/external/tensorflow/tensorflow/core/api_def/base_api/ |
D | api_def_Squeeze.pbtxt | 6 The `input` to squeeze. 21 index starts at 0. It is an error to squeeze a dimension that is not 1. Must 36 shape(squeeze(t)) ==> [2, 3] 43 shape(squeeze(t, [2, 4])) ==> [1, 2, 3, 1]
|
/external/tensorflow/tensorflow/python/compiler/tensorrt/test/ |
D | unary_test.py | 51 q = array_ops.squeeze(q, axis=-2) 57 q = array_ops.squeeze(q, axis=3) 82 q = array_ops.squeeze(q, axis=[-1, -2, 3]) 87 q = array_ops.squeeze(q, axis=[5, 2, 3]) 99 array_ops.squeeze(q, name=output_name)
|
D | base_test.py | 66 array_ops.squeeze(pool, name=output_name) 123 array_ops.squeeze(s, name=output_name) 175 array_ops.squeeze(n, name=output_name) 228 array_ops.squeeze(n, name=output_name) 261 array_ops.squeeze(n, name=output_name) 295 array_ops.squeeze(n, name=output_name) 344 array_ops.squeeze(add3, name=output_name)
|
/external/tensorflow/tensorflow/core/grappler/optimizers/ |
D | remapper.cc | 96 Conv2DWithSqueezeAndBiasAdd(const NodeDef* conv2d, const NodeDef* squeeze, in Conv2DWithSqueezeAndBiasAdd() 98 : conv2d(conv2d), squeeze(squeeze), bias_add(bias_add) {} in Conv2DWithSqueezeAndBiasAdd() 101 const NodeDef* squeeze = nullptr; member 304 const auto squeeze = ctx.graph_view.GetRegularFanin(bias_input_port); in FindConv2DWithSqueezeAndBias() local 306 if (!squeeze.node || !IsSqueeze(*squeeze.node) || in FindConv2DWithSqueezeAndBias() 307 !HaveSameDataType(bias_add, squeeze.node, "T") || in FindConv2DWithSqueezeAndBias() 308 HasControlFaninOrFanout(ctx.graph_view, squeeze.node) || in FindConv2DWithSqueezeAndBias() 309 !HasSingleFanoutNode(ctx.graph_view, squeeze.node) || in FindConv2DWithSqueezeAndBias() 310 IsInPreserveSet(ctx, squeeze.node)) in FindConv2DWithSqueezeAndBias() 315 if (!GetNodeAttr(*squeeze.node, "squeeze_dims", &dims).ok()) return false; in FindConv2DWithSqueezeAndBias() [all …]
|
/external/tensorflow/tensorflow/contrib/timeseries/examples/ |
D | multivariate.py | 83 mean=numpy.squeeze(current_prediction["mean"], axis=(0, 1)), 84 cov=numpy.squeeze(current_prediction["covariance"], axis=(0, 1))) 100 all_observations = numpy.squeeze(numpy.concatenate(values, axis=1), axis=0) 101 all_times = numpy.squeeze(numpy.concatenate(times, axis=1), axis=0)
|
D | known_anomaly.py | 54 return tf.equal(tf.squeeze(features["is_changepoint"], axis=-1), "yes") 129 mean = np.squeeze(np.concatenate( 131 variance = np.squeeze(np.concatenate(
|
/external/tensorflow/tensorflow/contrib/crf/python/kernel_tests/ |
D | crf_test.py | 70 sequence_score = array_ops.squeeze(sequence_score, [0]) 105 sequence_score = array_ops.squeeze(sequence_score, [0]) 132 unary_score = array_ops.squeeze(unary_score, [0]) 148 binary_score = array_ops.squeeze(binary_score, [0]) 199 log_norm = array_ops.squeeze(log_norm, [0]) 272 sequence_score = array_ops.squeeze(sequence_score, [0]) 328 sequence_score = array_ops.squeeze(sequence_score, [0]) 341 actual_max_sequence = array_ops.squeeze(actual_max_sequence, [0]) 342 actual_max_score = array_ops.squeeze(actual_max_score, [0])
|
/external/tensorflow/tensorflow/python/kernel_tests/ |
D | shape_ops_test.py | 279 np_ans = np.squeeze(x, axis=tuple(squeeze_dims)) 280 tensor = array_ops.squeeze(x, squeeze_dims) 283 np_ans = np.squeeze(x) 284 tensor = array_ops.squeeze(x) 346 tensor = array_ops.squeeze(np.zeros([1, 1, 1]), []) 356 tensor = array_ops.squeeze([[[False]]], []) 369 self.assertRaises(ValueError, array_ops.squeeze, input_1x1x3, [2]) 375 self.assertRaises(ValueError, array_ops.squeeze, 377 self.assertRaises(ValueError, array_ops.squeeze, 379 self.assertRaises(ValueError, array_ops.squeeze, [all …]
|
/external/tensorflow/tensorflow/python/ops/ |
D | confusion_matrix.py | 71 predictions = array_ops.squeeze(predictions, [-1]) 73 labels = array_ops.squeeze(labels, [-1]) 82 lambda: array_ops.squeeze(predictions, [-1]), 88 lambda: array_ops.squeeze(labels, [-1]),
|
/external/tensorflow/tensorflow/lite/kernels/ |
D | squeeze.cc | 26 namespace squeeze { namespace 91 static TfLiteRegistration r = {nullptr, nullptr, squeeze::Prepare, in Register_SQUEEZE() 92 squeeze::Eval}; in Register_SQUEEZE()
|
