Help required to implement the below model using Bi-GRU
0
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As you can see in above images I need to model Bi-GRUs stacked as shown in table which takes input (N,1,64) and outputs (N,204). The input data is binary number stream and so is output data. Can anyone please help me get started?
Thank you.
keras rnn autoencoder
asked 2 days ago
Sank_BESank_BE
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0
$begingroup$
As you can see in above images I need to model Bi-GRUs stacked as shown in table which takes input (N,1,64) and outputs (N,204). The input data is binary number stream and so is output data. Can anyone please help me get started?
Thank you.
keras rnn autoencoder
asked 2 days ago
Sank_BESank_BE
11
New contributor
Sank_BE is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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add a comment |
0
0
0
$begingroup$
As you can see in above images I need to model Bi-GRUs stacked as shown in table which takes input (N,1,64) and outputs (N,204). The input data is binary number stream and so is output data. Can anyone please help me get started?
Thank you.
keras rnn autoencoder
asked 2 days ago
Sank_BESank_BE
11
New contributor
Sank_BE is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
As you can see in above images I need to model Bi-GRUs stacked as shown in table which takes input (N,1,64) and outputs (N,204). The input data is binary number stream and so is output data. Can anyone please help me get started?
Thank you.
keras rnn autoencoder
keras rnn autoencoder
asked 2 days ago
Sank_BESank_BE
11
New contributor
Sank_BE is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 days ago
Sank_BESank_BE
11
New contributor
Sank_BE is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 days ago
Sank_BESank_BE
11
New contributor
Sank_BE is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 days ago
Sank_BESank_BE
11
asked 2 days ago
Sank_BESank_BE
11
11
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Sank_BE is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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New contributor
Sank_BE is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Sank_BE is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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1 Answer
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from keras.layers import LSTM
from keras.models import Sequential
from keras.layers import LSTM
from keras.layers import Dense,BatchNormalization
model = Sequential()
model.add(LSTM(800, return_sequences=True,
input_shape=(1, 64))) # returns a sequence of vectors of dimension 32
model.add(BatchNormalization()) # returns a sequence of vectors of dimension 32
model.add(LSTM(800)) # return a single vector of dimension 32
model.add(BatchNormalization())
model.add(Dense(204, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='Adam',metrics=['accuracy'])
model.summary()
I figured it out on my own.
answered 2 days ago
Sank_BESank_BE
11
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1 Answer
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1 Answer
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0
$begingroup$
from keras.layers import LSTM
from keras.models import Sequential
from keras.layers import LSTM
from keras.layers import Dense,BatchNormalization
model = Sequential()
model.add(LSTM(800, return_sequences=True,
input_shape=(1, 64))) # returns a sequence of vectors of dimension 32
model.add(BatchNormalization()) # returns a sequence of vectors of dimension 32
model.add(LSTM(800)) # return a single vector of dimension 32
model.add(BatchNormalization())
model.add(Dense(204, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='Adam',metrics=['accuracy'])
model.summary()
I figured it out on my own.
answered 2 days ago
Sank_BESank_BE
11
New contributor
Sank_BE is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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add a comment |
0
$begingroup$
from keras.layers import LSTM
from keras.models import Sequential
from keras.layers import LSTM
from keras.layers import Dense,BatchNormalization
model = Sequential()
model.add(LSTM(800, return_sequences=True,
input_shape=(1, 64))) # returns a sequence of vectors of dimension 32
model.add(BatchNormalization()) # returns a sequence of vectors of dimension 32
model.add(LSTM(800)) # return a single vector of dimension 32
model.add(BatchNormalization())
model.add(Dense(204, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='Adam',metrics=['accuracy'])
model.summary()
I figured it out on my own.
answered 2 days ago
Sank_BESank_BE
11
New contributor
Sank_BE is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
0
0
0
$begingroup$
from keras.layers import LSTM
from keras.models import Sequential
from keras.layers import LSTM
from keras.layers import Dense,BatchNormalization
model = Sequential()
model.add(LSTM(800, return_sequences=True,
input_shape=(1, 64))) # returns a sequence of vectors of dimension 32
model.add(BatchNormalization()) # returns a sequence of vectors of dimension 32
model.add(LSTM(800)) # return a single vector of dimension 32
model.add(BatchNormalization())
model.add(Dense(204, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='Adam',metrics=['accuracy'])
model.summary()
I figured it out on my own.
answered 2 days ago
Sank_BESank_BE
11
New contributor
Sank_BE is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
from keras.layers import LSTM
from keras.models import Sequential
from keras.layers import LSTM
from keras.layers import Dense,BatchNormalization
model = Sequential()
model.add(LSTM(800, return_sequences=True,
input_shape=(1, 64))) # returns a sequence of vectors of dimension 32
model.add(BatchNormalization()) # returns a sequence of vectors of dimension 32
model.add(LSTM(800)) # return a single vector of dimension 32
model.add(BatchNormalization())
model.add(Dense(204, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='Adam',metrics=['accuracy'])
model.summary()
I figured it out on my own.
answered 2 days ago
Sank_BESank_BE
11
New contributor
Sank_BE is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
answered 2 days ago
Sank_BESank_BE
11
New contributor
Sank_BE is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
answered 2 days ago
Sank_BESank_BE
11
answered 2 days ago
Sank_BESank_BE
11
11
New contributor
Sank_BE is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Sank_BE is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Sank_BE is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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