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https://github.com/zebrajr/tensorflow.git
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Go: Update generated wrapper functions for TensorFlow ops.
PiperOrigin-RevId: 381374492 Change-Id: Ie94eb36485d8835b7400759db4d15f0e7fa442d6
This commit is contained in:
A. Unique TensorFlower
TensorFlower Gardener
parent
4cdece0345
commit
e8253e1138
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@ -23504,29 +23504,6 @@ func ExperimentalRandomDataset(scope *Scope, seed tf.Output, seed2 tf.Output, ou
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return op.Output(0)
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}
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// Creates a dataset that uses a custom thread pool to compute `input_dataset`.
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//
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// Arguments:
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//
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// num_threads: Identifies the number of threads to use for the private threadpool.
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//
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//
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func PrivateThreadPoolDataset(scope *Scope, input_dataset tf.Output, num_threads tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) {
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if scope.Err() != nil {
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return
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}
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attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes}
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opspec := tf.OpSpec{
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Type: "PrivateThreadPoolDataset",
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Input: []tf.Input{
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input_dataset, num_threads,
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},
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Attrs: attrs,
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}
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op := scope.AddOperation(opspec)
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return op.Output(0)
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}
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// Returns a batched matrix tensor with new batched diagonal values.
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//
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// Given `input` and `diagonal`, this operation returns a tensor with the
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@ -28312,6 +28289,200 @@ func SparseCrossV2(scope *Scope, indices []tf.Output, values []tf.Output, shapes
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return op.Output(0), op.Output(1), op.Output(2)
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}
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// BlockLSTMV2Attr is an optional argument to BlockLSTMV2.
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type BlockLSTMV2Attr func(optionalAttr)
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// BlockLSTMV2CellClip sets the optional cell_clip attribute to value.
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//
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// value: Value to clip the 'cs' value to.
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// If not specified, defaults to 0
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func BlockLSTMV2CellClip(value float32) BlockLSTMV2Attr {
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return func(m optionalAttr) {
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m["cell_clip"] = value
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}
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}
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// BlockLSTMV2UsePeephole sets the optional use_peephole attribute to value.
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//
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// value: Whether to use peephole weights.
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// If not specified, defaults to false
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func BlockLSTMV2UsePeephole(value bool) BlockLSTMV2Attr {
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return func(m optionalAttr) {
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m["use_peephole"] = value
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}
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}
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// Computes the LSTM cell forward propagation for all the time steps.
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//
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// This is equivalent to applying LSTMBlockCell in a loop, like so:
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//
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// ```python
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// for x1 in unpack(x):
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// i1, cs1, f1, o1, ci1, co1, h1 = LSTMBlock(
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// x1, cs_prev, h_prev, w, wci, wcf, wco, b)
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// cs_prev = cs1
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// h_prev = h1
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// i.append(i1)
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// cs.append(cs1)
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// f.append(f1)
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// o.append(o1)
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// ci.append(ci1)
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// co.append(co1)
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// h.append(h1)
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// return pack(i), pack(cs), pack(f), pack(o), pack(ci), pack(ch), pack(h)
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//
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// Note that unlike LSTMBlockCell (and BlockLSTM) which uses ICFO gate layout,
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// this op uses IFCO. So in order for the following snippet to be equivalent
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// all gate-related outputs should be reordered.
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// ```
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//
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// Arguments:
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// seq_len_max: Maximum time length actually used by this input. Outputs are padded
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// with zeros beyond this length.
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// x: The sequence input to the LSTM, shape (timelen, batch_size, num_inputs).
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// cs_prev: Value of the initial cell state.
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// h_prev: Initial output of cell (to be used for peephole).
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// w: The weight matrix.
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// wci: The weight matrix for input gate peephole connection.
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// wcf: The weight matrix for forget gate peephole connection.
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// wco: The weight matrix for output gate peephole connection.
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// b: The bias vector.
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//
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// Returns:
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// i: The input gate over the whole time sequence.
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// cs: The cell state before the tanh over the whole time sequence.
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// f: The forget gate over the whole time sequence.
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// o: The output gate over the whole time sequence.
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// ci: The cell input over the whole time sequence.
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// co: The cell after the tanh over the whole time sequence.
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// h: The output h vector over the whole time sequence.
