(self, inputs_shape)
| 1516 | return self._output_size |
| 1517 | |
| 1518 | def build(self, inputs_shape): |
| 1519 | input_size = tensor_shape.dimension_value( |
| 1520 | tensor_shape.TensorShape(inputs_shape).with_rank(2)[1]) |
| 1521 | if input_size is None: |
| 1522 | raise ValueError("Could not infer input size from inputs.get_shape()[-1]") |
| 1523 | |
| 1524 | num_proj = self._num_units if self._num_proj is None else self._num_proj |
| 1525 | |
| 1526 | # Variables for the NAS cell. `recurrent_kernel` is all matrices multiplying |
| 1527 | # the hiddenstate and `kernel` is all matrices multiplying the inputs. |
| 1528 | self.recurrent_kernel = self.add_variable( |
| 1529 | "recurrent_kernel", [num_proj, self._NAS_BASE * self._num_units]) |
| 1530 | self.kernel = self.add_variable( |
| 1531 | "kernel", [input_size, self._NAS_BASE * self._num_units]) |
| 1532 | |
| 1533 | if self._use_bias: |
| 1534 | self.bias = self.add_variable("bias", |
| 1535 | shape=[self._NAS_BASE * self._num_units], |
| 1536 | initializer=init_ops.zeros_initializer) |
| 1537 | |
| 1538 | # Projection layer if specified |
| 1539 | if self._num_proj is not None: |
| 1540 | self.projection_weights = self.add_variable( |
| 1541 | "projection_weights", [self._num_units, self._num_proj]) |
| 1542 | |
| 1543 | self.built = True |
| 1544 | |
| 1545 | def call(self, inputs, state): |
| 1546 | """Run one step of NAS Cell. |
nothing calls this directly
no test coverage detected