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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# Import der benötigten Bibliotheken\n",
"import numpy as np\n",
"import tensorflow as tf\n",
"from tensorflow.keras.applications import ResNet50\n",
"from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
"from tensorflow.keras.layers import Dense, GlobalAveragePooling2D, Dropout\n",
"from tensorflow.keras.models import Model\n",
"from tensorflow.keras.optimizers import Adam\n",
"from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Vorbereitung der Daten\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 78 images belonging to 6 classes.\n",
"Found 16 images belonging to 6 classes.\n"
]
}
],
"source": [
"# Daten-Vorbereitung\n",
"base_dir = 'C:\\Daten\\Studium Wirtschaftsinformatik\\Semester 6 TZ\\KI-Anwendungen\\Übungen\\Übung2\\Abschluss\\Datensätze\\Strohhüte' # Pfad zum übergeordneten Ordner, der die Klassenordner enthält\n",
"datagen = ImageDataGenerator(\n",
" rescale=1./255,\n",
" rotation_range=40,\n",
" width_shift_range=0.2,\n",
" height_shift_range=0.2,\n",
" shear_range=0.2,\n",
" zoom_range=0.2,\n",
" horizontal_flip=True,\n",
" fill_mode='nearest',\n",
" validation_split=0.2 # Behalte die Aufteilung für Training und Validation bei\n",
")\n",
"train_generator = datagen.flow_from_directory(\n",
" base_dir,\n",
" target_size=(224, 224), # Assuming using ResNet input dimensions\n",
" batch_size=32, # Adjust according to your system capability\n",
" class_mode='categorical',\n",
" subset='training' # Use the 'subset' argument for splitting\n",
")\n",
"\n",
"validation_generator = datagen.flow_from_directory(\n",
" base_dir,\n",
" target_size=(224, 224),\n",
" batch_size=32,\n",
" class_mode='categorical',\n",
" subset='validation'\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Modell Setup"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Modell-Setup\n",
"base_model = ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3))\n",
"base_model.trainable = False # Zuerst wird das Basismodell eingefroren\n",
"\n",
"x = GlobalAveragePooling2D()(base_model.output)\n",
"x = Dense(1024, activation='relu')(x)\n",
"x = Dropout(0.5)(x) # Dropout hinzugefügt, um Overfitting zu reduzieren\n",
"predictions = Dense(3, activation='softmax')(x)\n",
"\n",
"model = Model(inputs=base_model.input, outputs=predictions)\n",
"model.compile(optimizer=Adam(learning_rate=0.001), loss='categorical_crossentropy', metrics=['accuracy'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Training des Models"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/20\n"
]
},
{
"ename": "ValueError",
"evalue": "Arguments `target` and `output` must have the same shape. Received: target.shape=(None, 6), output.shape=(None, 3)",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[4], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;66;03m# Trainieren des Modells\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m history \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 3\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrain_generator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 4\u001b[0m \u001b[43m \u001b[49m\u001b[43msteps_per_epoch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrain_generator\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msamples\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mtrain_generator\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 5\u001b[0m \u001b[43m \u001b[49m\u001b[43mvalidation_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalidation_generator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 6\u001b[0m \u001b[43m \u001b[49m\u001b[43mvalidation_steps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalidation_generator\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msamples\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mvalidation_generator\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 7\u001b[0m \u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m20\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 8\u001b[0m \u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\n\u001b[0;32m 9\u001b[0m \u001b[43m \u001b[49m\u001b[43mModelCheckpoint\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mbest_model.keras\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msave_best_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 10\u001b[0m \u001b[43m \u001b[49m\u001b[43mEarlyStopping\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmonitor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mval_loss\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpatience\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 11\u001b[0m \u001b[43m \u001b[49m\u001b[43mReduceLROnPlateau\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmonitor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mval_loss\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfactor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpatience\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 12\u001b[0m \u001b[43m \u001b[49m\u001b[43m]\u001b[49m\n\u001b[0;32m 13\u001b[0m \u001b[43m)\u001b[49m\n",
"File \u001b[1;32mc:\\Users\\Jeremy Kuwegu\\anaconda3\\envs\\kia\\lib\\site-packages\\keras\\src\\utils\\traceback_utils.py:122\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 119\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[0;32m 120\u001b[0m \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[0;32m 121\u001b[0m \u001b[38;5;66;03m# `keras.config.disable_traceback_filtering()`\u001b[39;00m\n\u001b[1;32m--> 122\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 123\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m 124\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n",
