在卷积层中调用模型文件

from mnist import model
data = read_data_sets('MNIST_data', one_hot=True)
with tf.variable_scope('convolutional'):
    x = tf.placeholder(tf.float32, [None, 784])
    keep_prob = tf.placeholder(tf.float32)
    y, variables = model.convolutional(x,keep_prob)

_y = tf.placeholder(tf.float32, [None, 10])
cross_entropy = -tf.reduce_sum(_y * tf.log(y))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(_y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
saver=tf.train.Saver(variables)
with tf.Session() as sess:
    merged_summary_op = tf.summary.merge_all()
    summary_writer = tf.summary.FileWriter('/tmp/mnist_log/1', sess.graph)
    summary_writer.add_graph(sess.graph)

1.定义完卷积的模型之后，定义卷积的文件：model->convolutional

import os

from mnist import model

import tensorflow as tf

from mnist import input_data

data = input_data.read_data_sets('MNIST_data', one_hot=True)

# model

with tf.variable_scope('convolutional'):

    x = tf.placeholder(tf.float32, [None, 784], name='x')

    keep_prob = tf.placeholder(tf.float32)

    y, variables = model.convolutional(x, keep_prob)

# train

y_ = tf.placeholder(tf.float32, [None, 10], name='y')

cross_entropy = -tf.reduce_sum(y_ * tf.log(y))

train_step = -tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)  # 随机梯度下降的方式

correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))    # 判断参数是否相等

accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

saver = tf.train.Saver(variables)

with tf.Session() as sess:

    merged_summary_op = tf.summary.merge_all()  # 合并参数，操作符

    summary_writer = tf.summary.FileWriter('/tmp/mnist_log/1', sess.graph)      # 将参数的路径、输入输出图放到哪里

    summary_writer.add_graph(sess.graph)    # 把图加进来

    sess.run(tf.global_variables_initializer())

Adam0pitimizer 随机梯度下降