From 2a9a5d84b7e904a27b37149c90bb2494e4fd18a5 Mon Sep 17 00:00:00 2001
From: leufen1 <l.leufen@fz-juelich.de>
Date: Tue, 22 Feb 2022 09:20:29 +0100
Subject: [PATCH] corrected input variable
---
mlair/model_modules/branched_input_networks.py | 12 +++++++-----
1 file changed, 7 insertions(+), 5 deletions(-)
diff --git a/mlair/model_modules/branched_input_networks.py b/mlair/model_modules/branched_input_networks.py
index 51bef4f9..4345d48b 100644
--- a/mlair/model_modules/branched_input_networks.py
+++ b/mlair/model_modules/branched_input_networks.py
@@ -8,7 +8,7 @@ from mlair.model_modules.loss import var_loss
from mlair.model_modules.recurrent_networks import RNN
-class BranchInputRNN(RNN): # pragma: no cover
+class BranchedInputRNN(RNN): # pragma: no cover
"""A recurrent neural network with multiple input branches."""
def __init__(self, input_shape, output_shape, *args, **kwargs):
@@ -37,7 +37,9 @@ class BranchInputRNN(RNN): # pragma: no cover
for branch in range(len(self._input_shape)):
shape_b = self._input_shape[branch]
x_input_b = keras.layers.Input(shape=shape_b)
- x_in_b = keras.layers.Reshape((shape_b[0], reduce((lambda x, y: x * y), shape_b[1:])))(x_input_b)
+ x_input.append(x_input_b)
+ x_in_b = keras.layers.Reshape((shape_b[0], reduce((lambda x, y: x * y), shape_b[1:])),
+ name=f"reshape_branch{branch + 1}")(x_input_b)
for layer, n_hidden in enumerate(conf):
return_sequences = (layer < len(conf) - 1)
@@ -69,7 +71,7 @@ class BranchInputRNN(RNN): # pragma: no cover
x_concat = self.dropout(self.dropout_rate)(x_concat)
x_concat = keras.layers.Dense(self._output_shape)(x_concat)
- out = self.activation_output(name=f"{self.activation_output_name}_output")(x_in)
+ out = self.activation_output(name=f"{self.activation_output_name}_output")(x_concat)
self.model = keras.Model(inputs=x_input, outputs=[out])
print(self.model.summary())
@@ -78,8 +80,8 @@ class BranchInputRNN(RNN): # pragma: no cover
"metrics": ["mse", "mae", var_loss]}
def _update_model_name(self, rnn_type):
- # n_input = str(reduce(lambda x, y: x * y, self._input_shape))
- n_input = f"{len(self._input_shape)}x{str(reduce(lambda x, y: x * y, self._input_shape[0]))}"
+ n_input = f"{len(self._input_shape)}x{self._input_shape[0][0]}x" \
+ f"{str(reduce(lambda x, y: x * y, self._input_shape[0][1:]))}"
n_output = str(self._output_shape)
self.model_name = rnn_type.upper()
if isinstance(self.layer_configuration, tuple) and len(self.layer_configuration) == 2:
--
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