Padding needed when using other data to support odd number of input data

[maystroh]

First of all, I would like to thank you to publish the code of the interesting study you made. I'm trying to do something similar with PPG signals so I tried your code on my data but I got an issue:

Traceback (most recent call last):
  File "ecg/train.py", line 117, in <module>
    train(args, params)
  File "ecg/train.py", line 103, in train
    callbacks=[checkpointer, reduce_lr, stopping])
  File "/usr/local/lib/python2.7/dist-packages/keras/engine/training.py", line 1039, in fit
    validation_steps=validation_steps)
  File "/usr/local/lib/python2.7/dist-packages/keras/engine/training_arrays.py", line 199, in fit_loop
    outs = f(ins_batch)
  File "/usr/local/lib/python2.7/dist-packages/keras/backend/tensorflow_backend.py", line 2715, in __call__
    return self._call(inputs)
  File "/usr/local/lib/python2.7/dist-packages/keras/backend/tensorflow_backend.py", line 2675, in _call
    fetched = self._callable_fn(*array_vals)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1439, in __call__
    run_metadata_ptr)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/errors_impl.py", line 528, in __exit__
    c_api.TF_GetCode(self.status.status))
tensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [16,15437,32] vs. [16,15438,32]
	 [[{{node add_4/add}} = Add[T=DT_FLOAT, _class=["loc:@training/Adam/gradients/add_4/add_grad/Reshape_1"], _device="/job:localhost/replica:0/task:0/device:GPU:0"](max_pooling1d_4/Squeeze, conv1d_9/add)]]
	 [[{{node loss/mul/_1985}} = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_20932_loss/mul", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]
srun: error: cn09: task 0: Exited with exit code 1

I've modified the line 62 in network.py in order to pad the inputs of the pooling layer

shortcut = MaxPooling1D(pool_size=subsample_length, padding='same')(layer)

This solved the issue.