3.2 tensorflow 第一步

from tensorflow.examples.tutorials.mnist import input_dataimport tensorflow as tfsess = tf.InteractiveSession()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(tf.float32, [None, 10])cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices = [1]))  train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)tf.global_variables_initializer().run()for i in range(1000):    batch_xs, batch_ys = mnist.train.next_batch(100)    train_step.run({x:batch_xs, y_: batch_ys})correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))print(accuracy.eval({x:mnist.test.images, y_: mnist.test.labels}))

tf.reduce_sum(y_ * tf.log(y), reduction_indices = [1]):

1. tf.reduce_sum(input_tensor, reduction_indices=None,
   keep_dims=False, name=None)

计算输入tensor元素的和，或者安照reduction_indices指定的轴进行求和
‘x’ is [[1, 1, 1] [1, 1, 1]]
tf.reduce_sum(x) ==> 6
tf.reduce_sum(x, 0) ==> [2, 2, 2]
tf.reduce_sum(x, 1) ==> [3, 3]
tf.reduce_sum(x, 1, keep_dims=True) ==> [[3], [3]]
tf.reduce_sum(x, [0, 1]) ==> 6