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(I am working on Jupter notebook with python version 3.6.12, running Tensorflow 2.4.0 version.) I have a dataset that consists of 5 input features and 3 output features (that requires to be predicted). My features are string values of integers and looks like as follows:

Input (training) features:

        A      B      C      D       E
57    00101  01000  01001  01000   00110
203   00111  01001  01000  01000   00110
559   00010  01001  01001  01000   00110
247   00101  01001  01001  01000   00110
1111  00111  01001  01000  01000   00110
...     ...    ...    ...    ...     ...
167   10000  00101  01000  10000   00110
908   00100  01000  01001  01000   00111
166   00010  01001  01001  01000   00110
1106  01001  00101  01000  10000   00110
996   00111  01001  01000  01000   00110

[930 rows x 5 columns]

Output (training) features:

        O1     O2         O3
57    10000  00101      00100
203   10000  00100      00100
559   10000  00101      00011
247   10000  00110      00110
1111  10000  01000      00110
...     ...    ...        ...
167   10000  00110      00111
908   00011  00010      00001
166   10000  00101      00011
1106  00010  00011      00001
996   10000  00100      00101

[930 rows x 3 columns]

Then I converted my data into array to be used further in LSTM, which looks like follows (just my input data shown below for example):

[['00101' '01000' '01001' '01000' '00110']
 ['00111' '01001' '01000' '01000' '00110']
 ['00010' '01001' '01001' '01000' '00110']
 ...
 ['00010' '01001' '01001' '01000' '00110']
 ['01001' '00101' '01000' '10000' '00110']
 ['00111' '01001' '01000' '01000' '00110']]

My code looks like:

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten, LSTM
from tensorflow.keras.layers import Convolution2D, MaxPooling2D
from tensorflow.keras.layers import Input, LSTM, concatenate, Dense, Lambda
from tensorflow.keras.models import Model

#Then I reshaped my input:
x_tr_re = np.array([int(k) for s in x_tr.flatten() for k in s]).reshape(-1, 5, 5)
x_te_re = np.array([int(a) for c in x_te.flatten() for a in c]).reshape(-1, 5, 5)
print(x_te_re.shape,x_tr_re.shape, y_tr1.shape)
(405, 5, 5) (930, 5, 5) (930, 3)

#my model:
input_1 = Input(shape=(x_tr_re.shape[1],x_tr_re.shape[2]), name = 'input_1')
lstm1   = LSTM(50, name = 'lstm1')(input_1)
output1 = Dense(3, activation = "softmax", name ='out1')(lstm1)
model   = Model(inputs=input_1, outputs=output1)
model.compile(optimizer = 'adam', loss = 'mean_squared_error',metrics = ['MAE'])
model.fit( x_te_re, y_te1, epochs = 1, batch_size = 10)

I get the following error:

---------------------------------------------------------------------------
UnimplementedError                        Traceback (most recent call last)
<ipython-input-9-c7d71b522ac7> in <module>
      4 model   = Model(inputs=input_1, outputs=output1)
      5 model.compile(optimizer = 'adam', loss = 'mean_squared_error',metrics = ['MAE'])
----> 6 model.fit( x_te_re, y_te, epochs = 1, batch_size = 10)

~\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\keras\engine\training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
   1098                 _r=1):
   1099               callbacks.on_train_batch_begin(step)
-> 1100               tmp_logs = self.train_function(iterator)
   1101               if data_handler.should_sync:
   1102                 context.async_wait()

~\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\eager\def_function.py in __call__(self, *args, **kwds)
    826     tracing_count = self.experimental_get_tracing_count()
    827     with trace.Trace(self._name) as tm:
--> 828       result = self._call(*args, **kwds)
    829       compiler = "xla" if self._experimental_compile else "nonXla"
    830       new_tracing_count = self.experimental_get_tracing_count()

~\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\eager\def_function.py in _call(self, *args, **kwds)
    886         # Lifting succeeded, so variables are initialized and we can run the
    887         # stateless function.
--> 888         return self._stateless_fn(*args, **kwds)
    889     else:
    890       _, _, _, filtered_flat_args = \

~\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\eager\function.py in __call__(self, *args, **kwargs)
   2941        filtered_flat_args) = self._maybe_define_function(args, kwargs)
   2942     return graph_function._call_flat(
-> 2943         filtered_flat_args, captured_inputs=graph_function.captured_inputs)  # pylint: disable=protected-access
   2944 
   2945   @property

