Dataset is now located at: C:\Users\user\Documents\UCI HAR Dataset-20171205T222020Z-001\UCI HAR Dataset/
Some useful info to get an insight on dataset's shape and normalisation:
features shape, labels shape, each features mean, each features standard deviation
(2947, 128, 9) (2947, 6) 0.0991399 0.395671
the dataset is therefore properly normalised, as expected.
TIMES
Tensor("Slice_10:0", shape=(128, ?, 1), dtype=float32)
NEWX
Tensor("Slice_11:0", shape=(128, ?, 9), dtype=float32)
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-23-e98a56ad362c> in <module>()
    635     Y = tf.placeholder(tf.float32, [None, config.n_classes])
    636 
--> 637     pred_Y = LSTM_Network(X, config)
    638 
    639     # Loss,optimizer,evaluation

<ipython-input-23-e98a56ad362c> in LSTM_Network(_X, config)
    544     initial_states = [LSTMStateTuple(tf.zeros([config.batch_size, config.n_hidden], tf.float32), tf.zeros([config.batch_size, config.n_hidden], tf.float32)) for _ in range(config.n_inputs)]
    545     # Get PLSTM cell output
--> 546     outputs, states = multiPLSTM([lstm_cell_1, lstm_cell_2], _X, config.n_steps,config.n_inputs, initial_states)
    547     # lstm cell output before change - for stm moder
    548     #outputs, states = tf.contrib.rnn.static_rnn(lstm_cells, _X, dtype=tf.float32)

<ipython-input-23-e98a56ad362c> in multiPLSTM(cells, inputs, lens, n_input, initial_states)
    421 
    422     for k, cell, initial_state in zip(range(len(cells)), cells, initial_states):
--> 423         newX = tf.concat(2, [newX, times])
    424         with tf.variable_scope("{}".format(k)):
    425             outputs, initial_state = tf.nn.dynamic_rnn(cell, newX, dtype=tf.float32,

C:\Users\user\Anaconda3\envs\py35\lib\site-packages\tensorflow\python\ops\array_ops.py in concat(values, axis, name)
   1027       ops.convert_to_tensor(axis,
   1028                             name="concat_dim",
-> 1029                             dtype=dtypes.int32).get_shape(
   1030                             ).assert_is_compatible_with(tensor_shape.scalar())
   1031       return identity(values[0], name=scope)

C:\Users\user\Anaconda3\envs\py35\lib\site-packages\tensorflow\python\framework\ops.py in convert_to_tensor(value, dtype, name, preferred_dtype)
    637       name=name,
    638       preferred_dtype=preferred_dtype,
--> 639       as_ref=False)
    640 
    641 

C:\Users\user\Anaconda3\envs\py35\lib\site-packages\tensorflow\python\framework\ops.py in internal_convert_to_tensor(value, dtype, name, as_ref, preferred_dtype)
    702 
    703         if ret is None:
--> 704           ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
    705 
    706         if ret is NotImplemented:

C:\Users\user\Anaconda3\envs\py35\lib\site-packages\tensorflow\python\framework\constant_op.py in _constant_tensor_conversion_function(v, dtype, name, as_ref)
    111                                          as_ref=False):
    112   _ = as_ref
--> 113   return constant(v, dtype=dtype, name=name)
    114 
    115 

C:\Users\user\Anaconda3\envs\py35\lib\site-packages\tensorflow\python\framework\constant_op.py in constant(value, dtype, shape, name, verify_shape)
    100   tensor_value = attr_value_pb2.AttrValue()
    101   tensor_value.tensor.CopyFrom(
--> 102       tensor_util.make_tensor_proto(value, dtype=dtype, shape=shape, verify_shape=verify_shape))
    103   dtype_value = attr_value_pb2.AttrValue(type=tensor_value.tensor.dtype)
    104   const_tensor = g.create_op(

C:\Users\user\Anaconda3\envs\py35\lib\site-packages\tensorflow\python\framework\tensor_util.py in make_tensor_proto(values, dtype, shape, verify_shape)
    368       nparray = np.empty(shape, dtype=np_dt)
    369     else:
--> 370       _AssertCompatible(values, dtype)
    371       nparray = np.array(values, dtype=np_dt)
    372       # check to them.

C:\Users\user\Anaconda3\envs\py35\lib\site-packages\tensorflow\python\framework\tensor_util.py in _AssertCompatible(values, dtype)
    300     else:
    301       raise TypeError("Expected %s, got %s of type '%s' instead." %
--> 302                       (dtype.name, repr(mismatch), type(mismatch).__name__))
    303 
    304 

TypeError: Expected int32, got list containing Tensors of type '_Message' instead.

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