import numpy as npimport 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_datamnist = input_data.read_data_sets("MNIST_data/", one_hot = True)复制代码
Extracting MNIST_data/train-images-idx3-ubyte.gzExtracting MNIST_data/train-labels-idx1-ubyte.gzExtracting MNIST_data/t10k-images-idx3-ubyte.gzExtracting 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]复制代码
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, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ], dtype=float32)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
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
329.698114.41669538.31432320.21347848.92667426.5362728.65308643.46419516.7572439.73138812.25160832.37905524.37107518.1379158.97284524.20766329.9319767.547547310.57671928.01723514.36422811.022556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