Keras CNN blueprint

fit.py

import glob

import imageio
from keras import layers
from keras import losses
from keras import models
from keras import optimizers
import numpy as np
import pandas as pd



masks = pd.read_csv('data/train_ship_segmentations.csv', index_col=0)['EncodedPixels']
target = masks.notnull()[~masks.index.duplicated()].astype(np.uint8)


def bake_model():

    init = layers.Input(shape=(768, 768, 3))

    x = layers.Conv2D(32, (3, 3), activation='relu', padding='same')(init)
    x = layers.Flatten()(x)

    out = layers.Dense(1, activation='sigmoid')(x)

    model = models.Model(inputs=init, outputs=out)
    loss = losses.binary_crossentropy
    optimizer = optimizers.SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
    model.compile(loss=loss, optimizer=optimizer, metrics=['accuracy'])

    return model



def generate(batch_size):

    features = np.zeros((batch_size, 768, 768, 3))
    labels = np.zeros((batch_size, 1))
    i = 0

    while True:

        for file in glob.glob('data/train/*.jpg'):

            features[i] = imageio.imread(file)
            labels[i] = target[file.split('/')[-1]]
            i += 1

            if i == batch_size:
                i = 0
                yield (features, labels)



def main():

    model = bake_model()

    model.fit_generator(
        generate(batch_size=16),
        steps_per_epoch=100,
        epochs=10,
        verbose=1
    )


if __name__ == "__main__":
    main()