90 lines
2.3 KiB
Python
90 lines
2.3 KiB
Python
# Ten plik był odpalany na remote serwerze jupyter notebook, stąd dziwne komentarze i cała baza danych w zipie
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# Skuteczność sieci to mniej więcej 80% na zbiorze walidacyjnym, ale jest przeuczona, bo nie augmentowaliśmy danych
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import os
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os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
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os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
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from tensorflow.keras import Sequential
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from tensorflow.keras.callbacks import EarlyStopping
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from tensorflow.keras.layers import Dense, Conv2D, MaxPooling2D, Flatten, Input
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from tensorflow.keras.callbacks import ModelCheckpoint
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save_best = ModelCheckpoint('./best_model_newD.h5', monitor='val_loss', save_best_only=True)
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es = EarlyStopping(monitor="val_loss", mode="min", patience=5)
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EPOCHS = 100
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# In[2]:
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from tensorflow.keras.preprocessing.image import ImageDataGenerator
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root_path = "./Database/"
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gen = ImageDataGenerator(
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rescale=1./255,
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validation_split=0.2
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)
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# Load Data
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train_dataset = gen.flow_from_directory(
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root_path,
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class_mode="categorical",
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classes=os.listdir(root_path),
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shuffle=True,
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batch_size=32,
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target_size=(128,128),
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subset="training",
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color_mode='grayscale'
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)
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validation_dataset = gen.flow_from_directory(
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root_path,
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class_mode="categorical",
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classes=os.listdir(root_path),
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shuffle=True,
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batch_size=32,
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target_size=(128,128),
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subset="validation",
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color_mode='grayscale'
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)
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# In[4]:
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model = Sequential()
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model.add(Input(shape=(128,128)))
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model.add(Conv2D(filters = 64, kernel_size = (3, 3), activation='relu', input_shape=(128, 128, 1)))
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model.add(MaxPooling2D((2, 2)))
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model.add(Conv2D(filters = 32, kernel_size = (3, 3), activation='relu', input_shape=(64, 64, 1)))
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model.add(MaxPooling2D((2, 2)))
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model.add(Conv2D(filters = 16, kernel_size = (3, 3), activation='relu', input_shape=(32, 32, 1)))
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model.add(MaxPooling2D((2, 2)))
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model.add(Flatten())
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model.add(Dense(128*128, activation='relu'))
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model.add(Dense(1000, activation='relu'))
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# model.add(Dropout(0.2))
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model.add(Dense(25, activation='relu'))
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# model.add(Dropout(0.2))
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model.add(Dense(5, activation='softmax'))
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model.compile(optimizer='adam',
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loss='categorical_crossentropy',
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metrics=['accuracy'])
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model.summary()
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# In[5]:
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history = model.fit(train_dataset, epochs = EPOCHS, validation_data=(validation_dataset), callbacks = [es, save_best])
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# In[ ]:
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