用cnn構建多層神經網絡來識別mnist中的圖片
阿新 • • 發佈：2017-10-28
argv padding out load 神經網絡 dir sco ack import
mnist.py
import tensorflow as tf import numpy as np import argparse import sys import urllib import os import gzip SOURCE_URL = r‘http://yann.lecun.com/exdb/mnist/‘ TRAIN_SIZE = 55000 TEST_SIZE = 10000 VALIDATE_SIZE = 5000 IMAGE_SIZE = 28 NUMBER_CHANNEL = 1 TRAIN_DATA = ‘train-images-idx3-ubyte.gz‘ TRAIN_LABELS= ‘train-labels-idx1-ubyte.gz‘ TEST_DATA = ‘t10k-images-idx3-ubyte.gz‘ TEST_LABELS = ‘t10k-labels-idx1-ubyte.gz‘ class Mnist: def __init__(self, FLAGS): self.FLAGS = FLAGS self.start = 0 self.train_size = TRAIN_SIZE self.validate_size = VALIDATE_SIZE self.test_size = TEST_SIZE self._maybe_download(TRAIN_DATA) self.train_data= self._extract_images(TRAIN_DATA) self.validate_data, self.train_data = self._get_validate_data(self.train_data, self.validate_size) self._maybe_download(TRAIN_LABELS) self.train_labels = self._extract_labels(TRAIN_LABELS) self.train_labels = self._one_hot(self.train_labels) self.validate_labels, self.train_labels= self._get_validate_data(self.train_labels, self.validate_size) self._maybe_download(TEST_DATA) self.test_data = self._extract_images(TEST_DATA) self._maybe_download(TEST_LABELS) self.test_labels = self._extract_labels(TEST_LABELS) self.test_labels = self._one_hot(self.test_labels) if self.num_images != self.num_labels: raise Error(‘number of images and number of labels don\‘t match‘) self.train_data, self.train_labels = self._shuffle(self.train_data, self.train_labels) print(‘Done preparing data‘) def _shuffle(self, images, labels): perm = np.arange(self.train_size) np.random.shuffle(perm) return images[perm], labels[perm] def _read32(self, bufstream): dt = np.dtype(‘>i4‘) buf = bufstream.read(4) data = np.frombuffer(buf, dtype = dt)[0] data.astype(np.int32) return data def _maybe_download(self, filename): filepath = os.path.join(self.FLAGS.data_dir, filename) if not os.path.isdir(self.FLAGS.data_dir): os.path.mkdir(self.FLAGS.data_dir) if not os.path.isfile(filepath): #def _progress(): urllib.request.urlopen(SOURCE_URL, filepath) def _extract_images(self, filename): filepath = os.path.join(self.FLAGS.data_dir, filename) with gzip.open(filepath) as bufstream: magic = self._read32(bufstream) print(magic) if not magic == 2051: raise ValueError("2051 error") num_data = self._read32(bufstream) self.num_images = num_data rows = self._read32(bufstream) self.image_size = rows cols = self._read32(bufstream) print(‘num_data %d rows %d cols %d‘% (num_data, rows, cols)) buf = bufstream.read(num_data*rows*cols*NUMBER_CHANNEL) data = np.frombuffer(buf, dtype = np.uint8) data.astype(np.float32) data = data.reshape(num_data, rows*cols*NUMBER_CHANNEL) return data def _extract_labels(self, filename): filepath = os.path.join(self.FLAGS.data_dir, filename) with gzip.open(filepath) as bufstream: magic = self._read32(bufstream) if magic != 2049: raise ValueError(‘2049 error‘) num_labels = self._read32(bufstream) self.num_labels = num_labels print(‘num_labels %d‘%num_labels) buf = bufstream.read(num_labels*1) labels = np.frombuffer(buf, dtype = np.uint8) labels.astype(np.int32) return labels def _get_validate_data(self, data, validate_size): if validate_size > data.shape[0]: raise Error(‘validate size out of bound‘) validate_data = data[:validate_size,...] data_left = data[validate_size:,...] return validate_data, data_left def _one_hot(self, labels): num_labels = labels.shape[0] one_hot_labels = np.zeros([num_labels,10],dtype = np.float32) one_hot_labels[[range(num_labels)], labels] = 1.0 return one_hot_labels def get_batch(self, batch_size): end = self.start +batch_size tmp_start = self.start if end >= self.train_size: self.start = end%self.train_size tmp_data = np.vstack((self.train_data, self.train_data[:batch_size,...])) tmp_labels = np.vstack((self.train_labels, self.train_labels[:batch_size,...])) return tmp_data[tmp_start:end,...],tmp_labels[tmp_start:end,...] else: self.start = end return self.train_data[tmp_start:end,...], self.train_labels[tmp_start:end,...]
