Comments (2)
import recurrentshop
import numpy
import keras.layers
from keras.layers import Dense, Input, LSTM
from keras.models import Model
prior_out = Input(batch_shape=(1,5),name='prior_out')
main_input = Input(batch_shape=(1,5),name='main_input')
h_tm1 = Input(batch_shape=(1, 10))
c_tm1 = Input(batch_shape=(1, 10))
added_layer = keras.layers.add([prior_out,main_input])
# hidden = LSTM(10,stateful=True,return_sequences=False,name='hidden')(added_layer)
hidden, h, c = recurrentshop.cells.LSTMCell(10)([added_layer, h_tm1, c_tm1])
out = Dense(5,name='out')(hidden)
rnn = recurrentshop.RecurrentModel(input=main_input, initial_states=[prior_out, h_tm1, c_tm1], output=out, final_states=[out, h, c])
rnn_input = Input(batch_shape=(1,1,5),name='rnn_input')
rnn_output = rnn(rnn_input)
model = Model(inputs=rnn_input,outputs=rnn_output)
model.compile(optimizer='adam',loss='mae')
print model.predict(numpy.random.random((3,1,5)),batch_size=1)
from recurrentshop.
Thanks @farizrahman4u , and there it was right on the front page, I just wasn't getting what it was doing.
from recurrentshop.
Related Issues (20)
- Dropout in Readout Model
- RecurrentModel without Cell
- AssertionError: Required 2 inputs, received 3.
- ImportError: cannot import name 'RecurrentSequentiaĺ'
- Error when combining teacherforce/readout with return_states
- Setting states for rnn forecasting
- update version HOT 1
- TypeError: can only concatenate tuple (not "int") to tuple HOT 3
- RNN with missing inputs
- Installation issue
- Decoder variable output_length? HOT 2
- How to transition between readout and teacher forcing?
- load model error with attention model
- Tensorflow2.0 support?
- AttributeError: 'Node' object has no attribute 'output_masks' HOT 1
- NameError: name 'K' is not defined HOT 1
- NameError: name 'activations' is not defined HOT 2
- NameError: name 'activations' is not defined HOT 2
- Error fix
- ImportError: cannot import name 'tensorflow_backend' HOT 10
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