92 lines
2.5 KiB
Python
92 lines
2.5 KiB
Python
from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
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from keras.models import Sequential
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from keras.layers import Conv2D, MaxPooling2D
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from keras.layers import Activation, Dropout, Flatten, Dense
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from keras import backend as K
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import json
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import numpy as np
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import matplotlib.pyplot as plt
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img_width, img_height = 256, 256
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train_data_dir = 'data/train'
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validation_data_dir = 'data/validation'
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nb_train_samples = 140
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nb_validation_samples = 40
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epochs = 20
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batch_size = 16
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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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else:
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input_shape = (img_width, img_height, 3)
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model = Sequential()
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model.add(Conv2D(32, (2, 2), input_shape=input_shape))
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model.add(Activation('relu'))
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model.add(MaxPooling2D(pool_size=(2, 2)))
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model.add(Conv2D(32, (2, 2)))
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model.add(Activation('relu'))
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model.add(MaxPooling2D(pool_size=(2, 2)))
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model.add(Conv2D(64, (2, 2)))
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model.add(Activation('relu'))
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model.add(MaxPooling2D(pool_size=(2, 2)))
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model.add(Flatten())
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model.add(Dense(64))
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model.add(Activation('relu'))
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model.add(Dropout(0.5))
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model.add(Dense(1))
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model.add(Activation('sigmoid'))
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model.compile(loss='binary_crossentropy',
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optimizer='rmsprop',
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metrics=['accuracy'])
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train_datagen = ImageDataGenerator(
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rotation_range=45,
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width_shift_range=0.3,
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height_shift_range=0.3,
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rescale=1./255,
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shear_range=0.25,
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zoom_range=0.1,
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horizontal_flip=True)
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validation_datagen = ImageDataGenerator(rescale=1. / 255)
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train_generator = train_datagen.flow_from_directory(
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train_data_dir,
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target_size=(img_width, img_height),
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batch_size=batch_size,
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class_mode='binary')
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validation_generator = validation_datagen.flow_from_directory(
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validation_data_dir,
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target_size=(img_width, img_height),
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batch_size=batch_size,
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class_mode='binary')
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model.fit_generator(
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train_generator,
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steps_per_epoch=nb_train_samples // batch_size,
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epochs=epochs,
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validation_data=validation_generator,
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validation_steps=nb_validation_samples // batch_size)
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model.save_weights('model_food_dirty.h5')
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results = model.evaluate(train_generator)
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print(results)
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''' test na pojedynczym elemencie
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test_image = load_img('data/train/dirty/dirty016.png', target_size=(img_width, img_height))
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test_image = img_to_array(test_image)
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test_image = test_image.reshape((1,) + test_image.shape)
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# 0 -> dirty, 1 -> food
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result = model.predict(test_image)
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print(result)
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'''
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