{ "cells": [ { "cell_type": "markdown", "id": "53b766a3-8e54-40b0-92ba-12fcbe9025ec", "metadata": {}, "source": [ "# Praxisbeispiel - Bildklassifikation mit CNNs\n", "\n", "## Ziel:\n", "Einführung in Convolutional Neural Networks mit TensorFlow/Keras anhand eines Bildklassifikationsproblems.\n" ] }, { "cell_type": "markdown", "id": "d4dee2c8-8af5-4492-ae60-87e3181697b6", "metadata": {}, "source": [ "## 1. Bibliotheken laden" ] }, { "cell_type": "code", "execution_count": 1, "id": "bcb16094-dffa-4960-be59-37041ef1cd94", "metadata": { "execution": { "iopub.execute_input": "2026-03-24T17:24:25.394188Z", "iopub.status.busy": "2026-03-24T17:24:25.393908Z", "iopub.status.idle": "2026-03-24T17:24:50.802366Z", "shell.execute_reply": "2026-03-24T17:24:50.801820Z", "shell.execute_reply.started": "2026-03-24T17:24:25.394167Z" } }, "outputs": [], "source": [ "import tensorflow as tf\n", "from tensorflow.keras import layers, models\n", "from tensorflow.keras.datasets import mnist\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "id": "d0f3b447-e147-4440-9d57-510303f0b327", "metadata": {}, "source": [ "## 2. Daten laden" ] }, { "cell_type": "code", "execution_count": 2, "id": "722a1383-267a-4526-8832-721336a47f04", "metadata": { "execution": { "iopub.execute_input": "2026-03-24T17:24:50.803083Z", "iopub.status.busy": "2026-03-24T17:24:50.802862Z", "iopub.status.idle": "2026-03-24T17:24:50.873718Z", "shell.execute_reply": "2026-03-24T17:24:50.873236Z", "shell.execute_reply.started": "2026-03-24T17:24:50.803069Z" } }, "outputs": [], "source": [ "(x_train, y_train), (x_test, y_test) = mnist.load_data()" ] }, { "cell_type": "markdown", "id": "468189a4-a053-418e-b880-9ae50e5b5de0", "metadata": {}, "source": [ "## 3. Daten einsehen" ] }, { "cell_type": "code", "execution_count": 3, "id": "ccf8205f-d371-455b-8d92-e2ebf407f635", "metadata": { "execution": { "iopub.execute_input": "2026-03-24T17:24:50.874043Z", "iopub.status.busy": "2026-03-24T17:24:50.873960Z", "iopub.status.idle": "2026-03-24T17:24:50.877721Z", "shell.execute_reply": "2026-03-24T17:24:50.877046Z", "shell.execute_reply.started": "2026-03-24T17:24:50.874035Z" }, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "array([[[0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " ...,\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0]],\n", "\n", " [[0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " ...,\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0]],\n", "\n", " [[0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " ...,\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0]],\n", "\n", " ...,\n", "\n", " [[0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " ...,\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0]],\n", "\n", " [[0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " ...,\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0]],\n", "\n", " [[0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " ...,\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0]]], shape=(60000, 28, 28), dtype=uint8)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x_train" ] }, { "cell_type": "markdown", "id": "d990992b-150d-4473-af09-0a366ef7cff0", "metadata": {}, "source": [ "## 4. Daten vorbereiten (Data Preprocessing)" ] }, { "cell_type": "markdown", "id": "620c9750-9f53-4071-be66-05a251ab8a4d", "metadata": {}, "source": [ "### Normalisierung\n", "\n", "Normalisieren von Bilderdaten ist wiederum anders als von numerischen Daten. \n", "\n", "Der Grund für die Normalisierung der Bilder(daten) ist die Vermeidung der Möglichkeit von explodierenden Gradienten aufgrund des großen Pixelbereichs [0, 255] und die Verbesserung der Konvergenzgeschwindigkeit. \n", "Daher kann entweder \n", "1. man jedes Bild normalisieren, so dass der Pixelbereich sich in [-1, 1] befindet \n", "oder\n", "2. man teilt jeden Wert durch den maximalen Pixelwert, d.h. 255, so dass der Bereich der Pixel im Bereich [0, 1] liegt.\n", "\n", "Ein weiterer Grund für die Normalisierung von Bilddaten ist wenn man Transfer Learning verwendet. \n", "Wenn z. B. ein bereits trainiertes Modell verwendet wird, das mit Bildern trainiert wurde, deren Pixel im Bereich [0, 1] liegen, sollte man sicherstellen, dass die neuen Werte, die man dem Modell liefert, im gleichen Bereich liegen. Andernfalls werden die Ergebnisse verfälscht werden." ] }, { "cell_type": "code", "execution_count": 4, "id": "6bcbf900-e870-4dbb-ab9f-1de80f2377a0", "metadata": { "execution": { "iopub.execute_input": "2026-03-24T17:24:58.714606Z", "iopub.status.busy": "2026-03-24T17:24:58.714059Z", "iopub.status.idle": "2026-03-24T17:24:58.799017Z", "shell.execute_reply": "2026-03-24T17:24:58.798270Z", "shell.execute_reply.started": "2026-03-24T17:24:58.714564Z" } }, "outputs": [], "source": [ "# Normalisierung\n", "x_train, x_test = x_train / 255.0, x_test / 255.0 " ] }, { "cell_type": "markdown", "id": "07dee44a-37e2-4dd6-a6ae-9b8bcb7c4bf3", "metadata": {}, "source": [ "### Dimension erweitern\n", "\n", "Zuden existierenden Dimensionen der Bilddaten fügen wir eine neue Dimension hinzu. \n", "Diese neue Dimension stellt die Anzahl der in den Daten vorhandenen Kanäle dar.\n", "\n", "Bei Farbbildern wären dies 3 Kanäle, die den roten, grünen und blauen Kanal darstellen. \n", "In diesem Fall handelt es sich um Schwarz-Weiß-Bilder, so dass nur 1 Kanal vorhanden ist." ] }, { "cell_type": "code", "execution_count": 5, "id": "54f9b079-3d31-4b6e-bcda-54f9b525c6f0", "metadata": { "execution": { "iopub.execute_input": "2026-03-24T17:25:00.917007Z", "iopub.status.busy": "2026-03-24T17:25:00.916727Z", "iopub.status.idle": "2026-03-24T17:25:00.919951Z", "shell.execute_reply": "2026-03-24T17:25:00.919150Z", "shell.execute_reply.started": "2026-03-24T17:25:00.916980Z" } }, "outputs": [], "source": [ "# Dimension erweitern\n", "x_train = x_train[..., tf.newaxis] \n", "x_test = x_test[..., tf.newaxis]" ] }, { "cell_type": "markdown", "id": "614c8498-fdb3-41ca-807c-79c395860466", "metadata": {}, "source": [ "## 5. Modell definieren" ] }, { "cell_type": "markdown", "id": "d0c0b7e5-dd65-45a3-a53d-843b89266edb", "metadata": {}, "source": [ "CNN Layers:\n", "- Conv2D:\n", "\n", "\n", " Die am häufigsten verwendete Art der Faltung ist die 2D-Faltungsschicht und wird üblicherweise als conv2D abgekürzt. Ein Filter oder ein Kernel in einer conv2D-Schicht „gleitet“ über die 2D-Eingangsdaten und führt eine elementweise Multiplikation durch. Das Ergebnis ist die Summierung der Ergebnisse zu einem einzigen Ausgabepixel.