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cifar10.py
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cifar10.py
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from keras import backend as K
import keras
from keras.datasets import cifar10
from keras.models import Sequential, model_from_json
from keras.layers import Dense, Dropout, Activation, Flatten, Input, GlobalAveragePooling2D, Lambda
from keras.layers import Convolution2D, MaxPooling2D, ZeroPadding2D, Conv2D
from keras.preprocessing.image import ImageDataGenerator
from keras.utils import np_utils
from keras_contrib.layers.normalization import GroupNormalization
import numpy as np
K.set_image_data_format('channels_first')
from tensorflow.python.platform import flags
FLAGS = flags.FLAGS
def set_flags(batch_size):
flags.DEFINE_integer('BATCH_SIZE', batch_size, 'Size of training batches')
flags.DEFINE_integer('NUM_CLASSES', 10, 'Number of classification classes')
flags.DEFINE_integer('IMAGE_ROWS', 32, 'Input row dimension')
flags.DEFINE_integer('IMAGE_COLS', 32, 'Input column dimension')
flags.DEFINE_integer('NUM_CHANNELS', 3, 'Input depth dimension')
def load_data(one_hot=True):
# the data, shuffled and split between train and test sets
(X_train, y_train), (X_test, y_test) = cifar10.load_data()
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255.0
X_test /= 255.0
print('X_train shape:', X_train.shape)
print(X_train.shape[0], 'train samples')
print(X_test.shape[0], 'test samples')
print("Loaded CIFAR-10 dataset.")
if one_hot:
# convert class vectors to binary class matrices
y_train = np_utils.to_categorical(y_train, FLAGS.NUM_CLASSES).astype(np.float32)
y_test = np_utils.to_categorical(y_test, FLAGS.NUM_CLASSES).astype(np.float32)
return X_train, y_train, X_test, y_test
def modelZ():
model = Sequential()
model.add(Conv2D(96, (3, 3), padding = 'same', input_shape=(FLAGS.NUM_CHANNELS, FLAGS.IMAGE_ROWS, FLAGS.IMAGE_COLS)))
model.add(GroupNormalization(axis=1))
model.add(Activation('elu'))
model.add(Conv2D(96, (3, 3), padding = 'same'))
model.add(GroupNormalization(axis=1))
model.add(Activation('elu'))
model.add(Conv2D(96, (3, 3), padding = 'same', strides = 2))
model.add(GroupNormalization(axis=1))
model.add(Activation('elu'))
model.add(Dropout(0.5))
model.add(Conv2D(192, (3, 3) , padding = 'same'))
model.add(GroupNormalization(axis=1))
model.add(Activation('elu'))
model.add(Conv2D(192, (3, 3), padding = 'same'))
model.add(GroupNormalization(axis=1))
model.add(Activation('elu'))
model.add(Conv2D(192, (3, 3), padding = 'same', strides = 2))
model.add(GroupNormalization(axis=1))
model.add(Activation('elu'))
model.add(Dropout(0.5))
model.add(Conv2D(192, (3, 3), padding = 'same'))
model.add(GroupNormalization(axis=1))
model.add(Activation('elu'))
model.add(Conv2D(192, (1, 1),padding='valid'))
model.add(GroupNormalization(axis=1))
model.add(Activation('elu'))
model.add(Conv2D(10, (1, 1), padding='valid'))
model.add(GlobalAveragePooling2D())
return model
def modelA():
weight_decay = 0.0001
model = Sequential()
model.add(Conv2D(96, (5, 5), padding='same', kernel_regularizer=keras.regularizers.l2(weight_decay), kernel_initializer="he_normal", input_shape=(FLAGS.NUM_CHANNELS, FLAGS.IMAGE_ROWS, FLAGS.IMAGE_COLS)))
model.add(Activation('elu'))
model.add(Conv2D(96, (1, 1), padding='same', kernel_regularizer=keras.regularizers.l2(weight_decay), kernel_initializer="he_normal"))
model.add(Activation('elu'))
model.add(MaxPooling2D(pool_size=(3, 3),strides=(2,2),padding = 'same'))
model.add(Dropout(0.5))
model.add(Conv2D(192, (5, 5), padding='same', kernel_regularizer=keras.regularizers.l2(weight_decay), kernel_initializer="he_normal"))
