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July 19, 2018 16:35
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nasnet model for model_to_estimator
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| # 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) |
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