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setup_mnist.py
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## setup_mnist.py -- mnist data and model loading code
##
## Copyright (C) 2017-2018, IBM Corp.
## Copyright (C) 2017, Lily Weng <[email protected]>
## and Huan Zhang <[email protected]>
## Copyright (C) 2016, Nicholas Carlini <[email protected]>.
##
## This program is licenced under the Apache 2.0 licence,
## contained in the LICENCE file in this directory.
import tensorflow as tf
import numpy as np
import os
import pickle
import gzip
import urllib.request
from tensorflow.contrib.keras.api.keras.models import Sequential
from tensorflow.contrib.keras.api.keras.layers import Dense, Dropout, Activation, Flatten
from tensorflow.contrib.keras.api.keras.layers import Conv2D, MaxPooling2D
from tensorflow.contrib.keras.api.keras.layers import Lambda
from tensorflow.contrib.keras.api.keras.models import load_model
from tensorflow.contrib.keras.api.keras import backend as K
def extract_data(filename, num_images):
with gzip.open(filename) as bytestream:
bytestream.read(16)
buf = bytestream.read(num_images*28*28)
data = np.frombuffer(buf, dtype=np.uint8).astype(np.float32)
data = (data / 255) - 0.5
data = data.reshape(num_images, 28, 28, 1)
return data
def extract_labels(filename, num_images):
with gzip.open(filename) as bytestream:
bytestream.read(8)
buf = bytestream.read(1 * num_images)
labels = np.frombuffer(buf, dtype=np.uint8)
return (np.arange(10) == labels[:, None]).astype(np.float32)
class MNIST:
def __init__(self):
if not os.path.exists("data"):
os.mkdir("data")
files = ["train-images-idx3-ubyte.gz",
"t10k-images-idx3-ubyte.gz",
"train-labels-idx1-ubyte.gz",
"t10k-labels-idx1-ubyte.gz"]
for name in files:
urllib.request.urlretrieve('http://yann.lecun.com/exdb/mnist/' + name, "data/"+name)
train_data = extract_data("data/train-images-idx3-ubyte.gz", 60000)
train_labels = extract_labels("data/train-labels-idx1-ubyte.gz", 60000)
self.test_data = extract_data("data/t10k-images-idx3-ubyte.gz", 10000)
self.test_labels = extract_labels("data/t10k-labels-idx1-ubyte.gz", 10000)
VALIDATION_SIZE = 5000
self.validation_data = train_data[:VALIDATION_SIZE, :, :, :]
self.validation_labels = train_labels[:VALIDATION_SIZE]
self.train_data = train_data[VALIDATION_SIZE:, :, :, :]
self.train_labels = train_labels[VALIDATION_SIZE:]
class MNISTModel:
def __init__(self, restore = None, session=None, use_softmax=False, use_brelu = False, activation = "relu"):
def bounded_relu(x):
return K.relu(x, max_value=1)
if use_brelu:
activation = bounded_relu
print("inside MNISTModel: activation = {}".format(activation))
self.num_channels = 1
self.image_size = 28
self.num_labels = 10
model = Sequential()
model.add(Conv2D(32, (3, 3),
input_shape=(28, 28, 1)))
model.add(Activation(activation))
model.add(Conv2D(32, (3, 3)))
model.add(Activation(activation))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3)))
model.add(Activation(activation))
model.add(Conv2D(64, (3, 3)))
model.add(Activation(activation))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(200))
model.add(Activation(activation))
model.add(Dense(200))
model.add(Activation(activation))
model.add(Dense(10))
# output log probability, used for black-box attack
if use_softmax:
model.add(Activation('softmax'))
if restore:
model.load_weights(restore)
layer_outputs = []
for layer in model.layers:
if isinstance(layer, Conv2D) or isinstance(layer, Dense):
layer_outputs.append(K.function([model.layers[0].input], [layer.output]))
self.model = model
self.layer_outputs = layer_outputs
def predict(self, data):
return self.model(data)
class TwoLayerMNISTModel:
def __init__(self, restore = None, session=None, use_softmax=False):
self.num_channels = 1
self.image_size = 28
self.num_labels = 10
model = Sequential()
model.add(Flatten(input_shape=(28, 28, 1)))
model.add(Dense(1024))
model.add(Lambda(lambda x: x * 10))
model.add(Activation('softplus'))
model.add(Lambda(lambda x: x * 0.1))
model.add(Dense(10))
# output log probability, used for black-box attack
if use_softmax:
model.add(Activation('softmax'))
if restore:
model.load_weights(restore)
layer_outputs = []
for layer in model.layers:
if isinstance(layer, Conv2D) or isinstance(layer, Dense):
layer_outputs.append(K.function([model.layers[0].input], [layer.output]))
self.layer_outputs = layer_outputs
self.model = model
def predict(self, data):
return self.model(data)