tf.Session()与tf.InteractiveSission()的区别

tf.Session()：在程序图计算中，tf.Session()是先构建好operation，然后再构建会话，然后在会话中执行operation。
tf.InteractiveSession():在程序图计算中，tf.InteractiveSession()是先构建好会话，然后再构建operation。

代码实例：
tf.Session:

```python
import tensorflow as tf

input1 = tf.placeholder(tf.int32)
input2 = tf.placeholder(tf.int32)

output = tf.add(input1,input2)
sess = tf.Session()

print(sess.run(output,feed_dict={input1:[100,200],input2:[1,1]}))
print(output.dtype)

代码实例：
tf.InteractiveSession()

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import time

# 声明输入图片数据，类别
x = tf.placeholder('float', [None, 784])
y_ = tf.placeholder('float', [None, 10])
# 输入图片数据转化
x_image = tf.reshape(x, [-1, 28, 28, 1])

#第一层卷积层，初始化卷积核参数、偏置值，该卷积层5*5大小，一个通道，共有6个不同卷积核
filter1 = tf.Variable(tf.truncated_normal([5, 5, 1, 6]))
bias1 = tf.Variable(tf.truncated_normal([6]))
conv1 = tf.nn.conv2d(x_image, filter1, strides=[1, 1, 1, 1], padding='SAME')
h_conv1 = tf.nn.sigmoid(conv1 + bias1)

maxPool2 = tf.nn.max_pool(h_conv1, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME')

filter2 = tf.Variable(tf.truncated_normal([5, 5, 6, 16]))
bias2 = tf.Variable(tf.truncated_normal([16]))
conv2 = tf.nn.conv2d(maxPool2, filter2, strides=[1, 1, 1, 1], padding='SAME')
h_conv2 = tf.nn.sigmoid(conv2 + bias2)

maxPool3 = tf.nn.max_pool(h_conv2, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME')

filter3 = tf.Variable(tf.truncated_normal([5, 5, 16, 120]))
bias3 = tf.Variable(tf.truncated_normal([120]))
conv3 = tf.nn.conv2d(maxPool3, filter3, strides=[1, 1, 1, 1], padding='SAME')
h_conv3 = tf.nn.sigmoid(conv3 + bias3)



# 全连接层
# 权值参数
W_fc1 = tf.Variable(tf.truncated_normal([7 * 7 * 120, 80]))
# 偏置值
b_fc1 = tf.Variable(tf.truncated_normal([80]))
# 将卷积的产出展开
h_pool2_flat = tf.reshape(h_conv3, [-1, 7 * 7 * 120])
# 神经网络计算，并添加sigmoid激活函数
h_fc1 = tf.nn.sigmoid(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)


# 输出层，使用softmax进行多分类
W_fc2 = tf.Variable(tf.truncated_normal([80, 10]))
b_fc2 = tf.Variable(tf.truncated_normal([10]))
y_conv = tf.nn.softmax(tf.matmul(h_fc1, W_fc2) + b_fc2)
# 损失函数
cross_entropy = -tf.reduce_sum(y_ * tf.log(y_conv))
# 使用GDO优化算法来调整参数
train_step = tf.train.GradientDescentOptimizer(0.001).minimize(cross_entropy)

sess = tf.InteractiveSession()
# 测试正确率
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

# 所有变量进行初始化
sess.run(tf.global_variables_initializer())

# 获取mnist数据
mnist_data_set = input_data.read_data_sets('MNIST_data', one_hot=True)

# 进行训练
start_time = time.time()
for i in range(2000):
    # 获取训练数据
    batch_xs, batch_ys = mnist_data_set.train.next_batch(200)

    # 每迭代100个 batch，对当前训练数据进行测试，输出当前预测准确率
    if i % 2 == 0:
        train_accuracy = accuracy.eval(feed_dict={x: batch_xs, y_: batch_ys})
        print("step %d, training accuracy %g" % (i, train_accuracy))
        # 计算间隔时间
        end_time = time.time()
        print('time: ', (end_time - start_time))
        start_time = end_time
    # 训练数据
    train_step.run(feed_dict={x: batch_xs, y_: batch_ys})

# 关闭会话
sess.close()