I'm getting the following error when I try to run my LSTM program (for variable length inputs). 

> 
> TypeError: Inconsistency in the inner graph of scan 'scan\_fn' : an
>  input and an output are associated with the same recurrent state and
>  should have the same type but have type 'TensorType(float64, col)' and
>  'TensorType(float64, matrix)' respectively.
> 
> 
> 

My program is based on the LSTM example for imdb sentiment analysis problem as in here: <http://deeplearning.net/tutorial/lstm.html> . My data is not of imdb but it is sensor data.

I shared my source code: [**lstm\_var\_length.py**](https://ohio.box.com/s/n3onzqub3cutgzdtsb2fws6u0m5buxmt) and data: **[data.npz](https://ohio.box.com/s/vvkhamka60hphidizwrxvp49aw5wda7m)**. (Click on the files)

From the error above and a few google searches made me understand that I have some issue with the vector/matrix dimensions in my function. Below are the function definitions where this problem occurs: 

```
def lstm_layer(shared_params, input_ex, options):
"""
LSTM Layer implementation. (Variable Length inputs)

Parameters
----------
shared_params: shared model parameters W, U, b etc
input_ex: input example (say dimension: 36 x 100 i.e 36 features and 100 time units)
options: Neural Network model options

Output / returns
----------------
output of each lstm cell [h_0, h_1, ..... , h_t]
"""

def slice(param, slice_no, height):
    return param[slice_no*height : (slice_no+1)*height, :]

def cell(wxb, ht_1, ct_1):
    pre_activation = tensor.dot(shared_params['U'], ht_1)
    pre_activation += wxb

    height = options['hidden_dim']
    ft = tensor.nnet.sigmoid(slice(pre_activation, 0, height))
    it = tensor.nnet.sigmoid(slice(pre_activation, 1, height))
    c_t = tensor.tanh(slice(pre_activation, 2, height))
    ot = tensor.nnet.sigmoid(slice(pre_activation, 3, height))

    ct = ft * ct_1 + it * c_t
    ht = ot * tensor.tanh(ct)

    return ht, ct

wxb = tensor.dot(shared_params['W'], input_ex) + shared_params['b']
num_frames = input_ex.shape[1]
result, updates = theano.scan(cell,
                              sequences=[wxb.transpose()],
                              outputs_info=[tensor.alloc(numpy.asarray(0., dtype=floatX),
                                                         options['hidden_dim'], 1),
                                            tensor.alloc(numpy.asarray(0., dtype=floatX),
                                                         options['hidden_dim'], 1)],
                              n_steps=num_frames)

return result[0]  # only ht is needed

def build_model(shared_params, options):
"""
Build the complete neural network model and return the symbolic variables

Parameters
----------
shared_params: shared, model parameters W, U, b etc
options: Neural Network model options

return
------
x, y, f_pred_prob, f_pred, cost
"""

x = tensor.matrix(name='x', dtype=floatX)
y = tensor.iscalar(name='y') # tensor.vector(name='y', dtype=floatX)

num_frames = x.shape[1]
# lstm outputs from each cell
lstm_result = lstm_layer(shared_params, x, options)
# mean pool from the lstm cell outputs
pool_result = lstm_result.sum(axis=1)/(1. * num_frames)
# Softmax / Logistic Regression
pred = tensor.nnet.softmax(tensor.dot(shared_params['softmax_W'], pool_result) +
                           shared_params['softmax_b'])
# predicted probability function
theano.printing.debugprint(pred)
f_pred_prob = theano.function([x], pred, name='f_pred_prob', mode='DebugMode') # 'DebugMode' <-- Problem seems to occur at this point
# predicted class
f_pred = theano.function([x], pred.argmax(axis=0), name='f_pred')
# cost of the model: -ve log likelihood
offset = 1e-8   # an offset to prevent log(0)
cost = -tensor.log(pred[y-1, 0] + offset)    # y = 1,2,...n but indexing is 0,1,..(n-1)

return x, y, f_pred_prob, f_pred, cost

```

The error above is caused when trying to compile **f\_pred\_prob** theano function.

Exception and call stack is below: 

```
File "/home/inblueswithu/Documents/Theano_Trails/lstm_var_length.py", line 450, in 
    main()
  File "/home/inblueswithu/Documents/Theano_Trails/lstm_var_length.py", line 447, in main
  train_lstm(model_options, train, valid)
 File "/home/inblueswithu/Documents/Theano_Trails/lstm_var_length.py", line 314, in train_lstm
  (x, y, f_pred_prob, f_pred, cost) = build_model(shared_params, options)
File "/home/inblueswithu/Documents/Theano_Trails/lstm_var_length.py", line 95, in build_model
  f_pred_prob = theano.function([x], pred, name='f_pred_prob', mode='DebugMode') # 'DebugMode'
File "/usr/local/lib/python2.7/dist-packages/theano/compile/function.py", line 320, in function
  output_keys=output_keys)
File "/usr/local/lib/python2.7/dist-packages/theano/compile/pfunc.py", line 479, in pfunc
  output_keys=output_keys)
File "/usr/local/lib/python2.7/dist-packages/theano/compile/function_module.py", line 1777, in orig_function
  defaults)
File "/usr/local/lib/python2.7/dist-packages/theano/compile/debugmode.py", line 2571, in create
  storage_map=storage_map)
File "/usr/local/lib/python2.7/dist-packages/theano/gof/link.py", line 690, in make_thunk
  storage_map=storage_map)[:3]
File "/usr/local/lib/python2.7/dist-packages/theano/compile/debugmode.py", line 1809, in make_all
  no_recycling)
File "/usr/local/lib/python2.7/dist-packages/theano/scan_module/scan_op.py", line 730, in make_thunk
  self.validate_inner_graph()
File "/usr/local/lib/python2.7/dist-packages/theano/scan_module/scan_op.py", line 249, in validate_inner_graph
  (self.name, type_input, type_output))
TypeError: Inconsistency in the inner graph of scan 'scan_fn' : an input and an output are associated with the same recurrent state and should have the same type but have type 'TensorType(float64, col)' and 'TensorType(float64, matrix)' respectively. 
```

I have been doing all the debugging for a week, but could not find the issue. I doubt the initializations at outputs\_info in theano.scan to be the problem but when I delete the second dimension (1) I get an error in slice function even before getting to **f\_pred\_prob** function (near *lstm\_result*). I'm not sure where the problem lies.

A simple execution of this program by placing the data file in the same directory as the python source file can recreate this issue.

Please help me out.

Thanks & Regards, 
inblueswithu