Tensorflow: TypeError: 'numpy.float32' object is not iterable

Question

我是神经网络领域的初学者，我正在构建一个神经网络并希望通过将 'xx' 作为输入来预测 'yy' 中的值，但我遇到了 TypeError : 'numpy.float32' 对象不可迭代。我试过改变一些东西，但它导致了一些其他错误。谁能告诉我为什么会出现此错误以及如何更正它？

import tensorflow as tf

xx=(
        [178.72,218.38,171.1],
        [211.57,215.63,173.13],
        [196.25,196.69,116.91],
        [121.88,132.07,85.02],
        [117.04,135.44,112.54],
        [118.13,124.04,97.98],
        [116.73,125.88,99.04],
        [118.75,125.01,110.16],
        [109.69,111.72,69.07],
        [76.57,96.88,67.38],
        [91.69,128.43,87.57],
        [117.57,146.43,117.57]
      )

yy=(
        [212.09],
        [195.58],
        [127.6],
        [116.5],
        [117.95],
        [117.55],
        [117.55],
        [110.39],
        [74.33],
        [91.08],
        [121.75],
        [127.3]
       )


x=tf.placeholder(tf.float32,[None,3])
y=tf.placeholder(tf.float32,[None,1])
n1=5
n2=5
classes=12

def neuralnetwork(data):

    hl1={'weights':tf.Variable(tf.random_normal([3,n1])),'biases':tf.Variable(tf.random_normal([n1]))}   

    hl2={'weights':tf.Variable(tf.random_normal([n1,n2])),'biases':tf.Variable(tf.random_normal([n2]))}

    op={'weights':tf.Variable(tf.random_normal([n2,classes])),'biases':tf.Variable(tf.random_normal([classes]))}

    l1=tf.add(tf.matmul(data,hl1['weights']),hl1['biases'])
    l1=tf.nn.relu(l1)
    l2=tf.add(tf.matmul(l1,hl2['weights']),hl2['biases'])
    l2=tf.nn.relu(l2)
    output=tf.matmul(l2,op['weights'])+op['biases']
    return output

def train(x):
        pred=neuralnetwork(x)
       # cost=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred,labels=y))
        sq = tf.square(pred-y)
        loss=tf.reduce_mean(sq)

        optimizer = tf.train.GradientDescentOptimizer(0.5)
        train = optimizer.minimize(loss)

        #optimizer=tf.train.RMSPropOptimizer(0.01).minimize(cost)
        epochs=10



        with tf.Session() as sess:
            sess.run(tf.global_variables_initializer())
            for epoch in range(epochs):
               for i in range (int(1)):
                   batch_x=xx
                   batch_y=yy
                  # a=tf.shape(xx)
                   #print(sess.run(a))
                   i,c=sess.run(loss,feed_dict={x:batch_x, y: batch_y})
                   epoch_loss+=c
                   print("Epoch ",epoch," completed out of ",epochs, 'loss:', epoch_loss)

train(x)

Answer 1

错误在 i,c=sess.run(loss,feed_dict={x:batch_x, y: batch_y})。您正在返回一个值，但在输出中有两个变量。只需删除 i。像这样：c=sess.run(loss,feed_dict={x:batch_x, y: batch_y})。另外，在上面定义 epoch_loss 。

Tensorflow: TypeError: 'numpy.float32' object is not iterable

Tensorflow: TypeError: 'numpy.float32' object is not iterable

python

numpy

typeerror

tensorflow

tensor