CNTK入门05：一个全连接前馈模型用于分类手写识别图片

CNTK入门05：一个全连接前馈模型用于分类手写识别图片

------- A fully connected feed-forward model for classification of MNIST images.

1.  .\Examples\Image\DataSets\MNIST下有两个文件，运行Python install_mnist.py下载手写识别图片。

其中60000作为测试训练数据，10000张作为测试数据。

代码：install_mnist.py
from __future__ import print_function
import os
import mnist_utils as ut

if __name__ == "__main__":
#改变当前工作目录到指定的路径
os.chdir(os.path.abspath(os.path.dirname(__file__)))
train = ut.load('http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz',
'http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz', 60000)
print ('Writing train text file...')
ut.savetxt(r'./Train-28x28_cntk_text.txt', train)
print ('Done.')

test = ut.load('http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz',
'http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz', 10000)
print ('Writing test text file...')
ut.savetxt(r'./Test-28x28_cntk_text.txt', test)
print ('Done.')


上面主函数调用了如下的文件中的函数：mnist_utils.py
from __future__ import print_function
try:
from urllib.request import urlretrieve
except ImportError:
from urllib import urlretrieve
import sys
import gzip
import shutil
import os
import struct
import numpy as np

def loadData(src, cimg):
print ('Downloading ' + src)
#retrieve,取回；src 下载地址，./delete.me是存储地址；返回tuple(filename,header)
#filename 表示保存到本地的路径，header 表示服务器的响应头
gzfname, h = urlretrieve(src, './delete.me')
print ('Done.')
try:
with gzip.open(gzfname) as gz:
#按照给定的格式(fmt)解析字节流string，返回解析出来的tuple
n = struct.unpack('I', gz.read(4))
# Read magic number.
if n[0] != 0x3080000:
raise Exception('Invalid file: unexpected magic number.')
# Read number of entries.
n = struct.unpack('>I', gz.read(4))[0]
if n != cimg:
raise Exception('Invalid file: expected {0} entries.'.format(cimg))
crow = struct.unpack('>I', gz.read(4))[0]
ccol = struct.unpack('>I', gz.read(4))[0]
if crow != 28 or ccol != 28:
raise Exception('Invalid file: expected 28 rows/cols per image.')
# Read data.
res = np.fromstring(gz.read(cimg * crow * ccol), dtype = np.uint8)
finally:
os.remove(gzfname)
return res.reshape((cimg, crow * ccol))

def loadLabels(src, cimg):
print ('Downloading ' + src)
gzfname, h = urlretrieve(src, './delete.me')
print ('Done.')
try:
with gzip.open(gzfname) as gz:
n = struct.unpack('I', gz.read(4))
# Read magic number.
if n[0] != 0x1080000:
raise Exception('Invalid file: unexpected magic number.')
# Read number of entries.
n = struct.unpack('>I', gz.read(4))
if n[0] != cimg:
raise Exception('Invalid file: expected {0} rows.'.format(cimg))
# Read labels.
res = np.fromstring(gz.read(cimg), dtype = np.uint8)
finally:
os.remove(gzfname)
return res.reshape((cimg, 1))#改变矩阵为cimg行，1列

#参数：训练的数据，训练数据的标签类别，图片张数
def load(dataSrc, labelsSrc, cimg):
data = loadData(dataSrc, cimg)
labels = loadLabels(labelsSrc, cimg)
#
return np.hstack((data, labels))

def savetxt(filename, ndarray):
with open(filename, 'w') as f:
labels = list(map(' '.join, np.eye(10, dtype=np.uint).astype(str)))
for row in ndarray:
row_str = row.astype(str)
label_str = labels[row[-1]]
feature_str = ' '.join(row_str[:-1])
f.write('|labels {} |features {}\n'.format(label_str, feature_str))

在目录 .\Examples\Image\Classification\MLP\Python\SimpleMNIST.py是手写识别的程序；
 Python
SimpleMNIST.py 运行即可：
import argparse
import numpy as np
import sys
import os
from cntk.train import Trainer, minibatch_size_schedule
from cntk.io import MinibatchSource, CTFDeserializer, StreamDef, StreamDefs, INFINITELY_REPEAT
from cntk.device import cpu, try_set_default_device
from cntk.learners import adadelta, learning_rate_schedule, UnitType
from cntk.ops import input, relu, element_times, constant
from cntk.losses import cross_entropy_with_softmax
from cntk.metrics import classification_error
from cntk.train.training_session import *
from cntk.logging import ProgressPrinter, TensorBoardProgressWriter

abs_path = os.path.dirname(os.path.abspath(__file__))
sys.path.append(os.path.join(abs_path, "..", "..", "..", "..", "common"))
from nn import fully_connected_classifier_net

def check_path(path):
if not os.path.exists(path):
readme_file = os.path.normpath(os.path.join(
os.path.dirname(path), "..", "README.md"))
raise RuntimeError(
"File '%s' does not exist. Please follow the instructions at %s to download and prepare it." % (path, readme_file))

def create_reader(path, is_training, input_dim, label_dim):
return MinibatchSource(CTFDeserializer(path, StreamDefs(
features = StreamDef(field='features', shape=input_dim, is_sparse=False),
labels = StreamDef(field='labels', shape=label_dim, is_sparse=False)
)), randomize=is_training, max_sweeps = INFINITELY_REPEAT if is_training else 1)

