(inputs, num_outputs, kernel_shape, strides=[1, 1], scope=None, train=True)
Args:
    inputs: nhwc
    num_outputs: int
    kernel_shape: [kernel_h, kernel_w]
  Returns:
    outputs: nhw(num_outputs)
Args:
    inputs: nhwc
    num_outputs: int
    kernel_shape: [kernel_h, kernel_w]
  Returns:
    outputs: nhw(num_outputs)
Args : inputs : nhwc num_outputs : int kernel_shape : [ kernel_h kernel_w ] Returns : outputs : nhw ( num_outputs )
def resnet_block(inputs, num_outputs, kernel_shape, strides=[1, 1], scope=None, train=True):
  """
  Args:
    inputs: nhwc
    num_outputs: int
    kernel_shape: [kernel_h, kernel_w]
  Returns:
    outputs: nhw(num_outputs)
  """
  with tf.variable_scope(scope) as scope:
    conv1 = conv2d(inputs, num_outputs, kernel_shape, strides=[1, 1], mask_type=None, scope="conv1")
    bn1 = batch_norm(conv1, train=train, scope='bn1')
    relu1 = tf.nn.relu(bn1)
    conv2 = conv2d(relu1, num_outputs, kernel_shape, strides=[1, 1], mask_type=None, scope="conv2")
    bn2 = batch_norm(conv2, train=train, scope='bn2')
    output = inputs + bn2

    return output
def resnet_block ( inputs , num_outputs , kernel_shape , strides = [ 1 , 1 ] , scope = None , train = True ) : with tf . variable_scope ( scope ) as scope : conv1 = conv2d ( inputs , num_outputs , kernel_shape , strides = [ 1 , 1 ] , mask_type = None , scope = "conv1" ) bn1 = batch_norm ( conv1 , train = train , scope = 'bn1' ) relu1 = tf . nn . relu ( bn1 ) conv2 = conv2d ( relu1 , num_outputs , kernel_shape , strides = [ 1 , 1 ] , mask_type = None , scope = "conv2" ) bn2 = batch_norm ( conv2 , train = train , scope = 'bn2' ) output = inputs + bn2 return output