mnist_cnn.py
import tensorflow as tf
import numpy as np
import mnist
import argparse
import sys
import os
from tensorflow.examples.tutorials.mnist import input_data
FLAGS = tf.flags.FLAGS
def _weight(name, shape):
return tf.Variable(tf.truncated_normal(stddev = 0.1, shape = shape, name = name, dtype = tf.float32))
def _bias(name, shape):
return tf.Variable(tf.constant(0.1, shape = shape, name = name, dtype= tf.float32))
def _conv(x, w):
return tf.nn.conv2d(x, w, strides = [1,1,1,1], padding= ‘SAME‘)
def _max_pool(x):
return tf.nn.max_pool(x, ksize = [1,2,2,1], strides = [1,2,2,1], padding = ‘SAME‘)
def _rm_prefiles(paths):
for path in paths:
for root, dirs, names in os.walk(path):
for filename in names:
os.remove(os.path.join(root, filename))
def cnn_structure(x):
x_images =tf.reshape(x,[-1,28,28,1])
with tf.name_scope(‘conv1‘):
w_cov1 = _weight(‘w_cov1‘, [5,5,1,32])
with tf.name_scope(‘w_cov1‘):
tf.summary.scalar(‘w_cov1_mean‘, tf.reduce_mean(w_cov1))
tf.summary.scalar(‘w_cov1_dev‘, tf.reduce_mean(tf.square(w_cov1- tf.reduce_mean(w_cov1))))
tf.summary.histogram(‘w_cov1_hist‘, w_cov1)
b_cov1 = _bias(‘b_cov1‘, [32])
h_cov1 = tf.nn.relu(_conv(x_images, w_cov1) + b_cov1 )
h_pool1 = _max_pool(h_cov1)
with tf.name_scope(‘conv2‘):
w_cov2 = _weight(‘w_cov2‘, [5,5,32,64])
b_cov2 = _bias(‘b_cov2‘, [64])
h_cov2 = tf.nn.relu(_conv(h_pool1, w_cov2) + b_cov2)
h_pool2 = _max_pool(h_cov2)
h_pool2_flat = tf.reshape(h_pool2, [-1, 3136])
with tf.name_scope(‘fc1‘):
w_fc1 = _weight(‘w_fc1‘, [7*7*64, 1024])
b_fc1 = _bias(‘b_fc1‘, [1024])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, w_fc1) + b_fc1)
with tf.name_scope(‘dropout‘):
keep_prob = tf.placeholder(dtype = tf.float32)
h_dropout = tf.nn.dropout(h_fc1, keep_prob)
with tf.name_scope(‘fc2‘):
w_fc2 = _weight(‘w_fc2‘, [1024, 10])
b_fc2 = _bias(‘b_fc2‘, [10])
y = tf.matmul(h_dropout, w_fc2) + b_fc2
saver = tf.train.Saver({‘w_cov1‘:w_cov1, ‘b_cov1‘:b_cov1, ‘w_cov2‘:w_cov2, ‘b_cov2‘:b_cov2})
return y, keep_prob, saver
def main(_):
mnistdata = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)
mymnist = mnist.Mnist(FLAGS)
x = tf.placeholder(shape = [None, 784], dtype = tf.float32)
y_ = tf.placeholder(shape = [None, 10], dtype = tf.float32)
y, keep_prob, saver = cnn_structure(x)
with tf.name_scope(‘train_section‘):
with tf.name_scope(‘Cross_entropy‘):
cross_entropy = tf.reduce_sum(tf.nn.softmax_cross_entropy_with_logits(logits = y, labels = y_))
tf.summary.scalar(‘cross_entropy‘, cross_entropy)
