logits and labels must be broadcastable traceback

[under94]

Hello,
I'm new in tensorflow.
I'm trying the resnet_v2_101 but i get an exception and i can't solve it.
My images are 488x488x3 my batchs size and num classes are 2.

So, this is my code:

num_classes = 2
net_input = tf.placeholder(tf.float32,shape=[None,None,None,3])
    net_output = tf.placeholder(tf.float32,shape=[None,None,None,num_classes]) 

   #build network
    network,_ = resnet_v2.resnet_v2_101(tf.zeros([2,488,488,3]),num_classes , is_training=True, scope='resnet_v2_101')

sess.run(tf.global_variables_initializer())


        loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=network, labels=net_output))

        opt = tf.train.RMSPropOptimizer(learning_rate=0.0001, decay=0.995).minimize(loss, var_list=[var for var in tf.trainable_variables()])

        saver=tf.train.Saver(max_to_keep=1000)
        sess.run(tf.global_variables_initializer())

        # Do the training here
        for epoch in range(args.epoch_start_i, args.num_epochs):

            current_losses = []

            # Equivalent to shuffling
            id_list = np.random.permutation( len(train_input_names) )

            num_iters = int(np.floor(len(id_list) / args.batch_size))

            #for i in range(num_iters):
            description_train = '[i]Train Epoch {:>2}/{}'.format(epoch + 1, args.num_epochs)
            for i in tqdm(range(num_iters), desc=description_train, unit='batch'):

                input_image_batch = []
                output_image_batch = []

                # Collect a batch of images
                for j in range(args.batch_size):
                    index = i*args.batch_size + j
                    id = id_list[index]
                    input_image = utils.load_image(train_input_names[id])
                    output_image = utils.load_image(train_output_names[id])

                    with tf.device('/cpu:0'):
                        input_image, output_image = data_augmentation(input_image, output_image)

                        # Prep the data. Make sure the labels are in one-hot format
                        input_image = np.float32(input_image) / 255.0
                        output_image = np.float32(helpers.one_hot_it(label=output_image, label_values=label_values))

                        input_image_batch.append(np.expand_dims(input_image, axis=0))
                        output_image_batch.append(np.expand_dims(output_image, axis=0))

                    input_image_batch = np.squeeze(np.stack(input_image_batch, axis=1))
                    output_image_batch = np.squeeze(np.stack(output_image_batch, axis=1))

                # Do the training
                _,current=sess.run([opt,loss],feed_dict={net_input:input_image_batch,net_output:output_image_batch})

I get this traceback:
InvalidArgumentError (see above for traceback): logits and labels must be broadcastable: logits_size=[512,2048] labels_size=[100352,2]

How can i solve it?

System information

• What is the top-level directory of the model you are using: https://github.com/tensorflow/models/blob/master/research/slim/nets/resnet_v2.py
• Have I written custom code (as opposed to using a stock example script provided in TensorFlow):
• OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Windows 10
• TensorFlow installed from (source or binary): pip install tensorflow-gpu
• TensorFlow version (use command below): 1.10.0
• Bazel version (if compiling from source): /
• CUDA/cuDNN version: 9.0
• GPU model and memory: NVIDIA Tesla V100
• Exact command to reproduce:

[tensorflowbutler]

Thank you for your post. We noticed you have not filled out the following field in the issue template. Could you update them if they are relevant in your case, or leave them as N/A? Thanks.
What is the top-level directory of the model you are using
Have I written custom code
OS Platform and Distribution
TensorFlow installed from
TensorFlow version
Bazel version
CUDA/cuDNN version
GPU model and memory
Exact command to reproduce

[under94]

Updated with template

[tensorflowbutler]

Hi There,
We are checking to see if you still need help on this, as this seems to be considerably old issue. Please update this issue with the latest information, code snippet to reproduce your issue and error you are seeing.
If we don't hear from you in the next 7 days, this issue will be closed automatically. If you don't need help on this issue any more, please consider closing this.

[amahendrakar]

Automatically closing due to lack of recent activity. Please update the issue when new information becomes available, and we will reopen the issue. Thanks!