MNIST手寫數字識別 tensorflow

神經網路一半包含三層，輸入層、隱含層、輸出層。如下圖所示：

現以手寫數字識別為例：

輸入為784個變數，輸出為10個節點，10個節點再通過softmax啟用函式轉化為**值。

**如下，準確率可達0.9226

import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("mnist_data/", one_hot=true)
sess = 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(0.1).minimize(cross_entropy)
tf.global_variables_initializer().run()
for i in range(10000):
batch_xs, batch_ys = mnist.train.next_batch(128)
train_step.run()
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())

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