Searched refs:_inputs (Results 1 – 18 of 18) sorted by relevance
61 self._inputs = np.array([[-1.], [-.5], [0.], [.5], [1.]], np.float32)62 self._batch_size = len(self._inputs)83 x = constant_op.constant(self._inputs)115 self.assertAllClose(m, self._NextM(self._inputs, 1., m_prev, c_prev))116 self.assertAllClose(c, self._NextC(self._inputs, 1., m_prev, c_prev))128 self._NextM(self._inputs, weight, m_prev, c_prev))130 self._NextC(self._inputs, weight, m_prev, c_prev))147 x_seq = [constant_op.constant(self._inputs)] * seq_length167 x_seq = [constant_op.constant(self._inputs)] * seq_length200 m0 = self._NextM(self._inputs, weight1, m_init, c_init)[all …]
347 self._inputs = []354 inputs = _concat_along_batch_dim(self._inputs)409 self._inputs.append(inputs)414 result = len(self._inputs)457 self._inputs = []470 inputs = _concat_along_batch_dim(self._inputs)511 self._inputs.append(inputs)516 return len(self._inputs)611 self._inputs = []631 inputs = _concat_along_batch_dim(self._inputs)[all …]
859 self._inputs = inputs870 (self._inputs,) + tuple(self._outputs_grads))874 input_size = self._inputs.shape[1] + self._has_bias894 inputs = self._inputs896 inputs = append_homog(self._inputs)932 self._inputs = inputs945 (self._inputs,) + tuple(self._outputs_grads))964 with maybe_colocate_with(self._inputs):970 self._inputs,1084 self._inputs = inputs[all …]
89 self._inputs = np.random.rand(16, 4).astype(np.float32)94 tf_inputs = constant_op.constant(self._inputs, dtype=dtypes.float32)108 tf_inputs = constant_op.constant(self._inputs, dtype=dtypes.float32)111 expected_mean = np.mean(self._inputs, axis=(0))112 expected_var = np.var(self._inputs, axis=(0))145 tf_inputs = constant_op.constant(self._inputs, dtype=dtypes.float32)178 tf_inputs = constant_op.constant(self._inputs, dtype=dtypes.float32)201 tf_inputs = constant_op.constant(self._inputs, dtype=dtypes.float32)228 self._inputs = np.zeros((16, 4))233 self._inputs[i, j] = 1[all …]
150 self._inputs = np.zeros((16, 4))155 self._inputs[i, j] = 1169 tf_inputs = constant_op.constant(self._inputs, dtype=dtypes.float32)194 inputs = constant_op.constant(self._inputs, dtype=dtypes.float32)220 inputs = constant_op.constant(self._inputs, dtype=dtypes.float32)247 inputs = constant_op.constant(self._inputs, dtype=dtypes.float32)268 self._inputs = np.zeros((16, 4))273 self._inputs[i, j] = 1287 tf_inputs = constant_op.constant(self._inputs, dtype=dtypes.float32)309 inputs = constant_op.constant(self._inputs, dtype=dtypes.float32)[all …]
232 self._inputs = np.zeros((16, 4))237 self._inputs[i, j] = 1245 tf_inputs = constant_op.constant(self._inputs, dtype=dtypes.float32)266 self._inputs = np.random.rand(16, 4).astype(np.float32)277 tf_inputs = constant_op.constant(self._inputs, dtype=dtypes.float32)280 expected_mean = np.mean(self._inputs, axis=(0))281 expected_var = np.var(self._inputs, axis=(0))315 tf_inputs = constant_op.constant(self._inputs, dtype=dtypes.float32)348 tf_inputs = constant_op.constant(self._inputs, dtype=dtypes.float32)373 tf_inputs = constant_op.constant(self._inputs, dtype=dtypes.float32)[all …]
87 self._inputs = constant_op.constant(inputs, dtype=dtypes.float32)89 self._predictions, self._scale = TestModel(self._inputs)225 self._inputs = constant_op.constant(inputs, dtype=dtypes.float32)227 self._predictions, self._scale = TestModel(self._inputs)
68 self._inputs = np.zeros((16, 4))73 self._inputs[i, j] = 187 tf_inputs = constant_op.constant(self._inputs, dtype=dtypes.float32)115 inputs = constant_op.constant(self._inputs, dtype=dtypes.float32)140 all_inputs = constant_op.constant(self._inputs, dtype=dtypes.float32)176 inputs = constant_op.constant(self._inputs, dtype=dtypes.float32)204 inputs = constant_op.constant(self._inputs, dtype=dtypes.float32)233 inputs = constant_op.constant(self._inputs, dtype=dtypes.float32)
139 def _inputs(self): member in RankSampledSoftmaxLossTest157 inputs=self._inputs(),176 inputs=self._inputs(),194 inputs=self._inputs(),210 inputs=self._inputs(),223 inputs=self._inputs(),
56 self._inputs = [74 if not self._inputs:78 if len(op.inputs) != len(self._inputs):81 for input_tensor, input_pattern in zip(op.inputs, self._inputs):
144 self._inputs = inputs if isinstance(inputs, list) else [inputs]365 inputs = self._inputs370 self._inputs, num_clusters, initial_clusters, self._distance_metric,587 self._inputs = inputs601 [array_ops.shape(i)[0] for i in self._inputs])610 return embedding_lookup(self._inputs, indices, partition_strategy='div')615 inp = self._inputs[0]638 first_shard = self._inputs[0]713 lambda: array_ops.concat(self._inputs, 0),731 return self._initial_clusters(self._inputs, self._num_remaining)
24 self._inputs = inputs47 for i in self._inputs:71 for i in self._inputs:
175 self._inputs = []212 self._inputs.append(inp)219 return self._inputs
2010 self._inputs = inputs2013 return iter(self._inputs)2016 return len(self._inputs)2019 return bool(self._inputs)2025 return self._inputs[i]2044 def _inputs(self): member in Operation2049 @_inputs.setter2050 def _inputs(self, value): member in Operation
187 self._inputs = inputs206 return self._inputs
98 self._inputs = array_ops.placeholder(130 self._inputs, initial_state=self._initial_state, training=training)140 return self._inputs
2221 'python', '<@(_inputs)', '<(PRODUCT_DIR)/natives_blob_host.bin'2228 'python', '<@(_inputs)', '<(PRODUCT_DIR)/natives_blob.bin'2237 'python', '<@(_inputs)', '<(PRODUCT_DIR)/natives_blob.bin'
5474 auto _inputs = _o->inputs.size() ? _fbb.CreateVector(_o->inputs) : 0;5483 _inputs,5516 auto _inputs = _o->inputs.size() ? _fbb.CreateVector(_o->inputs) : 0;5523 _inputs,