sciann-v0.7.0.0

Author: ehsanhaghighat

SciANN.egg-info/PKG-INFO

@@ -1,12 +1,11 @@
 Metadata-Version: 2.1
 Name: SciANN
-Version: 0.6.8.6
+Version: 0.7.0.0
 Summary: A Keras/Tensorflow wrapper for scientific computations and physics-informed deep learning using artificial neural networks.
 Home-page: https://github.com/sciann/sciann
 Author: Ehsan Haghighat
 Author-email: [email protected]
 License: MIT
-Platform: UNKNOWN
 Classifier: Development Status :: 4 - Beta
 Classifier: Intended Audience :: Developers
 Classifier: Intended Audience :: Education
@@ -30,5 +29,3 @@ Read the documentation at: https://sciann.com
 
 SciANN is compatible with Python 2.7-3.6
 and is distributed under the MIT license.
-
-

SciANN.egg-info/SOURCES.txt

@@ -69,7 +69,6 @@ docs/theme/css/theme_extra.css
 docs/theme/js/jquery-2.1.1.min.js
 docs/theme/js/modernizr-2.8.3.min.js
 docs/theme/js/theme.js
-examples/.DS_Store
 examples/example-fitting-1d-lstm.py
 examples/example-fitting-1d-rnn.py
 examples/example-fitting-1d.py

SciANN.egg-info/requires.txt

@@ -3,9 +3,8 @@ scipy
 six
 pyyaml
 h5py
-sklearn
+scikit-learn
 pybtex
-tensorflow<=2.9.5,>=2.6.0
 
 [tests]
 pytest

install_sciann_macos_arm.sh

@@ -0,0 +1,20 @@
+
+export ENV_NAME=tf29
+export TF_VER=2.9.0
+export PY_VER=3.9
+
+# source ~/.miniconda_zshrc
+
+conda create -n $ENV_NAME python=$PY_VER -y
+conda activate $ENV_NAME
+conda install -c apple tensorflow-deps=$TF_VER -y
+pip install tensorflow-macos==$TF_VER
+
+# pip install tensorflow-metal
+
+conda install -c conda-forge -y pandas jupyterlab scipy scikit-learn matplotlib 
+pip install plotly
+pip install -e .
+
+cd examples
+python example-fitting-1d.py 

sciann/__init__.py

@@ -41,7 +41,7 @@
 __credits__ = []
 __url__ = "http://github.com/sciann/sciann]"
 __license__ = "MIT"
-__version__ = "0.6.8.6"
+__version__ = "0.7.0.0"
 __cite__ = \
     '@article{haghighat2021sciann, \n' +\
     '    title={SciANN: A Keras/TensorFlow wrapper for scientific computations and physics-informed deep learning using artificial neural networks}, \n' +\

sciann/functionals/functional.py

@@ -5,11 +5,13 @@
 from __future__ import division
 from __future__ import print_function
 
+import time
 from keras import backend as K
 graph_unique_name = K.get_graph().unique_name
 
 from keras.layers import Dense
 from keras.layers import Activation
+from keras.layers import Dropout
 from keras.layers import Concatenate
 from keras.layers import Lambda
 # from keras.layers import BatchNormalization
@@ -46,6 +48,9 @@
         Last layer will have a linear output.
     output_activation: defaulted to "linear".
         Activation function to be applied to the network output.
+    dropout_rate: Float.
+        Dropout rate for the hidden layers. Active only if > 0.
+        NOTE: Do not use dropout for PINNs.
     res_net: (True, False). Constructs a resnet architecture.
         Defaulted to False.
     kernel_initializer: Initializer of the `Kernel`, from `k.initializers`.
@@ -68,6 +73,7 @@ def Functional(
         hidden_layers=None,
         activation="tanh",
         output_activation="linear",
+        dropout_rate=0.,
         res_net=False,
         kernel_initializer=None,
         bias_initializer=None,
@@ -116,6 +122,11 @@ def Functional(
         )
     else:
         bias_initializer = [bias_initializer for l in len(hidden_layers) * [activation] + [output_activation]]
+    # check dropout rate.
+    if dropout_rate > 0.0:
+        print("\n NOTE: Dropout layer does not work with PINN setup!!! \n ")
+        time.sleep(1)
+
     # prepare regularizers.
     kernel_regularizer = default_regularizer(kernel_regularizer)
     bias_regularizer = default_regularizer(bias_regularizer)
@@ -222,6 +233,11 @@ def Functional(
         if res_net is True:
             layer = Lambda(lambda xs: (1-xs[0])*xs[1] + xs[0]*xs[2], name=graph_unique_name("ResLayer"))
             net[-1] = layer([net[-1]] + res_outputs[:2])
+        # adding dropout at the end of each layer.
+        if dropout_rate > 0.0:
+            layer = Dropout(dropout_rate, name=graph_unique_name("Dropout"))
+            layers.append(layer)
+            net[-1] = layer(net[-1])
 
     # Assign to the output variable
     if len(net) == 1:

setup.py

@@ -15,7 +15,7 @@
 
 setup(
     name='SciANN',
-    version='0.6.8.6',
+    version='0.7.0.0',
     description='A Keras/Tensorflow wrapper for scientific computations and physics-informed deep learning using artificial neural networks.',
     long_description=long_description,
     author='Ehsan Haghighat',
@@ -27,9 +27,9 @@
                       'six',
                       'pyyaml',
                       'h5py',
-                      'sklearn',
+                      'scikit-learn',
                       'pybtex',
-                      'tensorflow>=2.6.0,<=2.9.5',
+                      #'tensorflow>=2.6.0,<=2.9.5',
                       ],
     extras_require={
           'visualize': ['pydot>=1.2.4', 'matplotlib'],