Shaping data for LSTM, and feeding output of dense layers to LSTM

Question

I\'m trying to figure out the proper syntax for the model I\'m trying to fit. It\'s a time-series prediction problem, and I want to use a few dense layers to improve the re

Answer 1

For first question, i am doing same thing, i didn't get any error, please share your error.

Note: I will give you example using functional API, which gives little more freedom(personal opinion)

from keras.layers import Dense, Flatten, LSTM, Activation
from keras.layers import Dropout, RepeatVector, TimeDistributed
from keras import Input, Model

seq_length = 15
input_dims = 10
output_dims = 8
n_hidden = 10
model1_inputs = Input(shape=(seq_length,input_dims,))
model1_outputs = Input(shape=(output_dims,))

net1 = LSTM(n_hidden, return_sequences=True)(model1_inputs)
net1 = LSTM(n_hidden, return_sequences=False)(net1)
net1 = Dense(output_dims, activation='relu')(net1)
model1_outputs = net1

model1 = Model(inputs=model1_inputs, outputs = model1_outputs, name='model1')

## Fit the model
model1.summary()


_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_11 (InputLayer)        (None, 15, 10)            0         
_________________________________________________________________
lstm_8 (LSTM)                (None, 15, 10)            840       
_________________________________________________________________
lstm_9 (LSTM)                (None, 10)                840       
_________________________________________________________________
dense_9 (Dense)              (None, 8)                 88        
_________________________________________________________________

To your second problem, there are two method:

1. If you are sending data without making sequence, which is of dims as (batch, input_dims), then use can use this method RepeatVector, which repeat the same weights by n_steps, which is nothing but rolling_steps in LSTM.

{

seq_length = 15
input_dims = 16
output_dims = 8
n_hidden = 20
lstm_dims = 10
model1_inputs = Input(shape=(input_dims,))
model1_outputs = Input(shape=(output_dims,))

net1 = Dense(n_hidden)(model1_inputs)
net1 = Dense(n_hidden)(net1)

net1 = RepeatVector(3)(net1)
net1 = LSTM(lstm_dims, return_sequences=True)(net1)
net1 = LSTM(lstm_dims, return_sequences=False)(net1)
net1 = Dense(output_dims, activation='relu')(net1)
model1_outputs = net1

model1 = Model(inputs=model1_inputs, outputs = model1_outputs, name='model1')

## Fit the model
model1.summary()

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_13 (InputLayer)        (None, 16)                0         
_________________________________________________________________
dense_13 (Dense)             (None, 20)                340       
_________________________________________________________________
dense_14 (Dense)             (None, 20)                420       
_________________________________________________________________
repeat_vector_2 (RepeatVecto (None, 3, 20)             0         
_________________________________________________________________
lstm_14 (LSTM)               (None, 3, 10)             1240      
_________________________________________________________________
lstm_15 (LSTM)               (None, 10)                840       
_________________________________________________________________
dense_15 (Dense)             (None, 8)                 88        
=================================================================

2. If you are sending the sequence of dims (seq_len, input_dims), then you can TimeDistributed, which repeats the same weights of dense layer on the entire sequence.

{

seq_length = 15
input_dims = 10
output_dims = 8
n_hidden = 10
lstm_dims = 6
model1_inputs = Input(shape=(seq_length,input_dims,))
model1_outputs = Input(shape=(output_dims,))

net1 = TimeDistributed(Dense(n_hidden))(model1_inputs)
net1 = LSTM(output_dims, return_sequences=True)(net1)
net1 = LSTM(output_dims, return_sequences=False)(net1)
net1 = Dense(output_dims, activation='relu')(net1)
model1_outputs = net1

model1 = Model(inputs=model1_inputs, outputs = model1_outputs, name='model1')

## Fit the model
model1.summary()


_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_17 (InputLayer)        (None, 15, 10)            0         
_________________________________________________________________
time_distributed_3 (TimeDist (None, 15, 10)            110       
_________________________________________________________________
lstm_18 (LSTM)               (None, 15, 8)             608       
_________________________________________________________________
lstm_19 (LSTM)               (None, 8)                 544       
_________________________________________________________________
dense_19 (Dense)             (None, 8)                 72        
=================================================================

Note: I stacked two layer, on doing so, in the first layer i used return_sequence, which return the output at each time step, which is used by second layer, where it is return output only at last time_step.