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func BlockLSTMV2(scope *Scope, seq_len_max tf.Output, x tf.Output, cs_prev tf.Output, h_prev tf.Output, w tf.Output, wci tf.Output, wcf tf.Output, wco tf.Output, b tf.Output, optional ...BlockLSTMV2Attr) (i tf.Output, cs tf.Output, f tf.Output, o tf.Output, ci tf.Output, co tf.Output, h tf.Output) {
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if scope.Err() != nil {
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return
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}
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attrs := map[string]interface{}{}
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for _, a := range optional {
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a(attrs)
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}
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opspec := tf.OpSpec{
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Type: "BlockLSTMV2",
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Input: []tf.Input{
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seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b,
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},
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Attrs: attrs,
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}
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op := scope.AddOperation(opspec)
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return op.Output(0), op.Output(1), op.Output(2), op.Output(3), op.Output(4), op.Output(5), op.Output(6)
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}
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// Extract `patches` from `images` and put them in the "depth" output dimension.
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//
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// Arguments:
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// images: 4-D Tensor with shape `[batch, in_rows, in_cols, depth]`.
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// ksizes: The size of the sliding window for each dimension of `images`.
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// strides: How far the centers of two consecutive patches are in
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// the images. Must be: `[1, stride_rows, stride_cols, 1]`.
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// rates: Must be: `[1, rate_rows, rate_cols, 1]`. This is the
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// input stride, specifying how far two consecutive patch samples are in the
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// input. Equivalent to extracting patches with
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// `patch_sizes_eff = patch_sizes + (patch_sizes - 1) * (rates - 1)`, followed by
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// subsampling them spatially by a factor of `rates`. This is equivalent to
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// `rate` in dilated (a.k.a. Atrous) convolutions.
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// padding: The type of padding algorithm to use.
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//
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// Returns 4-D Tensor with shape `[batch, out_rows, out_cols, ksize_rows *
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// ksize_cols * depth]` containing image patches with size
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// `ksize_rows x ksize_cols x depth` vectorized in the "depth" dimension. Note
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// `out_rows` and `out_cols` are the dimensions of the output patches.
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func ExtractImagePatches(scope *Scope, images tf.Output, ksizes []int64, strides []int64, rates []int64, padding string) (patches tf.Output) {
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if scope.Err() != nil {
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return
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}
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attrs := map[string]interface{}{"ksizes": ksizes, "strides": strides, "rates": rates, "padding": padding}
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opspec := tf.OpSpec{
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Type: "ExtractImagePatches",
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Input: []tf.Input{
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images,
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},
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Attrs: attrs,
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}
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op := scope.AddOperation(opspec)
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return op.Output(0)
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}
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// Forwards the value of an available tensor from `inputs` to `output`.
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//
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// `Merge` waits for at least one of the tensors in `inputs` to become available.
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// It is usually combined with `Switch` to implement branching.
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//
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// `Merge` forwards the first tensor to become available to `output`, and sets
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// `value_index` to its index in `inputs`.
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//
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// Arguments:
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// inputs: The input tensors, exactly one of which will become available.
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//
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// Returns:
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// output: Will be set to the available input tensor.
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// value_index: The index of the chosen input tensor in `inputs`.
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func Merge(scope *Scope, inputs []tf.Output) (output tf.Output, value_index tf.Output) {
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if scope.Err() != nil {
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return
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}
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opspec := tf.OpSpec{
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Type: "Merge",
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Input: []tf.Input{
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tf.OutputList(inputs),
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},
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}
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op := scope.AddOperation(opspec)
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return op.Output(0), op.Output(1)
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}
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// PaddedBatchDatasetV2Attr is an optional argument to PaddedBatchDatasetV2.
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type PaddedBatchDatasetV2Attr func(optionalAttr)
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// PaddedBatchDatasetV2ParallelCopy sets the optional parallel_copy attribute to value.
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// If not specified, defaults to false
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func PaddedBatchDatasetV2ParallelCopy(value bool) PaddedBatchDatasetV2Attr {
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return func(m optionalAttr) {
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m["parallel_copy"] = value
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}
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}
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// Creates a dataset that batches and pads `batch_size` elements from the input.
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//
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// Arguments:
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//
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// batch_size: A scalar representing the number of elements to accumulate in a
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// batch.
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// padded_shapes: A list of int64 tensors representing the desired padded shapes
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// of the corresponding output components. These shapes may be partially
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// specified, using `-1` to indicate that a particular dimension should be
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// padded to the maximum size of all batch elements.
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// padding_values: A list of scalars containing the padding value to use for
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// each of the outputs.
