TensorFlow實現MNIST手寫體識別
阿新 • • 發佈:2018-11-03
# -*- coding: utf-8 -*- #匯入資料集 from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("MINST_data/",one_hot = True) #檢視資料集相關資訊 print(mnist.train.images.shape,mnist.train.labels.shape) print(mnist.test.images.shape,mnist.test.labels.shape) print(mnist.validation.images.shape,mnist.validation.labels.shape) #匯入tensorflow import tensorflow as tf sess = tf.InteractiveSession() x = tf.placeholder(tf.float32,[None,784]) #初始化權重,偏置 W = tf.Variable(tf.zeros([784,10])) b = tf.Variable(tf.zeros([10])) #呼叫softmax函式估算對每一類別的概率 y = tf.nn.softmax(tf.matmul(x,W) + b) #設定損失函式 y_ = tf.placeholder(tf.float32,[None,10]) cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1])) #設定學習速率為0.5,優化目標為cross_entropy train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy) tf.global_variables_initializer().run() #迭代訓練,每次隨機取出100條資料進行訓練 for i in range(1000): batch_xs,batch_ys = mnist.train.next_batch(100) train_step.run({x:batch_xs,y_:batch_ys}) #計算輸出學習結果,準確率為91%左右 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}))