#TensorFlow实现Logistic 回归 import tensorflow as tf #导入手写数字集 from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) #学习参数 learning_rate = 0.01 training_epoches = 25 batch_size = 100 display_step = 1 #构造图 x = tf.placeholder(tf.float32,[None,784]) y = tf.placeholder(tf.float32,[None,10]) W = tf.Variable(tf.zeros([784,10])) b = tf.Variable(tf.zeros([10])) prediction = tf.nn.softmax(tf.matmul(x,W) + b) cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(prediction),reduction_indices=1)) optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) init = tf.global_variables_initializer() with tf.Session() as sess: sess.run(init) for epoch in range(training_epoches): avg_cost = 0. total_batch = int(mnist.train.num_examples/batch_size) for i in range(total_batch): batch_xs,batch_ys = mnist.train.next_batch(batch_size) _,c =sess.run([optimizer,cost],feed_dict={x:batch_xs,y:batch_ys}) avg_cost += c / total_batch if (epoch+1) % display_step == 0: print("Epoch:","%0.4d" %(epoch+1),"cost","{:.9f}".format(avg_cost)) print("训练结束") correct_prediction = tf.equal(tf.argmax(prediction,1),tf.argmax(y,1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.int32)) print("Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels}))
时间: 2024-11-08 20:40:54