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// drop_remainder: A scalar representing whether the last batch should be dropped in case its size
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// is smaller than desired.
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//
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func PaddedBatchDatasetV2(scope *Scope, input_dataset tf.Output, batch_size tf.Output, padded_shapes []tf.Output, padding_values []tf.Output, drop_remainder tf.Output, output_shapes []tf.Shape, optional ...PaddedBatchDatasetV2Attr) (handle tf.Output) {
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if scope.Err() != nil {
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return
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}
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attrs := map[string]interface{}{"output_shapes": output_shapes}
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for _, a := range optional {
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a(attrs)
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}
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opspec := tf.OpSpec{
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Type: "PaddedBatchDatasetV2",
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Input: []tf.Input{
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input_dataset, batch_size, tf.OutputList(padded_shapes), tf.OutputList(padding_values), drop_remainder,
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},
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Attrs: attrs,
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}
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op := scope.AddOperation(opspec)
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return op.Output(0)
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}
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// CudnnRNNAttr is an optional argument to CudnnRNN.
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type CudnnRNNAttr func(optionalAttr)
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@ -33442,6 +33613,182 @@ func TensorArraySplitV2(scope *Scope, handle tf.Output, value tf.Output, lengths
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return op.Output(0)
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}
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// Creates a dataset that uses a custom thread pool to compute `input_dataset`.
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//
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// Arguments:
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//
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// num_threads: Identifies the number of threads to use for the private threadpool.
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//
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//
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func PrivateThreadPoolDataset(scope *Scope, input_dataset tf.Output, num_threads tf.Output, output_types []tf.DataType, output_shapes []tf.Shape) (handle tf.Output) {
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if scope.Err() != nil {
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return
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}
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attrs := map[string]interface{}{"output_types": output_types, "output_shapes": output_shapes}
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opspec := tf.OpSpec{
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Type: "PrivateThreadPoolDataset",
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Input: []tf.Input{
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input_dataset, num_threads,
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},
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Attrs: attrs,
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}
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op := scope.AddOperation(opspec)
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return op.Output(0)
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}
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// Returns the diagonal part of the tensor.
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//
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// This operation returns a tensor with the `diagonal` part
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// of the `input`. The `diagonal` part is computed as follows:
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//
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// Assume `input` has dimensions `[D1,..., Dk, D1,..., Dk]`, then the output is a
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// tensor of rank `k` with dimensions `[D1,..., Dk]` where:
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//
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// `diagonal[i1,..., ik] = input[i1, ..., ik, i1,..., ik]`.
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//
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// For example:
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//
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// ```
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// # 'input' is [[1, 0, 0, 0]
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// [0, 2, 0, 0]
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// [0, 0, 3, 0]
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// [0, 0, 0, 4]]
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//
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// tf.diag_part(input) ==> [1, 2, 3, 4]
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// ```
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//
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// Arguments:
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// input: Rank k tensor where k is even and not zero.
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//
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// Returns The extracted diagonal.
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func DiagPart(scope *Scope, input tf.Output) (diagonal tf.Output) {
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if scope.Err() != nil {
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return
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}
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opspec := tf.OpSpec{
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Type: "DiagPart",
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Input: []tf.Input{
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input,
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},
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}
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op := scope.AddOperation(opspec)
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return op.Output(0)
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}
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// CudnnRNNParamsToCanonicalAttr is an optional argument to CudnnRNNParamsToCanonical.
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type CudnnRNNParamsToCanonicalAttr func(optionalAttr)
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// CudnnRNNParamsToCanonicalRnnMode sets the optional rnn_mode attribute to value.
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// If not specified, defaults to "lstm"
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func CudnnRNNParamsToCanonicalRnnMode(value string) CudnnRNNParamsToCanonicalAttr {
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return func(m optionalAttr) {
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m["rnn_mode"] = value
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}
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}
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// CudnnRNNParamsToCanonicalInputMode sets the optional input_mode attribute to value.
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// If not specified, defaults to "linear_input"
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func CudnnRNNParamsToCanonicalInputMode(value string) CudnnRNNParamsToCanonicalAttr {
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return func(m optionalAttr) {
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m["input_mode"] = value
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}
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}
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// CudnnRNNParamsToCanonicalDirection sets the optional direction attribute to value.
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// If not specified, defaults to "unidirectional"
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func CudnnRNNParamsToCanonicalDirection(value string) CudnnRNNParamsToCanonicalAttr {
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return func(m optionalAttr) {
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m["direction"] = value
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}
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}
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// CudnnRNNParamsToCanonicalDropout sets the optional dropout attribute to value.
