import tensorflow as tf
graph = tf.Graph()
with graph.as_default():
    with tf.name_scope("variables"):
        # 记录数据流图运行次数的 Variable 对象
        global_step = tf.Variable(0, dtype=tf.int32, trainable=False, name="global_step")
        # 追踪该模型的所有输出随时间的累加和的 Variable 对象
        total_output = tf.Variable(0.0, dtype=tf.float32, trainable=False, name="total_output")
    
    with tf.name_scope("transformation"):
        # 独立的输入层
        with tf.name_scope("input"):
            # 创建输出占用符， 用于接收一个向量
            a = tf.placeholder(tf.float32, shape=[None], name="input_placeholder_a")
        
        # 独立的中间层
        with tf.name_scope("intermediate_layer"):
            b = tf.reduce_prod(a, name="product_b")
            c = tf.reduce_sum(a, name="sum_c")
        # 独立的输出层
        with tf.name_scope("output"):
            output = tf.add(b, c, name="output")
    with tf.name_scope("update"):
        # 用最新的输出更新 Variable 对象 total_output
        update_total = total_output.assign_add(output)
        # 将前面的 Variable 对象 global_step 曾1，只要数据流图运行，该操作需要进行
        increment_step = global_step.assign_add(1)
    with tf.name_scope("summaries"):
        avg = tf.div(update_total, tf.cast(increment_step, tf.float32), name="average")
        # 为输出节点创建汇总数据
        tf.summary.scalar('output_summary', output)
        tf.summary.scalar('total_summary', update_total)
        tf.summary.scalar('average_summary', avg)
    with tf.name_scope("global_ops"):
        # 初始化Op
        init = tf.initialize_all_variables()
        # 将所有汇总数据合并到一个Op中
        merged_summaries = tf.summary.merge_all()

# 用明确定义的 Graph 对象启动一个会话
sess = tf.Session(graph=graph)
# 开启一个 summary.FileWriter 对象， 保存汇总数据
writer = tf.summary.FileWriter('./improved_graph', graph)
# 初始化 Variable 对象
sess.run(init)
# 为方便代码复用，定义辅助函数 run_graph()
# 用以输入张量以运行数据流图，并保存汇总数据
def run_graph(input_tensor):
    feed_dict = {a: input_tensor}
    _, step, summary = sess.run([output, increment_step, merged_summaries], feed_dict=feed_dict)
    writer.add_summary(summary, global_step=step)
# 用不同的输入运行该数据流图
run_graph([2, 8])
run_graph([3, 1, 3, 3])
run_graph([8])
run_graph([1, 2, 3])
run_graph([11, 4])
run_graph([4, 1])
run_graph([7, 3, 1])
run_graph([6, 3])
run_graph([0, 2])
run_graph([4, 5, 6])
# 将汇总数据写入磁盘
writer.flush()
# 关闭 summary.FileWriter 对象
writer.close()
# 关闭 Session 对象
sess.close()

# 导入tensorflow
import tensorflow as tf
# 模型参数
W = tf.Variable([.3], dtype=tf.float32)
b = tf.Variable([-.3], dtype=tf.float32)
# 模型输入和输出
x = tf.placeholder(tf.float32)
# 定义线性模型
linear_model = W*x + b
y = tf.placeholder(tf.float32)
# 损耗
loss = tf.reduce_sum(tf.square(linear_model - y)) # sum of the squares
# 优化器：梯度下降
optimizer = tf.train.GradientDescentOptimizer(0.01)
train = optimizer.minimize(loss)
# 训练集数据
x_train = [1, 2, 3, 4]
y_train = [0, -1, -2, -3]
# 训练循环
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init) # reset values to wrong
for i in range(1000):
  sess.run(train, {x: x_train, y: y_train})
# 计算训练的准确度
curr_W, curr_b, curr_loss = sess.run([W, b, loss], {x: x_train, y: y_train})
print("W: %s b: %s loss: %s"%(curr_W, curr_b, curr_loss))