Keras/Finetuning

finetuning.py

from keras.preprocessing.image import ImageDataGenerator
from keras.optimizers import SGD
from keras.models import Model
from keras.utils import np_utils
from keras.preprocessing import image
from keras.applications.inception_resnet_v2 import InceptionResNetV2
from keras.applications.inception_resnet_v2 import preprocess_input, decode_predictions
from keras.layers import Dense, GlobalAveragePooling2D
from sklearn.model_selection import train_test_split
import numpy as np
import os
import glob


def load_data(path, pattern):
    class_names={}
    class_id=0
    x = []
    y = []

    for d in glob.glob(os.path.join(path, '*')):
        clname = os.path.basename(d)
        for f in glob.glob(os.path.join(d, pattern)):
            if not clname in class_names:
                class_names[clname]=class_id
                class_id += 1

            img = image.load_img(f, target_size=(224, 224))
            npi = image.img_to_array(img)
            npi = preprocess_input(npi)
            for i in range(4):
                npi=np.rot90(npi, i)
                x.append(npi)
                y.append(class_names[clname])

    return np.array(x), np.array(y), class_names

x, y, class_names = load_data('flower_photos', '*.jpg')
num_classes = len(class_names)


x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1)
y_train = np_utils.to_categorical(y_train, num_classes)
y_test = np_utils.to_categorical(y_test, num_classes)


model = InceptionResNetV2(weights='imagenet', include_top=False)

x = model.output
x = GlobalAveragePooling2D()(x)
predictions = Dense(num_classes, activation='softmax')(x) 
model = Model(input=model.input, output=predictions)


LAYERS_TO_FREEZE=700
for layer in model.layers[:LAYERS_TO_FREEZE]:
    layer.trainable = False


model.compile(optimizer="adam", loss='categorical_crossentropy', metrics=['accuracy'])


model.fit(x_train, y_train, batch_size=128, epochs=1, verbose=1, validation_split=0.1)
score = model.evaluate(x_test, y_test, verbose=0)
print 'Testing set accuracy:', score[1]