Tensorflow Sonar Training

.gitignore

@@ -15,5 +15,7 @@ etc/
 share/
 .DS_Store
 env/
-simulations/
-log.txt
+# simulations/
+simpylc/
+accessories/
+env/Scripts

install/install_requirements.sh

@@ -18,48 +18,15 @@ case "${os}" in
     *)          machine="UNKNOWN:${os}"
 esac
 
-function install_with_conda {
-  if (! command -v "conda" &> /dev/null ) then
-    echo "Install Miniconda first\n" && exit 0
-  fi
-
-  conda install --yes -c conda-forge \
-    beautifulsoup4 \
-    py-cpuinfo \
-    jupyter_core \
-    jupyterlab \
-    keras \
-    Keras-Preprocessing \
-    lidar \
-    matplotlib-base \
-    numexpr \
-    pandas \
-    Pillow \
-    pyopengl \
-    pytables \
-    scikit-image \
-    scikit-learn \
-    scipy \
-    seaborn \
-    selenium \
-    statsmodels \
-    sympy \
-    tensorflow \
-    tensorboard-plugin-wit \
-    tensorflow-estimator \
-    widgetsnbextension
-}
-
 # Install all required libraries t
 function install_with_pip {
   # Upgrade pip
-  python3 -m pip install --upgrade pip
+  python -m pip install --upgrade pip
 
   # Install setuptools
-  python3 -m pip install setuptools
-  python3 -m pip install --no-cache-dir -r install/pip/requirements.txt
+  python -m pip install setuptools
+  python -m pip install --no-cache-dir -r install/pip/requirements.txt
 
 }
 
-install_with_conda
 install_with_pip 

install/pip/requirements.txt

@@ -1,6 +1,17 @@
+jupyterlab
+matplotlib
 matplotlib-venn
 numpy
+Pillow
 ptpython
 scipy
 SimPyLC==3.10.0
-sonar
+sonar
+numexpr
+pandas
+keras
+Keras-Preprocessing
+scikit-image
+scikit-learn
+ptpython
+

log.txt

@@ -0,0 +1 @@
+error during connect: This error may indicate that the docker daemon is not running.: Get "http://%2F%2F.%2Fpipe%2Fdocker_engine/v1.24/containers/python-ai-jupyter/json": open //./pipe/docker_engine: The system cannot find the file specified.

manual_car/1. run-car-sim.sh

@@ -0,0 +1,4 @@
+#!/usr/bin/env bash
+
+export LIBGL_ALLOW_SOFTWARE=1
+python3 simpylc/simulations/car_manual_flex_track/world.py

manual_car/2. run-manual-client.sh

@@ -0,0 +1,4 @@
+#!/usr/bin/env bash
+
+cd simpylc/simulations/car_manual_flex_track/control_client/
+python3 manual_client.py

norm_sonar_01_model/keras_metadata.pb

@@ -0,0 +1,10 @@
+
+�/root"_tf_keras_sequential*�.{"name": "sequential", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": false, "class_name": "Sequential", "config": {"name": "sequential", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, null]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "normalization_input"}}, {"class_name": "Normalization", "config": {"name": "normalization", "trainable": true, "batch_input_shape": {"class_name": "__tuple__", "items": [null, null]}, "dtype": "float32", "axis": {"class_name": "__tuple__", "items": [-1]}, "mean": null, "variance": null}}, {"class_name": "InputLayer", "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 3]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "input_1"}}, {"class_name": "Dense", 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"beta_2": 0.9990000128746033, "epsilon": 1e-07, "amsgrad": false}}}}2

+�
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+�root.layer_with_weights-1"_tf_keras_layer*�{"name": "dense", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": true, "class_name": "Dense", "config": {"name": "dense", "trainable": true, "dtype": "float32", "units": 16, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 3}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 4}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 5, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 3}}, "shared_object_id": 17}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 3]}}2

+�root.layer_with_weights-2"_tf_keras_layer*�{"name": "dense_1", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": true, "class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "dtype": "float32", "units": 16, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 6}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 7}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 8, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 16}}, "shared_object_id": 18}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 16]}}2

+�root.layer_with_weights-3"_tf_keras_layer*�{"name": "dense_2", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": true, "class_name": "Dense", "config": {"name": "dense_2", "trainable": true, "dtype": "float32", "units": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 9}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 10}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 11, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 16}}, "shared_object_id": 19}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 16]}}2

+�
[root.keras_api.metrics.0"_tf_keras_metric*�
{"class_name": "Mean", "name": "loss", "dtype": "float32", "config": {"name": "loss", "dtype": "float32"}, "shared_object_id": 20}2