model.add(Activation('elu'))
model.add(Conv2D(192, (1, 1),padding='same', kernel_regularizer=keras.regularizers.l2(weight_decay), kernel_initializer="he_normal"))
model.add(Activation('elu'))
model.add(MaxPooling2D(pool_size=(3, 3),strides=(2,2),padding = 'same'))
model.add(Dropout(0.5))
model.add(Conv2D(256, (3, 3), padding='same', kernel_regularizer=keras.regularizers.l2(weight_decay), kernel_initializer="he_normal"))
model.add(Activation('elu'))
model.add(Conv2D(256, (1, 1), padding='same', kernel_regularizer=keras.regularizers.l2(weight_decay), kernel_initializer="he_normal"))
model.add(Activation('elu'))
model.add(Conv2D(FLAGS.NUM_CLASSES, (1, 1), padding='same', kernel_regularizer=keras.regularizers.l2(weight_decay), kernel_initializer="he_normal"))
model.add(GlobalAveragePooling2D())
return model
def modelB():
weight_decay = 0.0001
model = Sequential()
model.add(Conv2D(192, (5, 5), padding='same', kernel_regularizer=keras.regularizers.l2(weight_decay), kernel_initializer="he_normal", input_shape=(FLAGS.NUM_CHANNELS, FLAGS.IMAGE_ROWS, FLAGS.IMAGE_COLS)))
model.add(Activation('elu'))
model.add(Conv2D(96, (1, 1), padding='same', kernel_regularizer=keras.regularizers.l2(weight_decay), kernel_initializer="he_normal"))
model.add(Activation('elu'))
model.add(MaxPooling2D(pool_size=(3, 3),strides=(2,2),padding = 'same'))
model.add(Dropout(0.5))
model.add(Conv2D(192, (5, 5), padding='same', kernel_regularizer=keras.regularizers.l2(weight_decay), kernel_initializer="he_normal"))
model.add(Activation('elu'))
model.add(Conv2D(192, (1, 1),padding='same', kernel_regularizer=keras.regularizers.l2(weight_decay), kernel_initializer="he_normal"))
model.add(Activation('elu'))
model.add(MaxPooling2D(pool_size=(3, 3),strides=(2,2),padding = 'same'))
model.add(Dropout(0.5))
model.add(Conv2D(256, (3, 3), padding='same', kernel_regularizer=keras.regularizers.l2(weight_decay), kernel_initializer="he_normal"))
model.add(Activation('elu'))
model.add(Conv2D(256, (1, 1), padding='same', kernel_regularizer=keras.regularizers.l2(weight_decay), kernel_initializer="he_normal"))
model.add(Activation('elu'))
model.add(Conv2D(FLAGS.NUM_CLASSES, (1, 1), padding='same', kernel_regularizer=keras.regularizers.l2(weight_decay), kernel_initializer="he_normal"))
model.add(GlobalAveragePooling2D())
return model
def modelC():
model = Sequential()
model.add(Conv2D(96, (3, 3), activation='elu', padding = 'same', input_shape=(FLAGS.NUM_CHANNELS, FLAGS.IMAGE_ROWS, FLAGS.IMAGE_COLS)))
model.add(Dropout(0.2))
model.add(Conv2D(96, (3, 3), activation='elu', padding = 'same'))
model.add(Conv2D(96, (3, 3), activation='elu', padding = 'same', strides = 2))
model.add(Dropout(0.5))
model.add(Conv2D(192, (3, 3), activation='elu', padding = 'same'))
model.add(Conv2D(192, (3, 3), activation='elu', padding = 'same', strides = 2))
model.add(Dropout(0.5))
model.add(Conv2D(256, (3, 3), padding = 'same'))
model.add(Activation('elu'))
model.add(Conv2D(256, (1, 1),padding='valid'))
model.add(Activation('elu'))
model.add(Conv2D(FLAGS.NUM_CLASSES, (1, 1), padding='valid'))
model.add(GlobalAveragePooling2D())
return model
def model_select(type=0):
models = [modelZ, modelA, modelB, modelC]
return models[type]()
def data_flow(X_train):
datagen = ImageDataGenerator()
datagen.fit(X_train)
return datagen
def load_model(model_path, type=0):
try:
with open(model_path+'.json', 'r') as f:
json_string = f.read()
model = model_from_json(json_string)
except IOError:
model = model_select(type=type)
model.load_weights(model_path)
return model