\n", " \n", " Parameter bei der Erstellung einer Conv2D Schicht:\n", "\n", " 1. 32: Anzahl von Filtern in dieser Convolution-Schicht. Hierfür wird immer empfohlen, Potenzen von 2 als Werte zu verwenden.\n", " 2. (3, 3): bestimmt die Dimensionen des Kernels. Übliche Abmessungen sind 1×1, 3×3, 5×5 oder 7×7, entsprechend als (1, 1), (3, 3), (5, 5) oder (7, 7)-Tupel übergeben.\n", " Es muss hier eine ganze Zahl oder ein Tupel/Liste von 2 ganzen Zahlen, die die Höhe und Breite des 2D-Faltungsfensters angeben. Zudem muss dieser Parameter eine ungerade ganze Zahl sein.\n", " 4. activation=\"..\": gibt den Namen der Aktivierungsfunktion an, die nach der Faltung/convolution verwendet werden soll. (siehe unten)\n", " \n", " \n", "- MaxPooling2D (more details: https://www.geeksforgeeks.org/cnn-introduction-to-pooling-layer/?ref=header_outind) \n", "\n", " Die Pooling-Schicht wird in CNNs verwendet, um die räumlichen Dimensionen (Breite und Höhe) der eingegebenen Merkmalskarten zu reduzieren und gleichzeitig die wichtigsten Informationen beizubehalten. Dabei wird ein zweidimensionaler Filter über jeden Kanal einer Merkmalskarte gezogen und die Merkmale innerhalb des vom Filter abgedeckten Bereichs zusammengefasst.\n", "\n", " Zudem hilft es die Dimensionalität zu verringern, da Pooling-Schichten die räumliche Größe der Feature-Matrix reduzieren, somit die Anzahl der Parameter und Berechnungen im Network verringert wird. So wird das Modell schneller und effizienter. Außerdem trägt die Reduzierung der räumlichen Dimensionen dazu bei, Overfitting zu verhindern. \n", "\n", "- Flatten:\n", "\n", "\n", " Eine flache Schicht des neuronalen Netzes wird verwendet, um die mehrdimensionale Ausgabe der vorhergehenden Schicht in ein eindimensionales Feld umzuwandeln, bevor sie zur weiteren Verarbeitung in eine vollständig verbundene Schicht (dense layers) eingespeist wird.\n", "\n", " Zudem reduziert es die Dimension in den Daten und vereinfacht die Modellarchitektur.\n", "\n", " \n", "- Dense:\n", " Die Dense Schicht ist eine vollständig verbundene Schicht." ] }, { "cell_type": "markdown", "id": "5aa5e504-e04f-4907-8677-4ce8335d992a", "metadata": {}, "source": [ "### (typische) Aktivierungsfunktionen \n", "- relu:\n", " \n", " Die ReLU-Aktivierungsfunktion wird verwendet, um Nichtlinearität in ein neuronales Netz einzuführen. Sie trägt dazu bei, das Problem des verschwindenden Gradienten beim Training von Modellen des maschinellen Lernens zu entschärfen, und ermöglicht es neuronalen Netzen, komplexere Beziehungen in Daten zu lernen.\n", " Wenn eine Modelleingabe positiv ist, gibt die ReLU-Funktion denselben Wert aus. Wenn eine Modelleingabe negativ ist, gibt die ReLU-Funktion den Wert Null aus.\n", "\n", "- softmax:\n", "\n", " Die Softmax-Funktion, die häufig in der letzten Schicht eines neuronalen Netzmodells für Klassifizierungsaufgaben verwendet wird, wandelt rohe Ausgabeergebnisse - auch als Logits bekannt - in Wahrscheinlichkeiten um, indem sie den Exponentialwert jeder Ausgabe nimmt und diese Werte normalisiert, indem sie durch die Summe aller Exponentialwerte dividiert wird.\n", "\n", " https://botpenguin.com/glossary/softmax-function" ] }, { "cell_type": "code", "execution_count": 6, "id": "169980df-2df4-42af-b2b9-eb2cf15671dc", "metadata": { "execution": { "iopub.execute_input": "2026-03-24T17:25:06.577378Z", "iopub.status.busy": "2026-03-24T17:25:06.576964Z", "iopub.status.idle": "2026-03-24T17:25:06.623104Z", "shell.execute_reply": "2026-03-24T17:25:06.622704Z", "shell.execute_reply.started": "2026-03-24T17:25:06.577347Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/veit/cusy/trn/ai-tutorial/.venv/lib/python3.13/site-packages/keras/src/layers/convolutional/base_conv.py:113: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", " super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n" ] } ], "source": [ "# Modell definieren\n", "model = models.Sequential(\n", " [\n", " layers.Conv2D(32, (3, 3), activation=\"relu\", input_shape=(28, 28, 1)),\n", " layers.MaxPooling2D((2, 2)),\n", " layers.Conv2D(64, (3, 3), activation=\"relu\"),\n", " layers.MaxPooling2D((2, 2)),\n", " layers.Conv2D(64, (3, 3), activation=\"relu\"),\n", " layers.Flatten(),\n", " layers.Dense(64, activation=\"relu\"),\n", " layers.Dense(10, activation=\"softmax\"),\n", " ]\n", ")" ] }, { "cell_type": "markdown", "id": "0dcef3d2-9b6e-4c35-b0af-24816e1fefac", "metadata": {}, "source": [ "Gute Visualisierungen solch ähnlicher Struktur können hier gefunden werden:\n", "\n", "https://miro.medium.com/v2/resize:fit:1400/format:webp/1*vkQ0hXDaQv57sALXAJquxA.jpeg\n", "\n", "https://miro.medium.com/v2/resize:fit:1400/format:webp/1*uAeANQIOQPqWZnnuH-VEyw.jpeg\n", "\n", "(Credits: Sumit Saha, Towards Data Science — „A Comprehensive Guide to Convolutional Neural Networks — the ELI5 way“; Artikel auf Medium, Link im Build ausgelassen da Medium oft 403 zurückgibt.)" ] }, { "cell_type": "markdown", "id": "cc005536-b5d2-43a4-a27a-195b9fc0a6ff", "metadata": {}, "source": [ "## 6. Modell kompilieren" ] }, { "cell_type": "code", "execution_count": 7, "id": "0af77c33-c547-47b5-8549-f77169a4585d", "metadata": { "execution": { "iopub.execute_input": "2026-03-24T17:25:13.767467Z", "iopub.status.busy": "2026-03-24T17:25:13.767200Z", "iopub.status.idle": "2026-03-24T17:25:13.779669Z", "shell.execute_reply": "2026-03-24T17:25:13.778897Z", "shell.execute_reply.started": "2026-03-24T17:25:13.767448Z" } }, "outputs": [], "source": [ "# Modell kompilieren\n", "model.compile(\n", " optimizer=\"adam\",\n", " loss=\"sparse_categorical_crossentropy\",\n", " metrics=[\"accuracy\"],\n", ")" ] }, { "cell_type": "markdown", "id": "322f7757-10d8-48b6-8b6c-89bdd6f83d3f", "metadata": {}, "source": [ "## 7. Training" ] }, { "cell_type": "code", "execution_count": 8, "id": "159676e6-736b-473b-93a0-1cc4e759a9f6", "metadata": { "execution": { "iopub.execute_input": "2026-03-24T17:25:15.551533Z", "iopub.status.busy": "2026-03-24T17:25:15.551261Z", "iopub.status.idle": "2026-03-24T17:26:24.189539Z", "shell.execute_reply": "2026-03-24T17:26:24.188796Z", "shell.execute_reply.started": "2026-03-24T17:25:15.551514Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/5\n", "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 6ms/step - accuracy: 0.9534 - loss: 0.1507 - val_accuracy: 0.9856 - val_loss: 0.0488\n", "Epoch 2/5\n", "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 7ms/step - accuracy: 0.9859 - loss: 0.0463 - val_accuracy: 0.9840 - val_loss: 0.0533\n", "Epoch 3/5\n", "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 7ms/step - accuracy: 0.9890 - loss: 0.0352 - val_accuracy: 0.9899 - val_loss: 0.0327\n", "Epoch 4/5\n", "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 8ms/step - accuracy: 0.9917 - loss: 0.0267 - val_accuracy: 0.9890 - val_loss: 0.0365\n", "Epoch 5/5\n", "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 8ms/step - accuracy: 0.9933 - loss: 0.0206 - val_accuracy: 0.9913 - val_loss: 0.0293\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.fit(x_train, y_train, epochs=5, validation_data=(x_test, y_test))" ] }, { "cell_type": "code", "execution_count": 9, "id": "54e16a32-c8c8-46c5-89a8-d9ed94151f50", "metadata": { "execution": { "iopub.execute_input": "2026-03-24T17:26:39.574434Z", "iopub.status.busy": "2026-03-24T17:26:39.573963Z", "iopub.status.idle": "2026-03-24T17:26:39.603088Z", "shell.execute_reply": "2026-03-24T17:26:39.602446Z", "shell.execute_reply.started": "2026-03-24T17:26:39.574412Z" } }, "outputs": [ { "data": { "text/plain": [ "[array([[[[-2.20920473e-01, -7.94491619e-02, -6.55768160e-03,\n", " -1.67344511e-01, 4.06573564e-02, 9.16692317e-02,\n", " 1.59453467e-01, 7.33715445e-02, 5.15761822e-02,\n", " 1.51928384e-02, 2.35505372e-01, 4.93809208e-02,\n", " 1.42657220e-01, 2.56727114e-02, 1.32331820e-02,\n", " -2.01917097e-01, -4.89609018e-02, -1.42563239e-01,\n", " -6.69733211e-02, 2.49209125e-02, -7.21121654e-02,\n", " -1.09157324e-01, -8.19490664e-03, 1.74524680e-01,\n", " -1.00255124e-01, -1.63895801e-01, -3.22987698e-02,\n", " 7.25202933e-02, -7.89696723e-02, -7.60411248e-02,\n", " 1.13907091e-01, 8.21158662e-02]],\n", " \n", " [[ 5.29988930e-02, -3.60815167e-01, 8.38052407e-02,\n", " 8.62759426e-02, -2.92696562e-02, 1.95439041e-01,\n", " -8.02631304e-02, 3.64616476e-02, -5.28480746e-02,\n", " 9.72734243e-02, 3.02234530e-01, 6.07382506e-02,\n", " -4.71719094e-02, -2.30713248e-01, 1.07276186e-01,\n", " -9.69349965e-02, 1.92033976e-01, 1.25463903e-01,\n", " -5.52276522e-02, -2.13665634e-01, -2.00767651e-01,\n", " 1.63289726e-01, -2.85120636e-01, 1.77466944e-02,\n", " -2.83153430e-02, 5.96216954e-02, 1.53643191e-01,\n", " 5.43419598e-03, -2.71694232e-02, 1.95470929e-01,\n", " 2.06592754e-01, 1.70671437e-02]],\n", " \n", " [[-5.47746487e-04, -1.36313200e-01, 8.81105885e-02,\n", " -2.55618989e-02, 7.11751878e-02, -6.61918297e-02,\n", " -1.64077580e-01, -3.78607780e-01, 4.54623550e-02,\n", " -2.05549300e-01, -1.88396767e-01, -1.82072908e-01,\n", " -1.70547172e-01, -3.04500729e-01, 2.47008756e-01,\n", " 2.53357351e-01, -7.01055750e-02, 6.28316775e-02,\n", " 1.77779451e-01, -1.61328502e-02, -3.20997864e-01,\n", " 2.01325834e-01, -2.91512072e-01, -2.32901767e-01,\n", " 2.49616206e-02, 1.62868239e-02, -5.37151694e-02,\n", " 1.03772536e-01, 1.63174868e-01, -6.53485805e-02,\n", " 2.14372754e-01, -2.71023810e-01]]],\n", " \n", " \n", " [[[ 1.42177984e-01, -2.17010319e-01, 9.53710154e-02,\n", " -4.18881699e-02, 1.02515344e-03, -2.59148449e-01,\n", " 1.81316257e-01, -9.85033810e-02, 2.48357803e-02,\n", " 2.43994445e-02, 2.73050010e-01, -3.21224742e-02,\n", " 2.15805799e-01, -1.41091710e-02, -1.48583993e-01,\n", " -4.66286957e-01, -2.22558990e-01, 1.09945469e-01,\n", " -1.88796729e-01, 7.46641532e-02, 1.89557299e-01,\n", " 1.98169306e-01, 5.76510653e-02, 5.60208224e-02,\n", " 1.07446268e-01, -2.59754658e-01, 1.21596009e-01,\n", " 1.59706790e-02, 1.14251323e-01, 1.59909436e-03,\n", " 8.54656473e-02, -9.69819427e-02]],\n", " \n", " [[ 1.88999146e-01, 7.98606724e-02, 1.16631072e-02,\n", " -3.39207388e-02, -3.54416929e-02, 8.63766763e-03,\n", " -2.72432286e-02, -6.11493349e-01, 1.58249393e-01,\n", " 2.14425549e-01, -1.93805862e-02, 9.79106128e-02,\n", " 9.72954091e-03, 1.18725277e-01, 1.04483915e-02,\n", " 1.14888199e-01, 2.30073556e-01, 4.49464247e-02,\n", " -1.67520523e-01, -2.00613514e-02, 1.89994350e-01,\n", " -9.05582309e-03, 1.67186335e-02, -1.18125580e-01,\n", " 1.98961824e-01, 2.28117947e-02, -5.56683308e-03,\n", " 8.84333551e-02, 2.03834504e-01, 1.20300345e-01,\n", " 9.64103267e-02, 1.91511348e-01]],\n", " \n", " [[ 1.11986905e-01, 2.57325739e-01, -1.25424592e-02,\n", " 7.45124668e-02, 5.58233149e-02, -3.95383984e-02,\n", " -2.58078814e-01, -6.42040223e-02, 5.15178069e-02,\n", " 1.18728228e-01, -4.18353975e-01, 1.07019544e-01,\n", " -3.48158747e-01, -1.88075006e-01, 2.58432478e-01,\n", " 2.29934245e-01, -9.77288261e-02, 9.79964361e-02,\n", " -1.46402325e-02, -8.91939923e-02, 1.14863291e-01,\n", " -8.29333961e-02, -1.08123459e-01, -3.71893406e-01,\n", " 9.89398956e-02, 1.76147595e-01, 5.95058911e-02,\n", " -6.12055920e-02, 5.77863082e-02, -8.17916244e-02,\n", " 3.68913338e-02, 1.32307917e-01]]],\n", " \n", " \n", " [[[ 6.95364773e-02, 2.51678437e-01, -7.89162815e-02,\n", " 7.30204210e-02, -5.05873002e-02, -2.11244464e-01,\n", " 2.47483812e-02, -5.00733912e-01, -1.59897149e-01,\n", " -1.66043520e-01, 3.22126932e-02, -1.77697733e-03,\n", " 1.44355744e-01, 1.34088621e-01, -3.73311490e-01,\n", " -1.74173132e-01, -2.68429250e-01, -3.05692144e-02,\n", " -3.95541161e-01, 1.52650297e-01, -7.71371648e-02,\n", " -1.06454082e-01, 1.21975549e-01, 2.92527050e-01,\n", " 1.42879114e-01, 4.06415462e-02, 1.45763099e-01,\n", " -2.76064485e-01, 1.98648381e-03, -3.79160307e-02,\n", " -3.14531267e-01, -4.84688580e-02]],\n", " \n", " [[-1.32999852e-01, 1.15969032e-01, 8.20346400e-02,\n", " 1.49580285e-01, 8.36936533e-02, -2.40755174e-02,\n", " 2.23825455e-01, 2.18706429e-02, -2.20397890e-01,\n", " -1.25085041e-01, -4.10221338e-01, 1.22000620e-01,\n", " 1.70271412e-01, 1.73176065e-01, -3.11257094e-01,\n", " -1.08548351e-01, 2.13634506e-01, 1.22949667e-01,\n", " 1.02562025e-01, 5.71810305e-02, 2.48645842e-02,\n", " -1.72762349e-01, 2.07926556e-01, 2.27537408e-01,\n", " -7.71648884e-02, 8.04939717e-02, 8.10799748e-02,\n", " -5.40046021e-02, -1.46931589e-01, 1.57796115e-01,\n", " -3.49180013e-01, -9.06167030e-02]],\n", " \n", " [[-1.37200013e-01, 1.06969319e-01, -6.87700957e-02,\n", " 5.88214435e-02, -2.52333209e-02, 5.31174615e-02,\n", " -2.06401169e-01, 3.80136132e-01, -1.18226081e-01,\n", " 5.33868819e-02, -9.06113386e-02, 7.10080052e-03,\n", " -1.97398961e-01, 1.79805905e-01, -9.28899422e-02,\n", " 1.52331367e-01, -1.22799454e-02, 