Tensorflow one layer neural network training handwritten digit recognition

# coding:utf-8
#  Import tensorflow Deep learning library and handwriting data set 
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

#  Define the number of training iterations of the network 、 Learning rate 、droup
max_steps = 50000
learning_rate = 0.001
dropout = 0.9

#  Where the downloaded data and model are saved 
data_dir = "MNIST_data/"
log_dir = "/tmp/tensorflow/mnist/logs/mnist_with_summaries"

#  download mnist Data sets 
mnist = input_data.read_data_sets(data_dir, one_hot=True)
sess = tf.InteractiveSession()

#  Define the input data for the network 
with tf.name_scope('input'):
    x = tf.placeholder(tf.float32, [None, 784], name='x-input')
    y_ = tf.placeholder(tf.float32, [None, 10], name='y-input')

with tf.name_scope('input_reshape'):
    image_shaped_input = tf.reshape(x, [-1, 28, 28, 1])
    tf.summary.image('input', image_shaped_input, 10)

#  Define the initialization method of model weight and offset term 
def weight_variable(shape):
    initial = tf.truncated_normal(shape, stddev=0.1)
    return tf.Variable(initial)

def bias_variable(shape):
    initial =tf.constant(0.1, shape=shape)
    return tf.Variable(initial)


#  Definition Variable  Data summary of variables 
def variable_summaries(var):
    with tf.name_scope('summaries'):
        mean = tf.reduce_mean(var)
        tf.summary.scalar('mean',mean)
        with tf.name_scope('stddev'):
            stddev =tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
        tf.summary.scalar('stddev',stddev)
        tf.summary.scalar('max', tf.reduce_max(var))
        tf.summary.scalar('min', tf.reduce_min(var))
        tf.summary.histogram('histogram', var)

#  Design MLP Multilayer neural network training data 
def nn_layer(input_tensor, input_dim, outpu_dim, layer_name, act=tf.nn.relu):
    with tf.name_scope(layer_name):
        with tf.name_scope('weights'):
            weights =weight_variable([input_dim, outpu_dim])
            variable_summaries(weights)
        with tf.name_scope('biases'):
            biases =bias_variable([outpu_dim])
            variable_summaries(biases)
        with tf.name_scope('Wx_plux_b'):
            preactivate =tf.matmul(input_tensor, weights)+biases
            tf.summary.histogram('pre_activations', preactivate)
            activations =act(preactivate,name='activation')
        tf.summary.histogram('activations',activations)
        return activations

#  Create a layer of neural network 
hidden1 = nn_layer(x, 784, 500, 'layer1')

with tf.name_scope('dropout'):
    keep_prob = tf.placeholder(tf.float32)
    tf.summary.scalar('dropout_keep_probality', keep_prob)
    dropped =tf.nn.dropout(hidden1, keep_prob)

#  Output 
y = nn_layer(dropped, 500, 10, 'layer2', act=tf.identity)

#  Solving cross loss entropy 
with tf.name_scope('cross_entropy'):
    diff = tf.nn.softmax_cross_entropy_with_logits(logits=y, labels=y_)
    with tf.name_scope('total'):
        cross_entropy =tf.reduce_mean(diff)
tf.summary.scalar('cross_entropy',cross_entropy)


# Adam  The optimizer optimizes the loss , Predict the correct number of samples and calculate the correct rate accuray
with tf.name_scope('train'):
    train_step = tf.train.AdamOptimizer(learning_rate).minimize(cross_entropy)
with tf.name_scope('accuracy'):
    with tf.name_scope('correct_prediction'):
        correct_prediction = tf.equal(tf.arg_max(y, 1),tf.arg_max(y_, 1))
    with tf.name_scope('accuracy'):
        accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
tf.summary.scalar('accuracy', accuracy)

# tf.summary  Summary ,tf.summary.FileWrite Used for training and test data storage , Also initialize all variables 
merged = tf.summary.merge_all()
train_writer =tf.summary.FileWriter(log_dir + '/train',sess.graph)
test_writer =tf.summary.FileWriter(log_dir + '/test')
tf.global_variables_initializer().run()

#  Collect data for training , Divided into training and test data 
def feed_dict(train):
     if train:
         xs, ys =mnist.train.next_batch(100)
         k = dropout
     else:
         xs, ys = mnist.test.images, mnist.test.labels
         k= 1.0
     return {x: xs, y_:ys, keep_prob: k}


#  Create a model Saver 
saver = tf.train.Saver()
for i in range(max_steps):
    if i % 10 == 0:
        summary,acc = sess.run([merged, accuracy],feed_dict=feed_dict(False))
        test_writer.add_summary(summary, i)
        print('Accuracy at step %s: %s'% (i, acc))
    else:
        if i % 100 ==99:
            run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
            run_metadata = tf.RunMetadata()
            summary, _ =sess.run([merged,train_step], feed_dict=feed_dict(True),options=run_options, run_metadata=run_metadata)
            train_writer.add_run_metadata(run_metadata, 'step%03d' % i)
            train_writer.add_summary(summary, i)
            saver.save(sess, log_dir+"/model.ckpt", i)
            print('Adding run metadata for',i)
        else:
            summary, _ = sess.run([merged,train_step],feed_dict=feed_dict(True))
            train_writer.add_summary(summary, i)
train_writer.close()
test_writer.close()





 The recognition rate can reach 98% 
Accuracy at step 12150: 0.9809
Accuracy at step 12160: 0.9809
Accuracy at step 12170: 0.9817
Accuracy at step 12180: 0.9815
Accuracy at step 12190: 0.9815
Adding run metadata for 12199
Accuracy at step 12200: 0.9827
Accuracy at step 12210: 0.9832
Accuracy at step 12220: 0.9829
Accuracy at step 12230: 0.9829
Accuracy at step 12240: 0.9826
Accuracy at step 12250: 0.9828
Accuracy at step 12260: 0.9822