这里主要将《Tensorflow实战》第4.4节多层感知机的代码扩展到了两个隐含层
但是出现了一个问题：为什么识别率只有11.35%呢？ 一个隐含层是98%~~
更改学习率 迭代batch数，还有隐含层节点数之后正常了。
但是识别率并没有明显提升
代码如下
# -*- coding: utf-8 -*-
"""
Created on Fri Dec 15 16:21:21 2017
@author: Administrator
"""
#%%
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
# Create the model
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
sess = tf.InteractiveSession()
in_units = 784
h1_units = 500
h2_units = 100
W1 = tf.Variable(tf.truncated_normal([in_units, h1_units], stddev=0.1))
b1 = tf.Variable(tf.zeros([h1_units]))
W2 = tf.Variable(tf.truncated_normal([h1_units, h2_units], stddev=0.1))
b2 = tf.Variable(tf.zeros([h2_units]))
W3 = tf.Variable(tf.zeros([h2_units, 10]))
b3 = tf.Variable(tf.zeros([10]))
x = tf.placeholder(tf.float32, [None, in_units])
keep_prob = tf.placeholder(tf.float32)
hidden1 = tf.nn.relu(tf.matmul(x, W1) + b1)
hidden1_drop = tf.nn.dropout(hidden1, keep_prob)
hidden2 = tf.nn.relu(tf.matmul(hidden1_drop, W2) + b2)
hidden2_drop = tf.nn.dropout(hidden2, keep_prob)
y = tf.nn.softmax(tf.matmul(hidden2_drop, W3) + b3)
# Define loss and optimizer
y_ = tf.placeholder(tf.float32, [None, 10])
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
train_step = tf.train.AdagradOptimizer(0.3).minimize(cross_entropy)
# Train
tf.global_variables_initializer().run()
for i in range(1000):
batch_xs, batch_ys = mnist.train.next_batch(1000)
train_step.run({x: batch_xs, y_: batch_ys, keep_prob: 1.0})
# Test trained model
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(accuracy.eval({x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))