"File \u001b[1;32mc:\\Users\\Jeremy Kuwegu\\anaconda3\\envs\\kia\\lib\\site-packages\\keras\\src\\backend\\tensorflow\\nn.py:554\u001b[0m, in \u001b[0;36mcategorical_crossentropy\u001b[1;34m(target, output, from_logits, axis)\u001b[0m\n\u001b[0;32m 552\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m e1, e2 \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(target\u001b[38;5;241m.\u001b[39mshape, output\u001b[38;5;241m.\u001b[39mshape):\n\u001b[0;32m 553\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m e1 \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m e2 \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m e1 \u001b[38;5;241m!=\u001b[39m e2:\n\u001b[1;32m--> 554\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m 555\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mArguments `target` and `output` must have the same shape. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 556\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mReceived: \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 557\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtarget.shape=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtarget\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, output.shape=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00moutput\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 558\u001b[0m )\n\u001b[0;32m 560\u001b[0m output, from_logits \u001b[38;5;241m=\u001b[39m _get_logits(\n\u001b[0;32m 561\u001b[0m output, from_logits, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSoftmax\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcategorical_crossentropy\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 562\u001b[0m )\n\u001b[0;32m 563\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m from_logits:\n",
"\u001b[1;31mValueError\u001b[0m: Arguments `target` and `output` must have the same shape. Received: target.shape=(None, 6), output.shape=(None, 3)"
]
}
],
"source": [
"# Trainieren des Modells\n",
"history = model.fit(\n",
" train_generator,\n",
" steps_per_epoch=train_generator.samples // train_generator.batch_size,\n",
" validation_data=validation_generator,\n",
" validation_steps=validation_generator.samples // validation_generator.batch_size,\n",
" epochs=20,\n",
" callbacks=[\n",
" ModelCheckpoint('best_model.keras', save_best_only=True),\n",
" EarlyStopping(monitor='val_loss', patience=5),\n",
" ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=2)\n",
" ]\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Fine Tuning des Modells"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/10\n",
"\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m148s\u001b[0m 12s/step - accuracy: 0.7070 - loss: 1.0460 - val_accuracy: 0.6094 - val_loss: 0.9729\n",
"Epoch 2/10\n",
"\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 135ms/step - accuracy: 1.0000 - loss: 0.0769 - val_accuracy: 0.5714 - val_loss: 1.0434\n",
"Epoch 3/10\n",
"\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m100s\u001b[0m 10s/step - accuracy: 0.9677 - loss: 0.1108 - val_accuracy: 0.5469 - val_loss: 0.9639\n",
"Epoch 4/10\n",
"\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 111ms/step - accuracy: 1.0000 - loss: 0.0381 - val_accuracy: 0.7143 - val_loss: 0.9019\n",
"Epoch 5/10\n",
"\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m96s\u001b[0m 10s/step - accuracy: 0.9992 - loss: 0.0220 - val_accuracy: 0.2969 - val_loss: 1.1206\n",
"Epoch 6/10\n",
"\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 164ms/step - accuracy: 1.0000 - loss: 0.0226 - val_accuracy: 0.1429 - val_loss: 1.1233\n",
"Epoch 7/10\n",
"\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m97s\u001b[0m 10s/step - accuracy: 1.0000 - loss: 0.0062 - val_accuracy: 0.1719 - val_loss: 1.4363\n",
"Epoch 8/10\n",
"\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 103ms/step - accuracy: 0.9688 - loss: 0.0287 - val_accuracy: 0.1429 - val_loss: 1.4406\n",
"Epoch 9/10\n",
"\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m98s\u001b[0m 10s/step - accuracy: 0.9907 - loss: 0.0160 - val_accuracy: 0.2344 - val_loss: 1.4151\n",
"Epoch 10/10\n",
"\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 101ms/step - accuracy: 1.0000 - loss: 0.0038 - val_accuracy: 0.0000e+00 - val_loss: 1.6847\n"
]
}
],
"source": [
"# Fine-Tuning des Modells\n",
"for layer in base_model.layers:\n",
" layer.trainable = True\n",
"\n",
"model.compile(optimizer=Adam(learning_rate=0.0001), loss='categorical_crossentropy', metrics=['accuracy'])\n",
"history_fine = model.fit(\n",
" train_generator,\n",
" steps_per_epoch=train_generator.samples // train_generator.batch_size,\n",
" validation_data=validation_generator,\n",
" validation_steps=validation_generator.samples // validation_generator.batch_size,\n",
" epochs=10\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Bewertung und Ergebnisse"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 3s/step - accuracy: 0.2708 - loss: 1.4508\n",
"Performance vor dem Fine-Tuning: 0.640625\n",
"Performance nach dem Fine-Tuning: 0.0\n"
]
}
],
"source": [
"# Ergebnisse bewerten\n",
"eval_result = model.evaluate(validation_generator, steps=validation_generator.samples // validation_generator.batch_size)\n",
"print(f'Performance vor dem Fine-Tuning: {history.history[\"val_accuracy\"][-1]}')\n",
"print(f'Performance nach dem Fine-Tuning: {history_fine.history[\"val_accuracy\"][-1]}')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
]
}
],
"source": [
"model.save('mein_modell.h5') "
]
}
],
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|