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// If not specified, defaults to 0
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func CudnnRNNParamsToCanonicalDropout(value float32) CudnnRNNParamsToCanonicalAttr {
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return func(m optionalAttr) {
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m["dropout"] = value
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}
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}
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// CudnnRNNParamsToCanonicalSeed sets the optional seed attribute to value.
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// If not specified, defaults to 0
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func CudnnRNNParamsToCanonicalSeed(value int64) CudnnRNNParamsToCanonicalAttr {
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return func(m optionalAttr) {
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m["seed"] = value
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}
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}
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// CudnnRNNParamsToCanonicalSeed2 sets the optional seed2 attribute to value.
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// If not specified, defaults to 0
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func CudnnRNNParamsToCanonicalSeed2(value int64) CudnnRNNParamsToCanonicalAttr {
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return func(m optionalAttr) {
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m["seed2"] = value
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}
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}
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// Retrieves CudnnRNN params in canonical form.
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//
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// Retrieves a set of weights from the opaque params buffer that can be saved and
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// restored in a way compatible with future runs.
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//
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// Note that the params buffer may not be compatible across different GPUs. So any
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// save and restoration should be converted to and from the canonical weights and
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// biases.
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//
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// num_layers: Specifies the number of layers in the RNN model.
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// num_units: Specifies the size of the hidden state.
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// input_size: Specifies the size of the input state.
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// num_params: number of parameter sets for all layers.
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// Each layer may contain multiple parameter sets, with each set consisting of
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// a weight matrix and a bias vector.
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// weights: the canonical form of weights that can be used for saving
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// and restoration. They are more likely to be compatible across different
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// generations.
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// biases: the canonical form of biases that can be used for saving
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// and restoration. They are more likely to be compatible across different
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// generations.
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// rnn_mode: Indicates the type of the RNN model.
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// input_mode: Indicate whether there is a linear projection between the input and
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// The actual computation before the first layer. 'skip_input' is only allowed
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// when input_size == num_units; 'auto_select' implies 'skip_input' when
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// input_size == num_units; otherwise, it implies 'linear_input'.
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// direction: Indicates whether a bidirectional model will be used.
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// dir = (direction == bidirectional) ? 2 : 1
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// dropout: dropout probability. When set to 0., dropout is disabled.
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// seed: the 1st part of a seed to initialize dropout.
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// seed2: the 2nd part of a seed to initialize dropout.
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func CudnnRNNParamsToCanonical(scope *Scope, num_layers tf.Output, num_units tf.Output, input_size tf.Output, params tf.Output, num_params int64, optional ...CudnnRNNParamsToCanonicalAttr) (weights []tf.Output, biases []tf.Output) {
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if scope.Err() != nil {
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return
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}
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attrs := map[string]interface{}{"num_params": num_params}
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for _, a := range optional {
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a(attrs)
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}
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opspec := tf.OpSpec{
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Type: "CudnnRNNParamsToCanonical",
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Input: []tf.Input{
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num_layers, num_units, input_size, params,
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},
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Attrs: attrs,
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}
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op := scope.AddOperation(opspec)
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if scope.Err() != nil {
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return
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}
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var idx int
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var err error
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if weights, idx, err = makeOutputList(op, idx, "weights"); err != nil {
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scope.UpdateErr("CudnnRNNParamsToCanonical", err)
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return
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}
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if biases, idx, err = makeOutputList(op, idx, "biases"); err != nil {
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scope.UpdateErr("CudnnRNNParamsToCanonical", err)
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return
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}
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return weights, biases
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}
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// Computes the number of elements in the given queue.
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//
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// Arguments:
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@ -35257,159 +35604,6 @@ func TensorArraySizeV3(scope *Scope, handle tf.Output, flow_in tf.Output) (size
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return op.Output(0)
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}
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// Returns the diagonal part of the tensor.
