title: Keras Unet: A `Concatenate` layer requires inputs with matching shapes except for the concat axis Error off by 1
body: <p>I have skimmed through the many threads that have this same sort of error, but none seem to be similar to the issue I am facing. Below is a simple Unet architecture:</p>
  <pre><code>tdata = np.zeros([100,500,500,5])
  
  def unet2(input_data):
  
      inputs = layers.Input((input_data.shape[1],input_data.shape[2],input_data.shape[3]))
  
      conv1 = layers.Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(inputs)
      conv1 = layers.Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv1)
      pool1 = layers.MaxPooling2D(pool_size=(2, 2))(conv1)
      conv2 = layers.Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool1)
      conv2 = layers.Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv2)
      pool2 = layers.MaxPooling2D(pool_size=(2, 2))(conv2)
      conv3 = layers.Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool2)
      conv3 = layers.Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv3)
      pool3 = layers.MaxPooling2D(pool_size=(2, 2))(conv3)
      conv4 = layers.Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool3)
      conv4 = layers.Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv4)
      drop4 = layers.Dropout(0.5)(conv4)
      pool4 = layers.MaxPooling2D(pool_size=(2, 2))(drop4)
  
      conv5 = layers.Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool4)
      conv5 = layers.Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv5)
      drop5 = layers.Dropout(0.5)(conv5)
  
      up6 = layers.Conv2D(512, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(layers.UpSampling2D(size = (2,2))(drop5))
      merge6 = layers.concatenate([drop4,up6], axis = 3)
      conv6 = layers.Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge6)
      conv6 = layers.Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv6)
  
      up7 = layers.Conv2D(256, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(layers.UpSampling2D(size = (2,2))(conv6))
      merge7 = layers.concatenate([conv3,up7], axis = 3)
      conv7 = layers.Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge7)
      conv7 = layers.Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv7)
  
      up8 = layers.Conv2D(128, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(layers.UpSampling2D(size = (2,2))(conv7))
      merge8 = layers.concatenate([conv2,up8], axis = 3)
      conv8 = layers.Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge8)
      conv8 = layers.Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv8)
  
      up9 = layers.Conv2D(64, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(layers.UpSampling2D(size = (2,2))(conv8))
      merge9 = layers.concatenate([conv1,up9], axis = 3)
      conv9 = layers.Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge9)
      conv9 = layers.Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
      conv9 = layers.Conv2D(2, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
      conv10 = layers.Conv2D(1, 1, activation = 'sigmoid')(conv9)
  
      model = Model(input = input_data, output = conv10)
  
      model.compile(optimizer = Adam(lr = 1e-4), loss = 'binary_crossentropy', metrics = ['accuracy'])
  
  </code></pre>
  <p>I then simply run the unet as:</p>
  <pre><code>unet2(tdata)
  </code></pre>
  <p>where tdata represents a sample dataset that I might have that would be ready for the Unet. Here, the dimensions are [N,x,y,v], where N = number of samples, x = x-dimension, y = y-dimension, and v = number of variables. In other words, I have 100 samples of 500x500 data consisting of 5 variables. When I run this exact code I get the error:</p>
  <pre><code>ValueError: A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got inputs shapes: [(None, 125, 125, 256), (None, 124, 124, 256)]
  </code></pre>
  <p>at the 'merge7' part. Therefore, there is an offset of 1 issue somewhere that I cannot seem to pinpoint - why is this issue occurring and how best to fix this?</p>
  <p>My versions of Keras are 2.4.0 and tf = 2.3.1</p>