3.64275798e-02,\n", " 1.87574059e-01, -1.57145098e-01, 1.05322644e-01,\n", " -1.85443774e-01, 2.74148762e-01, -4.11142455e-03,\n", " -1.98894858e-01, 1.44984841e-01, -6.28811195e-02,\n", " 9.43158865e-02, -2.57780373e-01, 2.70449929e-02,\n", " -1.70540676e-01, 1.29828125e-01]]]], dtype=float32),\n", " array([-0.11964628, -0.00853754, -0.12067217, -0.14822854, -0.12620844,\n", " -0.06389564, -0.0206668 , 0.10146309, -0.01160986, -0.0636593 ,\n", " 0.01471836, -0.14386651, -0.01522872, -0.01808114, 0.03797244,\n", " -0.01213757, -0.10286602, -0.15156734, -0.03810443, -0.05511363,\n", " -0.02217144, -0.01239665, -0.01430051, -0.02510639, -0.11962832,\n", " -0.06226835, -0.11643692, -0.08747373, -0.07341461, -0.13219371,\n", " -0.00681282, -0.0792897 ], dtype=float32),\n", " array([[[[-2.87141770e-01, 2.72438042e-02, -5.11086211e-02, ...,\n", " -2.40444839e-02, 1.01456687e-01, 8.34921598e-02],\n", " [ 9.85200480e-02, -3.03475372e-02, -1.06990263e-01, ...,\n", " 1.24072209e-02, 1.46683957e-03, 6.44328520e-02],\n", " [-3.54707181e-01, -8.89599137e-03, 2.07795147e-02, ...,\n", " -4.01142761e-02, 9.51791555e-02, -4.14183103e-02],\n", " ...,\n", " [-2.02539966e-01, -3.63311879e-02, -2.17840932e-02, ...,\n", " -6.80495948e-02, 1.22527994e-01, 1.30075544e-01],\n", " [-1.31371379e-01, 7.45408759e-02, 1.43486951e-02, ...,\n", " -1.47490846e-02, -1.23880312e-01, -2.19554886e-01],\n", " [-2.69234836e-01, 4.33345251e-02, 6.68406114e-02, ...,\n", " -1.35258496e-01, 3.69792357e-02, -2.93387752e-02]],\n", " \n", " [[-1.70327663e-01, -7.54242241e-02, 8.91283154e-03, ...,\n", " 4.74838354e-03, 5.11242747e-02, 7.47668967e-02],\n", " [-7.14420602e-02, -1.40910763e-02, -6.69435337e-02, ...,\n", " 1.48552790e-01, -5.73910326e-02, -8.31986070e-02],\n", " [-1.86255619e-01, -8.67929980e-02, -1.16934348e-02, ...,\n", " -3.16584222e-02, -5.45332991e-02, -1.20528033e-02],\n", " ...,\n", " [-1.63636878e-01, 1.20767683e-01, 1.70605853e-02, ...,\n", " -1.79877669e-01, -7.97864422e-02, 5.68156652e-02],\n", " [ 4.56921235e-02, -2.06424311e-01, 1.15143880e-01, ...,\n", " 6.30126074e-02, 2.74168272e-02, -1.09053887e-01],\n", " [-1.16048634e-01, 8.29766542e-02, -2.95195766e-02, ...,\n", " -7.05948025e-02, -3.92884351e-02, -3.66110057e-02]],\n", " \n", " [[-2.18459725e-01, -1.14658348e-01, -2.64537372e-02, ...,\n", " 3.04673240e-02, -7.59111866e-02, 7.08029866e-02],\n", " [-2.48813793e-01, 7.48770386e-02, 3.39205116e-02, ...,\n", " 4.24497165e-02, -1.24660566e-01, -1.75484538e-01],\n", " [ 2.22609541e-03, -8.50358829e-02, -5.62276132e-03, ...,\n", " 9.36959088e-02, -2.45707426e-02, 5.13431355e-02],\n", " ...,\n", " [-8.24836642e-02, 6.77229790e-03, -9.19606239e-02, ...,\n", " 3.59022468e-02, -5.44607081e-02, 7.43014291e-02],\n", " [ 1.80594563e-01, -1.15248241e-01, 8.66105705e-02, ...,\n", " -1.86142817e-01, 5.24160229e-02, -2.89593667e-01],\n", " [-1.07689261e-01, -2.31931537e-01, -1.13854095e-01, ...,\n", " -7.43491426e-02, 2.18279902e-02, -8.69931057e-02]]],\n", " \n", " \n", " [[[ 1.15748502e-01, 5.72851598e-02, 2.51108687e-02, ...,\n", " -8.96187946e-02, 1.04319990e-01, -3.71962748e-02],\n", " [ 1.82982773e-01, -4.83286101e-03, -1.28907442e-01, ...,\n", " -1.11754186e-01, -6.75523803e-02, -4.66443114e-02],\n", " [ 3.09388675e-02, 6.55728728e-02, -5.33341244e-02, ...,\n", " 2.35964637e-02, 6.05051368e-02, 6.25400692e-02],\n", " ...,\n", " [ 6.82009384e-02, -7.47776553e-02, 1.71388611e-02, ...,\n", " -8.33416060e-02, 3.54118571e-02, 1.21152230e-01],\n", " [ 2.89506763e-01, 1.62052214e-01, 1.35738179e-02, ...,\n", " 9.97384042e-02, -8.80349651e-02, -1.25831261e-01],\n", " [ 6.69354275e-02, 3.15060541e-02, 9.49378535e-02, ...,\n", " -1.20546669e-01, 2.51164548e-02, -2.73378957e-02]],\n", " \n", " [[-6.62067309e-02, -1.27493262e-01, 1.90546200e-01, ...,\n", " -7.00002015e-02, 1.87407099e-02, 7.63981277e-03],\n", " [ 8.92735645e-02, -2.33126190e-02, 1.99162975e-01, ...,\n", " 5.10066077e-02, -1.35354355e-01, 1.17260136e-01],\n", " [-2.89361179e-02, 4.83213887e-02, 8.49392712e-02, ...,\n", " -7.56066144e-02, 1.99991986e-02, -7.93926269e-02],\n", " ...,\n", " [-4.43059132e-02, 2.80666295e-02, -2.20480189e-01, ...,\n", " -1.93504795e-01, -8.76336545e-02, 2.26779915e-02],\n", " [-1.76911280e-01, 6.28711507e-02, 1.97020710e-01, ...,\n", " -5.29049262e-02, -1.16330191e-01, -4.87897173e-02],\n", " [-3.60079855e-02, 1.44704401e-01, 8.19573402e-02, ...,\n", " 8.52851272e-02, 4.86281924e-02, -1.21772606e-02]],\n", " \n", " [[-1.99234173e-01, -1.16440035e-01, 1.86773926e-01, ...,\n", " -2.26203632e-02, 3.25202607e-02, 7.40338638e-02],\n", " [-9.39182490e-02, 1.32643972e-02, 7.39964992e-02, ...,\n", " 1.37882322e-01, 6.55246004e-02, 1.47754569e-02],\n", " [-6.64413646e-02, -1.96749461e-03, 2.17679627e-02, ...,\n", " 1.05354842e-02, -3.58506516e-02, -2.94759739e-02],\n", " ...,\n", " [-7.96286911e-02, -1.87324286e-02, -1.46458060e-01, ...,\n", " 1.10436425e-01, -2.83425394e-02, 8.48354921e-02],\n", " [-2.12218508e-01, -1.49263233e-01, 1.61070332e-01, ...,\n", " -1.30533174e-01, -7.25617409e-02, 1.50137311e-02],\n", " [-1.06645055e-01, 7.01546073e-02, -5.47982287e-03, ...,\n", " 1.36719510e-01, -1.37953460e-01, 6.12698458e-02]]],\n", " \n", " \n", " [[[ 7.90937394e-02, -3.04283248e-03, -2.13030726e-01, ...,\n", " -1.97056204e-01, 1.70838699e-01, 1.06945910e-01],\n", " [-1.39185111e-03, 1.04038410e-01, -1.64143920e-01, ...,\n", " -5.09495437e-02, -4.16874848e-02, -6.91269934e-02],\n", " [ 1.51556566e-01, -1.30391970e-01, -8.60519260e-02, ...,\n", " -8.31990689e-02, 2.68980358e-02, -8.78927577e-03],\n", " ...,\n", " [ 1.69954374e-01, -3.19773331e-02, -1.34970054e-01, ...,\n", " -2.81187028e-01, 6.58432543e-02, -1.05596848e-01],\n", " [ 7.10185468e-02, -1.74731594e-02, 3.74397226e-02, ...,\n", " 5.72115779e-02, -2.69202795e-02, 4.12565559e-01],\n", " [ 9.22055170e-02, -6.78981692e-02, -1.34359986e-01, ...,\n", " -1.15458816e-01, 1.87833626e-02, 9.39635113e-02]],\n", " \n", " [[ 8.26287791e-02, 3.35943513e-02, -6.68488666e-02, ...,\n", " -2.88449496e-01, 8.33515264e-03, 1.11621633e-01],\n", " [-2.13350151e-02, -8.93757585e-03, -1.97948098e-01, ...,\n", " 1.58864949e-02, -6.34026304e-02, -5.35957329e-02],\n", " [ 1.33022532e-01, -8.74736384e-02, -1.36090174e-01, ...,\n", " -3.66353057e-02, -3.16930078e-02, 1.15373187e-01],\n", " ...,\n", " [ 9.74067375e-02, -1.07977875e-01, -2.71959424e-01, ...,\n", " 4.87390943e-02, 