diff options
Diffstat (limited to 'tensorflow/python/ops/nn_ops.py')
| -rw-r--r-- | tensorflow/python/ops/nn_ops.py | 32 |
1 files changed, 25 insertions, 7 deletions
diff --git a/tensorflow/python/ops/nn_ops.py b/tensorflow/python/ops/nn_ops.py index 51ec1c313b..4a8ac42161 100644 --- a/tensorflow/python/ops/nn_ops.py +++ b/tensorflow/python/ops/nn_ops.py @@ -278,7 +278,8 @@ def with_space_to_batch( For N=3, the valid values are "NDHWC" (default) and "NCDHW". Returns: - The output Tensor as described above. + The output Tensor as described above, dimensions will vary based on the op + provided. Raises: ValueError: if `padding` is invalid or the arguments are incompatible. @@ -529,17 +530,16 @@ def convolution(input, filter, # pylint: disable=redefined-builtin of N `strides` (defaulting [1]*N), this computes for each N-D spatial output position (x[0], ..., x[N-1]): + ``` output[b, x[0], ..., x[N-1], k] = - sum_{z[0], ..., z[N-1], q} - filter[z[0], ..., z[N-1], q, k] * padded_input[b, x[0]*strides[0] + dilation_rate[0]*z[0], ..., x[N-1]*strides[N-1] + dilation_rate[N-1]*z[N-1], q] - + ``` where `padded_input` is obtained by zero padding the input using an effective spatial filter shape of `(spatial_filter_shape-1) * dilation_rate + 1` and output striding `strides` as described in the @@ -682,6 +682,7 @@ def pool(input, # pylint: disable=redefined-builtin 0 <= x[i] < output_spatial_shape[i], 0 <= c < num_channels: + ``` output[b, x[0], ..., x[N-1], c] = REDUCE_{z[0], ..., z[N-1]} input[b, @@ -689,6 +690,7 @@ def pool(input, # pylint: disable=redefined-builtin ... x[N-1]*strides[N-1] - pad_before[N-1] + dilation_rate[N-1]*z[N-1], c], + ``` where the reduction function REDUCE depends on the value of `pooling_type`, and pad_before is defined based on the value of `padding` as described in the @@ -698,10 +700,12 @@ def pool(input, # pylint: disable=redefined-builtin In the case that `data_format` starts with `"NC"`, the `input` and output are simply transposed as follows: + ``` pool(input, data_format, **kwargs) = tf.transpose(pool(tf.transpose(input, [0] + range(2,N+2) + [1]), **kwargs), [0, N+1] + range(1, N+1)) + ``` Args: input: Tensor of rank N+2, of shape @@ -740,6 +744,7 @@ def pool(input, # pylint: disable=redefined-builtin If padding = "SAME": output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides[i]) + If padding = "VALID": output_spatial_shape[i] = ceil((input_spatial_shape[i] - (window_shape[i] - 1) * dilation_rate[i]) @@ -844,9 +849,14 @@ def atrous_conv2d(value, filters, rate, padding, name=None): More specifically: - output[b, i, j, k] = sum_{di, dj, q} filters[di, dj, q, k] * - value[b, i + rate * di, j + rate * dj, q] - + ``` + output[batch, height, width, out_channel] = + sum_{dheight, dwidth, in_channel} ( + filters[dheight, dwidth, in_channel, out_channel] * + value[batch, height + rate * dheight, width + rate * dwidth, in_channel] + ) + ``` + Atrous convolution allows us to explicitly control how densely to compute feature responses in fully convolutional networks. Used in conjunction with bilinear interpolation, it offers an alternative to `conv2d_transpose` in @@ -932,6 +942,14 @@ def atrous_conv2d(value, filters, rate, padding, name=None): Returns: A `Tensor` with the same type as `value`. + Output shape with `'VALID`` padding is: + + [batch, height - 2 * (filter_width - 1), + width - 2 * (filter_height - 1), out_channels]. + + Output shape with `'SAME'` padding is: + + [batch, height, width, out_channels]. Raises: ValueError: If input/output depth does not match `filters`' shape, or if |