+�
\root.keras_api.metrics.1"_tf_keras_metric*�
{"class_name": "MeanMetricWrapper", "name": "accuracy", "dtype": "float32", "config": {"name": "accuracy", "dtype": "float32", "fn": "binary_accuracy"}, "shared_object_id": 15}2

+�
]root.keras_api.metrics.2"_tf_keras_metric*�
{"class_name": "MeanMetricWrapper", "name": "mae", "dtype": "float32", "config": {"name": "mae", "dtype": "float32", "fn": "mean_absolute_error"}, "shared_object_id": 16}2

notebooks/csv/marslander.csv

@@ -0,0 +1,2 @@
+length,width,weight,deckHeight,robotArmLength,numberOfSolarPanels,scienceInstruments,image
+6,1.56,360,"(83, 108)",1.8,2,"('seismometer', 'heat probe', 'radio science experiment')",../pics/mars.nasa.jpg

notebooks/opdrachten/challenge/matrix.py

@@ -0,0 +1,63 @@
+#!/usr/bin/env python
+
+# CHALLENGE:
+#   Fix nethod multiply
+#   Implement size_of
+
+class Matrix:
+
+    def __init__(self, vectorList):
+        self.vectorList = vectorList
+
+    def __add__(self, matrix2):
+        vectorList2 = matrix2.vectorList
+        vectorList3 = [self.addVectors(v1, v2) for (v1, v2) in zip(vectorList1, vectorList2)]
+        return Matrix(vectorList3)
+
+    def addVectors(self, v1, v2):
+        if len(v1) != len(v2):
+            return None
+        else:
+            v3 = [sum(tup) for tup in zip(v1, v2)]
+            return v3
+
+    # Challenge: Needs to be corrected and finished
+    def multiply(self, matrix2):
+        vectorList2 = matrix2.vectorList
+        vectorColumn2 = list()
+
+        for i in range(0, len(vectorList2)):
+            vectorColumn2.append(vectorList2[i])
+
+        print(vectorColumn2)
+
+        #  columns of matrix2
+
+        vectorList3 = [self.multiplyVectors(v1, v2) for (v1, v2) in zip(vectorList1, vectorColumn2)]
+        return Matrix(vectorList3)
+
+    def multiplyVectors(self, v1, v2):
+        if len(v1) != len(v2):
+            return None
+        else:
+            v3 = [a*b for (a, b) in zip(v1, v2)]
+            return v3
+
+    # Challenge: Overwrite this dunder method
+    # This method should return a tuple containing the Matrix dimensions
+    def __sizeof__(self):
+        pass
+
+    def __str__(self):
+        return f"{self.vectorList}"
+
+vectorList1 = ([1, 1, 1], [1, 1, 1])
+vectorList2 = ([1, 2, 3], [4, 5, 6])
+
+matrix1 = Matrix(vectorList1)
+matrix2 = Matrix(vectorList2)
+matrix3 = matrix1 + matrix2
+matrix4 = matrix1.multiply(matrix2)
+
+print(matrix4)
+# print(f"Matrix1 {matrix1} + Matrix2 {matrix2} = Matrix3 {matrix3}")

notebooks/opdrachten/examples/employee/employee.py

@@ -0,0 +1,47 @@
+# Encapsulation / Information hiding
+class Salary:
+    def __init__(self, pay):
+        self.pay = pay
+
+    def get_total(self):
+        return (self.pay * 12)
+
+class Employee:
+    # Constructor method with Arguments
+    # def __init__(self, firstName, lastName, pay, bonus):
+    def __init__(self, **kwargs):
+        self.firstname = kwargs['firstName']
+        self.lastname = kwargs['lastName']
+        self.pay = kwargs['pay']
+        self.bonus = kwargs['bonus']
+        # Composition
+        self.obj_salary = Salary(self.pay)
+
+    def getFullname(self):
+        return '{} {}'.format(self.firstname, self.lastname)
+
+    @property
+    def fullname(self):
+        return '{} {}'.format(self.firstname, self.lastname)
+
+    # Docstring
+    """
+    This function calculates and
+    returns the annual salary
+    """
+    @property
+    def annualSalary(self):
+        return self.obj_salary.get_total() + self.bonus
+
+    def main():
+        #  Object
+        employee1 = Employee(firstName="Marc", lastName="Rotsaert", pay=6000, bonus=500)
+        employee2 = Employee(firstName="Anton", lastName="Diepenhorst", pay=4500, bonus=1000)
+        print(f"Annual salary of {employee1.getFullname()} : {employee1.annualSalary}")
+        print(f"Annual salary of {employee2.getFullname()} : {employee2.annualSalary}")
+        print(f"__name__: {__name__}")
+
+    if __name__ == "main":
+        main()
+
+Employee.main()

