lab 1

.idea/.gitignore

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+# Default ignored files
+/shelf/
+/workspace.xml
+# Editor-based HTTP Client requests
+/httpRequests/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml

.idea/code.iml

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+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$">
+      <excludeFolder url="file://$MODULE_DIR$/.venv" />
+    </content>
+    <orderEntry type="jdk" jdkName="Python 3.13 virtualenv at ~/Disk-D/KPI/Semester_7/neural_networks/code/.venv" jdkType="Python SDK" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

.idea/inspectionProfiles/profiles_settings.xml

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+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/misc.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.13 virtualenv at ~/Disk-D/KPI/Semester_7/neural_networks/code/.venv" project-jdk-type="Python SDK" />
+</project>

.idea/modules.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/code.iml" filepath="$PROJECT_DIR$/.idea/code.iml" />
+    </modules>
+  </component>
+</project>

.idea/vcs.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="$PROJECT_DIR$" vcs="Git" />
+  </component>
+</project>

lab_1.py

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+import numpy as np
+from tensorflow.keras import Sequential, Input, layers, optimizers
+
+x = np.array([[0, 0, 0], [0, 0, 1], [0, 1, 0], [1, 0, 0], [0, 1, 1], [1, 0, 1], [1, 1, 0], [1, 1, 1]])
+y = np.array([0, 1, 1, 1, 0, 0, 0, 1])
+
+model = Sequential([
+    Input(shape=(3,)),
+    layers.Dense(3, activation="tanh"),
+    layers.Dense(1, activation="sigmoid"),
+])
+
+model.compile(optimizer=optimizers.Adam(learning_rate=0.05), loss="binary_crossentropy", metrics=["accuracy"])
+model.fit(x, y, epochs=100)
+
+loss, accuracy = model.evaluate(x, y)
+print(f"Loss: {loss}")
+print(f"Accuracy: {accuracy}")
+
+prediction = model.predict(x)
+for inp, pred in zip(x, prediction):
+    print(inp, round(pred[0]))