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AE_onepixel_mnist_model.py
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AE_onepixel_mnist_model.py
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import keras
import numpy as np
from keras import optimizers
from keras.datasets import mnist
from numbers import Number
from keras.models import Sequential, load_model
from keras.layers import Conv2D, Dense, Flatten, MaxPooling2D, Dropout, Lambda
from keras.callbacks import LearningRateScheduler, TensorBoard, ModelCheckpoint
from keras.preprocessing.image import ImageDataGenerator
from keras.regularizers import l2
from onepixel.networks.train_plot import PlotLearning
# Code taken from https://github.com/BIGBALLON/cifar-10-cnn
class CNNMNIST:
def __init__(self, epochs=200, batch_size=128, load_weights=True, p_fail = 0.1):
self.name = 'lenet'
self.model_filename = 'networks/models/lenet.h5'
self.num_classes = 10
self.input_shape = 28, 28, 1
self.batch_size = batch_size
self.epochs = epochs
self.iterations = 391
self.weight_decay = 0.0001
self.log_filepath = r'networks/models/lenet/'
self.p_fail = p_fail
if load_weights:
self._model = load_model(self.model_filename)
def count_params(self):
return self._model.count_params()
def build_model(self):
def IndependentCrashes(p_fail, input_shape = None):
""" Make dropout without scaling """
assert isinstance(p_fail, Number), "pfail must be a number"
return Lambda(lambda x: Dropout(p_fail)(x) * (1 - p_fail), input_shape = input_shape, name = 'Crashes')
model = Sequential()
model.add(IndependentCrashes(self.p_fail, input_shape = self.input_shape))
model.add(Conv2D(8, (3, 3), padding='valid', activation = 'relu', kernel_initializer='random_normal', input_shape=self.input_shape))
model.add(Conv2D(16, (3, 3), padding='valid', activation = 'relu', kernel_initializer='random_normal'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(Flatten())
model.add(Dense(50, activation = 'relu', kernel_initializer='he_normal' ))
model.add(Dense(10, activation = 'softmax', kernel_initializer='he_normal' ))
sgd = optimizers.Adadelta()
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
return model
def scheduler(self, epoch):
if epoch <= 60:
return 0.05
if epoch <= 120:
return 0.01
if epoch <= 160:
return 0.002
return 0.0004
def train(self):
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = np.reshape(x_train, (60000, 28,28,1))
x_test = np.reshape(x_test, (10000, 28,28,1))
y_train = keras.utils.to_categorical(y_train, self.num_classes)
y_test = keras.utils.to_categorical(y_test, self.num_classes)
# color preprocessing
# build network
model = self.build_model()
model.summary()
# Save the best model during each training checkpoint
checkpoint = ModelCheckpoint(self.model_filename,
monitor='val_loss',
verbose=0,
save_best_only= True,
mode='auto')
plot_callback = PlotLearning()
tb_cb = TensorBoard(log_dir=self.log_filepath, histogram_freq=0)
cbks = [checkpoint, plot_callback, tb_cb]
# using real-time data augmentation
print('Using real-time data augmentation.')
datagen = ImageDataGenerator(horizontal_flip=True,
width_shift_range=0.125,height_shift_range=0.125,fill_mode='constant',cval=0.)
datagen.fit(x_train)
# start traing
model.fit_generator(datagen.flow(x_train, y_train,batch_size=self.batch_size),
steps_per_epoch=self.iterations,
epochs=self.epochs,
callbacks=cbks,
validation_data=(x_test, y_test))
# save model
model.save(self.model_filename)
self._model = model
def color_process(self, imgs):
if imgs.ndim < 4:
imgs = np.array([imgs])
imgs = imgs.astype('float32')
mean = [125.307, 122.95, 113.865]
std = [62.9932, 62.0887, 66.7048]
for img in imgs:
for i in range(3):
img[:,:,i] = (img[:,:,i] - mean[i]) / std[i]
return imgs
def predict(self, img):
processed = img
return self._model.predict(processed, batch_size=self.batch_size)
def predict_one(self, img):
return self.predict(np.array([img]))[0]
def accuracy(self):
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
y_train = keras.utils.to_categorical(y_train, self.num_classes)
y_test = keras.utils.to_categorical(y_test, self.num_classes)
# color preprocessing
x_train, x_test = self.color_preprocessing(x_train, x_test)
return self._model.evaluate(x_test, y_test, verbose=0)[1]