title: Tensorflow Assign requires shapes of both tensors to match. lhs shape= [20] rhs shape= [48]
body: <p>I am a TensorFlow noob. I have trained a TensorFlow model from the open source implementation of deeppose and have to now run the model against a new set of images.</p>
  
  <p>The model was trained on images of size <code>100 * 100</code> so I have resized the new set of images to the same size. I have <code>149</code> new images to run the model against. When I run the model, I get the following error.</p>
  
  <pre><code>InvalidArgumentError (see above for traceback): Assign requires shapes
  of both tensors to match. lhs shape= [20] rhs shape= [48]
  </code></pre>
  
  <p>At the line</p>
  
  <pre><code>saver = tf.train.Saver(tf.all_variables())
  </code></pre>
  
  <p>I suspect the trained model size and the test image sizes are not matching. I am not clear how to fix this issue. I printed out the list of variables from the <code>tf.all_variables()</code> call. Here it is</p>
  
  <pre><code>Tensor("Placeholder:0", shape=(128, 100, 100, 3), dtype=float32)
  (11, 11, 3, 20)
  conv1/weights:0
  (20,)
  conv1/biases:0
  (5, 5, 20, 35)
  conv2/weights:0
  (35,)
  conv2/biases:0
  (3, 3, 35, 50)
  conv4/weights:0
  (50,)
  conv4/biases:0
  (3, 3, 50, 75)
  conv5/weights:0
  (75,)
  conv5/biases:0
  (300, 1024)
  local1/weights:0
  (1024,)
  local1/biases:0
  (1024, 1024)
  local2/weights:0
  (1024,)
  local2/biases:0
  (1024, 0)
  softmax_linear/weights:0
  (0,)
  softmax_linear/biases:0
  </code></pre>
  
  <p>I am not sure where the RHS parameter is coming from. I've looked at all config files and there doesn't seem to be any parameter specifying this config.</p>
  
  <p>Any help in resolving this will be greatly appreciated.</p>