2.47190297e-02, -1.40682802e-01],\n", " [-2.07911119e-01, 1.28951399e-02, 8.14483594e-03, ...,\n", " -2.23321274e-01, 4.29714397e-02, 3.81014705e-01],\n", " [ 6.49554059e-02, 2.36556102e-02, -1.01164579e-01, ...,\n", " 2.49743666e-02, 2.68268809e-02, -7.28107840e-02]],\n", " \n", " [[-1.30449221e-01, -1.08525708e-01, -3.88075076e-02, ...,\n", " 4.06191573e-02, -8.30720291e-02, 2.38718698e-04],\n", " [-9.00636911e-02, -1.04837172e-01, -1.56476468e-01, ...,\n", " 5.87328039e-02, -2.01005116e-02, -1.55294865e-01],\n", " [-1.15635164e-01, -2.36605536e-02, -2.63836324e-01, ...,\n", " 1.96182132e-02, 3.76341045e-02, 3.59329619e-02],\n", " ...,\n", " [-4.14120518e-02, 3.38348635e-02, -1.60050854e-01, ...,\n", " 6.38134032e-02, -7.75649399e-02, -4.17994931e-02],\n", " [-1.14109345e-01, 8.61790404e-02, -4.43402417e-02, ...,\n", " -1.10245809e-01, -1.29732698e-01, 2.76057720e-01],\n", " [-6.74327388e-02, 1.60675079e-01, -2.46481165e-01, ...,\n", " 1.09411940e-01, 4.32899371e-02, -7.89948832e-03]]]],\n", " shape=(3, 3, 32, 64), dtype=float32),\n", " array([ 0.06731296, -0.02459595, -0.02482229, -0.02666466, -0.05645316,\n", " 0.01924576, -0.08723029, -0.10761139, -0.048223 , -0.02819997,\n", " -0.06707903, -0.05372068, -0.07045425, -0.10744666, -0.01075376,\n", " -0.11981165, 0.00394488, -0.0332419 , -0.08568995, -0.05768878,\n", " -0.12302567, -0.01584917, -0.0035959 , -0.05352783, -0.00870674,\n", " -0.04014177, -0.01989994, -0.09150246, -0.01127943, 0.01514494,\n", " -0.04301269, 0.00545209, -0.07562286, 0.00512845, -0.03049872,\n", " -0.05419792, -0.0653836 , -0.06375174, 0.02577538, -0.01500314,\n", " -0.01567873, 0.03286972, 0.01425118, -0.04360417, -0.04756039,\n", " -0.05892737, -0.03354201, 0.02487741, -0.07415319, -0.09755213,\n", " -0.05163696, 0.0085671 , -0.00129141, -0.07027808, -0.03040549,\n", " -0.01221041, -0.00892876, -0.03424224, -0.07298737, -0.08493306,\n", " -0.01933318, -0.02557231, -0.09205134, -0.07720712], dtype=float32),\n", " array([[[[ 1.38326483e-02, -5.60028590e-02, -1.11946099e-01, ...,\n", " -9.23848376e-02, -4.39743586e-02, 1.12006485e-01],\n", " [ 1.07546322e-01, -4.26881202e-02, -1.57403445e-03, ...,\n", " 3.96482274e-02, 2.21814346e-02, 2.42187977e-01],\n", " [-4.24904115e-02, 3.52870498e-04, -6.20885938e-02, ...,\n", " 1.42450957e-02, -2.14181077e-02, -1.56311579e-02],\n", " ...,\n", " [-7.11930841e-02, -7.82431290e-02, -1.05166230e-02, ...,\n", " -2.45241448e-02, -6.72213510e-02, -1.86088294e-01],\n", " [-2.67039631e-02, -2.32834220e-02, 5.95758809e-03, ...,\n", " -2.33040042e-02, -6.80534318e-02, 1.66961864e-01],\n", " [ 6.92032427e-02, 3.10762487e-02, -7.18235523e-02, ...,\n", " 5.84568316e-03, 1.51486741e-02, -4.54714745e-02]],\n", " \n", " [[ 8.95656124e-02, -3.28155085e-02, -1.65620461e-01, ...,\n", " -9.39185619e-02, -8.11804458e-02, 1.16463840e-01],\n", " [ 5.78018427e-02, 1.85013115e-02, -7.45760649e-02, ...,\n", " -1.41571816e-02, -3.58420704e-03, -6.55916799e-03],\n", " [-9.83992741e-02, -7.88790286e-02, -7.09981397e-02, ...,\n", " -5.64843230e-02, -4.88687083e-02, -8.44919086e-02],\n", " ...,\n", " [ 7.02290796e-03, -7.61678442e-02, -1.21449027e-02, ...,\n", " 1.31721184e-01, 2.19784323e-02, 2.32389960e-02],\n", " [ 7.33735859e-02, 1.03031527e-02, -1.71359852e-02, ...,\n", " 3.50432955e-02, -9.20630395e-02, 2.83694472e-02],\n", " [-2.53777597e-02, -1.05815500e-01, -1.22579671e-02, ...,\n", " -1.20061614e-01, -1.14446633e-01, -2.14739367e-01]],\n", " \n", " [[-1.54941082e-01, -7.03022033e-02, -4.40699607e-02, ...,\n", " -6.72838762e-02, 5.89839853e-02, -2.56626815e-01],\n", " [-6.24654368e-02, 4.18878794e-02, -9.24368799e-02, ...,\n", " -1.66033939e-01, 1.26917372e-02, -1.43030174e-02],\n", " [-5.73151670e-02, -4.93946970e-02, -2.94560031e-03, ...,\n", " 8.56453329e-02, 5.83696598e-03, 9.15837362e-02],\n", " ...,\n", " [-1.01416223e-01, -1.69722792e-02, -2.14953441e-02, ...,\n", " -4.74646278e-02, 1.60891586e-03, -7.84632862e-02],\n", " [-8.33573714e-02, -1.05455138e-01, -6.92916960e-02, ...,\n", " 1.16767764e-01, -4.31388710e-03, -3.08036625e-01],\n", " [-1.53677627e-01, -1.79537218e-02, -4.93312813e-02, ...,\n", " -1.89838633e-02, -8.01014751e-02, -1.23914368e-01]]],\n", " \n", " \n", " [[[-2.21265808e-01, -3.21912989e-02, -7.94314817e-02, ...,\n", " -3.22166877e-03, -4.58807349e-02, 3.29253405e-01],\n", " [ 6.13443255e-02, -4.25678976e-02, 6.38110489e-02, ...,\n", " -1.41201005e-03, 5.61865531e-02, 9.56140757e-02],\n", " [-1.20172866e-01, -8.49731490e-02, 1.03288785e-01, ...,\n", " -7.98950121e-02, -4.49094810e-02, 1.39608949e-01],\n", " ...,\n", " [-7.61528537e-02, 3.20506580e-02, -2.50871405e-02, ...,\n", " -7.38966092e-02, -3.21425265e-04, 1.36282891e-01],\n", " [-1.44565761e-01, -4.32569981e-02, 2.04333365e-02, ...,\n", " -6.63700625e-02, -3.81267555e-02, -2.30036005e-02],\n", " [ 2.67012902e-02, -5.37795648e-02, -4.23380360e-02, ...,\n", " -5.63229509e-02, -5.12197688e-02, -3.12134009e-02]],\n", " \n", " [[-7.43731558e-02, -3.62176038e-02, 5.10122851e-02, ...,\n", " -3.51174027e-02, 3.87258045e-02, 2.27168292e-01],\n", " [ 1.16739683e-01, -2.23928560e-02, -5.57525223e-03, ...,\n", " -1.02788061e-01, -4.01335116e-03, 8.06760862e-02],\n", " [-1.64217681e-01, 8.26110918e-05, -4.12965678e-02, ...,\n", " 7.52085214e-03, -6.78892136e-02, -1.75480649e-01],\n", " ...,\n", " [ 8.83450210e-02, 5.34156477e-03, -7.77265951e-02, ...,\n", " 1.17669202e-01, -4.10092101e-02, -1.01568244e-01],\n", " [ 8.69038403e-02, -1.88934822e-02, -1.17780408e-02, ...,\n", " -7.16696978e-02, -9.30670425e-02, 1.23171233e-01],\n", " [-2.29017869e-01, 6.00396749e-03, 1.90518517e-02, ...,\n", " -1.41594961e-01, 2.73972610e-03, -2.70640224e-01]],\n", " \n", " [[-2.36580167e-02, -9.35083553e-02, 1.94609556e-02, ...,\n", " 7.69268163e-03, -1.22647034e-02, 9.45108756e-02],\n", " [ 9.35060829e-02, -2.52102762e-02, 3.71520706e-02, ...,\n", " -9.34997201e-02, 8.46573431e-03, -5.98869994e-02],\n", " [-3.28883454e-02, -2.59795431e-02, -9.67503898e-03, ...,\n", " -3.68000045e-02, 1.75331309e-02, 9.37586650e-03],\n", " ...,\n", " [-4.09095921e-03, -5.14208898e-02, -7.73036852e-02, ...,\n", " -9.90556255e-02, 2.79615112e-02, -2.30321847e-02],\n", " [ 1.59659505e-01, -1.82742421e-02, 5.14865806e-03, ...,\n", " -7.09384605e-02, 2.56752246e-03, -4.10889760e-02],\n", " [-6.88848943e-02, -7.74879605e-02, -3.57778445e-02, ...,\n", " 2.12062951e-02, -1.10647090e-01, -8.52660984e-02]]],\n", " \n", " \n", " [[[-2.46268973e-01, 