tensorflow识别mnist示例

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tensorflow识别mnist示例,并且可以使用tensorboard进行可视化

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

def add_layer(inputs, in_size, out_size, n_layer, activation_function = None):   
    layer_name="layer%s" % n_layer
    with tf.name_scope('layer'):
        with tf.name_scope('weights'):
            Weights = tf.Variable(tf.random_normal([in_size, out_size]))
            tf.summary.histogram(layer_name + '/weights', Weights)
        with tf.name_scope('biases'):
            biases = tf.Variable(tf.zeros([1, out_size])+0.1)
            tf.summary.histogram(layer_name + '/biases', biases)
        Wx_plus_b = tf.matmul(inputs, Weights) + biases
        Wx_plus_b = tf.nn.dropout(Wx_plus_b, 0.99)
        if activation_function is None:
            outputs = Wx_plus_b
        else:
            outputs = activation_function(Wx_plus_b)
        tf.summary.histogram(layer_name + '/outputs', outputs)
    return outputs

def compute_accuracy(v_xs, v_ys):
    global prediction
    y_pre = sess.run(prediction, feed_dict={xs: v_xs})
    correct_prediction = tf.equal(tf.argmax(y_pre,1), tf.argmax(v_ys,1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    result = sess.run(accuracy, feed_dict={xs: v_xs, ys: v_ys})
    return result

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

xs = tf.placeholder(tf.float32, [None, 784])
ys = tf.placeholder(tf.float32, [None, 10])

prediction = add_layer(xs, 784, 10, n_layer=1, activation_function=tf.nn.softmax)

with tf.name_scope('loss'):
    cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys*tf.log(prediction),reduction_indices = [1]))
    tf.summary.scalar('loss', cross_entropy)
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
sess = tf.Session()
merged = tf.summary.merge_all()
writer = tf.summary.FileWriter("logs/", sess.graph)
sess.run(tf.global_variables_initializer())

saver = tf.train.Saver()
save_path = saver.save(sess, "save/mnist.ckpt")

for i in range(1000):
    batch_xs, batch_ys = mnist.train.next_batch(100)
    sess.run(train_step, feed_dict={xs:batch_xs, ys:batch_ys})
    
    if i%50 == 0:
        print(compute_accuracy(
            mnist.test.images, mnist.test.labels))
        rs = sess.run(merged,feed_dict={xs:batch_xs,ys:batch_ys})
        writer.add_summary(rs, i)