| /external/tensorflow/tensorflow/contrib/rnn/ops/ |
| gru_ops.cc | 26 .Input("h_prev: T")
36 ShapeHandle x, h_prev;
38 TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 2, &h_prev));
41 DimensionHandle cell_size = c->Dim(h_prev, 1);
53 h_prev: State input from the previous GRU cell.
79 x_h_prev = [x, h_prev]
86 h_prevr = h_prev \circ r
93 h = (1-u) \circ c + u \circ h_prev
100 .Input("h_prev: T")
114 ShapeHandle x, h_prev, w_ru
[all...] |
| lstm_ops.cc | 28 .Input("h_prev: T")
67 xh = [x, h_prev]
92 h_prev: Output of the previous cell at previous time step.
111 .Input("h_prev: T")
158 h_prev: The previous h state.
183 .Input("h_prev: T")
226 x1, cs_prev, h_prev, w, wci, wcf, wco, b)
228 h_prev = h1
247 h_prev: Initial output of cell (to be used for peephole).
267 .Input("h_prev: T"
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| /external/tensorflow/tensorflow/contrib/rnn/python/ops/ |
| gru_ops.py | 83 x, h_prev, w_ru, w_c, b_ru, b_c = op.inputs
88 x, h_prev, w_ru, w_c, b_ru, b_c, r, u, c, d_h)
90 x_h_prev = array_ops.concat([x, h_prev], 1)
94 x_h_prevr = array_ops.concat([x, h_prev * r], 1)
117 x_h_prev = [x, h_prev]
124 h_prevr = h_prev \circ r
131 h = (1-u) \circ c + u \circ h_prev
197 def call(self, inputs, h_prev):
199 # Check cell_size == state_size from h_prev.
200 cell_size = h_prev.get_shape().with_rank(2)[1
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| lstm_ops.py | 46 h_prev,
64 xh = [x, h_prev]
88 h_prev: A `Tensor`. Must have the same type as `x`.
130 h_prev=h_prev,
148 h_prev=None,
164 h_prev: A `Tensor`. Must have the same type as `x`.
200 if cs_prev is None or h_prev is None:
205 if h_prev is None:
206 h_prev = zero_stat
[all...] |
| rnn_cell.py | [all...] |
| /external/tensorflow/tensorflow/contrib/rnn/kernels/ |
| gru_ops.h | 76 typename TTypes<T>::ConstMatrix h_prev,
83 // Concat x_h_prev = [x, h_prev].
85 x_h_prev.slice(h_offsets(), h_extends()).device(d) = h_prev;
104 // Concat x_h_prevr = [x,h_prev*r]
106 x_h_prevr.slice(h_offsets(), h_extends()).device(d) = h_prev * r;
119 // h= u*h_prev + (1-u)*c
120 h.device(d) = u * (h_prev - c) + c;
132 typename TTypes<T>::ConstMatrix h_prev,
149 d_u_bar.device(d) = d_h * (h_prev - c) * u * (u.constant(T(1)) - u);
160 d_r_bar.device(d) = (d_hr * h_prev * r) * (r.constant(T(1)) - r)
[all...] |
| lstm_ops_gpu.cu.cc | 194 __global__ void concat_xh(T* xh, const T* x, const T* h_prev,
217 xh[gid] = h_prev[output_row * cell_size + output_col - input_size];
226 typename TTypes<T>::ConstMatrix h_prev, typename TTypes<T>::ConstMatrix w,
246 xh.data(), x.data(), h_prev.data(), batch_size,
282 const T* h_prev, // [batch_size, cell_size]
360 typename TTypes<T>::ConstMatrix h_prev, typename TTypes<T>::ConstMatrix w,
382 cs_prev.data(), h_prev.data(), w.data(), wci.data(), wcf.data(),
415 typename TTypes<T>::ConstMatrix h_prev, \
424 use_peephole, x, cs_prev, h_prev, w, wci, \
433 typename TTypes<T>::ConstMatrix h_prev, \
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| lstm_ops.h | 160 typename TTypes<T>::ConstMatrix h_prev,
185 typename TTypes<T>::ConstMatrix h_prev, typename TTypes<T>::ConstMatrix w,
210 typename TTypes<T>::ConstMatrix h_prev, typename TTypes<T>::ConstMatrix w,
270 xh.slice(xh_h_offsets(), xh_h_extents()).device(d) = h_prev;
|
| lstm_ops.cc | 49 typename TTypes<T>::ConstMatrix h_prev, typename TTypes<T>::ConstMatrix w,
59 xh.slice(cell.xh_h_offsets(), cell.xh_h_extents()).device(d) = h_prev;
130 typename TTypes<T>::ConstMatrix h_prev, typename TTypes<T>::ConstMatrix w,
188 typename TTypes<T>::ConstMatrix h_prev, \
198 h_prev, w, wci, wcf, wco, b, xh, i, cs, f, o, ci, co, icfo, h); \
205 typename TTypes<T>::ConstMatrix h_prev, \
221 *this, ctx, d, use_peephole, x, cs_prev, h_prev, w, wci, wcf, wco, b, \
251 OP_REQUIRES_OK(ctx, ctx->input("h_prev", &h_prev_tensor));
283 errors::InvalidArgument("h_prev.dims(0) != batch_size: ",
288 "h_prev.dims(1) != cell_size: ", h_prev_tensor->dim_size(1)
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| gru_ops.cc | 38 OP_REQUIRES_OK(ctx, ctx->input("h_prev", &h_prev_tensor));
60 errors::InvalidArgument("h_prev.dims(0) != batch_size: ",
65 "h_prev.dims(1) != cell_size: ", h_prev_tensor->dim_size(1),
126 {"h_prev"}, "h",
181 OP_REQUIRES_OK(ctx, ctx->input("h_prev", &h_prev_tensor));
213 // Shape of 'h_prev' must be [batch_size, cell_size]
215 errors::InvalidArgument("h_prev.dims(0) != batch_size: ",
220 "h_prev.dims(1) != cell_size: ", h_prev_tensor->dim_size(1),
314 {"h_prev"}, "d_h_prev", TensorShape({batch_size, cell_size}),
393 typename TTypes<T>::ConstMatrix h_prev, \
[all...] |
