MNIST 数字识别和数据持久化–step by step 入门TensorFlow（三）
标签：tensorflow
我们将采用Jupyter notebook交互式编程的方式，通过一步步代码的讲解，学习Tensorflow 编程。推荐使用Jupyter notebook作为开发环境按照文中的代码step by step 的学习。
文章列表：
Tensorflow基础知识与神经网络构建–step by step 入门TensorFlow（一）
深层神经网络实现–step by step 入门TensorFlow（二）
MNIST 数字识别和数据持久化–step by step 入门TensorFlow（三）
一个完整的MNIST 数字识别代码
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
# 数据集参数
input_dim = 784
output_dim = 104
# 神经网络参数
h1_dim = 500
batch_size = 100
learning_rate_base = 0.8
learning_rate_decay = 0.99 # 学习率的衰减率
regularization_rate = 0.0001 # 正则化项系数
training_step = 30000
moving_average_decay = 0.99 # 滑动平均衰减率
def net(input_tensor, avg_class, w1,b1,w2,b2):
# 不使用滑动平均类
if avg_class == None:
h1 = tf.nn.relu(tf.matmul(input_tensor, w1) + b1)
output = tf.matmul(h1, w2) + b2
return output
# 使用滑动平均类
else:
h1 = tf.nn.relu(tf.matmul(input_tensor, avg_class.average(w1) + avg_class.average(b1)))
return tf.matmul(h1,avg_class.average(w2) + avg_class.average(b2))
def train(mnist):
x = tf.placeholder(tf.float32, shape=[None,784], name="x_input")
y_ = tf.placeholder(tf.float32, shape=[None, 10], name="y_input")
w1 = tf.Variable(tf.truncated_normal([input_dim, h1_dim], stddev=0.1))
b1 = tf.Variable(tf.constant(0.1, shape=[h1_dim]))
w2 = tf.Variable(tf.truncated_normal([h1_dim, output_dim], stddev=0.1))
b2 = tf.Variable(tf.constant(0.1, shape=[output_dim]))
y = net(x, None, w1, b1, w2, b2)
# 使用滑动平均
global_step = tf.Variable(0, trainable=False)
variable_averages = tf.train.ExponentialMovingAverage(moving_average_decay,global_step)
variable_averages_op = variable_averages.apply(tf.trainable_variables())
average_y = net(x, variable_averages, w1, b1, w2, b2)
# argmax(y_, 1) 提取第1维度最大的，即为1的那个，即把10个数的向量变为1个数的答案
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1))
cross_entropy_mean = tf.reduce_mean(cross_entropy)
# 计算L2正则化损失函数
regularizer = tf.contrib.layers.l2_regularizer(regularization_rate)
regularization =regularizer(w1) + regularizer(w2)
# 总损失
loss = cross_entropy_mean + regularization
learning_rate = tf.train.exponential_decay(learning_rate_base,
global_step,
mnist.train.num_examples / batch_size,
learning_rate_decay)
train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_step)
train_op = tf.group(train_step, variable_averages_op)
# average是batch*10的二维数组，tf.argmax选取第1维最大的坐标，也就识别结果，得到一个长度为batch的数组，equal判断数组的每一维是否相等，，相等则该维结果为True
correct_prediction = tf.equal(tf.argmax(average_y,1), tf.argmax(y_,1))
accuarcy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
with tf.Session() as sess:
tf.global_variables_initializer().run()
#tf.global_variables_initialize().run()
validate_feed = {x:mnist.validation.images, y_:mnist.validation.labels}
test_feed = {x:mnist.test.images, y_:mnist.test.labels}
for i in range(training_step):
# 每1000轮测试一次
if i % 1000 == 0:
# 用全部的数据集验证
validata_acc = sess.run(accuarcy, feed_dict=validate_feed)
print("%d step vali accuarcy %g" % (i, validata_acc))
xs, ys = mnist.train.next_batch(batch_size)
sess.run(train_op, feed_dict={x:xs, y_:ys})
test_acc = sess.run(accuarcy, feed_dict = test_feed)
print("test accuarcy %d " % test_acc)
def main(argv=None):
# print("ll")
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
# print("hh")
train(mnist)
if __name__ == '__main__':
# print("kk")
tf.app.run()
变量管理
在名字为foo的空间里创建名字为v的变量
import tensorflow as tf
with tf.variable_scope("foo"):
