title: Scipy convolve2d different from tensorflow conv2d
body: <p>Here is my code:</p>
  <pre><code>import tensorflow as tf
  import numpy as np
  from scipy import signal
  
  img2 = np.array([
      [10, 10, 10, 0, 0, 0],
      [10, 10, 10, 0, 0, 0],
      [10, 10, 10, 0, 0, 0],
      [10, 10, 10, 0, 0, 0],
      [10, 10, 10, 0, 0, 0],
      [10, 10, 10, 0, 0, 0]
  ]).astype(np.float32)
  k = np.array([
      [1., 0., -1.],
      [1., 0., -1.],
      [1., 0., -1.]
  ]).astype(np.float32)
  
  img_tf = tf.constant(tf.reshape(img2, (1, 6, 6, 1)), dtype=tf.float32)
  k_tf = tf.constant(tf.reshape(k, (3, 3, 1, 1)), dtype=tf.float32)
  conv_tf = tf.nn.conv2d(img_tf, k_tf, strides=[1, 1], padding=&quot;SAME&quot;)[0, ..., 0]
  print(&quot;conv_tf: &quot; + str(conv_tf))
  
  np_conv = np.array(signal.convolve2d(img2 , k, &quot;same&quot;), np.int32)
  print(&quot;sp_conv:\n&quot; + str(np_conv))
  </code></pre>
  <p>The output is:</p>
  <pre><code>conv_tf: tf.Tensor(
  [[-20.   0.  20.  20.   0.   0.]
   [-30.   0.  30.  30.   0.   0.]
   [-30.   0.  30.  30.   0.   0.]
   [-30.   0.  30.  30.   0.   0.]
   [-30.   0.  30.  30.   0.   0.]
   [-20.   0.  20.  20.   0.   0.]], shape=(6, 6), dtype=float32)
  sp_conv:
  [[ 20   0 -20 -20   0   0]
   [ 30   0 -30 -30   0   0]
   [ 30   0 -30 -30   0   0]
   [ 30   0 -30 -30   0   0]
   [ 30   0 -30 -30   0   0]
   [ 20   0 -20 -20   0   0]]
  </code></pre>
  <p>Now one seam as the opposite of the other one, why is there this difference?</p>