resnet.py

"""Demonstrate ResNet50 training on label-maker prepared data"""

from __future__ import print_function
import numpy as np
import keras
from keras.applications.resnet50 import ResNet50
from keras.preprocessing.image import ImageDataGenerator
from keras import backend as K

batch_size = 16
num_classes = 2
epochs = 50

# input image dimensions
img_rows, img_cols = 256, 256

# the data, shuffled and split between train and test sets
npz = np.load('data.npz')
x_train = npz['x_train']
y_train = npz['y_train']
x_test = npz['x_test']
y_test = npz['y_test']

if K.image_data_format() == 'channels_first':
    x_train = x_train.reshape(x_train.shape[0], 3, img_rows, img_cols)
    x_test = x_test.reshape(x_test.shape[0], 3, img_rows, img_cols)
    input_shape = (3, img_rows, img_cols)
else:
    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 3)
    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 3)
    input_shape = (img_rows, img_cols, 3)

x_train = x_train.astype('float32')
x_test = x_test.astype('float32')

print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')

# weight classes to account for differences in class frequencies
non_background = np.sum(y_train, axis=0)[1]
class_weight = {
    0: 1,
    1: (y_train.shape[0] - non_background) / non_background
}

x_train /= 255
x_test /= 255

# normalize the images
img_mean = np.mean(x_train, axis=(0, 1, 2))
img_std = np.std(x_train, axis=(0, 1, 2))
x_train -= img_mean
x_train /= img_std

x_test -= img_mean
x_test /= img_std

print('class_weight', class_weight)

model = ResNet50(weights=None,
                 input_shape=input_shape,
                 classes=num_classes)

model.compile(loss=keras.losses.categorical_crossentropy,
              optimizer=keras.optimizers.SGD(lr=0.01, momentum=0.0, decay=0.0, nesterov=False),
              metrics=['accuracy'])


datagen = ImageDataGenerator(
    rotation_range=180,  # randomly rotate images in the range (degrees, 0 to 180)
    horizontal_flip=True,  # randomly flip images
    vertical_flip=False
)

# Fit the model on the batches generated by datagen.flow().
model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),
                    steps_per_epoch=int(x_train.shape[0] / batch_size),
                    epochs=epochs,
                    validation_data=(x_test, y_test),
                    verbose=1,
                    class_weight=class_weight,
                    workers=4)

score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
model.save('model.h5')