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107 lines
4.4 KiB
Python
107 lines
4.4 KiB
Python
"""
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Adapted from keras example cifar10_cnn.py
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Train NASNet-CIFAR on the CIFAR10 small images dataset.
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"""
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from __future__ import print_function
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from keras.datasets import cifar10
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from keras.preprocessing.image import ImageDataGenerator
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from keras.utils import np_utils
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from keras.callbacks import ModelCheckpoint
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from keras.callbacks import ReduceLROnPlateau
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from keras.callbacks import CSVLogger
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from keras.optimizers import Adam
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from keras_contrib.applications.nasnet import NASNetCIFAR, preprocess_input
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import numpy as np
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weights_file = 'NASNet-CIFAR-10.h5'
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lr_reducer = ReduceLROnPlateau(factor=np.sqrt(0.5), cooldown=0, patience=5, min_lr=0.5e-5)
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csv_logger = CSVLogger('NASNet-CIFAR-10.csv')
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model_checkpoint = ModelCheckpoint(weights_file, monitor='val_predictions_acc', save_best_only=True,
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save_weights_only=True, mode='max')
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batch_size = 128
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nb_classes = 10
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nb_epoch = 600
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data_augmentation = True
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# input image dimensions
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img_rows, img_cols = 32, 32
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# The CIFAR10 images are RGB.
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img_channels = 3
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# The data, shuffled and split between train and test sets:
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(X_train, y_train), (X_test, y_test) = cifar10.load_data()
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# Convert class vectors to binary class matrices.
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Y_train = np_utils.to_categorical(y_train, nb_classes)
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Y_test = np_utils.to_categorical(y_test, nb_classes)
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X_train = X_train.astype('float32')
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X_test = X_test.astype('float32')
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# preprocess input
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X_train = preprocess_input(X_train)
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X_test = preprocess_input(X_test)
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# For training, the auxilary branch must be used to correctly train NASNet
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model = NASNetCIFAR((img_rows, img_cols, img_channels), use_auxilary_branch=True)
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model.summary()
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optimizer = Adam(lr=1e-3, clipnorm=5)
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model.compile(loss=['categorical_crossentropy', 'categorical_crossentropy'],
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optimizer=optimizer, metrics=['accuracy'], loss_weights=[1.0, 0.4])
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# model.load_weights('NASNet-CIFAR-10.h5', by_name=True)
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if not data_augmentation:
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print('Not using data augmentation.')
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model.fit(X_train, [Y_train, Y_train],
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batch_size=batch_size,
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epochs=nb_epoch,
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validation_data=(X_test, [Y_test, Y_test]),
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shuffle=True,
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verbose=2,
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callbacks=[lr_reducer, csv_logger, model_checkpoint])
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else:
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print('Using real-time data augmentation.')
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# This will do preprocessing and realtime data augmentation:
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datagen = ImageDataGenerator(
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featurewise_center=False, # set input mean to 0 over the dataset
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samplewise_center=False, # set each sample mean to 0
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featurewise_std_normalization=False, # divide inputs by std of the dataset
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samplewise_std_normalization=False, # divide each input by its std
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zca_whitening=False, # apply ZCA whitening
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rotation_range=0, # randomly rotate images in the range (degrees, 0 to 180)
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width_shift_range=0.1, # randomly shift images horizontally (fraction of total width)
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height_shift_range=0.1, # randomly shift images vertically (fraction of total height)
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horizontal_flip=True, # randomly flip images
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vertical_flip=False) # randomly flip images
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# Compute quantities required for featurewise normalization
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# (std, mean, and principal components if ZCA whitening is applied).
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datagen.fit(X_train)
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# wrap the ImageDataGenerator to yield two label batches [y, y] for each input batch X
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# When training a NASNet model, we have to use its auxilary training head
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# Therefore the model is technically a 1 input - 2 output model, and requires
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# the label to be duplicated for the auxilary head
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def image_data_generator_wrapper(image_datagenerator, batch_size):
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iterator = datagen.flow(X_train, Y_train, batch_size=batch_size)
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while True:
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X, y = next(iterator) # get the next batch
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yield X, [y, y] # duplicate the labels for each batch
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# Fit the model on the batches generated by datagen.flow().
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model.fit_generator(image_data_generator_wrapper(datagen, batch_size),
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steps_per_epoch=X_train.shape[0] // batch_size,
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validation_data=(X_test, [Y_test, Y_test]),
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epochs=nb_epoch, verbose=2,
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callbacks=[lr_reducer, csv_logger, model_checkpoint])
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scores = model.evaluate(X_test, [Y_test, Y_test], batch_size=batch_size)
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for score, metric_name in zip(scores, model.metrics_names):
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print("%s : %0.4f" % (metric_name, score))
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