在tensorflow中，构建模型流程比较程式化，有构建图表（Build Graph）和评估（evaluate）两部分，其中构建图表又分为：推理（Inference）、损失（Loss）、训练（train）。掌握了这些，对于快速上手工程开发很有帮助。
一、构建图表
1. 推理（Inference）
inference()函数会尽可能地构建图表，做到返回包含了预测结果(output prediction)的Tensor
Args:
images: 输入与占位符（Inputs and Placeholders)
hidden1_units: 第一个隐含层
hidden2_units: 第二个隐含层
Returns:
softmax_linear: 输出结果的logits Tensor
API备注：
tf.truncated_normal() 根据所得到的均值和标准差，生成一个随机分布，初始化权重
tf.nn.relu() 0/1阶跃函数
tf.matmul() 矩阵相乘
tf.Variable() 生成初始值变量，必须指定初始值
tf.name_scope() 与tf.Variable()组合使用，更加方便管理参数命名
def inference(images, hidden1_units, hidden2_units):
# Hidden 1
with tf.name_scope('hidden1'):
weights = tf.Variable(
tf.truncated_normal([IMAGE_PIXELS, hidden1_units],
stddev=1.0 / math.sqrt(float(IMAGE_PIXELS))),
name='weights')
biases = tf.Variable(tf.zeros([hidden1_units]),
name='biases')
hidden1 = tf.nn.relu(tf.matmul(images, weights) + biases)
# Hidden 2
with tf.name_scope('hidden2'):
weights = tf.Variable(
tf.truncated_normal([hidden1_units, hidden2_units],
stddev=1.0 / math.sqrt(float(hidden1_units))),
name='weights')
biases = tf.Variable(tf.zeros([hidden2_units]),
name='biases')
hidden2 = tf.nn.relu(tf.matmul(hidden1, weights) + biases)
# Linear
with tf.name_scope('softmax_linear'):
weights = tf.Variable(
tf.truncated_normal([hidden2_units, NUM_CLASSES],
stddev=1.0 / math.sqrt(float(hidden2_units))),
name='weights')
biases = tf.Variable(tf.zeros([NUM_CLASSES]),
name='biases')
logits = tf.matmul(hidden2, weights) + biases
return logits2. 损失（Loss）
计算预测lost
Args:
logits: Logits张量, float - [batch_size, NUM_CLASSES].
labels: 标签张量, int32 - [batch_size].
Returns:
loss: 损失
API备注:
tf.expand_dims() 维度增加一维
tf.sparse_to_dense() 将稀疏表示形式转换为稠密张量,转化为1-hot张量
tf.stack() 矩阵拼接函数,在新的张量阶上拼接，产生的张量的阶数将会增加
tf.concat() 将两个张量在某一个维度(axis)合并起来,产生的张量的阶数不会发生变化
tf.nn.softmax_cross_entropy_with_logits_v2() softmax的输出向量和样本的实际标签的交叉熵的对应向量，用来比较inference()函数与1-hot标签所输出的logits Tensor
def loss(logits, labels):
batch_size = tf.size(labels)
labels = tf.expand_dims(labels, 1)
indices = tf.expand_dims(tf.range(0, batch_size), 1)
concated = tf.concat([indices, labels], 1)
onehot_labels = tf.sparse_to_dense(
concated, tf.stack([batch_size, NUM_CLASSES]), 1.0, 0.0)
cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(logits=logits,
labels=onehot_labels,
name='xentropy')
loss = tf.reduce_mean(cross_entropy, name='xentropy_mean')
return loss3. 训练（train）
training()函数添加了通过梯度下降(gradient descent)将损失最小化所需的操作
创建优化器并应用梯度下降法迭代寻优
操作函数放在会话sess运行
Args:
loss: 损失张量
learning_rate: 学习速率
Returns:
train_op: 训练操作
API备注：
tf.summary.scalar() 对标量数据汇总和记录
tf.train.GradientDescentOptimizer() 按照要求的学习率(固定值)应用梯度下降法
optimizer.minimize() 使用minimize函数更新系统中的三角权重、增加全局步骤
def training(loss, learning_rate):
# Add a scalar summary for the snapshot loss.
tf.summary.scalar(loss.op.name, loss)
# Create the gradient descent optimizer with the given learning rate.
optimizer = tf.train.GradientDescentOptimizer(learning_rate)
# Create a variable to track the global step.
global_step = tf.Variable(0, name='global_step', trainable=False)
# Use the optimizer to apply the gradients that minimize the loss
# (and also increment the global step counter) as a single training step.
train_op = optimizer.minimize(loss, global_step=global_step)
return train_op二、评估（evaluate）
评估
预测标签并给出评估效果
Args:
logits: Logits张量
labels: 预测标签
Returns:
正确预测标签张量
API备注：
tf.nn.in_top_k() 用于计算预测的结果和实际结果的是否相等,返回一个bool类型的张量
tf.reduce_sum() 压缩求和，用于降维
def evaluation(logits, labels):
"""Evaluate the quality of the logits at predicting the label.
Args:
logits: Logits tensor, float - [batch_size, NUM_CLASSES].
labels: Labels tensor, int32 - [batch_size], with values in the
range [0, NUM_CLASSES).
Returns:
A scalar int32 tensor with the number of examples (out of batch_size)
that were predicted correctly.
"""
# For a classifier model, we can use the in_top_k Op.
# It returns a bool tensor with shape [batch_size] that is true for
# the examples where the label's is was in the top k (here k=1)
# of all logits for that example.
correct = tf.nn.in_top_k(logits, labels, 1)
# Return the number of true entries.
return tf.reduce_sum(tf.cast(correct, tf.int32))