44 lines
1.4 KiB
Python
44 lines
1.4 KiB
Python
import numpy
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from keras.datasets import mnist
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from keras.layers import Conv2D, MaxPooling2D
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from keras.layers import Dense, Dropout, Flatten
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from keras.models import Sequential
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from keras.utils import np_utils
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from keras_preprocessing.image import load_img, img_to_array
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img_rows, img_cols = 28, 28
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input_shape = (img_rows, img_cols, 1)
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producent = []
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def imageClass(model):
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model.add(Conv2D(75, kernel_size=(5, 5),
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activation='relu',
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input_shape=input_shape))
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model.add(MaxPooling2D(pool_size=(2, 2)))
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model.add(Dropout(0.2))
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model.add(Conv2D(100, (5, 5), activation='relu'))
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model.add(MaxPooling2D(pool_size=(2, 2)))
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model.add(Dropout(0.2))
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model.add(Flatten())
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model.add(Dense(500, activation='relu'))
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model.add(Dropout(0.5))
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model.add(Dense(10, activation='softmax'))
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model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
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nmodel = Sequential()
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imageClass(nmodel)
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nmodel.load_weights('model_weights.h5')
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def imgSkan():
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img_width, img_height = 28, 28
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new_image = load_img('cyfra.png', target_size=(img_width, img_height), color_mode = "grayscale")
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new_image = img_to_array(new_image)
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new_image = new_image.reshape((1,) + new_image.shape)
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prediction = nmodel.predict(new_image)
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prediction = numpy.argmax(prediction)
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print("Producent:", prediction)
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producent.append(prediction)
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