Krümel Branko
83df9de035
12 Monate Crumbforest-Wachstum, sortiert nach Waldlogik. Struktur: - 454 Dokumente in 7 organischen Ebenen - Lichtung → Waldrand → Waldmitte → Wurzeln → Krone → Unterholz → Samen - INDEX.md für jede Ebene (eigene Stimme) - WALDKARTE.md (Master-Navigation) - crumbcodex.md (Das Versprechen zwischen Krümel & Bits) - lichtung/willkommen.md (Nullfeld-Einstieg für Kinder) Philosophie: - Waldlogik statt Ordnerlogik - Tiefe, Licht, Wurzeln - wie ein echter Wald - Schutz für Krümel, Tiefe für Gräber, Poesie für Atmende Repos verbunden: - OZM-Keks-Handbuch-v1 (Das Wissen) - Crumb-Core-v.1 (Das Herz) - 194.164.194.191 - crumbmissions (Das Spiel) Wuuuhuuuuu! 🦉✨
54 lines
1.4 KiB
Python
54 lines
1.4 KiB
Python
import tensorflow as tf
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from tensorflow.keras.preprocessing.image import ImageDataGenerator
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from tensorflow.keras.applications import MobileNetV2
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from tensorflow.keras.layers import Dense, GlobalAveragePooling2D
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from tensorflow.keras.models import Model
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import os
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# Datenpfad
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data_dir = 'orchideen_data'
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img_height, img_width = 224, 224
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batch_size = 8
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# Datengenerator
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train_datagen = ImageDataGenerator(
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rescale=1./255,
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validation_split=0.2
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)
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train_generator = train_datagen.flow_from_directory(
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data_dir,
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target_size=(img_height, img_width),
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batch_size=batch_size,
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class_mode='categorical',
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subset='training'
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)
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val_generator = train_datagen.flow_from_directory(
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data_dir,
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target_size=(img_height, img_width),
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batch_size=batch_size,
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class_mode='categorical',
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subset='validation'
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)
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# Modell
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base_model = MobileNetV2(weights='imagenet', include_top=False, input_shape=(img_height, img_width, 3))
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x = base_model.output
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x = GlobalAveragePooling2D()(x)
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x = Dense(64, activation='relu')(x)
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predictions = Dense(2, activation='softmax')(x)
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model = Model(inputs=base_model.input, outputs=predictions)
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# Basis-Modell einfrieren
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for layer in base_model.layers:
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layer.trainable = False
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# Kompilieren und Trainieren
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model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
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model.fit(train_generator, validation_data=val_generator, epochs=10)
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# Speichern
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model.save('orchideen_model.h5')
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