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mnist.py
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mnist.py
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from keras.datasets import mnist
from keras.models import Sequential, model_from_json
from keras.layers import Dense, Dropout, Activation, Flatten, Input
from keras.layers import Conv2D, MaxPooling2D
from keras.preprocessing.image import ImageDataGenerator
from keras.utils import np_utils
import argparse
import numpy as np
import pdb
from tensorflow.python.platform import flags
FLAGS = flags.FLAGS
def set_mnist_flags():
try:
flags.DEFINE_integer('BATCH_SIZE', 64, 'Size of training batches')
except argparse.ArgumentError:
pass
flags.DEFINE_integer('NUM_CLASSES', 10, 'Number of classification classes')
flags.DEFINE_integer('IMAGE_ROWS', 28, 'Input row dimension')
flags.DEFINE_integer('IMAGE_COLS', 28, 'Input column dimension')
flags.DEFINE_integer('NUM_CHANNELS', 1, 'Input depth dimension')
def data_mnist(one_hot=True):
"""
Preprocess MNIST dataset
"""
# the data, shuffled and split between train and test sets
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train = X_train.reshape(X_train.shape[0],
FLAGS.IMAGE_ROWS,
FLAGS.IMAGE_COLS,
FLAGS.NUM_CHANNELS)
X_test = X_test.reshape(X_test.shape[0],
FLAGS.IMAGE_ROWS,
FLAGS.IMAGE_COLS,
FLAGS.NUM_CHANNELS)
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_test /= 255
print('X_train shape:', X_train.shape)
print(X_train.shape[0], 'train samples')
print(X_test.shape[0], 'test samples')
print ("Loaded MNIST test data.")
if one_hot:
# convert class vectors to binary class matrices
y_train = np_utils.to_categorical(y_train, FLAGS.NUM_CLASSES).astype(np.float32)
y_test = np_utils.to_categorical(y_test, FLAGS.NUM_CLASSES).astype(np.float32)
return X_train, y_train, X_test, y_test
def modelA():
model = Sequential()
model.add(Dropout(0.2, input_shape=(FLAGS.IMAGE_ROWS,
FLAGS.IMAGE_COLS,
FLAGS.NUM_CHANNELS)))
model.add(Conv2D(64, (8, 8),
subsample=(2, 2),
border_mode='same'))
model.add(Activation('relu'))
model.add(Conv2D(128, (6, 6),
subsample=(2, 2),
border_mode='valid'))
model.add(Activation('relu'))
model.add(Conv2D(128, (5, 5),
subsample=(1, 1)))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Flatten())
model.add(Dense(FLAGS.NUM_CLASSES))
return model
def data_gen_mnist(X_train):
datagen = ImageDataGenerator()
datagen.fit(X_train)
return datagen
def load_model_mnist(model_path):
try:
with open(model_path+'.json', 'r') as f:
json_string = f.read()
model = model_from_json(json_string)
except IOError:
model = modelA()
model.load_weights(model_path)
return model