diff options
Diffstat (limited to 'tensorflow/python/ops/nn.py')
| -rw-r--r-- | tensorflow/python/ops/nn.py | 27 |
1 files changed, 14 insertions, 13 deletions
diff --git a/tensorflow/python/ops/nn.py b/tensorflow/python/ops/nn.py index 8eccc63eb1..004ceb51c6 100644 --- a/tensorflow/python/ops/nn.py +++ b/tensorflow/python/ops/nn.py @@ -43,10 +43,10 @@ are as follows. If the 4-D `input` has shape `[batch, in_height, in_width, ...]` and the 4-D `filter` has shape `[filter_height, filter_width, ...]`, then - output.shape = [batch, - (in_height - filter_height + 1) / strides[1], - (in_width - filter_width + 1) / strides[2], - ...] + shape(output) = [batch, + (in_height - filter_height + 1) / strides[1], + (in_width - filter_width + 1) / strides[2], + ...] output[b, i, j, :] = sum_{di, dj} input[b, strides[1] * i + di, strides[2] * j + dj, ...] * @@ -58,7 +58,7 @@ vectors. For `depthwise_conv_2d`, each scalar component `input[b, i, j, k]` is multiplied by a vector `filter[di, dj, k]`, and all the vectors are concatenated. -In the formula for `output.shape`, the rounding direction depends on padding: +In the formula for `shape(output)`, the rounding direction depends on padding: * `padding = 'SAME'`: Round down (only full size windows are considered). * `padding = 'VALID'`: Round up (partial windows are included). @@ -81,7 +81,7 @@ In detail, the output is for each tuple of indices `i`. The output shape is - output.shape = (value.shape - ksize + 1) / strides + shape(output) = (shape(value) - ksize + 1) / strides where the rounding direction depends on padding: @@ -119,10 +119,10 @@ TensorFlow provides several operations that help you perform classification. ## Embeddings -TensorFlow provides several operations that help you compute embeddings. +TensorFlow provides library support for looking up values in embedding +tensors. @@embedding_lookup -@@embedding_lookup_sparse ## Evaluation @@ -336,15 +336,16 @@ def dropout(x, keep_prob, noise_shape=None, seed=None, name=None): By default, each element is kept or dropped independently. If `noise_shape` is specified, it must be [broadcastable](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) - to the shape of `x`, and only dimensions with `noise_shape[i] == x.shape[i]` - will make independent decisions. For example, if `x.shape = [b, x, y, c]` and - `noise_shape = [b, 1, 1, c]`, each batch and channel component will be + to the shape of `x`, and only dimensions with `noise_shape[i] == shape(x)[i]` + will make independent decisions. For example, if `shape(x) = [k, l, m, n]` + and `noise_shape = [k, 1, 1, n]`, each batch and channel component will be kept independently and each row and column will be kept or not kept together. Args: x: A tensor. - keep_prob: Float probability that each element is kept. - noise_shape: Shape for randomly generated keep/drop flags. + keep_prob: A Python float. The probability that each element is kept. + noise_shape: A 1-D `Tensor` of type `int32`, representing the + shape for randomly generated keep/drop flags. seed: A Python integer. Used to create a random seed. See [`set_random_seed`](constant_op.md#set_random_seed) for behavior. name: A name for this operation (optional). |