nasnet model for model_to_estimator

gistfile1.txt

# Download your data here: https://www.kaggle.com/c/dogs-vs-cats/data and split them into /train/dogs & /train/cats

from keras.preprocessing.image import ImageDataGenerator
from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
import tensorflow as tf

def test_nasnet(self):

    img_width, img_height = 150, 150
    train_samples = 1600
    train_data_dir = '/train'
    epochs = 50
    batch_size = 16
    steps = int(train_samples / batch_size)
    input_shape = (img_width, img_height, 3)

    train_datagen = ImageDataGenerator(
        rescale=1. / 255,
        shear_range=0.2,
        zoom_range=0.2,
        horizontal_flip=True)
    train_generator = train_datagen.flow_from_directory(
        train_data_dir,
        target_size=(img_width, img_height),
        batch_size=batch_size,
        class_mode='binary')
    train_data = np.zeros((steps, 16, img_width, img_height, 3))
    train_target = np.zeros((steps, 16, 2), np.int8)
    for i in range(steps):
      next_data = train_generator.next()
      train_data[i] = next_data[0]
      train_target[i] = to_categorical(next_data[1], num_classes=2)

    def train_input_fn():
      i = np.random.randint(100)
      return (train_data[i], train_target[i])

    model = nasnet.NASNetMobile(
        input_shape=input_shape, weights=None, classes=2)
    # model.summary()
    model.compile(
        optimizer='sgd',
        loss='categorical_crossentropy',
        metrics=['categorical_accuracy'])
    config = run_config_lib.RunConfig(save_summary_steps=1, save_checkpoints_steps=1, log_step_count_steps=1)
    nasnet_estimator = keras_lib.model_to_estimator(
        keras_model=model,
        model_dir='/result',
        config=config)

    for _ in range(epochs):
      nasnet_estimator.train(input_fn=train_input_fn, steps=2)
      nasnet_estimator.evaluate(input_fn=train_input_fn, steps=1)