podprojekt kacper update
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@ -4,6 +4,7 @@ from keras.models import Sequential
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from keras.layers import Conv2D, MaxPooling2D
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from keras.layers import Conv2D, MaxPooling2D
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from keras.layers import Activation, Dropout, Flatten, Dense, BatchNormalization
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from keras.layers import Activation, Dropout, Flatten, Dense, BatchNormalization
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from keras import backend as K
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from keras import backend as K
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img_width, img_height = 299, 299
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img_width, img_height = 299, 299
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train_data_dir = 'resources/zbior_uczacy'
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train_data_dir = 'resources/zbior_uczacy'
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@ -13,9 +14,8 @@ nb_validation_samples = 1574
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epochs = 1
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epochs = 1
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batch_size = 16
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batch_size = 16
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def stworzModel():
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def stworzModel():
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if K.image_data_format() == 'channels_first':
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if K.image_data_format() == 'channels_first':
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input_shape = (3, img_width, img_height)
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input_shape = (3, img_width, img_height)
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else:
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else:
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@ -42,6 +42,7 @@ def stworzModel():
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model.add(Activation('softmax'))
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model.add(Activation('softmax'))
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return model
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return model
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def trainModel():
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def trainModel():
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model = stworzModel()
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model = stworzModel()
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model.compile(loss='categorical_crossentropy',
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model.compile(loss='categorical_crossentropy',
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@ -80,6 +81,7 @@ def trainModel():
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model.save_weights('nowy_wytrenowany.h5')
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model.save_weights('nowy_wytrenowany.h5')
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def przewidz(path):
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def przewidz(path):
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model = stworzModel()
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model = stworzModel()
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model.load_weights('wytrenowany.h5')
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model.load_weights('wytrenowany.h5')
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@ -97,4 +99,3 @@ def przewidz(path):
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return "paper"
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return "paper"
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elif kategoria == 3:
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elif kategoria == 3:
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return "plastic"
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return "plastic"
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