the shape of the second input layer
auxiliary_input = Input(shape=(self.seq_len,), name='aux_input')
so now they can concatenate smoothly, but still not clear to me why ?
python machine-learning keras neural-network lstm
I am trying to build a multi-input multi-output model using keras functional api and I am following their code but I got that error:
ValueError: A
Concatenate
layer requires inputs with matching
shapes except for the concat axis. Got inputs shapes: [(None, 50),
(None, 50, 1)]
I have skipped the Embedding layer, here is the code:
def build_model(self):
main_input = Input(shape=(self.seq_len, 1), name='main_input')
print(main_input.shape)
# seq_len = 50
# A LSTM will transform the vector sequence into a single vector,
# containing information about the entire sequence
lstm_out = LSTM(self.seq_len,input_shape=(self.seq_len,1) )(main_input)
self.auxiliary_output = Dense(1, activation='sigmoid', name='aux_output')(lstm_out)
auxiliary_input = Input(shape=(self.seq_len,1), name='aux_input')
print(auxiliary_input.shape)
x = concatenate([lstm_out, auxiliary_input])
# We stack a deep densely-connected network on top
x = Dense(64, activation='relu')(x)
x = Dense(64, activation='relu')(x)
x = Dense(64, activation='relu')(x)
# And finally we add the main logistic regression layer
main_output = Dense(1, activation='sigmoid', name='main_output')(x)
self.model = Model(inputs=[main_input, auxiliary_input], outputs=[main_output, auxiliary_output])
print(self.model.summary())
self.model.compile(optimizer='rmsprop', loss='binary_crossentropy',
loss_weights=[1., 0.2])
I got that error in the concatenation step, although printing the shape of both layers are (?,50,1).
I do not know exactly why I got this, and what is the exact error in the input_shape of the first layer and why it does not give me the same shape as it should be using print(main_input.shape)
, and how to solve it ?
UPDATE:
I found a solution for the error by changing the shape of the second input layer
auxiliary_input = Input(shape=(self.seq_len,), name='aux_input')
so now they can concatenate smoothly, but still not clear to me why ?
python machine-learning keras neural-network lstm
python machine-learning keras neural-network lstm
edited Nov 25 '18 at 9:56
Kamal El-Saaid
asked Nov 24 '18 at 19:17
Kamal El-SaaidKamal El-Saaid
225
225
add a comment |
add a comment |
1 Answer
1
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For the second input, you specified before the bug that,
input_shape = (50,1)# seq_length=50
This means final shape is:
(None,50,1)
Now, when the first input passes through LSTM
, since you didn't specify return_sequences=True
it will return a tensor of shape (batch_size, units)
viz. (None, 50)
which you are concatenating with the above mentioned (None, 50, 1)
Your error went away because you changed the input shape for the second input as (50,)
so the final shape becomes (None,50)
which matches with output of LSTM
and hence it concatenated smoothly
add a comment |
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1 Answer
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active
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1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
For the second input, you specified before the bug that,
input_shape = (50,1)# seq_length=50
This means final shape is:
(None,50,1)
Now, when the first input passes through LSTM
, since you didn't specify return_sequences=True
it will return a tensor of shape (batch_size, units)
viz. (None, 50)
which you are concatenating with the above mentioned (None, 50, 1)
Your error went away because you changed the input shape for the second input as (50,)
so the final shape becomes (None,50)
which matches with output of LSTM
and hence it concatenated smoothly
add a comment |
For the second input, you specified before the bug that,
input_shape = (50,1)# seq_length=50
This means final shape is:
(None,50,1)
Now, when the first input passes through LSTM
, since you didn't specify return_sequences=True
it will return a tensor of shape (batch_size, units)
viz. (None, 50)
which you are concatenating with the above mentioned (None, 50, 1)
Your error went away because you changed the input shape for the second input as (50,)
so the final shape becomes (None,50)
which matches with output of LSTM
and hence it concatenated smoothly
add a comment |
For the second input, you specified before the bug that,
input_shape = (50,1)# seq_length=50
This means final shape is:
(None,50,1)
Now, when the first input passes through LSTM
, since you didn't specify return_sequences=True
it will return a tensor of shape (batch_size, units)
viz. (None, 50)
which you are concatenating with the above mentioned (None, 50, 1)
Your error went away because you changed the input shape for the second input as (50,)
so the final shape becomes (None,50)
which matches with output of LSTM
and hence it concatenated smoothly
For the second input, you specified before the bug that,
input_shape = (50,1)# seq_length=50
This means final shape is:
(None,50,1)
Now, when the first input passes through LSTM
, since you didn't specify return_sequences=True
it will return a tensor of shape (batch_size, units)
viz. (None, 50)
which you are concatenating with the above mentioned (None, 50, 1)
Your error went away because you changed the input shape for the second input as (50,)
so the final shape becomes (None,50)
which matches with output of LSTM
and hence it concatenated smoothly
answered Nov 25 '18 at 10:07
VitrioilVitrioil
894
894
add a comment |
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