Tensorflow初探之MNIST数据集学习
官方文档传送门
MNIST数据集是手写数字0~9的数据集，一般被用作机器学习领域的测试，相当于HelloWorld级别。
本程序先从网上导入数据，再利用最小梯度法进行训练使得样本交叉熵最小，最后给出训练之后程序的准确率。
交叉熵的定义：
y 是我们预测的概率分布, y' 是实际的分布。
该指标用来衡量学习结果与实际情况的差距。
import tensorflow.examples.tutorials.mnist.input_data as input_data
import tensorflow as tf
#initialize
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matmul(x,W) + b)
y_ = tf.placeholder("float", [None,10])
cross_entropy = -tf.reduce_sum(y_*tf.log(y))
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
#train
init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)
for i in range(1000):
batch_xs, batch_ys = mnist.train.next_batch(100)
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
#predict
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))
先用placeholder和variable初始化。
placeholder(占位符)一般用于导入数据，而Variable一般是与学习过程相关的变量。
之后，启动Session进行训练，本程序当中随机选取了100张图片进行训练。
最后运行的准确率为91.49%.
/home/jcole/PycharmProjects/MNIST-HelloWorld/venv/bin/python /home/jcole/PycharmProjects/MNIST-HelloWorld/venv/MNIST.py
WARNING:tensorflow:From /home/jcole/PycharmProjects/MNIST-HelloWorld/venv/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/datasets/base.py:198: retry (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.
Instructions for updating:
Use the retry module or similar alternatives.
WARNING:tensorflow:From /home/jcole/PycharmProjects/MNIST-HelloWorld/venv/MNIST.py:4: read_data_sets (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.
Instructions for updating:
Please use alternatives such as official/mnist/dataset.py from tensorflow/models.
WARNING:tensorflow:From /home/jcole/PycharmProjects/MNIST-HelloWorld/venv/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:260: maybe_download (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.
Instructions for updating:
Please write your own downloading logic.
WARNING:tensorflow:From /home/jcole/PycharmProjects/MNIST-HelloWorld/venv/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/datasets/base.py:219: retry..wrap..wrapped_fn (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.
Instructions for updating:
Please use urllib or similar directly.
Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.
Extracting MNIST_data/train-images-idx3-ubyte.gz
WARNING:tensorflow:From /home/jcole/PycharmProjects/MNIST-HelloWorld/venv/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:262: extract_images (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.
Instructions for updating:
Please use tf.data to implement this functionality.
WARNING:tensorflow:From /home/jcole/PycharmProjects/MNIST-HelloWorld/venv/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:267: extract_labels (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.
Instructions for updating:
Please use tf.data to implement this functionality.
WARNING:tensorflow:From /home/jcole/PycharmProjects/MNIST-HelloWorld/venv/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:110: dense_to_one_hot (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.
Instructions for updating:
Please use tf.one_hot on tensors.
Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.
Extracting MNIST_data/train-labels-idx1-ubyte.gz
Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.
Extracting MNIST_data/t10k-images-idx3-ubyte.gz
Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.
WARNING:tensorflow:From /home/jcole/PycharmProjects/MNIST-HelloWorld/venv/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:290: DataSet.__init__ (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.
Extracting MNIST_data/t10k-labels-idx1-ubyte.gz
Instructions for updating:
Please use alternatives such as official/mnist/dataset.py from tensorflow/models.
WARNING:tensorflow:From /home/jcole/PycharmProjects/MNIST-HelloWorld/venv/lib/python3.5/site-packages/tensorflow/python/util/tf_should_use.py:118: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.
Instructions for updating:
Use `tf.global_variables_initializer` instead.
2018-04-23 16:27:25.917321: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
0.9149
Process finished with exit code 0