Merge branch 'development' into example-simple

Author: pgleeson

.github/workflows/ci.yml

@@ -69,7 +69,10 @@ jobs:
           pip list
 
     - name: Install graphviz
-      uses: ts-graphviz/setup-graphviz@v1
+      uses: ts-graphviz/setup-graphviz@v2
+      with:
+        # Skip to run brew update command on macOS.
+        macos-skip-brew-update: 'true' # default false
 
     - name: Test interface ACT-R
       run: |
@@ -85,7 +88,6 @@ jobs:
         python -m pytest -v -m "neuroml" tests/
 
     - name: Test interface TensorFlow
-      if: ${{ matrix.python-version != '3.11'}}
       run: |
         python -m pip install .[tensorflow]
         dot -V

.gitignore

@@ -308,3 +308,5 @@ examples/TensorFlow/Keras/keras_to_MDF
 /examples/TensorFlow/Keras/MNIST/keras_to_MDF.1
 /examples/TensorFlow/Keras/IRIS/keras_to_MDF.1
 /checkout_pngs.sh
+/examples/TensorFlow/Keras/IRIS/keras_model_on_iris.keras
+/examples/TensorFlow/Keras/MNIST/kr_N_model.keras

examples/TensorFlow/Keras/IRIS/keras_model.py

@@ -82,7 +82,7 @@
 
 
 # Saving model in h5 for Ploting Nuetron visual
-model.save("keras_model_on_iris.h5")
+model.save("keras_model_on_iris.keras")
 
 
 # predict example for index 0

examples/TensorFlow/Keras/IRIS/keras_model_on_iris.h5

@@ -11,8 +11,10 @@
 from modelspec.utils import _val_info
 from modeci_mdf.execution_engine import EvaluableGraph
 
+exec(open("./keras_model.py").read())
+
 # load the keras model
-model = tf.keras.models.load_model("keras_model_on_iris.h5")
+# model = tf.keras.models.load_model("keras_model_on_iris.keras")
 
 # get the test data from iris dataset
 iris = load_iris()

examples/TensorFlow/Keras/IRIS/keras_to_MDF.py

@@ -35,47 +35,47 @@
 
 # Build Model
 print("Building the Model")
-kr_model = tf.keras.models.Sequential()
-kr_model.add(tf.keras.layers.Flatten())  # input layer
+model = tf.keras.models.Sequential()
+model.add(tf.keras.layers.Flatten())  # input layer
 
-kr_model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))  # hidden layers
-kr_model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))
-kr_model.add(tf.keras.layers.Dense(10, activation=tf.nn.softmax))  # output layer
+model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))  # hidden layers
+model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))
+model.add(tf.keras.layers.Dense(10, activation=tf.nn.softmax))  # output layer
 
 # Compile Model
 print("Compiling the model")
-kr_model.compile(
+model.compile(
     optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"]
 )
 
 # train the model
 print("Training the Model")
-kr_model.fit(x_train, y_train, epochs=3)
+model.fit(x_train, y_train, epochs=3)
 
 # loadthe model above
 # new_model = tf.keras.models.load_model("num_reader.model")
 
 
 # check if the model actually generalize the data.
 print("Accuracy of our model is:")
-val_loss, val_acc = kr_model.evaluate(x_test, y_test)
+val_loss, val_acc = model.evaluate(x_test, y_test)
 print(val_loss, val_acc)
 
 # Print summary
-kr_model.summary()
+model.summary()
 
 # plot the model
 print("Plotting the model")
-plot_model(kr_model, to_file="model_plot.png", show_shapes=True, show_layer_names=True)
+plot_model(model, to_file="model_plot.png", show_shapes=True, show_layer_names=True)
 
 
 # Saving model in h5 for Ploting Nuetron visual
-kr_model.save("kr_N_model.h5")
+model.save("kr_N_model.keras")
 
 
 # predict example for index 0
 print("Predict value at index 0:")
-predictions = kr_model.predict([x_test])
+predictions = model.predict([x_test])
 print("The predicted number at index 0 is", np.argmax(predictions[0]))
 
 # print the actual value at that index

examples/TensorFlow/Keras/MNIST/keras_model.py

@@ -1,12 +1,16 @@
+import sys
+
 import numpy as np
 import tensorflow as tf
 
 from modeci_mdf.interfaces.keras import keras_to_mdf
 from modelspec.utils import _val_info
 from modeci_mdf.execution_engine import EvaluableGraph
 
+exec(open("./keras_model.py").read())
+
 # load the keras model
-model = tf.keras.models.load_model("kr_N_model.h5")
+# model = tf.keras.models.load_model("kr_N_model.keras")
 
 # get 20 of the test images from the mnnist test dataset
 _, (x_test, y_test) = tf.keras.datasets.mnist.load_data()