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inc_agg_aug_full.py
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inc_agg_aug_full.py
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import numpy as np
np.random.seed(1337)
import os
os.environ["CUDA_VISIBLE_DEVICES"] = '0'
import pickle
import gc
import imgaug as ia
from imgaug import augmenters as iaa
import keras
from keras import applications
from keras import optimizers
from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D
from keras.callbacks import EarlyStopping
from keras.callbacks import ModelCheckpoint
from keras.layers.normalization import BatchNormalization
from keras.metrics import categorical_accuracy
from keras.models import Model, Sequential
from keras.preprocessing.image import ImageDataGenerator
from sklearn.cross_validation import train_test_split
from util import load_data_local_eval, load_data_full
VAL_SPLIT = 0.2
BATCH_SIZE = 23
checkpoint = 1
MODEL_NAME = 'inc_agg_aug_full'
# x_train, x_val, y_train, y_val = load_data_local_eval(VAL_SPLIT)
x_train, y_train = load_data_full()
def save_model():
global checkpoint
cp_name = '%s_cp%s.model' % (MODEL_NAME, checkpoint)
model.save(cp_name)
print('saved %s' % cp_name)
checkpoint += 1
base_model = applications.InceptionResNetV2( \
weights='imagenet', include_top=False, input_shape=x_train.shape[1:])
add_model = Sequential()
add_model.add(Flatten(input_shape=base_model.output_shape[1:]))
add_model.add(BatchNormalization())
add_model.add(Dense(256, activation='relu'))
add_model.add(BatchNormalization())
add_model.add(Dropout(0.5))
add_model.add(Dense(np.max(y_train) + 1, activation='softmax'))
model = Model(inputs=base_model.input, outputs=add_model(base_model.output))
model.compile(loss='sparse_categorical_crossentropy', optimizer=optimizers.Adam(lr=1e-5), \
metrics=['accuracy'])
base_model.trainable = False
train_datagen = ImageDataGenerator()
def train_model(train_datagen, epochs):
train_generator = train_datagen.flow(x_train, y_train, batch_size=BATCH_SIZE)
history = model.fit_generator(
train_generator,
steps_per_epoch=x_train.shape[0] // BATCH_SIZE,
epochs=epochs,
)
return history
train_model(train_datagen, 5)
save_model() # cp1
train_datagen = ImageDataGenerator(
rotation_range=30,
horizontal_flip=True,
vertical_flip=True,
)
train_model(train_datagen, 20)
train_datagen = ImageDataGenerator(
rotation_range=30,
width_shift_range=0.1,
height_shift_range=0.1,
shear_range=0.1,
zoom_range=0.1,
horizontal_flip=True,
vertical_flip=True,
)
train_model(train_datagen, 10)
save_model() # cp2
train_datagen = ImageDataGenerator(
rotation_range=40,
width_shift_range=0.15,
height_shift_range=0.15,
shear_range=0.15,
zoom_range=0.15,
horizontal_flip=True,
vertical_flip=True,
fill_mode='reflect'
)
train_model(train_datagen, 10)
save_model() # cp3
train_datagen = ImageDataGenerator(
rotation_range=90,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.3,
zoom_range=0.2,
horizontal_flip=True,
vertical_flip=True,
fill_mode='reflect'
)
train_model(train_datagen, 60)
save_model() # cp4
train_datagen = ImageDataGenerator(
rotation_range=90,
width_shift_range=0.25,
height_shift_range=0.25,
shear_range=0.4,
zoom_range=0.3,
horizontal_flip=True,
vertical_flip=True,
fill_mode='reflect'
)
train_model(train_datagen, 20)
save_model() # cp5
ia.seed(1)
sometimes = lambda aug: iaa.Sometimes(0.3, aug)
seq = iaa.Sequential([
iaa.Fliplr(0.5),
iaa.Fliplr(0.5),
iaa.Affine(
scale={"x": (0.7, 1.3), "y": (0.7, 1.3)},
translate_percent={"x": (-0.3, 0.3), "y": (-0.3, 0.3)},
rotate=(-60, 60),
shear=(-30, 30),
order=[0, 1],
mode='reflect',
),
])
def image_augment(i):
images = np.expand_dims(i, 0)
images = seq.augment_images(images)
return images[0]
train_datagen = ImageDataGenerator(preprocessing_function=image_augment)
train_model(train_datagen, 20)
save_model() # cp6
seq = iaa.Sequential([
iaa.Fliplr(0.5),
iaa.Fliplr(0.5),
iaa.Affine(
scale={"x": (0.6, 1.5), "y": (0.6, 1.5)},
translate_percent={"x": (-0.4, 0.4), "y": (-0.4, 0.4)},
rotate=(-90, 90),
shear=(-40, 40),
order=[0, 1],
mode='reflect',
),
])
train_datagen = ImageDataGenerator(preprocessing_function=image_augment)
train_model(train_datagen, 20)
save_model() # cp7
seq = iaa.Sequential([
iaa.Fliplr(0.5),
iaa.Fliplr(0.5),
iaa.Affine(
scale={"x": (0.5, 1.7), "y": (0.5, 1.7)},
translate_percent={"x": (-0.5, 0.5), "y": (-0.5, 0.5)},
rotate=(-90, 90),
shear=(-50, 50),
order=[0, 1],
mode='reflect',
),
])
train_datagen = ImageDataGenerator(preprocessing_function=image_augment)
train_model(train_datagen, 30)
save_model() # cp8
seq = iaa.Sequential([
iaa.Fliplr(0.5),
iaa.Fliplr(0.5),
iaa.Affine(
scale={"x": (0.45, 1.9), "y": (0.45, 1.9)},
translate_percent={"x": (-0.55, 0.55), "y": (-0.55, 0.55)},
rotate=(-90, 90),
shear=(-60, 60),
order=[0, 1],
mode='reflect',
),
])
train_datagen = ImageDataGenerator(preprocessing_function=image_augment)
train_model(train_datagen, 30)
save_model() # cp9
seq = iaa.Sequential([
iaa.Fliplr(0.5),
iaa.Fliplr(0.5),
iaa.Affine(
scale={"x": (0.4, 2), "y": (0.4, 2)},
translate_percent={"x": (-0.6, 0.6), "y": (-0.6, 0.6)},
rotate=(-90, 90),
shear=(-65, 65),
order=[0, 1],
mode='reflect',
),
])
train_datagen = ImageDataGenerator(preprocessing_function=image_augment)
train_model(train_datagen, 30)
save_model() # cp10
seq = iaa.Sequential([
iaa.Fliplr(0.5),
iaa.Fliplr(0.5),
iaa.Affine(
scale={"x": (0.35, 2.1), "y": (0.35, 2.1)},
translate_percent={"x": (-0.65, 0.65), "y": (-0.65, 0.65)},
rotate=(-90, 90),
shear=(-70, 70),
order=[0, 1],
mode='reflect',
),
])
train_datagen = ImageDataGenerator(preprocessing_function=image_augment)
train_model(train_datagen, 30)
save_model() # cp11
train_model(train_datagen, 40)
save_model() # cp12