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- '''This script goes along the blog post
- "Building powerful image classification models using very little data"
- from blog.keras.io.
- ```
- data/
- train/
- final/
- final001.png
- final002.png
- ...
- noisy/
- noisy001.png
- noisy002.png
- ...
- validation/
- final/
- final001.png
- final002.png
- ...
- noisy/
- noisy001.png
- noisy002.png
- ...
- ```
- '''
- import sys, os, getopt
- import json
- from keras.preprocessing.image import ImageDataGenerator
- from keras.models import Sequential
- from keras.layers import Conv2D, MaxPooling2D, AveragePooling2D
- from keras.layers import Activation, Dropout, Flatten, Dense, BatchNormalization
- from keras.optimizers import Adam
- from keras.regularizers import l2
- from keras import backend as K
- from keras.utils import plot_model
- import tensorflow as tf
- import numpy as np
- from modules.model_helper import plot_info
- from modules.image_metrics import svd_metric
- ##########################################
- # Global parameters (with default value) #
- ##########################################
- img_width, img_height = 100, 100
- train_data_dir = 'data/train'
- validation_data_dir = 'data/validation'
- nb_train_samples = 7200
- nb_validation_samples = 3600
- epochs = 50
- batch_size = 16
- input_shape = (3, img_width, img_height)
- ###########################################
- '''
- Method which returns model to train
- @return : DirectoryIterator
- '''
- def generate_model():
- model = Sequential()
- model.add(Conv2D(100, (2, 1), input_shape=input_shape))
- model.add(Activation('relu'))
- model.add(MaxPooling2D(pool_size=(2, 1)))
- model.add(Conv2D(80, (2, 1)))
- model.add(Activation('relu'))
- model.add(AveragePooling2D(pool_size=(2, 1)))
- model.add(Conv2D(50, (2, 1)))
- model.add(Activation('relu'))
- model.add(MaxPooling2D(pool_size=(2, 1)))
- model.add(Flatten())
- model.add(Dense(50, kernel_regularizer=l2(0.01)))
- model.add(Activation('relu'))
- model.add(BatchNormalization())
- model.add(Dropout(0.1))
- model.add(Dense(70, kernel_regularizer=l2(0.01)))
- model.add(Activation('relu'))
- model.add(BatchNormalization())
- model.add(Dropout(0.2))
- model.add(Dense(100, kernel_regularizer=l2(0.01)))
- model.add(Activation('relu'))
- model.add(BatchNormalization())
- model.add(Dropout(0.2))
- model.add(Dense(50, kernel_regularizer=l2(0.01)))
- model.add(Activation('relu'))
- model.add(BatchNormalization())
- model.add(Dropout(0.1))
- model.add(Dense(20, kernel_regularizer=l2(0.01)))
- model.add(Activation('relu'))
- model.add(BatchNormalization())
- model.add(Dropout(0.1))
- model.add(Dense(1))
- model.add(Activation('sigmoid'))
- model.compile(loss='binary_crossentropy',
- optimizer='rmsprop',
- metrics=['accuracy'])
- return model
- '''
- Method which loads train data
- @return : DirectoryIterator
- '''
- def load_train_data():
- # this is the augmentation configuration we will use for training
- train_datagen = ImageDataGenerator(
- #rescale=1. / 255,
- #shear_range=0.2,
- #zoom_range=0.2,
- #horizontal_flip=True,
- preprocessing_function=svd_metric.get_s_model_data)
- train_generator = train_datagen.flow_from_directory(
- train_data_dir,
- target_size=(img_width, img_height),
- batch_size=batch_size,
- class_mode='binary')
- return train_generator
- '''
- Method which loads validation data
- @return : DirectoryIterator
- '''
- def load_validation_data():
- # this is the augmentation configuration we will use for testing:
- # only rescaling
- test_datagen = ImageDataGenerator(
- #rescale=1. / 255,
- preprocessing_function=svd_metric.get_s_model_data)
- validation_generator = test_datagen.flow_from_directory(
- validation_data_dir,
- target_size=(img_width, img_height),
- batch_size=batch_size,
- class_mode='binary')
- return validation_generator
- def main():
- # update global variable and not local
- global batch_size
- global epochs
- global img_width
- global img_height
- global input_shape
- global train_data_dir
- global validation_data_dir
- global nb_train_samples
- global nb_validation_samples
- if len(sys.argv) <= 1:
- print('Run with default parameters...')
- print('classification_cnn_keras_svd.py --directory xxxx --output xxxxx --batch_size xx --epochs xx --img xx')
- sys.exit(2)
- try:
- opts, args = getopt.getopt(sys.argv[1:], "ho:d:b:e:i", ["help", "output=", "directory=", "batch_size=", "epochs=", "img="])
- except getopt.GetoptError:
- # print help information and exit:
- print('classification_cnn_keras_svd.py --directory xxxx --output xxxxx --batch_size xx --epochs xx --img xx')
- sys.exit(2)
- for o, a in opts:
- if o == "-h":
- print('classification_cnn_keras_svd.py --directory xxxx --output xxxxx --batch_size xx --epochs xx --img xx')
- sys.exit()
- elif o in ("-o", "--output"):
- filename = a
- elif o in ("-b", "--batch_size"):
- batch_size = int(a)
- elif o in ("-e", "--epochs"):
- epochs = int(a)
- elif o in ("-d", "--directory"):
- directory = a
- elif o in ("-i", "--img"):
- img_height = int(a)
- img_width = int(a)
- else:
- assert False, "unhandled option"
- # 3 because we have 3 color canals
- if K.image_data_format() == 'channels_first':
- input_shape = (3, img_width, img_height)
- else:
- input_shape = (img_width, img_height, 3)
- # configuration
- with open('config.json') as json_data:
- d = json.load(json_data)
- train_data_dir = d['train_data_dir']
- validation_data_dir = d['train_validation_dir']
- try:
- nb_train_samples = d[str(img_width)]['nb_train_samples']
- nb_validation_samples = d[str(img_width)]['nb_validation_samples']
- except:
- print("--img parameter missing of invalid (--image_width xx --img_height xx)")
- sys.exit(2)
- # load of model
- model = generate_model()
- model.summary()
- if(directory):
- print('Your model information will be saved into %s...' % directory)
- history = model.fit_generator(
- load_train_data(),
- steps_per_epoch=nb_train_samples // batch_size,
- epochs=epochs,
- validation_data=load_validation_data(),
- validation_steps=nb_validation_samples // batch_size)
- # if user needs output files
- if(filename):
- # update filename by folder
- if(directory):
- # create folder if necessary
- if not os.path.exists(directory):
- os.makedirs(directory)
- filename = directory + "/" + filename
- # save plot file history
- plot_info.save(history, filename)
- plot_model(model, to_file=str(('%s.png' % filename)), show_shapes=True)
- model.save_weights(str('%s.h5' % filename))
- if __name__ == "__main__":
- main()
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