I am running Deep Occlusion Framework developed by pierrebaque. In the
RunUnaries files i am getting following error.
---------------------------------------------------------------------------TypeError
Traceback (most recent call
last)<ipython-input-4-4a99bb3ab8e4> in <module>() 1 import UnariesNet---->
2 uNet = UnariesNet.unariesNet()
/media/kirmani/New
Volume/Office/DeepOcclusion-master/DeepOcclusion-master/UnariesNet.py in
__init__(self, load_pretrained) 117 self.train_func =
theano.function(inputs=[X,t_rois,Ybb,In(p_drop, value=0.5)], 118
outputs=[T.exp(log_p_out),loss],
updates=updates_loss_VGG,--> 119
allow_input_downcast=True,on_unused_input='warn') 120 121
self.test_func = theano.function(inputs=[X,t_rois,Ybb,In(p_drop, value=0.0)],
/home/kirmani/.local/lib/python2.7/site-packages/theano/compile/function.pyc in
function(inputs, outputs, mode, updates, givens, no_default_updates,
accept_inplace, name, rebuild_strict, allow_input_downcast, profile,
on_unused_input) 318 on_unused_input=on_unused_input,
319 profile=profile,--> 320
output_keys=output_keys) 321 # We need to add the flag check_aliased
inputs if we have any mutable or 322 # borrowed used defined inputs
/home/kirmani/.local/lib/python2.7/site-packages/theano/compile/pfunc.pyc in
pfunc(params, outputs, mode, updates, givens, no_default_updates,
accept_inplace, name, rebuild_strict, allow_input_downcast, profile,
on_unused_input, output_keys) 440
rebuild_strict=rebuild_strict, 441
copy_inputs_over=True,--> 442
no_default_updates=no_default_updates) 443 # extracting the arguments
444 input_variables, cloned_extended_outputs, other_stuff = output_vars
/home/kirmani/.local/lib/python2.7/site-packages/theano/compile/pfunc.pyc in
rebuild_collect_shared(outputs, inputs, replace, updates, rebuild_strict,
copy_inputs_over, no_default_updates) 205 ' function
to remove broadcastable dimensions.') 206 --> 207 raise
TypeError(err_msg, err_sug) 208 assert update_val.type ==
store_into.type 209
TypeError: ('An update must have the same type as the original shared variable
(shared_var=<TensorType(float32, matrix)>, shared_var.type=TensorType(float32,
matrix), update_val=Elemwise{add,no_inplace}.0,
update_val.type=TensorType(float64, matrix)).', 'If the difference is related
to the broadcast pattern, you can call the tensor.unbroadcast(var,
axis_to_unbroadcast[, ...]) function to remove broadcastable dimensions.')
I am new to theano and any help regarding this issue is highly appreciated.
Thanks
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