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tensorflowlstm
Paul Rousseau
Paul Rousseau

Reputation: 561

Tensorflow 1.0 LSTM Cell in dynamic_rnn throws dimension error

I am trying to implement an LSTM Model as a model_fn input to an Estimator. My X is only a .txt with a time series of prices. Before going into my first hidden layer, I try to define the lstm cell as:

  def lstm_cell():
   return tf.contrib.rnn.BasicLSTMCell(
   size, forget_bias=0.0, state_is_tuple=True)
  attn_cell = lstm_cell
  if is_training and keep_prob < 1:
   def attn_cell():
    return tf.contrib.rnn.DropoutWrapper(
    lstm_cell(), output_keep_prob=keep_prob)
  cell = tf.contrib.rnn.MultiRNNCell([attn_cell() for _ in  range(num_layers)], state_is_tuple=True)
  initial_state = cell.zero_state(batch_size, data_type())
  inputs = tf.unstack(X, num=num_steps, axis=0)  
  outputs = []
  outputs, state = tf.nn.dynamic_rnn(cell, inputs,
                           initial_state=initial_state)

This then is supposed to go into:

  first_hidden_layer = tf.contrib.layers.relu(outputs, 1000)

Unfortunately, it throws an error idicating that "ValueError: Dimension must be 1 but is 3 for 'transpose' (op: 'Transpose') with input shapes: [1], [3]." I gather that my problem is the "inputs" tensor. In its description, the inputs variable is supposed to be a tensor with form [batch_size,max_time,...], but Ihave no idea how to translate this into above structure since, through the estimator, only input values X and target values y are fed to the system. So my question would be how to create a tensor that can serve as an inputs variable to the dynamic_rnn class.

Thanks a lot.

Upvotes: 1

Views: 1402

Answers (1)

Yahia Zakaria
Yahia Zakaria

Reputation: 1206

I believe you don't need the line:

inputs = tf.unstack(X, num=num_steps, axis=0)

you can supply X directly to dynamic_rnn since dynamic_rnn doesn't take a list of tensors; It takes one tensor where the time axis is dimension 0 (if time_major == True) or dimension 1 (if time_major == False).

Actually, it seems that X has 2 dimensions only, since inputs is list of 1 dimensional tensors (as indicated by the error message). so you should replace the unstack line with:

inputs = tf.expand_dims(X, axis=2)

This will add a 3rd dimension of size 1 that is needed by dynamic_rnn

Upvotes: 1

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