学习进度笔记8

观看Tensorflow案例实战视频课程08 迭代完成逻辑回归模型

#PREDICTION
pred=tf.equal(tf.argmax(actv,1),tf.argmax(y,1))
#ACCURACY
accr=tf.reduce_mean(tf.cast(pred,"float"))
#INITIALIZER
init=tf.global_variables_initializer()

sess=tf.InteractiveSession()

arr=np.array([[31,23,4,24,27,34],
              [18,3,25,0,6,35],
              [28,14,33,22,20,8],
              [13,30,21,19,7,9],
              [16,1,26,32,2,29],
              [17,12,5,11,10,15]])
tf.rank(arr).eval()#维度
tf.shape(arr).eval()#行列
tf.argmax(arr,0).eval()#按列最大值索引
#0->31(arr[0,0])
#3->30(arr[3,1])
#2->33(arr[2,2])
tf.argmax(arr,1).eval()#按行最大值索引
#5->34(arr[0,5])
#5->35(arr[1,5])
#2->33(arr[2,2])

training_epochs=50
batch_size=100
display_step=5
#SESSION
sess=tf.Session()
sess.run(init)
#MINI-BATCH LEARNING
for epoch in range(training_epochs):
    avg_cost=0
    num_batch=int(mnist.train.num_examples/batch_size)
    for i in range(num_batch):
       batch_xs,batch_ys=mnist.train.next_batch(batch_size)
       sess.run(optm,feed_dict={x:batch_xs,y:batch_ys})
       feeds={x:batch_xs,y:batch_ys}
       avg_cost+=sess.run(cost,feed_dict=feeds)/num_batch
    #DISPLAY
    if epoch % display_step==0:
        feeds_train={x:batch_xs,y:batch_ys}
        feeds_test={x:mnist.test.images,y:mnist.test.labels}
        train_acc=sess.run(accr,feed_dict=feeds_train)
        test_acc=sess.run(accr,feed_dict=feeds_test)
        print("Epoch:%03d/%03d cost:%.9f train_acc:%.3f test_acc:%.3f"
              % (epoch,training_epochs,avg_cost,train_acc,test_acc))
print("DONE")