| /external/tensorflow/tensorflow/compiler/aot/tests/ |
| tfcompile_test.cc | 189 foo::bar::MatMulComp matmul; local
190 matmul.set_thread_pool(&device);
191 EXPECT_EQ(matmul.arg0_data(), matmul.args()[0]);
192 EXPECT_EQ(matmul.arg1_data(), matmul.args()[1]);
196 matmul.arg0(0, 0) = 1;
197 matmul.arg0(0, 1) = 2;
198 matmul.arg0(0, 2) = 3;
199 matmul.arg0(1, 0) = 4
[all...] |
| make_test_graphs.py | 89 math_ops.matmul(x, y, name='x_y_prod')
96 math_ops.matmul(x, y, name='x_y_prod')
120 z = math_ops.matmul(x, y, name='x_y_prod')
123 x = math_ops.matmul(a, b, name='a_b')
|
| /external/tensorflow/tensorflow/python/ops/ |
| linalg_grad.py | 42 return -math_ops.matmul(
43 ainv, math_ops.matmul(grad, ainv, adjoint_b=True), adjoint_a=True)
72 middle = math_ops.matmul(l, grad, adjoint_a=True)
77 grad_a = math_ops.matmul(
78 math_ops.matmul(l_inverse, middle, adjoint_a=True), l_inverse)
97 qdq = math_ops.matmul(q, dq, adjoint_a=True)
99 rdr = math_ops.matmul(r, dr, adjoint_b=True)
104 """Equiv to matmul(x, adjoint(matrix_inverse(r))) if r is upper-tri."""
109 grad_a = math_ops.matmul(q, dr + _TriangularSolve(tril, r))
110 grad_b = _TriangularSolve(dq - math_ops.matmul(q, qdq), r
[all...] |
| /external/tensorflow/tensorflow/contrib/timeseries/python/timeseries/state_space_models/ |
| kalman_filter.py | 170 math_ops.matmul(
207 prior_variance_transitioned = math_ops.matmul(
208 math_ops.matmul(transition_matrices, prior_state_var),
243 kalman_solve_rhs = math_ops.matmul(
253 math_ops.matmul(
258 gain_obs = math_ops.matmul(
266 posterior_state_var = math_ops.matmul(identity_minus_factor,
280 left_multiplied_state_var = math_ops.matmul(identity_minus_factor,
282 multiplied_state_var = math_ops.matmul(
285 return (multiplied_state_var + math_ops.matmul(
[all...] |
| test_utils.py | 41 true_single_step_update = math_ops.matmul(previous_matrix,
78 starting_transitioned = math_ops.matmul(
79 math_ops.matmul(transition_matrix, starting_noise),
87 evaled_noise_addition_transformed = math_ops.matmul(
88 math_ops.matmul(noise_transform, evaled_noise_addition),
|
| /external/tensorflow/tensorflow/python/profiler/ |
| tfprof_logger_test.py | 32 y = math_ops.matmul(a, b)
38 return math_ops.matmul(a, b)
73 # run_metadata has special name for MatMul, hence failed to fill shape.
