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230 lines
8.2 KiB
230 lines
8.2 KiB
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 37,
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'1.13.1'"
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]
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},
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"execution_count": 37,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import tensorflow as tf\n",
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"import tensorflow.examples.tutorials.mnist.input_data as input_data\n",
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"\n",
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"tf.__version__"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 36,
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
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"Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
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"Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
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"Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n"
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]
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}
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],
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"source": [
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"MNIST=input_data.read_data_sets(\"MNIST_data\", one_hot=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 34,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0.9149\n"
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]
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}
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],
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"source": [
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"# 定义一个占位符x\n",
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"x = tf.placeholder(tf.float32, [None, 784]) # 张量的形状是[None, 784]，None表第一个维度任意\n",
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"\n",
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"# 定义变量W,b,是可以被修改的张量，用来存放机器学习模型参数\n",
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"W = tf.Variable(tf.zeros([784, 10]))\n",
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"b = tf.Variable(tf.zeros([10]))\n",
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"\n",
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"# 实现模型, y是预测分布\n",
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"y = tf.nn.softmax(tf.matmul(x, W) + b)\n",
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"\n",
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"# 训练模型，y_是实际分布\n",
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"y_ = tf.placeholder(\"float\", [None, 10])\n",
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"cross_entropy = -tf.reduce_sum(y_ * tf.log(y)) # 交叉嫡，cost function\n",
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"\n",
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"# 使用梯度下降来降低cost，学习速率为0.01\n",
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"train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)\n",
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"\n",
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"# 初始化已经创建的变量\n",
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"init = tf.global_variables_initializer()\n",
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"\n",
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"# 在一个Session中启动模型，并初始化变量\n",
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"sess = tf.Session()\n",
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"sess.run(init)\n",
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"\n",
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"# # 训练模型，运行1000次，每次随机抽取100个\n",
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"for i in range(1, 1000):\n",
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" batch_xs, batch_ys = MNIST.train.next_batch(100)\n",
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" sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})\n",
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"\n",
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"# 验证正确率\n",
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"correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))\n",
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"accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float'))\n",
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"print(sess.run(accuracy, feed_dict={x: MNIST.test.images, y_: MNIST.test.labels}))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 41,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"当前迭代次数为0，训练正确率为0.8999999761581421\n",
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"当前迭代次数为200，训练正确率为0.9200000166893005\n",
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"当前迭代次数为400，训练正确率为0.9100000262260437\n",
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"当前迭代次数为600，训练正确率为0.9399999976158142\n",
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"当前迭代次数为800，训练正确率为0.8999999761581421\n",
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"当前迭代次数为1000，训练正确率为0.8799999952316284\n",
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"当前迭代次数为1200，训练正确率为0.9200000166893005\n",
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"当前迭代次数为1400，训练正确率为0.9100000262260437\n",
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"当前迭代次数为1600，训练正确率为0.9200000166893005\n",
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"当前迭代次数为1800，训练正确率为0.8399999737739563\n",
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"当前迭代次数为2000，训练正确率为0.8899999856948853\n",
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"当前迭代次数为2200，训练正确率为0.8700000047683716\n",
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"当前迭代次数为2400，训练正确率为0.8999999761581421\n",
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"当前迭代次数为2600，训练正确率为0.8899999856948853\n",
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"当前迭代次数为2800，训练正确率为0.8999999761581421\n",
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"当前迭代次数为3000，训练正确率为0.9100000262260437\n",
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"当前迭代次数为3200，训练正确率为0.949999988079071\n",
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"当前迭代次数为3400，训练正确率为0.9300000071525574\n",
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"当前迭代次数为3600，训练正确率为0.8999999761581421\n",
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"当前迭代次数为3800，训练正确率为0.9100000262260437\n",
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"当前迭代次数为4000，训练正确率为0.9700000286102295\n",
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"当前迭代次数为4200，训练正确率为0.8899999856948853\n",
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"当前迭代次数为4400，训练正确率为0.8999999761581421\n",
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"当前迭代次数为4600，训练正确率为0.9300000071525574\n",
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"当前迭代次数为4800，训练正确率为0.9399999976158142\n",
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"当前迭代次数为5000，训练正确率为0.8899999856948853\n",
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"当前迭代次数为5200，训练正确率为0.9100000262260437\n",
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"当前迭代次数为5400，训练正确率为0.9100000262260437\n",
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"当前迭代次数为5600，训练正确率为0.9100000262260437\n",
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"当前迭代次数为5800，训练正确率为0.9300000071525574\n",
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"当前迭代次数为6000，训练正确率为0.9200000166893005\n",
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"当前迭代次数为6200，训练正确率为0.8700000047683716\n",
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"当前迭代次数为6400，训练正确率为0.9200000166893005\n",
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"当前迭代次数为6600，训练正确率为0.9399999976158142\n",
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"当前迭代次数为6800，训练正确率为0.8999999761581421\n",
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"当前迭代次数为7000，训练正确率为0.9300000071525574\n",
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"当前迭代次数为7200，训练正确率为0.9300000071525574\n",
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"当前迭代次数为7400，训练正确率为0.9300000071525574\n",
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"当前迭代次数为7600，训练正确率为0.8799999952316284\n",
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"当前迭代次数为7800，训练正确率为0.9100000262260437\n",
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"当前迭代次数为8000，训练正确率为0.8999999761581421\n",
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"当前迭代次数为8200，训练正确率为0.9599999785423279\n",
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"当前迭代次数为8400，训练正确率为0.9300000071525574\n",
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"当前迭代次数为8600，训练正确率为0.8799999952316284\n",
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"当前迭代次数为8800，训练正确率为0.9200000166893005\n",
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"当前迭代次数为9000，训练正确率为0.8600000143051147\n",
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"当前迭代次数为9200，训练正确率为0.9700000286102295\n",
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"当前迭代次数为9400，训练正确率为0.9200000166893005\n",
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"当前迭代次数为9600，训练正确率为0.8899999856948853\n",
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"当前迭代次数为9800，训练正确率为0.8799999952316284\n"
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]
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}
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],
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"source": [
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" \n",
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"for i in range(10000):\n",
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" batch = MNIST.train.next_batch(100)\n",
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" if i % 200 == 0:\n",
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" print(\"当前迭代次数为{}，训练正确率为{}\".format(i, accuracy.eval(feed_dict={x:batch[0], y_: batch[1]},session=sess)))\n",
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" train_step.run(feed_dict={x: batch[0], y_: batch[1]},session=sess)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 45,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" 测试正确率为 0.912\n"
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]
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}
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],
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"source": [
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"#测试阶段\n",
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"print(\" 测试正确率为 %g\"%accuracy.eval(feed_dict={x: MNIST.test.images, y_: MNIST.test.labels},session=sess)) "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.3"
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},
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"toc": {
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"base_numbering": 1,
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"nav_menu": {},
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"number_sections": true,
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"sideBar": true,
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"skip_h1_title": false,
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"title_cell": "Table of Contents",
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"title_sidebar": "Contents",
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"toc_cell": false,
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"toc_position": {},
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"toc_section_display": true,
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"toc_window_display": false
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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