import numpy as np
import tensorflow as tf
复制代码 /anaconda3/envs/py35/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: compiletime version 3.6 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.5
return f(*args, **kwds)
/anaconda3/envs/py35/lib/python3.5/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
from ._conv import register_converters as _register_converters
复制代码 from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/" , one_hot = True )
复制代码 Extracting MNIST_data/train-images-idx3-ubyte.gz
Extracting MNIST_data/train-labels-idx1-ubyte.gz
Extracting MNIST_data/t10k-images-idx3-ubyte.gz
Extracting MNIST_data/t10k-labels-idx1-ubyte.gz
复制代码 len(mnist.train.images), len(mnist.train.labels)
复制代码 (55000, 55000)
复制代码 len(mnist.test.images), len(mnist.test.labels)
复制代码 (10000, 10000)
复制代码 mnist.train.images[0 ]
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复制代码 len(mnist.train.images[0 ])
复制代码 784
复制代码 import matplotlib.pyplot as plt
%matplotlib inline
复制代码 plt.imshow(mnist.train.images[1 ].reshape(28 ,28 ))
复制代码 <matplotlib.image.AxesImage at 0x1c27a1a550>
复制代码
mnist.train.labels[1 ]
复制代码 array([0., 0., 0., 1., 0., 0., 0., 0., 0., 0.])
复制代码 x = tf.placeholder("float" , shape=[None , 784 ])
y = tf.placeholder("float" , shape=[None , 10 ])
复制代码 weight = tf.Variable(tf.truncated_normal([784 ,10 ]))
bias = tf.Variable(tf.truncated_normal([10 ]))
复制代码 combine_input = tf.matmul(x, weight) + bias
复制代码 pred = tf.nn.softmax(combine_input)
复制代码 loss = -tf.reduce_sum(y * tf.log(pred))
复制代码 train_step = tf.train.GradientDescentOptimizer(0.01 ).minimize(loss)
复制代码 sess = tf.Session()
sess.run(tf.global_variables_initializer())
复制代码 for i in range(1100 ):
batch = mnist.train.next_batch(50 )
sess.run(train_step, feed_dict={x : batch[0 ], y:batch[1 ]})
if i%50 == 0 :
print(sess.run(loss, feed_dict={x : batch[0 ], y:batch[1 ]}))
复制代码 329.698
114.416695
38.314323
20.213478
48.926674
26.53627
28.653086
43.464195
16.75724
39.731388
12.251608
32.379055
24.371075
18.137915
8.972845
24.207663
29.931976
7.5475473
10.576719
28.017235
14.364228
11.022556
复制代码 correct_pred = tf.equal(tf.argmax(pred, 1 ), tf.argmax(y, 1 ))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
复制代码 acc = sess.run(accuracy, feed_dict={x:mnist.test.images, y:mnist.test.labels})
print(acc)
复制代码 0.8813
复制代码 
MNIST数据集与TensorFlow实现手写数字识别
本文介绍使用TensorFlow框架对MNIST数据集进行处理，实现手写数字识别的全过程。从数据集的加载到模型训练，再到最终的准确率评估，详细展示了如何构建和优化神经网络模型。
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