~\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\eager\function.py in _call_flat(self, args, captured_inputs, cancellation_manager)
   1917       # No tape is watching; skip to running the function.
   1918       return self._build_call_outputs(self._inference_function.call(
-> 1919           ctx, args, cancellation_manager=cancellation_manager))
   1920     forward_backward = self._select_forward_and_backward_functions(
   1921         args,

~\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\eager\function.py in call(self, ctx, args, cancellation_manager)
    558               inputs=args,
    559               attrs=attrs,
--> 560               ctx=ctx)
    561         else:
    562           outputs = execute.execute_with_cancellation(

~\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\eager\execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
     58     ctx.ensure_initialized()
     59     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
---> 60                                         inputs, attrs, num_outputs)
     61   except core._NotOkStatusException as e:
     62     if name is not None:

UnimplementedError:  Cast string to float is not supported
     [[node mean_squared_error/Cast (defined at <ipython-input-9-c7d71b522ac7>:6) ]] [Op:__inference_train_function_6931]

Function call stack:
train_function

Help to rectify my code would be highly appreciated. Also, if anyone can help me add an ATTENTION layer to this model, I want to see which model performs better, with attention or without attention.

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  • $\begingroup$ You can't run a model on Strings, you need to encode it first.. $\endgroup$
    – Dan
    Oct 12, 2021 at 9:02
  • $\begingroup$ @Dan can you suggest a way, in which the string values are used as encoded values, i.e. string value -'10000', to be encoded as 10000 only? $\endgroup$ Oct 12, 2021 at 16:01
  • $\begingroup$ It really depends on what they represent how to encode it. At first look it seems to be a binary string, so maybe one-hot encoding? But if they represent some scalar value an ordinal encoding.. as said it really depends on what it is. $\endgroup$
    – Dan
    Oct 13, 2021 at 7:32
  • $\begingroup$ This is an encoded representation of the numbers of respective cell values, I had to convert to string because i want to retain the zeroes in MSB. But as soon as I convert it to an array or something, it removes all zeroes in the MSB. Thats why I need help in converting these values, so that they can be fed to an LSTM model with attention. $\endgroup$ Oct 13, 2021 at 13:52

1 Answer 1

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I guess you should try one-hot encoding of your features (but as explained in comments, it's a wild guess w/o knowing what it represent)

import pandas as pd
values = [['10101', '01000', '01001', '01000', '00110'], ['00111', '01001', '01000', '01000', '00110'], ['00010', '01001', '01001', '01000', '00110']]
df = pd.DataFrame(values, columns=['A','B','C','D','E'])

for i in range(0, 5):
    df['A' + str(i+1)] = df['A'].str[i]
    df['B' + str(i+1)] = df['B'].str[i]
    df['C' + str(i+1)] = df['C'].str[i]
    df['D' + str(i+1)] = df['D'].str[i]
    df['E' + str(i+1)] = df['E'].str[i]

df = df.drop(['A', 'B', 'C', 'D', 'E'], axis=1)
df

And here the result:

idx A1  B1  C1  D1  E1  A2  B2  C2  D2  E2  ... A4  B4  C4  D4  E4  A5  B5  C5  D5  E5
0   1   0   0   0   0   0   1   1   1   0   ... 0   0   0   0   1   1   0   1   0   0
1   0   0   0   0   0   0   1   1   1   0   ... 1   0   0   0   1   1   1   0   0   0
2   0   0   0   0   0   0   1   1   1   0   ... 1   0   0   0   1   0   1   1   0   0

Finally, you'll have to adjust the shape of the inputs and do the same for the output.

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  • $\begingroup$ one input feature is further broken down into sub features, and likewise for output. But then, how would this be fed to Attention module? Will it be a time series problem. Then it would be dependent upon the sequence of input features fed to the model. I'm not clear in this case! If you could further elaborate your explanation? $\endgroup$ Oct 18, 2021 at 12:35
  • $\begingroup$ As said, this was just a wild guess based on the fact that you stated you didn't want to lose the MSB zeros (suggesting that it contains some informative value).. If it's just a numerical value why not use it as ordinal? This is something only who has the feature knowledge can answer. If there is a numerical relationship between values, you shall use it as ordinal (integer). If there's not, than this answer apply. The reasoning is the same independently the net architecture. $\endgroup$
    – Dan
    Oct 18, 2021 at 12:53
  • $\begingroup$ Ok Thank you, would apply this method and see the results. $\endgroup$ Oct 18, 2021 at 13:03

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