|
| /external/tensorflow/tensorflow/python/eager/ |
| tape_test.py | 38 mm = math_ops.matmul(a, b)
43 math_ops.matmul(dmm, b, transpose_b=True) +
44 math_ops.matmul(array_ops.ones_like(b * dr), b, transpose_b=True),
45 math_ops.matmul(a, dmm, transpose_b=True) +
46 math_ops.matmul(a, array_ops.ones_like(a) * dr, transpose_b=True)
89 mm = math_ops.matmul(a, b)
96 math_ops.matmul(
103 mm = math_ops.matmul(a, b)
110 math_ops.matmul(
117 mm = math_ops.matmul(a, b
[all...] |
| function_test.py | 45 matmul = function.defun(math_ops.matmul)
47 sq = matmul(t, t, transpose_a=True)
48 sq2 = matmul(sq, t, transpose_a=True)
53 matmul = function.defun(math_ops.matmul)
57 return matmul(a, a)
61 self.assertAllEqual(out, math_ops.matmul(t, t).numpy())
64 matmul = function.defun(math_ops.matmul)
[all...] |
| /external/tensorflow/tensorflow/python/training/ |
| server_lib_same_variables_no_clear_test.py | 42 v2 = math_ops.matmul(v0, v1)
49 new_v2 = math_ops.matmul(new_v0, new_v1)
|
| /external/tensorflow/tensorflow/cc/profiler/ |
| profiler_test.cc | 42 auto y = ops::MatMul(root.WithOpName("y"), a, x);
137 const GraphNodeProto* matmul = ExtractNode(ret, "y"); local
138 EXPECT_TRUE(matmul);
139 EXPECT_GT(matmul->exec_micros(), 0);
141 EXPECT_GT(matmul->accelerator_exec_micros(), 0);
143 EXPECT_EQ(matmul->accelerator_exec_micros(), 0);
164 const MultiGraphNodeProto* matmul2 = ExtractNode(ret3, "MatMul");
|
| /external/tensorflow/tensorflow/contrib/memory_stats/python/kernel_tests/ |
| memory_stats_ops_test.py | 64 c = math_ops.matmul(a, b)
65 d = math_ops.matmul(c, b)
80 c = math_ops.matmul(a, b)
|
| /external/tensorflow/tensorflow/examples/tutorials/mnist/ |
| mnist.py | 64 hidden1 = tf.nn.relu(tf.matmul(images, weights) + biases)
73 hidden2 = tf.nn.relu(tf.matmul(hidden1, weights) + biases)
82 logits = tf.matmul(hidden2, weights) + biases
|
| /external/tensorflow/tensorflow/tools/graph_transforms/ |
| fake_quantize_training_test.cc | 50 Output matmul = MatMul(root.WithOpName("matmul"), a_const, b_const); local
|
| /external/tensorflow/tensorflow/contrib/solvers/python/ops/ |
| util.py | 44 apply=lambda v: math_ops.matmul(matrix, v, adjoint_a=False),
45 apply_adjoint=lambda v: math_ops.matmul(matrix, v, adjoint_a=True))
65 # TODO(rmlarsen): Measure if we should just call matmul.
|
| /external/tensorflow/tensorflow/python/debug/examples/ |
| debug_errors.py | 40 y = tf.matmul(m, x, name="y")
41 z = tf.matmul(m, v, name="z")
|
| /external/tensorflow/tensorflow/contrib/distributions/python/kernel_tests/ |
| mvn_diag_plus_low_rank_test.py | 143 true_scale = np.diag(diag_large) + np.matmul(np.matmul(
145 true_covariance = np.matmul(true_scale, true_scale.T)
182 sample_covariance = math_ops.matmul(x, x, transpose_a=True) / n
375 np.eye(3) + np.matmul(np.matmul(u[0], np.diag(m[0])),
377 np.eye(3) + np.matmul(np.matmul(u[1], np.diag(m[1])),
380 cov = np.stack([np.matmul(scale[0], scale[0].T),
381 np.matmul(scale[1], scale[1].T)]
[all...] |
| /external/tensorflow/tensorflow/python/ops/linalg/ |
| linear_operator_low_rank_update.py | 85 operator.matmul(x)
92 `x` is a batch matrix with compatible shape for `matmul` and `solve` if
102 made from a rank `K` update of `base_operator` which performs `.matmul(x)` on
106 * `operator.matmul(x)` is `O(L_matmul*N*R + K*N*R)`
365 leading_term = l.matmul(x, adjoint=adjoint, adjoint_arg=adjoint_arg)
368 uh_x = math_ops.matmul(u, x, adjoint_a=True, adjoint_b=adjoint_arg)
369 d_uh_x = d.matmul(uh_x, adjoint=adjoint)
370 v_d_uh_x = math_ops.matmul(v, d_uh_x)
373 vh_x = math_ops.matmul(v, x, adjoint_a=True, adjoint_b=adjoint_arg)
374 d_vh_x = d.matmul(vh_x, adjoint=adjoint
[all...] |
| /external/tensorflow/tensorflow/examples/speech_commands/ |
