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