3.85357179e-02, -4.06927988e-02, ...,\n", " 3.74751054e-02, -9.25076082e-02, -1.29397631e-01],\n", " [ 7.63643160e-02, -6.98825344e-02, 2.06106082e-02, ...,\n", " 9.54258293e-02, 1.68073755e-02, 1.37710047e-03],\n", " [ 1.05941750e-01, -4.16890271e-02, 1.94569994e-02, ...,\n", " 1.56132326e-01, 1.72235724e-02, -4.77363281e-02],\n", " ...,\n", " [-1.09409325e-01, 5.61159924e-02, -1.12776212e-01, ...,\n", " -1.19272105e-01, -3.68169770e-02, 3.49496678e-02],\n", " [-8.11009202e-03, 3.49640884e-02, -6.70032576e-02, ...,\n", " -6.32542968e-02, 1.50984516e-02, -1.72102273e-01],\n", " [ 8.10830444e-02, 3.48706394e-02, -3.07865832e-02, ...,\n", " 1.29607707e-01, -5.81163615e-02, 1.03756957e-01]],\n", " \n", " [[-8.04790780e-02, -8.27980638e-02, -1.05671294e-01, ...,\n", " -7.03366250e-02, 4.25918251e-02, 1.75646335e-01],\n", " [-6.02779612e-02, 3.28739360e-02, -3.63444462e-02, ...,\n", " -1.12059116e-01, -6.25926554e-02, 1.38955384e-01],\n", " [-2.38637440e-02, -7.79598951e-02, 5.62047698e-02, ...,\n", " 2.28353627e-02, -2.37480737e-03, 5.32163940e-02],\n", " ...,\n", " [-3.15365382e-02, -3.44829215e-03, -6.26939237e-02, ...,\n", " -1.04815885e-01, 1.66660943e-03, -1.46268513e-02],\n", " [ 7.27598695e-03, 9.55763459e-03, -4.28966023e-02, ...,\n", " -4.77525927e-02, 1.72682526e-03, 7.72892591e-03],\n", " [ 1.72994703e-01, -6.68177381e-02, 5.22779580e-03, ...,\n", " 4.69456948e-02, -1.33015667e-04, -2.45814715e-02]],\n", " \n", " [[-9.57137123e-02, 7.37990811e-03, -5.69204502e-02, ...,\n", " 2.32768655e-01, -3.73396128e-02, 5.58465421e-02],\n", " [-1.84253957e-02, -2.04128996e-02, 1.02314362e-02, ...,\n", " 1.96217880e-01, -8.14723894e-02, -1.19025171e-01],\n", " [ 6.46807626e-02, 5.45739336e-03, 3.43166254e-02, ...,\n", " 1.29366472e-01, -2.66727451e-02, 6.07023761e-03],\n", " ...,\n", " [ 5.86102270e-02, 6.51542982e-03, -6.93500265e-02, ...,\n", " -1.63897485e-01, -6.41653836e-02, -3.82056534e-02],\n", " [ 3.81928682e-02, -6.24898523e-02, -1.06647953e-01, ...,\n", " -4.72425707e-02, 3.92622454e-03, 2.31024548e-02],\n", " [ 1.48619562e-01, -6.53460994e-02, -3.18123102e-02, ...,\n", " 1.43899456e-01, 2.96681821e-02, 8.48675147e-02]]]],\n", " shape=(3, 3, 64, 64), dtype=float32),\n", " array([ 2.4550732e-03, -2.5643468e-02, -4.2420231e-02, 5.1648982e-02,\n", " 1.5094544e-02, -6.1443243e-03, 3.4930601e-03, -5.4257452e-02,\n", " 1.7708238e-02, 2.5660655e-02, 2.4773186e-02, -1.1195319e-02,\n", " -3.9667740e-02, -3.3047036e-06, -4.8360862e-03, -2.7014380e-02,\n", " -1.5237678e-02, -1.6341314e-02, -3.7341344e-03, -2.7565246e-02,\n", " 5.5930126e-03, 6.0194261e-02, -4.5430576e-03, 5.3550620e-02,\n", " 1.5513954e-02, -1.8517613e-02, -7.9647377e-03, -2.3070274e-02,\n", " 2.3695156e-02, -3.1956777e-02, -1.0008575e-02, 8.9354776e-03,\n", " -1.7382259e-02, -2.1166353e-02, 2.7923085e-02, 2.8792849e-02,\n", " -3.2289915e-02, 2.5300624e-02, 1.9008113e-02, 2.5793573e-02,\n", " -2.9155096e-02, 2.2237476e-02, 3.1690761e-02, -3.3192068e-02,\n", " -1.3683923e-02, -3.7145395e-02, -9.2115039e-03, 7.1950540e-02,\n", " -3.7705472e-03, -2.8528892e-02, -6.3294269e-02, -1.5230717e-02,\n", " -2.2907145e-02, -4.2952418e-02, -3.5951860e-02, 9.5042344e-03,\n", " 2.7293764e-02, 4.1889604e-02, 4.0394571e-02, 1.2372457e-02,\n", " -3.5087023e-02, -3.0876681e-02, -2.3205196e-02, -2.1498492e-02],\n", " dtype=float32),\n", " array([[ 0.05708771, -0.06234324, -0.13463886, ..., -0.03414698,\n", " 0.06119309, -0.02520272],\n", " [-0.02455455, 0.07509242, 0.07041636, ..., 0.09118779,\n", " 0.08221027, -0.08331598],\n", " [-0.08261199, -0.04301149, -0.00914894, ..., -0.09411783,\n", " -0.08325154, -0.00983006],\n", " ...,\n", " [ 0.21591546, -0.0254946 , 0.07165627, ..., 0.19633831,\n", " -0.1346423 , 0.08037651],\n", " [ 0.04433294, 0.0122591 , -0.02097172, ..., 0.09993152,\n", " -0.07984101, 0.00207634],\n", " [-0.12268885, -0.03293889, 0.09699838, ..., 0.03583752,\n", " -0.06936302, -0.05591767]], shape=(576, 64), dtype=float32),\n", " array([ 0.01418659, -0.03349337, -0.06969396, -0.00489685, 0.02487434,\n", " -0.01691333, -0.01663163, -0.06567444, -0.01818012, -0.01410949,\n", " 0.06172884, 0.04483001, -0.00748309, -0.01995968, -0.00552262,\n", " 0.08356399, 0.08863807, -0.00358188, -0.00643371, -0.07654649,\n", " -0.01133776, -0.03647338, -0.04736697, -0.0670499 , -0.00294621,\n", " 0.0354447 , 0.04969401, -0.03378887, 0.09706189, 0.00067311,\n", " -0.05088674, -0.06142539, -0.02328563, -0.03018828, -0.04188383,\n", " 0.00369699, -0.02562218, -0.04497053, 0.00346254, -0.03978704,\n", " -0.054894 , -0.04106856, -0.02364217, -0.03416612, -0.04195937,\n", " -0.03278247, 0.03881833, -0.04748299, 0.0227492 , 0.07585373,\n", " 0.07963715, 0.0418564 , 0.00727706, -0.08144415, 0.00310017,\n", " 0.01189614, 0.05605335, 0.10206967, 0.01744832, -0.02168363,\n", " -0.00761675, -0.01551267, -0.0139414 , 0.03232931], dtype=float32),\n", " array([[-5.16570956e-02, 1.82396144e-01, 1.86560884e-01,\n", " 7.24766180e-02, 5.22688739e-02, 8.71893018e-02,\n", " -1.00037508e-01, 1.42719477e-01, -2.42041737e-01,\n", " -3.66162956e-01],\n", " [ 1.78905219e-01, -1.64840147e-01, -1.78555652e-01,\n", " 1.50711602e-02, 1.54459298e-01, 2.16973662e-01,\n", " 1.54395908e-01, -3.16675343e-02, 2.50614882e-01,\n", " -7.16972718e-05],\n", " [-1.62268102e-01, -3.70794564e-01, 1.01160772e-01,\n", " -1.99901089e-01, -1.34590287e-02, -2.16006264e-01,\n", " -2.27270067e-01, -1.05627939e-01, -2.30283424e-01,\n", " 9.00198817e-02],\n", " [ 2.54945874e-01, -2.30583414e-01, -3.24434698e-01,\n", " -4.64541800e-02, -1.14861749e-01, -3.08331758e-01,\n", " 2.65793532e-01, -4.29244488e-01, 2.64962077e-01,\n", " 1.47684097e-01],\n", " [ 1.89971998e-01, -1.26820460e-01, 6.93956316e-02,\n", " -3.10436875e-01, 2.72932410e-01, 1.20527938e-01,\n", " 4.83166575e-02, 2.14841276e-01, 2.21598551e-01,\n", " 1.94459423e-01],\n", " [-1.01403847e-01, -2.39520654e-01, -1.85177177e-01,\n", " -1.54134959e-01, -3.11129242e-01, 2.15920851e-01,\n", " -1.25315174e-01, 1.47254735e-01, 1.28699071e-03,\n", " -2.78044730e-01],\n", " [ 5.26516140e-02, -2.37085894e-01, 1.80669680e-01,\n", " -4.07854229e-01, 1.72871143e-01, -2.57931389e-02,\n", " 1.11917555e-02, -3.11586857e-01, -2.29372889e-01,\n", " -1.52407646e-01],\n", " [ 9.74876359e-02, -1.75573707e-01, 1.52443699e-03,\n", " 1.82614475e-01, -2.97166348e-01, 4.68500704e-02,\n", " 3.72588541e-03, -6.81607006e-03, -2.63306499e-01,\n", " 