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setup_mnist.py
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setup_mnist.py
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## setup_mnist.py -- mnist data and model loading code
##
## Copyright (C) 2016, Nicholas Carlini <nicholas@carlini.com>.
##
## This program is licenced under the BSD 2-Clause 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 keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.utils import np_utils
from keras.models import load_model
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, session=None, conv=True):
self.num_channels = 1
self.image_size = 28
self.num_labels = 10
model = Sequential()
if conv:
model.add(Conv2D(32, (3, 3),
input_shape=(28, 28, 1)))
model.add(Activation('relu'))
model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(200))
model.add(Activation('relu'))
model.add(Dense(200))
model.add(Activation('relu'))
model.add(Dense(10))
else:
model.add(Flatten(input_shape=(28, 28, 1)))
for p in [128, 128, 128, 128]:
model.add(Dense(p))
model.add(Activation('relu'))
model.add(Dense(10))
model.load_weights(restore)
model.compile(optimizer="sgd", loss='categorical_crossentropy', metrics=['accuracy'])
self.model = model
def predict(self, data):
return self.model(data)
def score(self, X, Y):
return self.model.test_on_batch(X, Y)[1]