votes up 5

The channel dimension of the inputs should be defined. Found `None`.

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Exception Class:

Raise code

return -1 - self.rank
      return -1

  def _get_input_channel(self, input_shape):
    channel_axis = self._get_channel_axis()
    if input_shape.dims[channel_axis].value is None:
      raise ValueError('The channel dimension of the inputs '
                       'should be defined. Found `None`.')
    return int(input_shape[channel_axis])

  def _get_padding_op(self):
    if self.padding == 'causal':
      op_padding = 'valid'
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Ways to fix

votes up 0 votes down

When initializing a Conv2DTranspose the channel dimension of the input_shape shouldn't be none.

for example for a 128x128 RGB pictures the input shape should be;

input_shape=(128, 128, 3)

Here the last element of the the tuple i.e 3 is indicating that the data(image) has 3 channels.

But if the value of this element is set to None, the given error is raised.



How to reproduce the error:

pipenv install tensorflow

import tensorflow as tf
model = tf.keras.Sequential()
                                          input_shape=(22,None)) # this causes the error

The error (output):


ValueError                                Traceback (most recent call last)

<ipython-input-22-dee6fd4b4f35> in <module>()
      1 import tensorflow as tf
      2 model = tf.keras.Sequential()
----> 3 model.add(tf.keras.layers.Conv2DTranspose(1, (1,1), strides=(2,2), input_shape=(2, 2,None)))
      4 model.summary()

7 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/layers/ in build(self, input_shape)
   1239     channel_axis = self._get_channel_axis()
   1240     if input_shape.dims[channel_axis].value is None:
-> 1241       raise ValueError('The channel dimension of the inputs '
   1242                        'should be defined. Found `None`.')
   1243     input_dim = int(input_shape[channel_axis])

ValueError: The channel dimension of the inputs should be defined. Found `None`.

How to fix:

The input shape parameter should be given a valid value.

import tensorflow as tf
model = tf.keras.Sequential()
                                          input_shape=(22,3)) # fixed

Expected output:

Model: "sequential_14"
Layer (type)                 Output Shape              Param #   
conv2d_transpose_4 (Conv2DTr (None, 4, 4, 1)           4         
Total params: 4
Trainable params: 4
Non-trainable params: 0

Jun 23, 2021 kellemnegasi answer
kellemnegasi 30.0k

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