/external/tensorflow/tensorflow/contrib/solvers/python/ops/ |
D | least_squares.py | 110 x=array_ops.squeeze(state.x), 111 r=array_ops.squeeze(state.r), 112 p=array_ops.squeeze(state.p),
|
D | linear_equations.py | 127 x=array_ops.squeeze(state.x), 128 r=array_ops.squeeze(state.r), 129 p=array_ops.squeeze(state.p),
|
/external/tensorflow/tensorflow/tools/compatibility/testdata/ |
D | test_file_v0_11.py | 124 self.assertAllEqual(tf.expand_dims(tf.squeeze(a, [0]), 0).eval(), 126 self.assertAllEqual(tf.squeeze(tf.expand_dims(a, 1), [1]).eval(), 129 tf.expand_dims(tf.squeeze([[1, 2, 3]], axis=[0]), dim=0).eval(), a) 131 tf.squeeze(tf.expand_dims([[1, 2, 3]], dim=1), axis=[1]).eval(), a) 134 tf.squeeze(tf.expand_dims([[1, 2, 3]], dim=1), axis=[1]).eval(), a)
|
/external/tensorflow/tensorflow/contrib/crf/python/ops/ |
D | crf.py | 95 array_ops.squeeze(inputs, [1]), 185 first_input = array_ops.squeeze(first_input, [1]) 508 state = array_ops.squeeze(state, axis=[1]) # [B] 539 squeezed_potentials = array_ops.squeeze(potentials, [1]) 555 initial_state = array_ops.squeeze(initial_state, axis=[1]) # [B, O] 583 decode_tags = array_ops.squeeze(decode_tags, axis=[2]) # [B, T - 1]
|
/external/tensorflow/tensorflow/contrib/framework/python/framework/ |
D | tensor_util.py | 111 labels = array_ops.squeeze(labels, [-1]) 113 predictions = array_ops.squeeze(predictions, [-1]) 122 lambda: array_ops.squeeze(predictions, [-1]), 128 lambda: array_ops.squeeze(labels, [-1]),
|
/external/tensorflow/tensorflow/contrib/distribute/python/ |
D | minimize_loss_test.py | 87 error = abs(numpy.add(numpy.squeeze(weights), numpy.squeeze(biases)) - 1) 121 error = abs(numpy.add(numpy.squeeze(weights), numpy.squeeze(biases)) - 1) 337 weight = numpy.squeeze(self.evaluate(v)) 466 numpy.add(numpy.squeeze(weights), numpy.squeeze(biases)) - 1)
|
D | step_fn_test.py | 63 error = abs(numpy.add(numpy.squeeze(weights), numpy.squeeze(biases)) - 1)
|
D | optimizer_v2_test.py | 65 error = abs(numpy.add(numpy.squeeze(weights), numpy.squeeze(biases)) - 1)
|
/external/tensorflow/tensorflow/contrib/timeseries/python/timeseries/state_space_models/ |
D | kalman_filter.py | 168 advanced_state = array_ops.squeeze( 251 posterior_state = prior_state + array_ops.squeeze( 329 observed_mean = array_ops.squeeze(
|
/external/tensorflow/tensorflow/contrib/tensor_forest/hybrid/python/ops/ |
D | training_ops.py | 115 dl_db = math_ops.reduce_mean(array_ops.squeeze(dl_du * du_df * df_db, [2]), 0) 194 dl_db = math_ops.reduce_mean(array_ops.squeeze(dl_du * du_df * df_db, [2]), 0) 272 dl_db = math_ops.reduce_mean(array_ops.squeeze(dl_du * du_df * df_db, [2]), 0)
|
/external/tensorflow/tensorflow/contrib/gan/python/features/python/ |
D | virtual_batchnorm_test.py | 93 vb_mean = array_ops.squeeze(vb_mean, batch_axis) 94 vb_variance = array_ops.squeeze(vb_variance, batch_axis) 142 vb_normed = array_ops.squeeze( 168 vbn_fixed_example = array_ops.squeeze(
|
/external/tensorflow/tensorflow/examples/wav_to_spectrogram/ |
D | wav_to_spectrogram.cc | 71 Output squeeze = Squeeze(root.WithOpName("squeeze"), expand_dims, in WavToSpectrogram() local 73 Output png_encoder = EncodePng(root.WithOpName("png_encoder"), squeeze); in WavToSpectrogram()
|
/external/tensorflow/tensorflow/c/ |
D | c_api_experimental_test.cc | 424 TFE_Op* squeeze = TFE_NewOp(eager_ctx_, "Squeeze", status_); in TEST_F() local 426 TFE_OpAddInput(squeeze, axis, status_); in TEST_F() 427 TFE_OpSetAttrType(squeeze, "T", TF_INT32); in TEST_F() 429 TFE_OpSetAttrIntList(squeeze, "squeeze_dims", boundaries.data(), in TEST_F() 433 squeeze, [this, &boundaries](TF_Operation* squeeze_graph_op) { in TEST_F() 446 TFE_DeleteOp(squeeze); in TEST_F()
|
/external/tensorflow/tensorflow/contrib/training/python/training/ |
D | sampling_ops.py | 110 tensor_list = [array_ops.squeeze(x, [0]) for x in batched] 247 val_list = [array_ops.squeeze(x, [0]) for x in batched[:-1]] 248 label = array_ops.squeeze(batched[-1], [0])
|
/external/tensorflow/tensorflow/python/keras/engine/ |
D | partial_batch_padding_handler.py | 100 prediction = np.squeeze(prediction, axis=0) 109 predictions.append(np.squeeze(prediction))
|