v = tf.get_variable("v", [1], initializer=tf.constant_initializer(3.0))
print(v)<tf.Variable 'foo/v:0' shape=(1,) dtype=float32_ref>
# 已存在不可重复创建
with tf.variable_scope("foo"):
v = tf.get_variable("v", [1])
print(v)---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-30-5a42879cdb76> in <module>()
1 with tf.variable_scope("foo"):
----> 2 v = tf.get_variable("v", [1])
3 print(v)
ValueError: Variable foo/v already exists, disallowed. Did you mean to set reuse=True in VarScope? Originally defined at:
# reuse就可调用原来创建的变量
with tf.variable_scope("foo", reuse=True):
v = tf.get_variable("v",[1])
print(v)<tf.Variable 'foo/v:0' shape=(1,) dtype=float32_ref>
# 不同变量域，变量之前未创建不可reuse=true
with tf.variable_scope("acc",reuse=True):
v = tf.get_variable("v", [1])
print(v)---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-32-4a89d8c222e3> in <module>()
1 with tf.variable_scope("acc",reuse=True):
----> 2 v = tf.get_variable("v", [1])
3 print(v)
...
ValueError: Variable acc/v does not exist, or was not created with tf.get_variable(). Did you mean to set reuse=None in VarScope?
# 嵌套中的reuse
with tf.variable_scope("root"):
print(tf.get_variable_scope().reuse)
with tf.variable_scope("go", reuse=True):
print(tf.get_variable_scope().reuse)
with tf.variable_scope("went"):
print(tf.get_variable_scope().reuse)
print(tf.get_variable_scope().reuse)
False
True
True
False
# 变量管理
v1 = tf.get_variable("v", [1])
print(v1.name)
with tf.variable_scope("root"):
v2 = tf.get_variable("v",[1])
print(v2.name)
with tf.variable_scope("first"):
with tf.variable_scope("second"):
v3 = tf.get_variable("v",[1])
print(v3.name)
v4 = tf.get_variable("v",[1])
print(v4.name)
with tf.variable_scope("",reuse=True):
v5 = tf.get_variable("first/second/v")
v6 = tf.get_variable("root/v")
print(v5==v3)
print(v6==v2)
v:0
root/v:0
first/second/v:0
first/v:0
True
True
tensorflow持久化
import tensorflow as tf
v1 = tf.Variable(tf.constant(1.3), [1], name='v1')
v2 = tf.Variable(tf.constant(.5),[1], name='v2')
re = v1+v2
saver = tf.train.Saver()
init_op = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init_op)
saver.save(sess,"checkpoint/model.ckpt")# 读取
v1 = tf.Variable(tf.constant(1.3), [1], name='v1')
v2 = tf.Variable(tf.constant(2.5),[1], name='v2')
re = v1+v2
saver = tf.train.Saver({"v1":v1,"v2":v2})
with tf.Session() as sess:
saver.restore(sess,"checkpoint/model.ckpt")
print(re.eval())INFO:tensorflow:Restoring parameters from checkpoint/model.ckpt
1.8
# 在滑动平均值可以运用
import tensorflow as tf
v = tf.Variable(1,dtype=tf.float32,name="v")
for i in tf.global_variables():
print(i.name)
ema = tf.train.ExponentialMovingAverage(0.99)
avg_op = ema.apply(tf.global_variables())
for i in tf.global_variables():
print(i.name)v:0
v:0
v/ExponentialMovingAverage:0
利用convert_variables_to_constants函数将变量及其取值以常量的方式保存
import tensorflow as tf
from tensorflow.python.framework import graph_util
v1 = tf.Variable(tf.constant(0.1),[1],name='v1')
v2 = tf.Variable(tf.constant(0.5),[1],name='v2')
re = v1+v2
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
graph_def = tf.get_default_graph().as_graph_def()
# 变量变为常数保存
output_graph_def = graph_util.convert_variables_to_constants(
sess, graph_def, ['add'])
# 将导出的模型存入文件
with tf.gfile.GFile("checkpoint/constant_model.pb","wb") as f:
f.write(output_graph_def.SerializeToString())
INFO:tensorflow:Froze 2 variables.
