CODEWITHSUNDEEP
pythonnumpytensorflowtypeerrortensor
xposure
xposure

Reputation: 35

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

I am a beginner in the world of neural nets, I am building a neural net and want to predict the values in 'yy' by taking 'xx' as an input but I am getting a TypeError: 'numpy.float32' object is not iterable. I have tried changing somethings but it results in some other error. can anyone tell me why I am getting this error and how to correct it?

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)

Upvotes: 0

Views: 2055

Answers (1)

talos1904
talos1904

Reputation: 972

The error is in i,c=sess.run(loss,feed_dict={x:batch_x, y: batch_y}). You are returning one value but have two variables in output. Just remove i. Like this: c=sess.run(loss,feed_dict={x:batch_x, y: batch_y}). Also, define epoch_loss above.

Upvotes: 1

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