with tf.name_scope(‘Adam_Optimizer‘):
optimizer = tf.train.AdamOptimizer(learning_rate = 0.0001)
train_steps = optimizer.minimize(cross_entropy)
with tf.name_scope(‘Test_section‘):
with tf.name_scope(‘Accuracy‘):
prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(prediction, tf.float32))
merged = tf.summary.merge_all()
_rm_prefiles([FLAGS.summary_dir, FLAGS.model_dir])
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
train_writer = tf.summary.FileWriter(FLAGS.summary_dir, sess.graph)
for i in range(FLAGS.train_steps):
if i%20 ==0:
j = int(i/int(FLAGS.train_steps/60))
sys.stdout.write(‘\r%4.2f%% ‘%(float(i)/float(FLAGS.train_steps)*100))
sys.stdout.write(‘->‘+‘#‘*j+(60-j)*‘ ‘+‘<-‘)
sys.stdout.flush()
#batch = mnistdata.train.next_batch(50)
#feed_dict = {x:batch[0], y_:batch[1],keep_prob:0.5}
batch_xs, batch_ys = mymnist.get_batch(FLAGS.batch_size)
feed_dict = {x: batch_xs, y_: batch_ys, keep_prob: 0.5}
_, summaries = sess.run([train_steps,merged], feed_dict = feed_dict)
train_writer.add_summary(summaries, i)
‘‘‘
if i%100 ==0:
feed_dict= {x:mymnist.test_data, y_:mymnist.test_labels, keep_prob : 1.0}
#feed_dict = {x: mnistdata.test.images, y_: mnistdata.test.labels, keep_prob: 1.0}
print(‘%5.2f%%‘%(sess.run(accuracy,feed_dict = feed_dict)*100))
‘‘‘
saver.save(sess, os.path.join(FLAGS.model_dir, ‘model.ckpt‘))
feed_dict= {x:mymnist.test_data, y_:mymnist.test_labels, keep_prob : 1.0}
#feed_dict = {x: mnistdata.test.images, y_: mnistdata.test.labels, keep_prob: 1.0}
print(‘%5.2f%%‘%(sess.run(accuracy,feed_dict = feed_dict)*100))
if __name__ ==‘__main__‘:
parser = argparse.ArgumentParser()
parser.add_argument("-d", "--data_dir", type = str, default = r‘W:\workspace\tensorflow\my\data‘,
help = "Directory for storing input data")
parser.add_argument(‘-m‘, "--model_dir", type = str, default = r‘W:\workspace\tensorflow\mnistexpert\model‘, help = ‘Directory for storing model‘)
parser.add_argument(‘-s‘,‘--summary_dir‘, type = str, default = r‘W:\workspace\tensorflow\mnistexpert\summary‘, help = ‘Directory for storing summary data‘)
parser.add_argument(‘-b‘, ‘--batch_size‘, type = int, default = 50, help = ‘Size of batch‘)
parser.add_argument(‘-t‘, ‘--train_steps‘, type = int, default = 20000, help = ‘Number of steps to train the model‘)
FLAGS, unparsed = parser.parse_known_args()
tf.app.run(main = main, argv = [sys.argv[0]]+ unparsed)
在這次實現中將深度神經網絡框架獨立了出來寫進了一個函數，從而使得代碼可讀性大大提高
用cnn構建多層神經網絡來識別mnist中的圖片