# Creates and trains a feedforward classification model for MNIST images

def simple_mnist(tensorboard_logdir=None):
input_dim = 784
num_output_classes = 10
num_hidden_layers = 1
hidden_layers_dim = 200

# Input variables denoting the features and label data
feature = input(input_dim, np.float32)
label = input(num_output_classes, np.float32)

# Instantiate the feedforward classification model
scaled_input = element_times(constant(0.00390625), feature)
z = fully_connected_classifier_net(
scaled_input, num_output_classes, hidden_layers_dim, num_hidden_layers, relu)

ce = cross_entropy_with_softmax(z, label)
pe = classification_error(z, label)

data_dir = os.path.join(abs_path, "..", "..", "..", "DataSets", "MNIST")

path = os.path.normpath(os.path.join(data_dir, "Train-28x28_cntk_text.txt"))
check_path(path)

reader_train = create_reader(path, True, input_dim, num_output_classes)

input_map = {
feature : reader_train.streams.features,
label : reader_train.streams.labels
}

# Training config
minibatch_size = 64
num_samples_per_sweep = 60000
num_sweeps_to_train_with = 10

# Instantiate progress writers.
#training_progress_output_freq = 100
progress_writers = [ProgressPrinter(
#freq=training_progress_output_freq,
tag='Training',
num_epochs=num_sweeps_to_train_with)]

if tensorboard_logdir is not None:
progress_writers.append(TensorBoardProgressWriter(freq=10, log_dir=tensorboard_logdir, model=z))

# Instantiate the trainer object to drive the model training
trainer = Trainer(z, (ce, pe), adadelta(z.parameters), progress_writers)

training_session(
trainer=trainer,
mb_source = reader_train,
mb_size = minibatch_size,
model_inputs_to_streams = input_map,
max_samples = num_samples_per_sweep * num_sweeps_to_train_with,
progress_frequency=num_samples_per_sweep
).train()

# Load test data
path = os.path.normpath(os.path.join(data_dir, "Test-28x28_cntk_text.txt"))
check_path(path)

reader_test = create_reader(path, False, input_dim, num_output_classes)

input_map = {
feature : reader_test.streams.features,
label : reader_test.streams.labels
}

# Test data for trained model
test_minibatch_size = 1024
num_samples = 10000
num_minibatches_to_test = num_samples / test_minibatch_size
test_result = 0.0
for i in range(0, int(num_minibatches_to_test)):
mb = reader_test.next_minibatch(test_minibatch_size, input_map=input_map)
eval_error = trainer.test_minibatch(mb)
test_result = test_result + eval_error

# Average of evaluation errors of all test minibatches
return test_result / num_minibatches_to_test

if __name__=='__main__':
# Specify the target device to be used for computing, if you do not want to
# use the best available one, e.g.
# try_set_default_device(cpu())

parser = argparse.ArgumentParser()
parser.add_argument('-tensorboard_logdir', '--tensorboard_logdir',
help='Directory where TensorBoard logs should be created', required=False, default=None)
args = vars(parser.parse_args())

error = simple_mnist(args['tensorboard_logdir'])
print("Error: %f" % error)


----------------------------------------------------------------------------------------------------------------------------
附录：
1. os.chdir(）  #改变当前工作目录到指定的路径
2.np.hstack()的解释：

stack() Join a sequence of arrays along a new axis.

hstack() Stack arrays in sequence horizontally (column wise).

dstack() Stack arrays in sequence depth wise (along third dimension).

concatenate() Join a sequence of arrays along an existing axis.

vsplit () Split array into a list of multiple sub-arrays vertically.

2.1 stack() ：通过个新轴，连接两个数组。

>>> arrays = [np.random.randn(3, 4) for _ in range(10)]
>>> np.stack(arrays, axis=0).shape
(10, 3, 4)
>>> np.stack(arrays, axis=1).shape
(3, 10, 4)
>>> np.stack(arrays, axis=2).shape
(3, 4, 10)
>>> a = np.array([1, 2, 3])
>>> b = np.array([2, 3, 4])
>>> np.stack((a, b))
array([[1, 2, 3],
[2, 3, 4]])
>>> np.stack((a, b), axis=-1)
array([[1, 2],
[2, 3],
[3, 4]])

np.random.randn(3, 4)返回一个3行4列的矩阵，数字是服从标准正态分布的。

2.2 numpy.hstack()函数

函数原型：numpy.hstack(tup)

其中tup是arrays序列，The arrays must have the same shape, except in the dimensioncorresponding to axis (the first, by default).