Converted 2 variables to const ops.
import tensorflow as tf
from tensorflow.python.platform import gfile
with tf.Session() as sess:
model_name = "check_point/constant_model.pb"
with gfile.FastGFile(model_name,"rb") as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
result = tf.import_graph_def(graph_def,return_elements={"add:0"})
print(sess.run(result))持久化原理及数据格式
import tensorflow as tf
v1 = tf.Variable(tf.constant(1.0),[1],name="v1")
v2 = tf.Variable(tf.constant(2.0),[1],name="w2")
result =v1+v2
saver = tf.train.Saver()
saver.export_meta_graph("checkpoint/model.cpk.meda.json", as_text=True)
meta_info_def {
stripped_op_list {
op {
name: "Add"
input_arg {
name: "x"
type_attr: "T"
}
input_arg {
name: "y"
type_attr: "T"
}
output_arg {
name: "z"
type_attr: "T"
}
attr {
name: "T"
type: "type"
allowed_values {
list {
type: DT_HALF
type: DT_FLOAT
type: DT_DOUBLE
type: DT_UINT8
type: DT_INT8
type: DT_INT16
type: DT_INT32
type: DT_INT64
type: DT_COMPLEX64
type: DT_COMPLEX128
type: DT_STRING
}
}
}
}
op {
name: "Assign"
input_arg {
name: "ref"
type_attr: "T"
is_ref: true
}
input_arg {
name: "value"
type_attr: "T"
}
output_arg {
name: "output_ref"
type_attr: "T"
is_ref: true
}
attr {
name: "T"
type: "type"
}
attr {
name: "validate_shape"
type: "bool"
default_value {
b: true
}
}
attr {
name: "use_locking"
type: "bool"
default_value {
b: true
}
}
allows_uninitialized_input: true
}
op {
name: "Const"
output_arg {
name: "output"
type_attr: "dtype"
}
attr {
name: "value"
type: "tensor"
}
attr {
name: "dtype"
type: "type"
}
}
op {
name: "Identity"
input_arg {
name: "input"
type_attr: "T"
}
output_arg {
name: "output"
type_attr: "T"
}
attr {
name: "T"
type: "type"
}
}
op {
name: "NoOp"
}
op {
name: "RestoreV2"
input_arg {
name: "prefix"
type: DT_STRING
}
input_arg {
name: "tensor_names"
type: DT_STRING
}
input_arg {
name: "shape_and_slices"
type: DT_STRING
}
output_arg {
name: "tensors"
type_list_attr: "dtypes"
}
attr {
name: "dtypes"
type: "list(type)"
has_minimum: true
minimum: 1
}
}
op {
name: "SaveV2"
input_arg {
name: "prefix"
type: DT_STRING
}
input_arg {
name: "tensor_names"
type: DT_STRING
}
input_arg {
name: "shape_and_slices"
type: DT_STRING
}
input_arg {
name: "tensors"
type_list_attr: "dtypes"
}
attr {
name: "dtypes"
type: "list(type)"
has_minimum: true
minimum: 1
}
}
op {
name: "VariableV2"
output_arg {
name: "ref"
type_attr: "dtype"
is_ref: true
}
attr {
name: "shape"
type: "shape"
}
attr {
name: "dtype"
type: "type"
}
attr {
name: "container"
type: "string"
default_value {
s: ""
}
}
attr {
name: "shared_name"
type: "string"
default_value {
s: ""
}
}
is_stateful: true
}
}
tensorflow_version: "1.2.1"
tensorflow_git_version: "b\'unknown\'"
}
graph_def {
node {
name: "Const"
op: "Const"
attr {
key: "_output_shapes"
value {
list {
shape {
}
}
}
}
attr {
key: "dtype"
value {
type: DT_FLOAT
}
}
attr {
key: "value"
value {
tensor {
dtype: DT_FLOAT
tensor_shape {
}
float_val: 1.0
}
}
}
}
...