| /external/tensorflow/tensorflow/python/kernel_tests/ |
| matrix_solve_op_test.py | 72 m * n).astype(np.complex128).reshape([m, n]))
73 matrix.imag = (np.random.normal(-5, 5, m * n).astype(np.complex128).reshape(
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| xent_op_test.py | 42 features - np.reshape(np.amax(features, axis=dim), one_only_on_dim))
43 probs = e / np.reshape(np.sum(e, axis=dim), one_only_on_dim)
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| init_ops_test.py | 168 actual = array_ops.reshape(x, [-1]).eval()
182 np.asarray(value).reshape(tuple(shape)),
192 actual = array_ops.reshape(x, [-1]).eval()
209 "2D-ndarray", np.asarray(value).reshape(tuple([2, 3])), shape, expected)
228 np.asarray(value).reshape(tuple([2, 4])), shape)
565 t = t.reshape((np.prod(t.shape[:-1]), t.shape[-1]))
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| losses_test.py | 613 predictions = np.asarray([.9, .2, .2, .8, .4, .6]).reshape((2, 3))
614 labels = np.asarray([1.0, 0.0, 1.0, 1.0, 0.0, 0.0]).reshape((2, 3))
691 np.asarray([1.2, 1.2, 1.2, 3.4, 3.4, 3.4]).reshape((2, 3)))
699 np.asarray([1.2, 1.2, 1.2, 0, 0, 0]).reshape(
708 np.asarray([1.2, 1.2, 1.2, 0, 0, 0]).reshape(
721 weights = np.array([3, 6, 5, 0, 4, 2]).reshape((2, 3))
[all...] |
| cast_op_test.py | 94 self._testAll(np.arange(-10, 10).reshape(2, 10))
116 self._testAll(np.random.normal(0, 10, 210).reshape([2, 3, 5, 7]))
117 self._testAll(np.random.normal(0, 1e6, 210).reshape([2, 3, 5, 7]))
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| gather_nd_op_test.py | 161 self.assertAllEqual(params[[3, 2, 1, 4, 4, 0]].reshape(2, 3, 2, 2),
181 indices_reshaped = indices.reshape([10, 10, 20, 5])
186 self.assertAllEqual(expected.reshape([10, 10, 20]), gather_nd_val)
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| /external/tensorflow/tensorflow/python/ops/ |
| nn_xent_test.py | 53 losses = np.array(self._SigmoidCrossEntropyWithLogits(x, y)).reshape(*sizes)
129 losses = np.array(self._WeightedCrossEntropy(x, y, q)).reshape(*sizes)
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| check_ops.py | 356 # reshape((-1,)) is the fastest way to get a flat array view.
357 x_np = x.numpy().reshape((-1,))
358 y_np = y.numpy().reshape((-1,))
384 x_vals.numpy().reshape((-1,))[:summarize],
385 y_vals.numpy().reshape((-1,))[:summarize],
[all...] |
| math_ops_test.py | 368 nums = np.arange(-10, 10, 1).reshape(20, 1)
369 divs = np.arange(-3, 4, 2).reshape(1, 4)
373 nums = np.arange(-10, 10, .25).reshape(80, 1)
374 divs = np.arange(-3, 0, .25).reshape(1, 12)
471 expanded_nums = np.reshape(
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| parsing_ops.py | 682 # Reshape to a scalar to ensure user gets an error if they
688 default_value = array_ops.reshape(default_value, [])
696 default_value = array_ops.reshape(default_value, dense_shape)
[all...] |
| /external/tensorflow/tensorflow/contrib/distributions/python/ops/ |
| vector_diffeomixture.py | 181 edges = array_ops.reshape(edges, shape=array_ops.concat([
570 ids = array_ops.reshape(ids, shape=concat_vectors(
589 array_ops.reshape(self.grid, shape=[-1]),
598 weight = array_ops.reshape(weight, shape=new_shape)
755 m = array_ops.reshape(
776 return array_ops.reshape(p, shape=expand_shape)
[all...] |
| /external/tensorflow/tensorflow/contrib/legacy_seq2seq/python/ops/ |
| seq2seq.py | 621 # To calculate W1 * h_t we use a 1-by-1 convolution, need to reshape before.