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//
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// This operation returns a tensor with the `diagonal` part
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// of the `input`. The `diagonal` part is computed as follows:
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//
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// Assume `input` has dimensions `[D1,..., Dk, D1,..., Dk]`, then the output is a
|
||||
// tensor of rank `k` with dimensions `[D1,..., Dk]` where:
|
||||
//
|
||||
// `diagonal[i1,..., ik] = input[i1, ..., ik, i1,..., ik]`.
|
||||
//
|
||||
// For example:
|
||||
//
|
||||
// ```
|
||||
// # 'input' is [[1, 0, 0, 0]
|
||||
// [0, 2, 0, 0]
|
||||
// [0, 0, 3, 0]
|
||||
// [0, 0, 0, 4]]
|
||||
//
|
||||
// tf.diag_part(input) ==> [1, 2, 3, 4]
|
||||
// ```
|
||||
//
|
||||
// Arguments:
|
||||
// input: Rank k tensor where k is even and not zero.
|
||||
//
|
||||
// Returns The extracted diagonal.
|
||||
func DiagPart(scope *Scope, input tf.Output) (diagonal tf.Output) {
|
||||
if scope.Err() != nil {
|
||||
return
|
||||
}
|
||||
opspec := tf.OpSpec{
|
||||
Type: "DiagPart",
|
||||
Input: []tf.Input{
|
||||
input,
|
||||
},
|
||||
}
|
||||
op := scope.AddOperation(opspec)
|
||||
return op.Output(0)
|
||||
}
|
||||
|
||||
// CudnnRNNParamsToCanonicalAttr is an optional argument to CudnnRNNParamsToCanonical.
|
||||
type CudnnRNNParamsToCanonicalAttr func(optionalAttr)
|
||||
|
||||
// CudnnRNNParamsToCanonicalRnnMode sets the optional rnn_mode attribute to value.
|
||||
// If not specified, defaults to "lstm"
|
||||
func CudnnRNNParamsToCanonicalRnnMode(value string) CudnnRNNParamsToCanonicalAttr {
|
||||
return func(m optionalAttr) {
|
||||
m["rnn_mode"] = value
|
||||
}
|
||||
}
|
||||
|
||||
// CudnnRNNParamsToCanonicalInputMode sets the optional input_mode attribute to value.
|
||||
// If not specified, defaults to "linear_input"
|
||||
func CudnnRNNParamsToCanonicalInputMode(value string) CudnnRNNParamsToCanonicalAttr {
|
||||
return func(m optionalAttr) {
|
||||
m["input_mode"] = value
|
||||
}
|
||||
}
|
||||
|
||||
// CudnnRNNParamsToCanonicalDirection sets the optional direction attribute to value.
|
||||
// If not specified, defaults to "unidirectional"
|
||||
func CudnnRNNParamsToCanonicalDirection(value string) CudnnRNNParamsToCanonicalAttr {
|
||||
return func(m optionalAttr) {
|
||||
m["direction"] = value
|
||||
}
|
||||
}
|
||||
|
||||
// CudnnRNNParamsToCanonicalDropout sets the optional dropout attribute to value.
|
||||
// If not specified, defaults to 0
|
||||
func CudnnRNNParamsToCanonicalDropout(value float32) CudnnRNNParamsToCanonicalAttr {
|
||||
return func(m optionalAttr) {
|
||||
m["dropout"] = value
|
||||
}
|
||||
}
|
||||
|
||||
// CudnnRNNParamsToCanonicalSeed sets the optional seed attribute to value.
|
||||
// If not specified, defaults to 0
|
||||
func CudnnRNNParamsToCanonicalSeed(value int64) CudnnRNNParamsToCanonicalAttr {
|
||||
return func(m optionalAttr) {
|
||||
m["seed"] = value
|
||||
}
|
||||
}
|
||||
|
||||
// CudnnRNNParamsToCanonicalSeed2 sets the optional seed2 attribute to value.
|
||||
// If not specified, defaults to 0
|
||||
func CudnnRNNParamsToCanonicalSeed2(value int64) CudnnRNNParamsToCanonicalAttr {
|
||||
return func(m optionalAttr) {
|
||||
m["seed2"] = value
|
||||
}
|
||||
}
|
||||
|
||||
// Retrieves CudnnRNN params in canonical form.
|
||||
//
|
||||
// Retrieves a set of weights from the opaque params buffer that can be saved and
|
||||
// restored in a way compatible with future runs.
|
||||
//
|
||||
// Note that the params buffer may not be compatible across different GPUs. So any
|
||||
// save and restoration should be converted to and from the canonical weights and
|
||||
// biases.
|
||||
//
|
||||
// num_layers: Specifies the number of layers in the RNN model.
|
||||
// num_units: Specifies the size of the hidden state.
|
||||
// input_size: Specifies the size of the input state.
|
||||
// num_params: number of parameter sets for all layers.
