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I am trying to use LSTM with inputs with different time steps (different number of frames). The input to the rnn.static_rnn should be a sequence of tf (not a tf!). So, I should convert my input to sequence. I tried to use tf.unstack and tf.split, but both of them need to know exact size of inputs, while one dimension of my inputs (time steps) is changing by different inputs. following is part of my code: 

n_input = 256*256 # data input (img shape: 256*256)
n_steps = None # timesteps
batch_size = 1
# tf Graph input
x = tf.placeholder("float", [ batch_size , n_input,n_steps])
y = tf.placeholder("float", [batch_size, n_classes])
# Permuting batch_size and n_steps
x1 = tf.transpose(x, [2, 1, 0])
x1 = tf.transpose(x1, [0, 2, 1])
x3=tf.unstack(x1,axis=0)
#or x3 = tf.split(x2, ?, 0)
# Define a lstm cell with tensorflow
lstm_cell = rnn.BasicLSTMCell(num_units=n_hidden, forget_bias=1.0)

# Get lstm cell output
outputs, states = rnn.static_rnn(lstm_cell, x3, dtype=tf.float32,sequence_length=None)

I got following error when I am using tf.unstack:

ValueError: Cannot infer num from shape (?, 1, 65536)

Also, there are some discussions here and here, but none of them were useful for me. Any help is appreciated.

miken32
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amor
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1 Answers1

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As explained in here, tf.unstack does not work if the argument is unspecified and non-inferrable.

In your code, after transpositions, x1 has the shape of [ n_steps, batch_size, n_input] and its value at axis=0 is set to None.

Fariborz Ghavamian
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