| models.py | 129 This is a very simple model with just one matmul and bias layer. As you'd
137 [MatMul]<-(weights)
158 logits = tf.matmul(fingerprint_input, weights) + bias
192 [MatMul]<-(weights)
266 final_fc = tf.matmul(flattened_second_conv, final_fc_weights) + final_fc_bias
291 [MatMul]<-(weights)
295 [MatMul]<-(weights)
299 [MatMul]<-(weights)
358 first_fc = tf.matmul(flattened_first_conv, first_fc_weights) + first_fc_bias
368 second_fc = tf.matmul(second_fc_input, second_fc_weights) + second_fc_bia
[all...] |
| /external/tensorflow/tensorflow/contrib/distributions/python/ops/ |
| mvn_linear_operator.py | 239 return self.scale.matmul(self.scale.to_dense(), adjoint_arg=True)
247 self.scale.matmul(self.scale.to_dense()))
250 self.scale.matmul(self.scale.to_dense(), adjoint_arg=True))
258 self.scale.matmul(self.scale.to_dense())))
261 self.scale.matmul(self.scale.to_dense(), adjoint_arg=True)))
|
| vector_exponential_linear_operator.py | 244 return self.scale.matmul(self.scale.to_dense(), adjoint_arg=True)
252 self.scale.matmul(self.scale.to_dense()))
255 self.scale.matmul(self.scale.to_dense(), adjoint_arg=True))
263 array_ops.matrix_diag_part(self.scale.matmul(self.scale.to_dense())))
267 self.scale.matmul(self.scale.to_dense(), adjoint_arg=True)))
|
| vector_laplace_linear_operator.py | 268 return 2. * self.scale.matmul(self.scale.to_dense(), adjoint_arg=True)
276 2. * self.scale.matmul(self.scale.to_dense()))
279 self.scale.matmul(self.scale.to_dense(), adjoint_arg=True))
287 self.scale.matmul(self.scale.to_dense())))
290 self.scale.matmul(self.scale.to_dense(), adjoint_arg=True)))
|
| /external/tensorflow/tensorflow/python/kernel_tests/linalg/ |
| linear_operator_identity_test.py | 87 y = operator.matmul(x)
150 operator.matmul(x)
159 y = operator.matmul(x)
171 operator_matmul = operator.matmul(x)
185 operator_matmul = operator.matmul(x)
207 # Expected result of matmul and solve.
210 operator_matmul = operator.matmul(x)
232 # Expected result of matmul and solve.
235 operator_matmul = operator.matmul(x)
343 y = operator.matmul(x
[all...] |
| /external/tensorflow/tensorflow/c/eager/ |
| c_api_test.cc | 49 TFE_Op* op = TFE_NewOp(ctx, "MatMul", status);
131 TFE_Op* matmul = MatMulOp(ctx, m, m); local
132 TFE_DeleteOp(matmul);
151 TFE_Op* matmul = MatMulOp(ctx, m, m); local
156 TFE_Execute(matmul, &retvals[0], &num_retvals, status);
160 TFE_DeleteOp(matmul);
355 TFE_Op* matmul = MatMulOp(ctx, hcpu, hgpu); local
356 TFE_OpSetDevice(matmul, gpu_device_name.c_str(), status.get());
360 TFE_Execute(matmul, &retvals[0], &num_retvals, status.get());
362 TFE_DeleteOp(matmul);
396 TFE_Op* matmul = MatMulOp(ctx, hcpu, hgpu); local
422 TFE_Op* matmul = MatMulOp(ctx, m, m); local
453 TFE_Op* matmul = MatMulOp(ctx, m, m); local
520 TFE_Op* matmul = MatMulOp(ctx, m, m); local
598 TFE_Op* matmul = MatMulOp(ctx, m, m); local
845 TFE_Op* matmul = TFE_NewOp(ctx, "MatMulFunction", status); local
[all...] |
| /external/tensorflow/tensorflow/core/grappler/costs/ |
| analytical_cost_estimator_test.cc | 80 auto matmul = ops::MatMul(s.WithOpName("matmul"), flat, w2); local
81 auto logits = ops::Add(s.WithOpName("logits"), matmul, b2);
|
| /external/tensorflow/tensorflow/python/kernel_tests/ |
| matmul_op_test.py | 15 """Tests for tensorflow.ops.math_ops.matmul."""
34 # TODO(yangzihao): Currently matmul autotuning is disabled by default. Use
67 print("Built without fp16 matmul support for Cuda, running test on CPU.")
70 # attributes such that tf.matmul(effective_a_np, effective_b_np, **kwargs)
79 res = math_ops.matmul(a, b, **kwargs_)
84 res = math_ops.matmul(a, b, **kwargs_)
109 # attributes such that tf.matmul(effective_a_np, effective_b_np, **kwargs)
121 res = math_ops.matmul(a, b, **kwargs_)
141 math_ops.matmul(a, b)
144 if op.name == "MatMul"
[all...] |