notebooks/opdrachten/examples/employee/matrix.py

@@ -0,0 +1,69 @@
+#!/usr/bin/env python
+
+class Matrix:
+
+    def __init__(self, vectorList):
+        self.vectorList = vectorList
+
+    def __add__(self, matrix2):
+        vectorList2 = matrix2.vectorList
+        vectorList3 = [self.addVectors(v1, v2) for (v1, v2) in zip(vectorList1, vectorList2)]
+        return Matrix(vectorList3)
+
+    def addVectors(self, v1, v2):
+        if len(v1) != len(v2):
+            return None
+        else:
+            v3 = [sum(tup) for tup in zip(v1, v2)]
+            return v3
+
+    def multiply(self, matrix2):
+        vectorList2 = matrix2.vectorList
+        vectorColumn2 = list()
+
+        for i in range(0, len(vectorList2)):
+            vectorColumn2.append(vectorList2[i])
+
+        print(vectorColumn2)
+
+        #  columns of matrix2
+
+        vectorList3 = [self.multiplyVectors(v1, v2) for (v1, v2) in zip(vectorList1, vectorColumn2)]
+        return Matrix(vectorList3)
+
+    def multiplyVectors(self, v1, v2):
+        if len(v1) != len(v2):
+            return None
+        else:
+            v3 = [a*b for (a, b) in zip(v1, v2)]
+            return v3
+
+    def __str__(self):
+        return f"{self.vectorList}"
+
+# vectorList1 = ([1, 1, 1], [1, 1, 1])
+# vectorList2 = ([1, 2, 3], [4, 5, 6])
+
+
+
+# print(matrix4)
+# print(f"Matrix1 {matrix1} + Matrix2 {matrix2} = Matrix3 {matrix3}")
+
+    def main():
+        #  Object
+        vectorList1 = Matrix([1, 1, 1], [1, 1, 1])
+        vectorList2 = Matrix([1, 2, 3], [4, 5, 6])
+
+        print(matrix4)
+        print(f"Matrix1 {matrix1} + Matrix2 {matrix2} = Matrix3 {matrix3}")
+        print(f"__name__: {__name__}")
+
+    if __name__ == "main":
+        main()
+
+matrix1 = Matrix(vectorList1)
+matrix2 = Matrix(vectorList2)
+matrix3 = matrix1 + matrix2
+matrix4 = matrix1.multiply(matrix2)
+
+Matrix.main()

notebooks/opdrachten/examples/employee/matrix_jeroen.py

@@ -0,0 +1,53 @@
+#!/usr/bin/env python
+
+class Matrix:
+
+    def __init__(self, vectorList):
+        self.vectorList = vectorList
+
+    def __add__(self, matrix2):
+        vectorList2 = matrix2.vectorList
+        vectorList3 = [self.addVectors(v1, v2) for (v1, v2) in zip(vectorList1, vectorList2)]
+        return Matrix(vectorList3)
+
+    def addVectors(self, v1, v2):
+        if len(v1) != len(v2):
+            return None
+        else:
+            v3 = [sum(tup) for tup in zip(v1, v2)]
+            return v3
+
+    def multiply(self, matrix2):
+        vectorList2 = matrix2.vectorList
+        vectorColumn2 = list()
+
+        for i in range(0, len(vectorList2)):
+            vectorColumn2.append(vectorList2[i])
+
+        print(vectorColumn2)
+
+        #  columns of matrix2
+
+        vectorList3 = [self.multiplyVectors(v1, v2) for (v1, v2) in zip(vectorList1, vectorColumn2)]
+        return Matrix(vectorList3)
+
+    def multiplyVectors(self, v1, v2):
+        if len(v1) != len(v2):
+            return None
+        else:
+            v3 = [a*b for (a, b) in zip(v1, v2)]
+            return v3
+
+    def __str__(self):
+        return f"{self.vectorList}"
+
+vectorList1 = ([1, 1, 1], [1, 1, 1])
+vectorList2 = ([1, 2, 3], [4, 5, 6])
+
+matrix1 = Matrix(vectorList1)
+matrix2 = Matrix(vectorList2)
+matrix3 = matrix1 + matrix2
+matrix4 = matrix1.multiply(matrix2)
+
+print(matrix4)
+print(f"Matrix1 {matrix1} + Matrix2 {matrix2} = Matrix3 {matrix3}")