fix the issue that keras_model.input_names is gone

henrysky · Jan 6, 2024 · 657044d · 657044d
1 parent 1e51ca1
commit 657044d
Show file tree

Hide file tree

Showing 3 changed files with 13 additions and 13 deletions.
diff --git a/astroNN/models/base_bayesian_cnn.py b/astroNN/models/base_bayesian_cnn.py
@@ -789,7 +789,7 @@ def predict(self, input_data, inputs_err=None, batch_size=None):
             inputs_err /= self.input_std["input"]
 
         # TODO: better way to handle named input
-        if "input_err" in self.keras_model.input_names:
+        if "input_err" in [i.name for i in self.keras_model.inputs]:
             input_data = {"input": input_data, "input_err": inputs_err}
         else:
             input_data = {"input": input_data}
@@ -1158,7 +1158,7 @@ def evaluate(
         norm_input_err = inputs_err / self.input_std["input"]
         norm_labels_err = labels_err / self.labels_std["output"]
 
-        if "input_err" in self.keras_model.input_names:
+        if "input_err" in [i.name for i in self.keras_model.inputs]:
             norm_data.update(
                 {"input_err": norm_input_err, "labels_err": norm_labels_err}
             )

diff --git a/astroNN/models/base_cnn.py b/astroNN/models/base_cnn.py
@@ -315,7 +315,7 @@ def pre_training_checklist_child(self, input_data, labels, sample_weight):
         ):  # only compile if there is no keras_model, e.g. fine-tuning does not required
             self.compile()
 
-        norm_data = self._tensor_dict_sanitize(norm_data, self.keras_model.input_names)
+        norm_data = self._tensor_dict_sanitize(norm_data, [i.name for i in self.keras_model.inputs])
         norm_labels = self._tensor_dict_sanitize(
             norm_labels, self.keras_model.output_names
         )
@@ -580,10 +580,10 @@ def predict(self, input_data):
             norm_data_remainder.update({name: input_array[name][data_gen_shape:]})
 
         norm_data_main = self._tensor_dict_sanitize(
-            norm_data_main, self.keras_model.input_names
+            norm_data_main, [i.name for i in self.keras_model.inputs]
         )
         norm_data_remainder = self._tensor_dict_sanitize(
-            norm_data_remainder, self.keras_model.input_names
+            norm_data_remainder, [i.name for i in self.keras_model.inputs]
         )
 
         # Data Generator for prediction
@@ -635,7 +635,7 @@ def evaluate(self, input_data, labels):
         :History: 2018-May-20 - Written - Henry Leung (University of Toronto)
         """
         self.has_model_check()
-        input_data = list_to_dict(self.keras_model.input_names, input_data)
+        input_data = list_to_dict([i.name for i in self.keras_model.inputs], input_data)
         labels = list_to_dict(self.keras_model.output_names, labels)
 
         # check if exists (existing means the model has already been trained (e.g. fine-tuning), so we do not need calculate mean/std again)
@@ -661,7 +661,7 @@ def evaluate(self, input_data, labels):
             norm_data = self.input_normalizer.normalize(input_data, calc=False)
             norm_labels = self.labels_normalizer.normalize(labels, calc=False)
 
-        norm_data = self._tensor_dict_sanitize(norm_data, self.keras_model.input_names)
+        norm_data = self._tensor_dict_sanitize(norm_data, [i.name for i in self.keras_model.inputs])
         norm_labels = self._tensor_dict_sanitize(
             norm_labels, self.keras_model.output_names
         )

diff --git a/astroNN/models/base_vae.py b/astroNN/models/base_vae.py
@@ -404,7 +404,7 @@ def pre_training_checklist_child(
         ):  # only compile if there is no keras_model, e.g. fine-tuning does not required
             self.compile()
 
-        norm_data = self._tensor_dict_sanitize(norm_data, self.keras_model.input_names)
+        norm_data = self._tensor_dict_sanitize(norm_data, [i.name for i in self.keras_model.inputs])
         norm_labels = self._tensor_dict_sanitize(
             norm_labels, self.keras_model.output_names
         )
@@ -564,7 +564,7 @@ def fit_on_batch(self, input_data, input_recon_target, sample_weight=None):
                 input_recon_target, calc=False
             )
 
-        norm_data = self._tensor_dict_sanitize(norm_data, self.keras_model.input_names)
+        norm_data = self._tensor_dict_sanitize(norm_data, [i.name for i in self.keras_model.inputs])
         norm_labels = self._tensor_dict_sanitize(
             norm_labels, self.keras_model.output_names
         )
@@ -670,10 +670,10 @@ def predict(self, input_data):
             norm_data_remainder.update({name: input_array[name][data_gen_shape:]})
 
         norm_data_main = self._tensor_dict_sanitize(
-            norm_data_main, self.keras_model.input_names
+            norm_data_main, [i.name for i in self.keras_model.inputs]
         )
         norm_data_remainder = self._tensor_dict_sanitize(
-            norm_data_remainder, self.keras_model.input_names
+            norm_data_remainder, [i.name for i in self.keras_model.inputs]
         )
 
         # Data Generator for prediction
@@ -951,7 +951,7 @@ def evaluate(self, input_data, labels):
         self.has_model_check()
         input_data = {"input": input_data}
         labels = {"output": labels}
-        input_data = list_to_dict(self.keras_model.input_names, input_data)
+        input_data = list_to_dict([i.name for i in self.keras_model.inputs], input_data)
         labels = list_to_dict(self.keras_model.output_names, labels)
 
         # check if exists (existing means the model has already been trained (e.g. fine-tuning), so we do not need calculate mean/std again)
@@ -977,7 +977,7 @@ def evaluate(self, input_data, labels):
             norm_data = self.input_normalizer.normalize(input_data, calc=False)
             norm_labels = self.labels_normalizer.normalize(labels, calc=False)
 
-        norm_data = self._tensor_dict_sanitize(norm_data, self.keras_model.input_names)
+        norm_data = self._tensor_dict_sanitize(norm_data, [i.name for i in self.keras_model.inputs])
         norm_labels = self._tensor_dict_sanitize(
             norm_labels, self.keras_model.output_names
         )