|
||||
// Each layer may contain multiple parameter sets, with each set consisting of
|
||||
// a weight matrix and a bias vector.
|
||||
// weights: the canonical form of weights that can be used for saving
|
||||
// and restoration. They are more likely to be compatible across different
|
||||
// generations.
|
||||
// biases: the canonical form of biases that can be used for saving
|
||||
// and restoration. They are more likely to be compatible across different
|
||||
// generations.
|
||||
// rnn_mode: Indicates the type of the RNN model.
|
||||
// input_mode: Indicate whether there is a linear projection between the input and
|
||||
// The actual computation before the first layer. 'skip_input' is only allowed
|
||||
// when input_size == num_units; 'auto_select' implies 'skip_input' when
|
||||
// input_size == num_units; otherwise, it implies 'linear_input'.
|
||||
// direction: Indicates whether a bidirectional model will be used.
|
||||
// dir = (direction == bidirectional) ? 2 : 1
|
||||
// dropout: dropout probability. When set to 0., dropout is disabled.
|
||||
// seed: the 1st part of a seed to initialize dropout.
|
||||
// seed2: the 2nd part of a seed to initialize dropout.
|
||||
func CudnnRNNParamsToCanonical(scope *Scope, num_layers tf.Output, num_units tf.Output, input_size tf.Output, params tf.Output, num_params int64, optional ...CudnnRNNParamsToCanonicalAttr) (weights []tf.Output, biases []tf.Output) {
|
||||
if scope.Err() != nil {
|
||||
return
|
||||
}
|
||||
attrs := map[string]interface{}{"num_params": num_params}
|
||||
for _, a := range optional {
|
||||
a(attrs)
|
||||
}
|
||||
opspec := tf.OpSpec{
|
||||
Type: "CudnnRNNParamsToCanonical",
|
||||
Input: []tf.Input{
|
||||
num_layers, num_units, input_size, params,
|
||||
},
|
||||
Attrs: attrs,
|
||||
}
|
||||
op := scope.AddOperation(opspec)
|
||||
if scope.Err() != nil {
|
||||
return
|
||||
}
|
||||
var idx int
|
||||
var err error
|
||||
if weights, idx, err = makeOutputList(op, idx, "weights"); err != nil {
|
||||
scope.UpdateErr("CudnnRNNParamsToCanonical", err)
|
||||
return
|
||||
}
|
||||
if biases, idx, err = makeOutputList(op, idx, "biases"); err != nil {
|
||||
scope.UpdateErr("CudnnRNNParamsToCanonical", err)
|
||||
return
|
||||
}
|
||||
return weights, biases
|
||||
}
|
||||
|
||||
// Creates a sequence of numbers.
|
||||
//
|
||||
// This operation creates a sequence of numbers that begins at `start` and
|
||||
|
|
@ -36438,200 +36632,6 @@ func PriorityQueueV2(scope *Scope, shapes []tf.Shape, optional ...PriorityQueueV
|
|||
return op.Output(0)
|
||||
}
|
||||
|
||||
// BlockLSTMV2Attr is an optional argument to BlockLSTMV2.
|
||||
type BlockLSTMV2Attr func(optionalAttr)
|
||||
|
||||
// BlockLSTMV2CellClip sets the optional cell_clip attribute to value.
|
||||
//
|
||||
// value: Value to clip the 'cs' value to.
|
||||
// If not specified, defaults to 0
|
||||
func BlockLSTMV2CellClip(value float32) BlockLSTMV2Attr {
|
||||
return func(m optionalAttr) {
|
||||
m["cell_clip"] = value
|
||||
}
|
||||
}
|
||||
|
||||
// BlockLSTMV2UsePeephole sets the optional use_peephole attribute to value.
|
||||
//
|
||||
// value: Whether to use peephole weights.
|
||||
// If not specified, defaults to false
|
||||
func BlockLSTMV2UsePeephole(value bool) BlockLSTMV2Attr {
|
||||
return func(m optionalAttr) {
|
||||
m["use_peephole"] = value
|
||||
}
|
||||
}
|
||||
|
||||
// Computes the LSTM cell forward propagation for all the time steps.
