From c4c88456a72f162777a5f9deba4e6a5457cc4cab Mon Sep 17 00:00:00 2001
From: Nikolai <osterei33@gmx.de>
Date: Tue, 21 Aug 2018 08:31:46 +0200
Subject: [PATCH] single neuron output works with multiple inputs
---
scripts/plot_NN_2D.py | 6 +++++-
utils.py | 13 ++++++++++---
2 files changed, 15 insertions(+), 4 deletions(-)
diff --git a/scripts/plot_NN_2D.py b/scripts/plot_NN_2D.py
index 65b7faf..112c1bf 100755
--- a/scripts/plot_NN_2D.py
+++ b/scripts/plot_NN_2D.py
@@ -126,11 +126,15 @@ if args.mode.startswith("mean"):
logscale=args.log, only_pixels=(not args.contour)
)
else:
+ if hasattr(c, "get_input_list"):
+ transform_function = lambda inp : c.get_input_list(c.scaler.transform(inp))
+ else:
+ transform_function = c.scaler.transform(inp)
plot_NN_vs_var_2D_all(
args.output_filename,
means=means,
model=c.model,
- transform_function=c.scaler.transform,
+ transform_function=transform_function,
varx_index=varx_index,
vary_index=vary_index,
xmin=varx_range[0], xmax=varx_range[1], nbinsx=varx_range[2],
diff --git a/utils.py b/utils.py
index 316eba9..daac2da 100644
--- a/utils.py
+++ b/utils.py
@@ -15,14 +15,21 @@ logger.addHandler(logging.NullHandler())
def get_single_neuron_function(model, layer, neuron, scaler=None, input_transform=None):
- f = K.function([model.input]+[K.learning_phase()], [model.layers[layer].output[:,neuron]])
+ inp = model.input
+ if not isinstance(inp, list):
+ inp = [inp]
+
+ f = K.function(inp+[K.learning_phase()], [model.layers[layer].output[:,neuron]])
def eval_single_neuron(x):
+ x_eval = x
if scaler is not None:
x_eval = scaler.transform(x)
+ if input_transform is not None:
+ x_eval = input_transform(x_eval)
else:
- x_eval = x
- return f([x_eval])[0]
+ x_eval = [x_eval]
+ return f(x_eval)[0]
return eval_single_neuron
--
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