Python gen_nn_ops._fractional_avg_pool_grad方法代码示例

# 需要导入模块: from tensorflow.python.ops import gen_nn_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_nn_ops import _fractional_avg_pool_grad [as 别名]
def _FractionalAvgPoolGrad(op, grad_0, unused_grad_1, unused_grad_2):
  """Returns gradient for FractionalAvgPool.

  Since FractionalAvgPool has three outputs, there are three gradients passed in
  for each of the outputs. Only the first one is useful, the other two gradients
  are empty.

  Args:
    op: The FractionalAvgPoolOp.
    grad_0: Gradient with respect to op.outputs[0]
    unused_grad_1: Gradient with respect to op.outputs[1]/row_seq. It is empty.
    unused_grad_2: Gradient with respect to op.outputs[2]/col_seq. It is empty.

  Returns:
    Input backprop for FractionalAvgPool op.
  """
  # pylint: disable=protected-access
  return gen_nn_ops._fractional_avg_pool_grad(op.inputs[0].get_shape(), grad_0,
                                              op.outputs[1], op.outputs[2],
                                              op.get_attr("overlapping")) 

# 需要导入模块: from tensorflow.python.ops import gen_nn_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_nn_ops import _fractional_avg_pool_grad [as 别名]
def testDirectNotUseOverlapping(self):
    for num_batches in [1, 3]:
      for row_window_size in [2, 5]:
        for col_window_size in [2, 4]:
          num_rows = row_window_size * 5
          num_cols = col_window_size * 7
          for num_channels in [1, 2]:
            input_shape = (num_batches, num_rows, num_cols, num_channels)
            with self.test_session() as _:
              input_tensor = tf.constant(self._GenerateRandomInputTensor(
                  input_shape).astype(np.float32))
              window_size = [1, row_window_size, col_window_size, 1]
              stride_size = [1, row_window_size, col_window_size, 1]
              padding = "VALID"
              output_tensor = tf.nn.avg_pool(input_tensor, window_size,
                                             stride_size, padding)
              output_data = output_tensor.eval()
              num_elements = 1
              for dim_size in output_data.shape:
                num_elements *= dim_size
              output_backprop = (self._PRNG.rand(num_elements) *
                                 1000).reshape(output_data.shape)
              input_backprop_tensor = gen_nn_ops._avg_pool_grad(
                  input_tensor.get_shape(), output_backprop, window_size,
                  stride_size, padding)
              input_backprop = input_backprop_tensor.eval()
              row_seq = list(range(0, num_rows + 1, row_window_size))
              col_seq = list(range(0, num_cols + 1, col_window_size))
              fap_input_backprop_tensor = gen_nn_ops._fractional_avg_pool_grad(
                  input_tensor.get_shape(),
                  output_backprop,
                  row_seq,
                  col_seq,
                  overlapping=False)
              fap_input_backprop = fap_input_backprop_tensor.eval()
              self.assertShapeEqual(input_backprop, fap_input_backprop_tensor)
              self.assertAllClose(input_backprop, fap_input_backprop) 

# 需要导入模块: from tensorflow.python.ops import gen_nn_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_nn_ops import _fractional_avg_pool_grad [as 别名]
def testDirectUseOverlapping(self):
    for num_batches in [1, 3]:
      for row_window_size in [2, 5]:
        for col_window_size in [2, 4]:
          num_rows = (row_window_size - 1) * 5 + 1
          num_cols = (col_window_size - 1) * 7 + 1
          for num_channels in [1, 2]:
            input_shape = (num_batches, num_rows, num_cols, num_channels)
            with self.test_session() as _:
              input_tensor = tf.constant(self._GenerateRandomInputTensor(
                  input_shape).astype(np.float32))
              window_size = [1, row_window_size, col_window_size, 1]
              stride_size = [1, row_window_size - 1, col_window_size - 1, 1]
              padding = "VALID"
              output_tensor = tf.nn.avg_pool(input_tensor, window_size,
                                             stride_size, padding)
              output_data = output_tensor.eval()
              num_elements = 1
              for dim_size in output_data.shape:
                num_elements *= dim_size
              output_backprop = (self._PRNG.rand(num_elements) *
                                 1000).reshape(output_data.shape)
              input_backprop_tensor = gen_nn_ops._avg_pool_grad(
                  input_tensor.get_shape(), output_backprop, window_size,
                  stride_size, padding)
              input_backprop = input_backprop_tensor.eval()
              row_seq = list(range(0, num_rows, row_window_size - 1))
              col_seq = list(range(0, num_cols, col_window_size - 1))
              row_seq[-1] += 1
              col_seq[-1] += 1
              fap_input_backprop_tensor = gen_nn_ops._fractional_avg_pool_grad(
                  input_tensor.get_shape(),
                  output_backprop,
                  row_seq,
                  col_seq,
                  overlapping=True)
              fap_input_backprop = fap_input_backprop_tensor.eval()
              self.assertShapeEqual(input_backprop, fap_input_backprop_tensor)
              self.assertAllClose(input_backprop, fap_input_backprop)