|
||||
//
|
||||
// This is equivalent to applying LSTMBlockCell in a loop, like so:
|
||||
//
|
||||
// ```python
|
||||
// for x1 in unpack(x):
|
||||
// i1, cs1, f1, o1, ci1, co1, h1 = LSTMBlock(
|
||||
// x1, cs_prev, h_prev, w, wci, wcf, wco, b)
|
||||
// cs_prev = cs1
|
||||
// h_prev = h1
|
||||
// i.append(i1)
|
||||
// cs.append(cs1)
|
||||
// f.append(f1)
|
||||
// o.append(o1)
|
||||
// ci.append(ci1)
|
||||
// co.append(co1)
|
||||
// h.append(h1)
|
||||
// return pack(i), pack(cs), pack(f), pack(o), pack(ci), pack(ch), pack(h)
|
||||
//
|
||||
// Note that unlike LSTMBlockCell (and BlockLSTM) which uses ICFO gate layout,
|
||||
// this op uses IFCO. So in order for the following snippet to be equivalent
|
||||
// all gate-related outputs should be reordered.
|
||||
// ```
|
||||
//
|
||||
// Arguments:
|
||||
// seq_len_max: Maximum time length actually used by this input. Outputs are padded
|
||||
// with zeros beyond this length.
|
||||
// x: The sequence input to the LSTM, shape (timelen, batch_size, num_inputs).
|
||||
// cs_prev: Value of the initial cell state.
|
||||
// h_prev: Initial output of cell (to be used for peephole).
|
||||
// w: The weight matrix.
|
||||
// wci: The weight matrix for input gate peephole connection.
|
||||
// wcf: The weight matrix for forget gate peephole connection.
|
||||
// wco: The weight matrix for output gate peephole connection.
|
||||
// b: The bias vector.
|
||||
//
|
||||
// Returns:
|
||||
// i: The input gate over the whole time sequence.
|
||||
// cs: The cell state before the tanh over the whole time sequence.
|
||||
// f: The forget gate over the whole time sequence.
|
||||
// o: The output gate over the whole time sequence.
|
||||
// ci: The cell input over the whole time sequence.
|
||||
// co: The cell after the tanh over the whole time sequence.
|
||||
// h: The output h vector over the whole time sequence.
|
||||
func BlockLSTMV2(scope *Scope, seq_len_max tf.Output, x tf.Output, cs_prev tf.Output, h_prev tf.Output, w tf.Output, wci tf.Output, wcf tf.Output, wco tf.Output, b tf.Output, optional ...BlockLSTMV2Attr) (i tf.Output, cs tf.Output, f tf.Output, o tf.Output, ci tf.Output, co tf.Output, h tf.Output) {
|
||||
if scope.Err() != nil {
|
||||
return
|
||||
}
|
||||
attrs := map[string]interface{}{}
|
||||
for _, a := range optional {
|
||||
a(attrs)
|
||||
}
|
||||
opspec := tf.OpSpec{
|
||||
Type: "BlockLSTMV2",
|
||||
Input: []tf.Input{
|
||||
seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b,
|
||||
},
|
||||
Attrs: attrs,
|
||||
}
|
||||
op := scope.AddOperation(opspec)
|
||||
return op.Output(0), op.Output(1), op.Output(2), op.Output(3), op.Output(4), op.Output(5), op.Output(6)
|
||||
}
|
||||
|
||||
// PaddedBatchDatasetV2Attr is an optional argument to PaddedBatchDatasetV2.
|
||||
type PaddedBatchDatasetV2Attr func(optionalAttr)
|
||||
|
||||
// PaddedBatchDatasetV2ParallelCopy sets the optional parallel_copy attribute to value.
|
||||
// If not specified, defaults to false
|
||||
func PaddedBatchDatasetV2ParallelCopy(value bool) PaddedBatchDatasetV2Attr {
|
||||
return func(m optionalAttr) {
|
||||
m["parallel_copy"] = value
|
||||
}
|
||||
}
|
||||
|
||||
// Creates a dataset that batches and pads `batch_size` elements from the input.
|
||||
//
|
||||
// Arguments:
|
||||
//
|
||||
// batch_size: A scalar representing the number of elements to accumulate in a
|
||||
// batch.
|
||||
// padded_shapes: A list of int64 tensors representing the desired padded shapes
|
||||
// of the corresponding output components. These shapes may be partially
|
||||
// specified, using `-1` to indicate that a particular dimension should be
|
||||
// padded to the maximum size of all batch elements.
|
||||
// padding_values: A list of scalars containing the padding value to use for
|
||||
// each of the outputs.
|
||||
// drop_remainder: A scalar representing whether the last batch should be dropped in case its size
|
||||
// is smaller than desired.
