def create_pattern_reconstruct_fn(layers,
patterns,
patterns_flipped,
return_all=False,
enforce_positivity_after_relu=True,
enforce_positivity_everywhere=False):
""" Attempts to reconstruct given input from specific layer
by first computing feature representation..."""
assert patterns_flipped == False, "Otherwise implement it"
inputs = create_suitable_theano_input_var(layers[-1])
outputs = lasagne.layers.get_output(layers[-1],
deterministic=True,
input_var=inputs,
inputs=inputs)
n_patterns = len([l for l in layers if hasattr(l, 'W')])
deconved_var = pattern_deconv(
outputs,
inputs,
layers,
patterns[:n_patterns],
patterns_flipped=patterns_flipped,
return_all=return_all,
enforce_positivity_after_relu=enforce_positivity_after_relu,
enforce_positivity_everywhere=enforce_positivity_everywhere)
pattern_deconv_fn = theano.function([inputs], deconved_var)
return pattern_deconv_fn
def create_pattern_deconv_fn(layers,
patterns,
patterns_flipped,
return_all=False,
enforce_positivity_after_relu=True,
enforce_positivity_everywhere=False):
assert patterns_flipped == False, "Otherwise implement it"
inputs = create_suitable_theano_input_var(layers[-1])
outputs = create_suitable_theano_output_var(layers[-1])
n_patterns = len([l for l in layers if hasattr(l, 'W')])
deconved_var = pattern_deconv(
outputs,
inputs,
layers,
patterns[:n_patterns],
patterns_flipped=patterns_flipped,
return_all=return_all,
enforce_positivity_after_relu=enforce_positivity_after_relu,
enforce_positivity_everywhere=enforce_positivity_everywhere)
try:
pattern_deconv_fn = theano.function([outputs, inputs], deconved_var)
except UnusedInputError:
pattern_deconv_fn = theano.function([outputs], deconved_var)
return pattern_deconv_fn
def create_pattern_reconstruct_fn(layers, patterns, patterns_flipped,
return_all=False,
enforce_positivity_after_relu=True,
enforce_positivity_everywhere=False):
""" Attempts to reconstruct given input from specific layer
by first computing feature representation..."""
assert patterns_flipped == False, "Otherwise implement it"
inputs = create_suitable_theano_input_var(layers[-1])
outputs = lasagne.layers.get_output(layers[-1],
deterministic=True, input_var=inputs, inputs=inputs)
n_patterns = len([l for l in layers if hasattr(l, 'W')])
deconved_var = pattern_deconv(outputs, inputs, layers, patterns[:n_patterns],
patterns_flipped=patterns_flipped,
return_all=return_all,
enforce_positivity_after_relu=enforce_positivity_after_relu,
enforce_positivity_everywhere=enforce_positivity_everywhere)
pattern_deconv_fn = theano.function([inputs], deconved_var)
return pattern_deconv_fn
def create_pattern_deconv_fn(layers, patterns, patterns_flipped,
return_all=False,
enforce_positivity_after_relu=True,
enforce_positivity_everywhere=False):
assert patterns_flipped == False, "Otherwise implement it"
inputs = create_suitable_theano_input_var(layers[-1])
outputs = create_suitable_theano_output_var(layers[-1])
n_patterns = len([l for l in layers if hasattr(l, 'W')])
deconved_var = pattern_deconv(outputs, inputs, layers, patterns[:n_patterns],
patterns_flipped=patterns_flipped,
return_all=return_all,
enforce_positivity_after_relu=enforce_positivity_after_relu,
enforce_positivity_everywhere=enforce_positivity_everywhere)
try:
pattern_deconv_fn = theano.function([outputs, inputs], deconved_var)
except UnusedInputError:
pattern_deconv_fn = theano.function([outputs], deconved_var)
return pattern_deconv_fn