1.41870350e-01],\n", " [ 2.06628874e-01, -2.04366744e-01, -3.61576468e-01,\n", " -2.70182043e-01, 1.07519627e-01, -2.00734496e-01,\n", " -3.27197790e-01, -1.40033245e-01, -3.32248926e-01,\n", " -1.36627182e-01],\n", " [-7.75504624e-04, 1.02751479e-01, -1.46293089e-01,\n", " -2.88504511e-01, 1.03341743e-01, 2.85819829e-01,\n", " -5.94628938e-02, 1.61925808e-01, -1.34175748e-01,\n", " 2.79708207e-01],\n", " [-3.29341471e-01, 5.26261181e-02, -2.42110178e-01,\n", " 2.05533952e-02, 8.31093490e-02, -3.27978402e-01,\n", " -3.25569212e-01, 1.07423492e-01, -3.75838876e-01,\n", " -9.64015797e-02],\n", " [ 1.03486061e-01, 2.48279169e-01, -1.97293177e-01,\n", " -9.47673097e-02, 1.32593885e-01, -3.43340188e-02,\n", " -3.10092688e-01, 3.17143261e-01, -1.05908655e-01,\n", " 1.29661053e-01],\n", " [ 1.75281182e-01, 1.93607450e-01, 5.06153442e-02,\n", " 1.52850389e-01, 3.47571522e-01, -6.66841120e-02,\n", " 1.84733897e-01, -2.60123700e-01, -2.34570295e-01,\n", " 1.70061544e-01],\n", " [-7.41054788e-02, -1.56462654e-01, 1.31585538e-01,\n", " 2.76431471e-01, -1.19654231e-01, -3.38970162e-02,\n", " 1.70878172e-02, 2.54455715e-01, -2.68775731e-01,\n", " 1.01187686e-02],\n", " [ 1.57435015e-01, 6.48213401e-02, -1.64385252e-02,\n", " 5.30404598e-02, -2.11611688e-01, -1.32774353e-01,\n", " -2.27555603e-01, -3.14288706e-01, -1.99659958e-01,\n", " 8.57193395e-03],\n", " [-4.45573151e-01, -1.75526902e-01, -2.18004555e-01,\n", " 3.72956097e-02, 1.33197144e-01, -1.57117695e-02,\n", " -3.10838133e-01, 8.82661194e-02, -3.29450928e-02,\n", " 4.38649580e-02],\n", " [-7.97981471e-02, -1.14686064e-01, 2.68548913e-02,\n", " 1.54006585e-01, -2.44160309e-01, -2.73801506e-01,\n", " -1.88529760e-01, -2.64297366e-01, 2.27251992e-01,\n", " -1.08027138e-01],\n", " [-7.94948190e-02, -3.89029942e-02, 2.16617256e-01,\n", " -1.28654197e-01, -5.58328303e-03, -1.61028028e-01,\n", " -3.77498806e-01, -1.45756423e-01, 1.44786075e-01,\n", " 1.27807304e-01],\n", " [-1.85091898e-01, 1.32755846e-01, -1.11977933e-02,\n", " -2.23677963e-01, -1.85212448e-01, -2.93326676e-01,\n", " -5.74105941e-02, 1.98006853e-01, -1.65121943e-01,\n", " 2.15950832e-01],\n", " [-1.87861118e-02, -9.10503343e-02, -3.07070632e-02,\n", " -1.54559031e-01, -1.01825327e-01, -1.42532066e-01,\n", " -1.91016406e-01, -1.35894924e-01, -2.96260208e-01,\n", " 2.77623355e-01],\n", " [ 1.66226804e-01, 1.63931400e-01, -2.25374743e-01,\n", " -1.02028362e-02, 2.01440603e-01, 1.10866509e-01,\n", " 5.08405641e-02, -1.63740918e-01, 2.22709626e-01,\n", " 2.42358387e-01],\n", " [ 1.41369656e-01, -1.40989572e-01, 2.89542705e-01,\n", " -1.51578054e-01, -9.12421048e-02, -1.67799622e-01,\n", " -4.33283627e-01, -9.54575464e-03, -6.95173163e-03,\n", " 2.52436459e-01],\n", " [ 2.85074830e-01, 5.97847905e-03, -2.32044280e-01,\n", " 1.00973941e-01, -3.39538038e-01, -2.47360080e-01,\n", " -1.88782454e-01, 5.32565312e-03, -8.85380581e-02,\n", " 7.35052899e-02],\n", " [ 1.78755045e-01, 1.47358239e-01, -1.23605870e-01,\n", " 8.52901489e-02, 6.41780673e-03, -1.41452134e-01,\n", " -2.91708142e-01, 2.11124029e-02, 4.82854396e-02,\n", " 2.42745891e-01],\n", " [ 1.85737789e-01, 1.87957972e-01, 2.53847670e-02,\n", " -2.70438284e-01, 2.61514783e-01, 2.35514328e-01,\n", " 2.39865452e-01, 2.03062356e-01, 1.16571411e-01,\n", " -1.47464484e-01],\n", " [ 1.38842374e-01, -2.01592192e-01, 3.09788495e-01,\n", " 2.54305005e-01, -6.27078786e-02, -3.04349035e-01,\n", " -2.23497748e-01, 5.78192845e-02, 1.97714180e-01,\n", " 4.28988263e-02],\n", " [ 1.78229138e-01, -3.21614921e-01, -4.55862820e-01,\n", " 2.52224684e-01, 3.56726684e-02, 3.06170166e-01,\n", " 2.47444674e-01, -4.52093869e-01, 1.44500747e-01,\n", " 1.26036644e-01],\n", " [ 1.94521084e-01, -2.03747347e-01, 2.21435785e-01,\n", " 6.18621632e-02, 1.60888791e-01, 2.47784197e-01,\n", " 2.60732651e-01, 2.75516063e-01, -4.30957926e-03,\n", " -1.68438897e-01],\n", " [-1.17162541e-01, 2.38961384e-01, -2.22619280e-01,\n", " 1.67741671e-01, -3.38397384e-01, 1.80762962e-01,\n", " -8.22924450e-02, -1.92300335e-01, 1.82826534e-01,\n", " -3.36759090e-01],\n", " [-3.54503095e-01, 9.51806828e-03, -1.26831204e-01,\n", " 1.11947395e-01, 1.86633497e-01, -1.78340208e-02,\n", " -2.74095953e-01, -2.73597576e-02, -7.75732845e-02,\n", " 2.32511282e-01],\n", " [ 2.31229320e-01, -2.29861483e-01, -2.50747025e-01,\n", " 1.54900640e-01, -2.51247019e-01, -9.20872092e-02,\n", " -4.38264571e-02, -6.01947010e-02, -3.70690674e-02,\n", " 5.72180841e-03],\n", " [ 1.29668742e-01, 1.67938948e-01, 2.71530509e-01,\n", " 9.87362117e-02, -1.11610256e-02, -9.49455947e-02,\n", " -7.03838691e-02, -1.90909311e-01, -9.61497501e-02,\n", " -2.22085848e-01],\n", " [-4.54211887e-03, -2.98274577e-01, -1.97932228e-01,\n", " -1.71578214e-01, 1.96355850e-01, 1.17920609e-02,\n", " -1.43543676e-01, 3.69117647e-01, -6.83321804e-02,\n", " 6.39851242e-02],\n", " [ 2.66058117e-01, -2.21391246e-01, -6.51206821e-02,\n", " 2.48825580e-01, 2.70042233e-02, 7.69974142e-02,\n", " 7.67013654e-02, -1.87259927e-01, -1.68538824e-01,\n", " 2.20064402e-01],\n", " [ 2.01051891e-01, 1.33354038e-01, 1.32612005e-01,\n", " 1.67933568e-01, -2.69793123e-01, 1.72866434e-01,\n", " 1.52714968e-01, 6.39446527e-02, -4.15093489e-02,\n", " -2.70082891e-01],\n", " [-1.14600575e-02, 1.41058400e-01, 2.51689047e-01,\n", " 4.42901673e-03, -1.33357257e-01, 2.12791517e-01,\n", " -1.77473668e-02, 2.59607017e-01, 1.19011775e-01,\n", " -3.02698433e-01],\n", " [-1.32166475e-01, 1.66911885e-01, 2.87316162e-02,\n", " -8.29543024e-02, -1.10832443e-02, -4.63532470e-02,\n", " 1.49000511e-01, -1.87354848e-01, -3.10452342e-01,\n", " 1.52855620e-01],\n", " [ 8.17507803e-02, -1.13676637e-01, -6.13319129e-02,\n", " -2.76785463e-01, -1.56265944e-01, -2.50247747e-01,\n", " 1.38710305e-01, 2.38066941e-01, -1.74162373e-01,\n", " -2.39063397e-01],\n", " [ 1.96594775e-01, -2.74036169e-01, -8.38667229e-02,\n", " 2.29640026e-02, 2.19124749e-01, -2.34084919e-01,\n", " 2.11351439e-01, -3.24498564e-01, 6.66174963e-02,\n", " -4.03303616e-02],\n", " [ 2.75130093e-01, -1.52934849e-01, -1.46691397e-01,\n", " -2.88175851e-01, -4.06669348e-01, 5.65760508e-02,\n", " 4.17029597e-02, 1.14666998e-01, -2.04257533e-01,\n", " -1.91317797e-01],\n", " [-1.70486182e-01, 8.48507062e-02, -1.51479170e-01,\n", " 1.15432711e-02, 1.90412775e-01, 1.97004318e-01,\n", " -1.39617264e-01, -2.48450980e-01, -2.18839403e-02,\n", " 4.33079228e-02],\n", " [ 1.66243352e-02, -3.51673663e-02, -1.18699059e-01,\n", " 