I am using keras and using a layer output for some modifications. Before, using the output ( a tensor variable ) I am converting it to numpy array and thus calling eval() on it, as below:
def convert_output(orig_output): conv_output = invoke_modifications(orig_output.eval(), 8) The code fails with following error:
File "<ipython-input-11-df86946997d5>", line 1, in <module> orig_output.eval() File "C:\ENV\p34\lib\site-packages\theano-0.9.0.dev4-py3.4.egg\theano\gof\graph.py", line 516, in eval self._fn_cache[inputs] = theano.function(inputs, self) File "C:\ENV\p34\lib\site-packages\theano-0.9.0.dev4-py3.4.egg\theano\compile\function.py", line 326, in function output_keys=output_keys) File "C:\ENV\p34\lib\site-packages\theano-0.9.0.dev4-py3.4.egg\theano\compile\pfunc.py", line 486, in pfunc output_keys=output_keys) File "C:\ENV\p34\lib\site-packages\theano-0.9.0.dev4-py3.4.egg\theano\compile\function_module.py", line 1783, in orig_function output_keys=output_keys).create( File "C:\ENV\p34\lib\site-packages\theano-0.9.0.dev4-py3.4.egg\theano\compile\function_module.py", line 1437, in __init__ accept_inplace) File "C:\ENV\p34\lib\site-packages\theano-0.9.0.dev4-py3.4.egg\theano\compile\function_module.py", line 176, in std_fgraph update_mapping=update_mapping) File "C:\ENV\p34\lib\site-packages\theano-0.9.0.dev4-py3.4.egg\theano\gof\fg.py", line 180, in __init__ self.__import_r__(output, reason="init") File "C:\ENV\p34\lib\site-packages\theano-0.9.0.dev4-py3.4.egg\theano\gof\fg.py", line 351, in __import_r__ self.__import__(variable.owner, reason=reason) File "C:\ENV\p34\lib\site-packages\theano-0.9.0.dev4-py3.4.egg\theano\gof\fg.py", line 396, in __import__ variable=r) theano.gof.fg.MissingInputError: An input of the graph, used to compute InplaceDimShuffle{x,x,x,x}(keras_learning_phase), was not provided and not given a value.Use the Theano flag exception_verbosity='high',for more information on this error. Backtrace when the variable is created: File "C:\Users\kak7lr\AppData\Roaming\JetBrains\PyCharm Community Edition 2016.3.2\helpers\pydev\_pydev_bundle\pydev_monkey_qt.py", line 71, in patched_import return original_import(name, *args, **kwargs) File "<frozen importlib._bootstrap>", line 2237, in _find_and_load File "<frozen importlib._bootstrap>", line 2226, in _find_and_load_unlocked File "<frozen importlib._bootstrap>", line 1200, in _load_unlocked File "<frozen importlib._bootstrap>", line 1129, in _exec File "<frozen importlib._bootstrap>", line 1471, in exec_module File "<frozen importlib._bootstrap>", line 321, in _call_with_frames_removed File "C:\ENV\p34\lib\site-packages\keras\backend\theano_backend.py", line 23, in <module> _LEARNING_PHASE = T.scalar(dtype='uint8', name='keras_learning_phase') # 0 = test, 1 = train I intend to invoke a conversion function on the output of the previous layer). The conversion function takes the input as a tensor variable and do the computation. However, I also want to visualize the data and I need to compute bit_length on each element of it.
For eg If layer A gives an output Y1. This output is used by Lambda layer L1 and it invokes the conversion method. Sample code:
Y1 = layer A output Lambda( lambda x: conversion_method(x))(Y1) def conversion_method( input_tensor ): # do the conversion and also calc bit length calc_integer_bits( input_tensor ) def calc_integer_bits(X): noib_list = list() for pos, each in enumerate(X): in_range = int(round(abs(each .max() - each .min()))) bit_length = in_range.bit_length() noib_list.append(bit_length) return noib_list I used similar scheme for converting the layer weights using model.get_weights(). The get_weights() method returns numpy array list and thus could easily iterate on each element and calc the bit length. But while converting the output is an issue as the output is a tensor variable and when i call eval() on it, gives the error I mentioned in previous post. I hope I was able to clear my intentions.