等价于：np.concatenate(tup, axis=1)

>>> a = np.array((1,2,3))
>>> b = np.array((2,3,4))
>>> np.hstack((a,b))
array([1, 2, 3, 2, 3, 4])
>>> a = np.array([[1],[2],[3]])
>>> b = np.array([[2],[3],[4]])
>>> np.hstack((a,b))
array([[1, 2],
[2, 3],
[3, 4]])

2.3 numpy.vstack()函数

函数原型：numpy.vstack(tup)

等价于：np.concatenate(tup, axis=0） if tup contains arrays thatare at least 2-dimensional.

程序实例：

>>> a = np.array([1, 2, 3])
>>> b = np.array([2, 3, 4])
>>> np.vstack((a,b))
array([[1, 2, 3],
[2, 3, 4]])
>>> a = np.array([[1], [2], [3]])
>>> b = np.array([[2], [3], [4]])
>>> np.vstack((a,b))
array([[1],
[2],
[3],
[2],
[3],
[4]])

2.4   numpy.dstack(）函数

函数原型：numpy.dstack(tup)

等价于：np.concatenate(tup, axis=2)

程序实例：

>>> a = np.array((1,2,3))
>>> b = np.array((2,3,4))
>>> np.dstack((a,b))
array([[[1, 2],
[2, 3],
[3, 4]]])

>>>

>>> a = np.array([[1],[2],[3]])
>>> b = np.array([[2],[3],[4]])
>>> np.dstack((a,b))
array([[[1, 2]],
[[2, 3]],
[[3, 4]]])

2.5 numpy.concatenate()函数

函数原型：numpy.concatenate((a1, a2, ...), axis=0)

程序实例：

>>> a = np.array([[1, 2], [3, 4]])
>>> b = np.array([[5, 6]])
>>> np.concatenate((a, b), axis=0)
array([[1, 2],
[3, 4],
[5, 6]])
>>> np.concatenate((a, b.T), axis=1)
array([[1, 2, 5],
[3, 4, 6]])

This function will not preserve masking of MaskedArray inputs.
>>>

>>> a = np.ma.arange(3)
>>> a[1] = np.ma.masked
>>> b = np.arange(2, 5)
>>> a
masked_array(data = [0 -- 2],
mask = [False  True False],
fill_value = 999999)
>>> b
array([2, 3, 4])
>>> np.concatenate([a, b])
masked_array(data = [0 1 2 2 3 4],
mask = False,
fill_value = 999999)
>>> np.ma.concatenate([a, b])
masked_array(data = [0 -- 2 2 3 4],
mask = [False  True False False False False],
fill_value = 999999)

2.6 numpy.vsplit()函数

函数原型：numpy.vsplit(ary, indices_or_sections)

程序实例：

>>> x = np.arange(16.0).reshape(4, 4)
>>> x
array([[  0.,   1.,   2.,   3.],
[  4.,   5.,   6.,   7.],
[  8.,   9.,  10.,  11.],
[ 12.,  13.,  14.,  15.]])
>>> np.vsplit(x, 2)
[array([[ 0.,  1.,  2.,  3.],
[ 4.,  5.,  6.,  7.]]),
array([[  8.,   9.,  10.,  11.],
[ 12.,  13.,  14.,  15.]])]
>>> np.vsplit(x, np.array([3, 6]))
[array([[  0.,   1.,   2.,   3.],
[  4.,   5.,   6.,   7.],
[  8.,   9.,  10.,  11.]]),
array([[ 12.,  13.,  14.,  15.]]),
array([], dtype=float64)]

With a higher dimensional array the split is still along the first axis.
>>>

>>> x = np.arange(8.0).reshape(2, 2, 2)
>>> x
array([[[ 0.,  1.],
[ 2.,  3.]],
[[ 4.,  5.],
[ 6.,  7.]]])
>>> np.vsplit(x, 2)
[array([[[ 0.,  1.],
[ 2.,  3.]]]),
array([[[ 4.,  5.],
[ 6.,  7.]]])]

------------------------------------------------------------------------------------
3.url.urlretrieve(filename[,
reporthook[, data]]])的使用

urllib.urlretrieve(url,filename)下载网络文件,第一个元素就是目标url,第二个参数是保存的文件绝对路径(含文件名),返回值是一个tuple(filename,header),filename
表示保存到本地的路径，header 表示服务器的响应头.如果urlretrieve仅提供1个参数,返回值的filename就是产生的临时文件名,函数执行完毕后该临时文件会被删除参数。参数reporthook是一个回调函数，当连接上服务器、以及相应的数据块传输完毕的时候会触发该回调。其中回调函数名称可任意，但是参数必须为三个。一般直接使用reporthook(block_read,block_size,total_size)定义回调函数，block_size是每次读取的数据块的大小，block_read是每次读取的数据块个数，taotal_size是一一共读取的数据量，单位是byte。可以使用reporthook函数来显示读取进度。