1.64179951e-01, 2.07475826e-01, 6.36996981e-03,\n", " -2.97149181e-01, 6.26099994e-03, -1.93936884e-01,\n", " 1.19045772e-01],\n", " [-2.34536842e-01, -2.92002052e-01, -8.14765990e-02,\n", " -1.23654418e-01, 8.89466256e-02, -1.06820785e-01,\n", " 5.86572997e-02, 1.38415217e-01, 6.30669817e-02,\n", " 2.67673820e-01],\n", " [ 2.24965349e-01, -1.34028852e-01, -3.06553453e-01,\n", " -1.28137171e-01, -3.60821225e-02, -2.40799621e-01,\n", " 3.14842671e-01, -1.33109808e-01, -3.24275881e-01,\n", " 2.14633778e-01],\n", " [ 1.45587787e-01, -4.68877405e-02, -2.01387659e-01,\n", " 8.34583715e-02, 1.81788400e-01, -2.07731719e-04,\n", " 1.24553092e-01, -1.99418649e-01, 1.07602052e-01,\n", " 8.28800648e-02],\n", " [-5.74691221e-02, 1.35082810e-03, -2.02241033e-01,\n", " 2.48051181e-01, -9.00799632e-02, 2.64533609e-01,\n", " -3.13308567e-01, 1.03611529e-01, -2.79019535e-01,\n", " 4.96034771e-02],\n", " [ 7.48019144e-02, -3.07263732e-01, -2.96288401e-01,\n", " 1.08347572e-01, 2.02868655e-01, 9.72235128e-02,\n", " 1.35799825e-01, -3.53739820e-02, 2.12206632e-01,\n", " 3.01186055e-01],\n", " [ 1.82164505e-01, -2.44176805e-01, -1.58329472e-01,\n", " -2.26171255e-01, -1.32030800e-01, 2.37682581e-01,\n", " -1.37904966e-02, 1.37954220e-01, 3.48205939e-02,\n", " -2.58509349e-02],\n", " [-3.58668923e-01, -7.95790367e-03, -2.96002775e-01,\n", " 1.31813928e-01, -3.23758125e-01, 3.19456100e-01,\n", " -2.04832956e-01, -3.81792724e-01, -2.01389521e-01,\n", " 7.19458684e-02],\n", " [-1.24115035e-01, 2.55270958e-01, -9.58794057e-02,\n", " -2.26069152e-01, 2.96746284e-01, -6.19703978e-02,\n", " 1.93892360e-01, -3.35892051e-01, 2.40316659e-01,\n", " -2.16129005e-01],\n", " [ 1.41175613e-01, 1.98802382e-01, 6.41119182e-02,\n", " -2.44437769e-01, 5.93550839e-02, -2.23857194e-01,\n", " -4.21661466e-01, 1.99799687e-01, 1.51105165e-01,\n", " -1.09498642e-01],\n", " [-9.89007577e-02, 1.57155842e-01, 1.59081534e-01,\n", " 3.08500469e-01, -1.02180459e-01, 2.31799990e-01,\n", " 2.73230374e-01, 2.31182531e-01, 2.42998704e-01,\n", " -3.09382677e-01],\n", " [ 1.45925164e-01, -8.92660543e-02, 1.45201921e-01,\n", " 2.41680801e-01, 7.16382116e-02, -1.47748515e-01,\n", " 1.98164552e-01, 3.24054360e-01, -2.22253263e-01,\n", " -2.05984369e-01],\n", " [-7.03814849e-02, 1.83393851e-01, -1.79929554e-01,\n", " -2.37406537e-01, -7.27819130e-02, -8.79087448e-02,\n", " 1.96527746e-02, -2.14605957e-01, 1.10800490e-01,\n", " -2.10513532e-01],\n", " [-8.59806463e-02, 1.76193550e-01, 1.73069030e-01,\n", " -9.20611024e-02, -2.36101240e-01, 1.21646918e-01,\n", " 2.78342962e-01, -8.48453045e-02, 1.64842486e-01,\n", " 1.68510452e-01],\n", " [-1.52394146e-01, -2.50590563e-01, 2.70139366e-01,\n", " 3.09226215e-01, -6.77545518e-02, 1.16110161e-01,\n", " 1.21644542e-01, 2.77483672e-01, -1.75407201e-01,\n", " -3.19735438e-01],\n", " [-1.29462734e-01, -2.01208040e-01, 1.04181226e-02,\n", " -1.35632172e-01, 2.17418656e-01, 9.36727524e-02,\n", " -1.58911109e-01, -1.90711603e-01, 2.38178521e-01,\n", " 5.52048944e-02],\n", " [-8.05972591e-02, 1.48052126e-01, -3.75475466e-01,\n", " 3.79764438e-02, 1.24343233e-02, -6.51395991e-02,\n", " 8.06608424e-02, 2.07118884e-01, 3.84490967e-01,\n", " 7.20644146e-02],\n", " [ 1.92045867e-01, -3.11668962e-02, 3.13478976e-01,\n", " 9.90396645e-03, -2.58267522e-01, -2.90753283e-02,\n", " 1.91320434e-01, -1.75890923e-01, 3.04402053e-01,\n", " -1.63461879e-01],\n", " [ 4.34739590e-02, -1.88699216e-01, 1.56138837e-01,\n", " 2.07077473e-01, -2.81859279e-01, 1.08558983e-01,\n", " -2.40348920e-01, -1.11143710e-02, -7.54054040e-02,\n", " -1.86252594e-02],\n", " [-5.11724763e-02, 3.47667307e-01, -2.60294467e-01,\n", " -9.30366665e-02, 1.42463103e-01, -2.65860975e-01,\n", " -9.15907025e-02, 2.44312793e-01, -4.10301000e-01,\n", " 1.86489657e-01],\n", " [-1.29526660e-01, 1.33382335e-01, 1.53816730e-01,\n", " -3.20706904e-01, -7.38243386e-02, -1.43649042e-01,\n", " 2.27065638e-01, -1.99850172e-01, 1.17242811e-02,\n", " -3.99768539e-02],\n", " [ 2.21772581e-01, -1.56109646e-01, -1.24776006e-01,\n", " 1.75048411e-01, -3.22463602e-01, 8.00449178e-02,\n", " -1.92207679e-01, 4.06748056e-02, -2.07004949e-01,\n", " 1.96689054e-01],\n", " [-1.71793252e-01, -1.18684553e-01, 2.27854028e-01,\n", " 2.94921517e-01, -2.24115521e-01, -3.83285373e-01,\n", " -1.00005284e-01, -1.30508587e-01, -3.95162925e-02,\n", " -3.12202543e-01]], dtype=float32),\n", " array([-0.08606234, 0.04817877, -0.04294467, 0.01732274, 0.02866258,\n", " -0.0510773 , -0.08001343, 0.02867183, 0.09399392, 0.00924843],\n", " dtype=float32)]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.get_weights()" ] }, { "cell_type": "markdown", "id": "1d97bde9-9dcc-46cd-a078-f26f1c276e87", "metadata": {}, "source": [ "## 8. Modell evaluieren" ] }, { "cell_type": "code", "execution_count": 10, "id": "5c224092-5868-4718-8921-bab572acf683", "metadata": { "execution": { "iopub.execute_input": "2026-03-24T17:26:41.559881Z", "iopub.status.busy": "2026-03-24T17:26:41.559470Z", "iopub.status.idle": "2026-03-24T17:26:42.886409Z", "shell.execute_reply": "2026-03-24T17:26:42.885603Z", "shell.execute_reply.started": "2026-03-24T17:26:41.559850Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9913 - loss: 0.0293\n", "Testgenauigkeit: 0.9912999868392944\n", "Test Loss: 0.02932381071150303\n" ] } ], "source": [ "# Evaluation\n", "test_loss, test_acc = model.evaluate(x_test, y_test)\n", "print(f\"Testgenauigkeit: {test_acc}\")\n", "print(f\"Test Loss: {test_loss}\")" ] }, { "cell_type": "markdown", "id": "646cbc80-31a8-4e7d-9776-8682ebec0752", "metadata": {}, "source": [ "## 9. Beispielhafte Vorhersage\n" ] }, { "cell_type": "code", "execution_count": 11, "id": "7e9759ce-76ae-49dc-ab07-e400c18da124", "metadata": { "execution": { "iopub.execute_input": "2026-03-24T17:26:46.238523Z", "iopub.status.busy": "2026-03-24T17:26:46.238246Z", "iopub.status.idle": "2026-03-24T17:26:47.639192Z", "shell.execute_reply": "2026-03-24T17:26:47.638653Z", "shell.execute_reply.started": "2026-03-24T17:26:46.238504Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "predictions = model.predict(x_test)\n", "plt.imshow(x_test[0].reshape(28, 28), cmap=\"gray\")\n", "plt.title(f\"Vorhergesagte Klasse: {predictions[0].argmax()}\")\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.13.0" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": {}, "version_major": 2, "version_minor": 0 } } }, "nbformat": 4, "nbformat_minor": 5 }