entfernt

backup.ipynb

-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "###########################################################################\n",
-    "# ImageGenerator with dataframes\n",
-    "###########################################################################\n",
-    "\n",
-    "#Size of our input images\n",
-    "SIZE = 128\n",
-    "# Size of ba\n",
-    "batch_size = 64\n",
-    "\n",
-    "# Pfad zum Ordner, der nur Bilder der Klasse A enthält\n",
-    "src_path = \"data/cell_images\"\n",
-    "\n",
-    "\n",
-    "# Klasse \"uninfected_train\"\n",
-    "# Pfad zum Ordner mit den Bildern\n",
-    "src_path_train = \"data/cell_images/uninfected_train\"\n",
-    "# Liste der Dateinamen im Ordner\n",
-    "file_list_train = os.listdir(src_path_train)\n",
-    "# Liste der Labels (Klassen) für die Bilder\n",
-    "labels_train = ['uninfected_train'] * len(file_list_train)  # Bilder im Ordner werden Klasse \"uninfected_train\" zugeordnet\n",
-    "# Erstellen eines DataFrames mit Dateinamen und den entsprechenden Labels\n",
-    "df_data_train = pd.DataFrame({'filename': file_list_train, 'label': labels_train})\n",
-    "\n",
-    "# Klasse \"uninfected_test\"\n",
-    "src_path_test = \"data/cell_images/uninfected_test\"\n",
-    "file_list_test = os.listdir(src_path_test)\n",
-    "labels_test = ['uninfected_test'] * len(file_list_test)\n",
-    "df_data_test = pd.DataFrame({'filename': file_list_test, 'label': labels_test})\n",
-    "\n",
-    "# Klasse \"parasitized\"\n",
-    "src_path_parasitized = \"data/cell_images/parasitized\"\n",
-    "file_list_parasitized = os.listdir(src_path_parasitized)\n",
-    "labels_parasitized = ['parasitized'] * len(file_list_parasitized)\n",
-    "df_data_parasitized = pd.DataFrame({'filename': file_list_parasitized, 'label': labels_parasitized})\n",
-    "\n",
-    "\n",
-    "#Define generators for training, validation and also anomaly data\n",
-    "# Konfigurieren des ImageDataGenerator für das Rescaling der Pixelwerte\n",
-    "datagen = ImageDataGenerator(rescale=1./255)\n",
-    "\n",
-    "# Erstellen eines ImageDataGenerator-Objekts, um Bilder und Labels zu laden, Klasse \"df_data_train\"\n",
-    "train_generator = datagen.flow_from_dataframe(\n",
-    "    df_data_train,\n",
-    "    src_path_train,             # Verzeichnis, das die Bilder enthält\n",
-    "    x_col='filename',           # Name der Spalte im DataFrame, die die Dateinamen enthält\n",
-    "    y_col='label',              # Name der Spalte im DataFrame, die die Labels enthält\n",
-    "    target_size=(SIZE, SIZE),   # Größe der Eingabebilder\n",
-    "    batch_size=batch_size,      # Anzahl der Bilder pro Batch\n",
-    "    class_mode='categorical',   # 'categorical' für Klassifikation, 'binary' für binäre Klassifikation\n",
-    "    shuffle=True\n",
-    ")\n",
-    "\n",
-    "validation_generator = datagen.flow_from_dataframe(\n",
-    "    df_data_test,\n",
-    "    src_path_test,\n",
-    "    x_col='filename',\n",
-    "    y_col='label',\n",
-    "    target_size=(SIZE, SIZE),\n",
-    "    batch_size=batch_size,\n",
-    "    class_mode='categorical',\n",
-    "    shuffle=True\n",
-    ")\n",
-    "\n",
-    "anomaly_generator = datagen.flow_from_dataframe(\n",
-    "    df_data_parasitized,\n",
-    "    src_path_parasitized,\n",
-    "    x_col='filename',\n",
-    "    y_col='label',\n",
-    "    target_size=(SIZE, SIZE),\n",
-    "    batch_size=batch_size,\n",
-    "    class_mode='categorical',\n",
-    "    shuffle=True\n",
-    ")"
-   ]
-  }
- ],
- "metadata": {
-  "language_info": {
-   "name": "python"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}