622 hidden = array_ops.reshape(attention_states,
649 y = array_ops.reshape(y, [-1, 1, 1, attention_vec_size])
656 array_ops.reshape(a, [-1, attn_length, 1, 1]) * hidden, [1, 2])
657 ds.append(array_ops.reshape(d, [-1, attn_size]))
[all...] |
| /external/tensorflow/tensorflow/compiler/tests/ |
| binary_ops_test.py | 766 np.array([], dtype=dtype).reshape((2, 0)),
767 np.array([], dtype=dtype).reshape((0, 3)),
822 np.array([], dtype=np.float32).reshape((2, 2, 0)),
823 np.array([], dtype=np.float32).reshape((2, 0, 3)),
829 np.array([], dtype=np.float32).reshape((0, 2, 4)),
830 np.array([], dtype=np.float32).reshape((0, 4, 3)),
831 expected=np.array([], dtype=np.float32).reshape(0, 2, 3))
835 x = np.arange(0, 3 * 5 * 2 * 7, dtype=np.float32).reshape((3, 5, 2, 7))
[all...] |
| /external/tensorflow/tensorflow/python/estimator/canned/ |
| linear_testing_utils.py | 541 data = data.reshape(batch_size, label_dimension)
606 data = data.reshape(batch_size, label_dimension)
[all...] |
| dnn_linear_combined_test.py | 268 data = data.reshape(batch_size, label_dimension)
332 data = data.reshape(batch_size, label_dimension)
533 x_data = data.reshape(batch_size, input_dimension)
534 y_data = self._as_label(np.reshape(data[:batch_size], (batch_size, 1)))
601 data = data.reshape(batch_size, input_dimension)
[all...] |
| /external/tensorflow/tensorflow/python/keras/_impl/keras/preprocessing/ |
| image.py | 363 x = x.reshape((1, x.shape[0], x.shape[1]))
365 x = x.reshape((x.shape[0], x.shape[1], 1))
657 flatx = np.reshape(x, (-1, np.prod(x.shape[-3:])))
659 x = np.reshape(whitex, x.shape)
816 self.mean = np.reshape(self.mean, broadcast_shape)
823 self.std = np.reshape(self.std, broadcast_shape)
830 flat_x = np.reshape(x, (x.shape[0], x.shape[1] * x.shape[2] * x.shape[3]))
[all...] |
| /external/tensorflow/tensorflow/compiler/xla/service/ |
| hlo_verifier.cc | 188 Status ShapeVerifier::HandleReshape(HloInstruction* reshape) {
190 TF_RETURN_IF_ERROR(CheckShape(reshape, reshape->shape()));
191 TF_RET_CHECK(ShapeUtil::ElementsIn(reshape->shape()) ==
192 ShapeUtil::ElementsIn(reshape->operand(0)->shape()));
[all...] |
| layout_assignment_test.cc | 321 // param -> log -> reshape -> tanh
329 auto reshape = local
332 HloInstruction::CreateUnary(bshape, HloOpcode::kTanh, reshape));
355 AsInt64Slice(reshape->shape().layout().minor_to_major());
517 // param0 -> concatenate -> reshape
529 auto reshape = builder.AddInstruction( local
533 module->AddEntryComputation(builder.Build(reshape));
[all...] |
| /external/tensorflow/tensorflow/contrib/gan/python/losses/python/ |
| losses_impl_test.py | 91 array_ops.reshape(self._discriminator_gen_outputs, [2, 2]))
98 array_ops.reshape(self._discriminator_real_outputs, [2, 2]),
99 array_ops.reshape(self._discriminator_gen_outputs, [2, 2]))
319 patch_args = {x: array_ops.reshape(y, [2, 2, 4]) for x, y in
326 patch_args = {x: array_ops.reshape(y, [2, 2, 4]) for x, y in
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| /external/tensorflow/tensorflow/contrib/framework/python/ops/ |
| variables.py | 502 feed_dict[placeholder_value] = var_value.reshape(var.get_shape())
629 feed_dict[placeholder_tensor] = ckpt_value.reshape(ckpt_value.shape)
636 slice_value = slice_value.reshape(var._save_slice_info.var_shape)
659 reshape_variables: Boolean, if True it would automatically reshape variables
688 saver = tf_saver.Saver(var_list, reshape=reshape_variables,
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| /external/tensorflow/tensorflow/core/kernels/ |
| quantized_instance_norm.cc | 356 float_mean.reshape(expand_spec).broadcast(broadcast_spec))))
363 float_mean.reshape(expand_spec).broadcast(broadcast_spec)) *
366 .reshape(expand_spec)
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| /external/tensorflow/tensorflow/python/eager/ |
| ops_test.py | 188 npt = np.arange(1, 19, dtype=np.float32).reshape(3, 2, 3)
202 npt = np.arange(1, 19, dtype=np.float32).reshape(3, 2, 3)
241 # The GPU kernel for the Reshape op requires that the
245 reshaped = array_ops.reshape(value, shape)
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| /external/tensorflow/tensorflow/python/estimator/inputs/ |
| numpy_io_test.py | 399 x = np.arange(16).reshape(4, 2, 2) * 1.0
400 y = np.arange(-48, -32).reshape(4, 2, 2)
440 y = np.arange(-48, -40).reshape(2, 4)
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| /external/tensorflow/tensorflow/contrib/kfac/python/ops/ |
| fisher_factors.py | 202 # "gradients/add_4_grad/Reshape:0" -> "gradients_add_4_grad_Reshape"
671 cov_diag_vec = array_ops.reshape(self.get_cov_var(), [-1])
677 return utils.matmul_diag_sparse(array_ops.reshape(damped_cov, [-1]), x)
692 return utils.matmul_diag_sparse(array_ops.reshape(inverse, [-1]), x)
818 flat_input_ids = array_ops.reshape(input_ids, [-1])
[all...] |
| /external/tensorflow/tensorflow/contrib/legacy_seq2seq/python/kernel_tests/ |
| seq2seq_test.py | 327 array_ops.reshape(e, [-1, 1, cell.output_size]) for e in enc_outputs
352 array_ops.reshape(e, [-1, 1, cell.output_size]) for e in enc_outputs
430 array_ops.reshape(e, [-1, 1, cell.output_size]) for e in enc_outputs
460 array_ops.reshape(e, [-1, 1, cell.output_size])
490 array_ops.reshape(e, [-1, 1, cell.output_size]) for e in enc_outputs
[all...] |
