In tensorflow, how to iterate over a sequence of inputs stored in a tensor?
Solution 1
You can convert a tensor into a list using the unpack function which converts the first dimension into a list. There is also a split function which does something similar. I use unstack in an RNN model I am working on.
y = tf.unstack(tf.transpose(y, (1, 0, 2)))
In this case y starts out with shape (BATCH_SIZE, TIME_STEPS, 128) I transpose it to make the time steps the outer dimension and then unpack it into a list of tensors, one per time step. Now every element in the y list if of shape (BATCH_SIZE, 128) and I can feed it into my RNN.
Solution 2
In TF>=1.0, tf.pack
and tf.unpack
are renamed to tf.stack
and tf.unstack
respectively
exAres
Updated on June 09, 2022Comments
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exAres almost 2 years
I am trying RNN on a variable length multivariate sequence classification problem.
I have defined following function to get the output of the sequence (i.e. the output of RNN cell after the final input from sequence is fed)
def get_sequence_output(x_sequence, initial_hidden_state): previous_hidden_state = initial_hidden_state for x_single in x_sequence: hidden_state = gru_unit(previous_hidden_state, x_single) previous_hidden_state = hidden_state final_hidden_state = hidden_state return final_hidden_state
Here
x_sequence
is tensor of shape(?, ?, 10)
where first ? is for batch size and second ? is for sequence length and each input element is of length 10.gru
function takes a previous hidden state and current input and spits out next hidden state (a standard gated recurrent unit).I am getting an error:
'Tensor' object is not iterable.
How do I iterate over a Tensor in sequence manner (reading single element at a time)?My objective is to apply
gru
function for every input from the sequence and get the final hidden state. -
Cospel over 7 yearsThis will not work if time_steps is not present(variable length of sequence).