|
||||
//
|
||||
func PaddedBatchDatasetV2(scope *Scope, input_dataset tf.Output, batch_size tf.Output, padded_shapes []tf.Output, padding_values []tf.Output, drop_remainder tf.Output, output_shapes []tf.Shape, optional ...PaddedBatchDatasetV2Attr) (handle tf.Output) {
|
||||
if scope.Err() != nil {
|
||||
return
|
||||
}
|
||||
attrs := map[string]interface{}{"output_shapes": output_shapes}
|
||||
for _, a := range optional {
|
||||
a(attrs)
|
||||
}
|
||||
opspec := tf.OpSpec{
|
||||
Type: "PaddedBatchDatasetV2",
|
||||
Input: []tf.Input{
|
||||
input_dataset, batch_size, tf.OutputList(padded_shapes), tf.OutputList(padding_values), drop_remainder,
|
||||
},
|
||||
Attrs: attrs,
|
||||
}
|
||||
op := scope.AddOperation(opspec)
|
||||
return op.Output(0)
|
||||
}
|
||||
|
||||
// Extract `patches` from `images` and put them in the "depth" output dimension.
|
||||
//
|
||||
// Arguments:
|
||||
// images: 4-D Tensor with shape `[batch, in_rows, in_cols, depth]`.
|
||||
// ksizes: The size of the sliding window for each dimension of `images`.
|
||||
// strides: How far the centers of two consecutive patches are in
|
||||
// the images. Must be: `[1, stride_rows, stride_cols, 1]`.
|
||||
// rates: Must be: `[1, rate_rows, rate_cols, 1]`. This is the
|
||||
// input stride, specifying how far two consecutive patch samples are in the
|
||||
// input. Equivalent to extracting patches with
|
||||
// `patch_sizes_eff = patch_sizes + (patch_sizes - 1) * (rates - 1)`, followed by
|
||||
// subsampling them spatially by a factor of `rates`. This is equivalent to
|
||||
// `rate` in dilated (a.k.a. Atrous) convolutions.
|
||||
// padding: The type of padding algorithm to use.
|
||||
//
|
||||
// Returns 4-D Tensor with shape `[batch, out_rows, out_cols, ksize_rows *
|
||||
// ksize_cols * depth]` containing image patches with size
|
||||
// `ksize_rows x ksize_cols x depth` vectorized in the "depth" dimension. Note
|
||||
// `out_rows` and `out_cols` are the dimensions of the output patches.
|
||||
func ExtractImagePatches(scope *Scope, images tf.Output, ksizes []int64, strides []int64, rates []int64, padding string) (patches tf.Output) {
|
||||
if scope.Err() != nil {
|
||||
return
|
||||
}
|
||||
attrs := map[string]interface{}{"ksizes": ksizes, "strides": strides, "rates": rates, "padding": padding}
|
||||
opspec := tf.OpSpec{
|
||||
Type: "ExtractImagePatches",
|
||||
Input: []tf.Input{
|
||||
images,
|
||||
},
|
||||
Attrs: attrs,
|
||||
}
|
||||
op := scope.AddOperation(opspec)
|
||||
return op.Output(0)
|
||||
}
|
||||
|
||||
// Forwards the value of an available tensor from `inputs` to `output`.
|
||||
//
|
||||
// `Merge` waits for at least one of the tensors in `inputs` to become available.
|
||||
// It is usually combined with `Switch` to implement branching.
|
||||
//
|
||||
// `Merge` forwards the first tensor to become available to `output`, and sets
|
||||
// `value_index` to its index in `inputs`.
|
||||
//
|
||||
// Arguments:
|
||||
// inputs: The input tensors, exactly one of which will become available.
|
||||
//
|
||||
// Returns:
|
||||
// output: Will be set to the available input tensor.
|
||||
// value_index: The index of the chosen input tensor in `inputs`.
|
||||
func Merge(scope *Scope, inputs []tf.Output) (output tf.Output, value_index tf.Output) {
|
||||
if scope.Err() != nil {
|
||||
return
|
||||
}
|
||||
opspec := tf.OpSpec{
|
||||
Type: "Merge",
|
||||
Input: []tf.Input{
|
||||
tf.OutputList(inputs),
|
||||
},
|
||||
}
|
||||
op := scope.AddOperation(opspec)
|
||||
return op.Output(0), op.Output(1)
|
||||
}
|
||||
|
||||
// Strip leading and trailing whitespaces from the Tensor.
|
||||
//
|
||||
// Examples:
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user