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import tensorflow as tf | |
sess = tf.Session() | |
tf.python.control_flow_ops = tf | |
from keras import backend as K | |
K.set_session(sess) | |
from keras.layers import ZeroPadding2D, Activation | |
from keras.layers.convolutional import Convolution2D, MaxPooling2D, AveragePooling2D | |
from keras.layers.core import Dense, Flatten | |
from keras.models import Sequential | |
from keras.optimizers import SGD | |
from keras.regularizers import l2 | |
from keras.initializations import normal | |
from keras.callbacks import LearningRateScheduler | |
from keras.datasets import cifar10 | |
from keras.utils.np_utils import to_categorical | |
from functools import partial | |
from math import floor | |
import numpy as np | |
# Helper functions | |
def schedule(epoch, decay, max_epoch=24): # Fixed interval lr step function | |
interval = max_epoch / decay | |
lr = .001 / (10 ** floor(epoch / interval)) | |
return lr | |
def LR_scheduler(decay): | |
return LearningRateScheduler(partial(schedule, decay=decay)) | |
def gaussian(shape, name=None, scale=.01): | |
return normal(shape, scale=scale, name=name) | |
# Network definition | |
weight_decay = .004 | |
filter_size = 5 | |
stride = (1, 1) | |
pool_stride = (2, 2) | |
pool_size = (3, 3) | |
padding_size = (2, 2) | |
nn = Sequential() | |
nn.add(Convolution2D(32, filter_size, filter_size, dim_ordering='tf', subsample=stride, input_shape=(32, 32, 3), | |
init=partial(gaussian, scale=.0001), W_regularizer=l2(weight_decay))) | |
nn.add(ZeroPadding2D(padding_size)) | |
nn.add(MaxPooling2D(pool_size=pool_size, strides=pool_stride)) | |
nn.add(Activation('relu')) | |
nn.add(Convolution2D(32, filter_size, filter_size, dim_ordering='tf', subsample=stride, activation='relu', | |
init=partial(gaussian, scale=.01), W_regularizer=l2(weight_decay))) | |
nn.add(ZeroPadding2D(padding_size)) | |
nn.add(AveragePooling2D(pool_size=pool_size, strides=pool_stride)) | |
nn.add(Convolution2D(64, filter_size, filter_size, dim_ordering='tf', subsample=stride, activation='relu', | |
init=partial(gaussian, scale=.01), W_regularizer=l2(weight_decay))) | |
nn.add(ZeroPadding2D(padding_size)) | |
nn.add(AveragePooling2D(pool_size=pool_size, strides=pool_stride)) | |
nn.add(Flatten()) | |
nn.add(Dense(64, activation='linear', init=partial(gaussian, scale=.1), W_regularizer=l2(weight_decay))) | |
nn.add(Dense(10, activation='softmax', init=partial(gaussian, scale=.1), W_regularizer=l2(weight_decay))) | |
nn.compile(loss='categorical_crossentropy', | |
optimizer=SGD(.001, momentum=.9), | |
metrics=['accuracy']) | |
# Get and format data: | |
(train, label), (test, test_label) = cifar10.load_data() | |
label, test_label = to_categorical(label), to_categorical(test_label) | |
# Mean subtraction | |
mean = np.mean(train, axis=0).astype(np.float32) | |
train = train.astype(np.float32) - mean | |
test = test.astype(np.float32) - mean | |
# Fit and evaluate network on CIFAR10 | |
lr_sched = LR_scheduler(3) | |
nn.fit(train, label, validation_data=(test, test_label), nb_epoch=24, batch_size=100, callbacks=[lr_sched]) | |
print("\n", "Final Accuracy:", nn.evaluate(test, test_label)[1]) |
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