title: The difference between tf.nn.conv2d_transpose and slim.conv2d_transpose
body: <p>What is the difference between this two function in Tensorflow</p>
  
  <pre><code>tf.nn.conv2d_transpose(
      value,
      filter,
      output_shape,
      strides,
      padding='SAME',
      data_format='NHWC',
      name=None
  )
  </code></pre>
  
  <p>It full defination is in <a href="https://www.tensorflow.org/api_docs/python/tf/nn/conv2d_transpose" rel="nofollow noreferrer">tf.nn.conv2d_transpose</a>
  slim.conv2d_transpose is define as follow:</p>
  
  <pre><code>tf.layers.conv2d_transpose(
      inputs,
      filters,
      kernel_size,
      strides=(1, 1),
      padding='valid',
      data_format='channels_last',
      activation=None,
      use_bias=True,
      kernel_initializer=None,
      bias_initializer=tf.zeros_initializer(),
      kernel_regularizer=None,
      bias_regularizer=None,
      activity_regularizer=None,
      kernel_constraint=None,
      bias_constraint=None,
      trainable=True,
      name=None,
      reuse=None
  )
  </code></pre>
  
  <p>full defination is in <a href="https://www.tensorflow.org/api_docs/python/tf/layers/conv2d_transpose" rel="nofollow noreferrer">slim.conv2d_transpose</a></p>
  
  <p>how can I define the output shape in slim.conv2d_transpose</p>