title: How to convert a Tensor with shape (None, 512) into a Tensor with shape (None, 12, 12, 512)
body: <p>I'm using Python 3.7.7., Tensorflow 2.1.0 and Functional Api to define an encoder with this summary:</p>
  <pre><code>Model: &quot;encoder&quot;
  _________________________________________________________________
  Layer (type)                 Output Shape              Param #   
  =================================================================
  input_1 (InputLayer)         [(None, 200, 200, 1)]     0         
  _________________________________________________________________
  conv1_1 (Conv2D)             (None, 200, 200, 64)      1664      
  _________________________________________________________________
  conv1_2 (Conv2D)             (None, 200, 200, 64)      102464    
  _________________________________________________________________
  pool1 (MaxPooling2D)         (None, 100, 100, 64)      0         
  _________________________________________________________________
  conv2_1 (Conv2D)             (None, 100, 100, 96)      55392     
  _________________________________________________________________
  conv2_2 (Conv2D)             (None, 100, 100, 96)      83040     
  _________________________________________________________________
  pool2 (MaxPooling2D)         (None, 50, 50, 96)        0         
  _________________________________________________________________
  conv3_1 (Conv2D)             (None, 50, 50, 128)       110720    
  _________________________________________________________________
  conv3_2 (Conv2D)             (None, 50, 50, 128)       147584    
  _________________________________________________________________
  pool3 (MaxPooling2D)         (None, 25, 25, 128)       0         
  _________________________________________________________________
  conv4_1 (Conv2D)             (None, 25, 25, 256)       295168    
  _________________________________________________________________
  conv4_2 (Conv2D)             (None, 25, 25, 256)       1048832   
  _________________________________________________________________
  pool4 (MaxPooling2D)         (None, 12, 12, 256)       0         
  _________________________________________________________________
  conv5_1 (Conv2D)             (None, 12, 12, 512)       1180160   
  _________________________________________________________________
  conv5_2 (Conv2D)             (None, 12, 12, 512)       2359808   
  _________________________________________________________________
  global_average_pooling2d (Gl (None, 512)               0         
  =================================================================
  Total params: 5,384,832
  Trainable params: 5,384,832
  Non-trainable params: 0
  _________________________________________________________________
  None
  </code></pre>
  <p>I get the encoder from a U-Net network with this code:</p>
  <pre><code>u_net: Model = get_unet_uncompiled(img_shape = (200, 200, 1))
  
  encoder_input = Model(inputs=u_net.layers[0].input, outputs=u_net.layers[14].output)
  encoder_output = GlobalAveragePooling2D()(encoder_input.layers[-1].output)
  
  encoder = Model(encoder_input.input, encoder_output, name='encoder')
  
  print(encoder.summary())
  </code></pre>
  <p>I want to up pooling its output to a shape of <code>(None, 12, 12, 512)</code>.</p>
  <p>I have tried this:</p>
  <pre><code>u_net: Model = get_unet_uncompiled(img_shape = (200, 200, 1))
  
  encoder_input = Model(inputs=u_net.layers[0].input, outputs=u_net.layers[14].output)
  encoder_output = GlobalAveragePooling2D()(encoder_input.layers[-1].output)
  
  up = UpSampling2D(size=(12, 12))(encoder_output)
  
  encoder = Model(encoder_input.input, up, name='encoder')
  </code></pre>
  <p>But I have this error:</p>
  <pre><code>ValueError: Input 0 of layer up_sampling2d is incompatible with the layer: expected ndim=4, found ndim=2. Full shape received: [None, 512]
  </code></pre>
  <p><strong>I have no idea about what I'm doing</strong>. Maybe <code>UpSampling2D</code> is wrong here.</p>
  <p>But, how can I convert it to shape <code>(None, 12, 12, 512)</code>?</p>