Stable

CHANGELOG.md

@@ -1,3 +1,25 @@
 # Changelog
 
+## [0.1.2](https://github.com/brsynth/amn_library/tree/0.1.2) (2026-03-31)
+
+[Full Changelog](https://github.com/brsynth/amn_library/compare/0.1.1...0.1.2)
+
+## [0.1.1](https://github.com/brsynth/amn_library/tree/0.1.1) (2026-03-30)
+
+[Full Changelog](https://github.com/brsynth/amn_library/compare/0.1.0...0.1.1)
+
+**Merged pull requests:**
+
+- v0.1.1 [\#2](https://github.com/brsynth/amn_library/pull/2) ([ramez17khaled](https://github.com/ramez17khaled))
+
+## [0.1.0](https://github.com/brsynth/amn_library/tree/0.1.0) (2026-03-30)
+
+[Full Changelog](https://github.com/brsynth/amn_library/compare/df1d126ffc9bc50ceed7a9fc4d66d1fa7e41490f...0.1.0)
+
+**Merged pull requests:**
+
+- init amn [\#1](https://github.com/brsynth/amn_library/pull/1) ([ramez17khaled](https://github.com/ramez17khaled))
+
+
+
 \* *This Changelog was automatically generated by [github_changelog_generator](https://github.com/github-changelog-generator/github-changelog-generator)*

amn/Build_Model.py

@@ -19,8 +19,8 @@
 def sharp_sigmoid(x):
     # Custom activation function
     return K.sigmoid(10000.0 * x)
-from tensorflow.keras.utils import get_custom_objects, CustomObjectScope
-from tensorflow.keras.layers import Activation, Lambda
+from keras.utils import get_custom_objects, CustomObjectScope
+from keras.layers import Activation, Lambda
 get_custom_objects().update({'sharp_sigmoid': Activation(sharp_sigmoid)})
 
 def my_mse(y_true, y_pred):
@@ -55,7 +55,7 @@ def CROP(dimension, start, end):
     # Crops (or slices) a Tensor on a given dimension from start to end
     # example : to crop tensor x[:, :, 5:10]
     # call x = crop(2,5,10)(x) to slice the second dimension
-    from tensorflow.keras.layers import Lambda
+    from keras.layers import Lambda
     def func(x):
         if dimension == 0:
             return x[start: end]
@@ -165,9 +165,9 @@ def Loss_all(V, Vinko, Vout, parameter):
 # Dense model
 # ##############################################################################
 
-from tensorflow.keras.layers import Input, Dense, Dropout, Flatten, Activation, BatchNormalization
-from tensorflow.keras.layers import Reshape, multiply
-from tensorflow.keras.layers import concatenate, add, subtract, dot
+from keras.layers import Input, Dense, Dropout, Flatten, Activation, BatchNormalization
+from keras.layers import Reshape, multiply
+from keras.layers import concatenate, add, subtract, dot
 
 def input_ANN_Dense(parameter, verbose=False):
     # Shape X and Y depending on the model used
@@ -180,7 +180,7 @@ def input_ANN_Dense(parameter, verbose=False):
 
 def Dense_layers(inputs, parameter, trainable=True, verbose=False):
     # Build a dense architecture with some hidden layers
-    from tensorflow.keras.regularizers import l2
+    from keras.regularizers import l2
 
     activation=parameter.activation
     n_hidden=parameter.n_hidden
@@ -614,9 +614,9 @@ def train_evaluate_model(parameter, failure=0, temperature=0, verbose=False):
 # ##############################################################################
 
 from amn.Build_Dataset import TrainingSet
-from tensorflow.keras.models import load_model
-from tensorflow.keras.models import Model
-from tensorflow.keras.models import model_from_json
+from keras.models import load_model
+from keras.models import Model
+from keras.models import model_from_json
 
 from sklearn.metrics import r2_score 
 from sklearn.metrics import accuracy_score, f1_score, matthews_corrcoef
@@ -760,7 +760,7 @@ def save(self, filename, verbose=False):
             h.write(s)
         self.model.save(filemodel)
 
-    def load(self, filename, fileparam, filemodel, output_dim=-1, verbose=False):
+    def load(self, filename, fileparam=None, filemodel=None, output_dim=-1, verbose=False):
         if filename == "" or filename is None:
             fileparam = fileparam
             filemodel = filemodel

amn/_version.py

@@ -1 +1 @@
-__version__ = "0.1.0"
+__version__ = "0.1.3"

environment.yaml

@@ -1,10 +1,10 @@
-name: reservoir
+name: test_reservoir
 channels:
   - defaults
   - conda-forge
 
 dependencies:
-  - python =3.8  
+  - python =3.11 
   - numpy =1.23.5
   - pandas
   - matplotlib
@@ -16,6 +16,6 @@ dependencies:
   - typing_extensions =4.5.0
   - pip
   - ipykernel
-  - tensorflow =2.4.1
+  - tensorflow =2.12.1
   - tensorflow-probability
   - importlib-resources

setup.py

@@ -55,5 +55,5 @@ def get_version():
         'License :: OSI Approved :: MIT License',
         'Operating System :: OS Independent',
     ],
-    python_requires               = '>=3.8',
+    python_requires               = '>=3.11',
 )

tests/data/IJN1463EXP_AMN_QP_param.csv

@@ -1 +1 @@
-./Dataset_input/Putida/IJN1463EXP,AMN_QP,3,['BIOMASS_KT2440_WT3'],1,174,4038,1,500,sigmoid,500,accuracy_score,0.001,0.25,10,0,5
+/home/rkhaled/amn_library/amn/models/IJN1463EXP,AMN_QP,3,['BIOMASS_KT2440_WT3'],1,174,4038,1,500,relu,500,r2_score,0.001,0.25,10,0,5