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/external/tensorflow/tensorflow/core/api_def/base_api/
Dapi_def_ExpandDims.pbtxt28 channels]`, you can make it a batch of 1 image with `expand_dims(image, 0)`,
35 shape(expand_dims(t, 0)) ==> [1, 2]
36 shape(expand_dims(t, 1)) ==> [2, 1]
37 shape(expand_dims(t, -1)) ==> [2, 1]
40 shape(expand_dims(t2, 0)) ==> [1, 2, 3, 5]
41 shape(expand_dims(t2, 2)) ==> [2, 3, 1, 5]
42 shape(expand_dims(t2, 3)) ==> [2, 3, 5, 1]
/external/tensorflow/tensorflow/python/kernel_tests/
Dconv1d_test.py39 x = array_ops.expand_dims(x, 0) # Add batch dimension
40 x = array_ops.expand_dims(x, 2) # And depth dimension
42 filters = array_ops.expand_dims(filters, 1) # in_channels
43 filters = array_ops.expand_dims(filters, 2) # out_channels
63 x = array_ops.expand_dims(x, 0) # Add batch dimension
64 x = array_ops.expand_dims(x, 2) # And depth dimension
67 filters = array_ops.expand_dims(filters, 1) # in_channels
68 filters = array_ops.expand_dims(filters, 2) # out_channels
Dnormalize_op_test.py43 norm = np.expand_dims(norm, d)
46 norm = np.expand_dims(norm, d)
54 return x / np.expand_dims(norm, axis)
Dattention_ops_test.py61 array_ops.expand_dims(array_ops.expand_dims(t_rows, 0), 3),
75 array_ops.expand_dims(array_ops.expand_dims(t_cols, 0), 3),
Dshape_ops_test.py178 np_ans = np.expand_dims(x, axis=dim)
180 tensor = array_ops.expand_dims(x, dim)
235 self.assertRaises(ValueError, array_ops.expand_dims,
237 self.assertRaises(ValueError, array_ops.expand_dims,
239 self.assertRaises(ValueError, array_ops.expand_dims,
241 self.assertRaises(ValueError, array_ops.expand_dims,
249 squeezed = array_ops.expand_dims(inp, 1)
259 self.assertAllEqual([7], array_ops.expand_dims(inp, 0))
260 self.assertAllEqual([7], array_ops.expand_dims(inp, -1))
263 self.assertAllEqual([True], array_ops.expand_dims(inp, 0))
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Ddraw_bounding_box_op_test.py87 bboxes = array_ops.expand_dims(bboxes, 0)
90 image = array_ops.expand_dims(image, 0)
Daggregate_ops_test.py67 [np.expand_dims(d, 0) for d in data]), axis=0)
80 expected = np.sum(np.vstack([np.expand_dims(data, 0)] * count),
Dmatrix_square_root_op_test.py54 [np.expand_dims(matrix1, 0),
55 np.expand_dims(matrix2, 0)])
/external/tensorflow/tensorflow/tools/compatibility/testdata/
Dtest_file_v0_11.py99 tf.expand_dims([[1, 2], [3, 4]], axis=1).eval(),
125 self.assertAllEqual(tf.expand_dims(tf.squeeze(a, [0]), 0).eval(),
127 self.assertAllEqual(tf.squeeze(tf.expand_dims(a, 1), [1]).eval(),
130 tf.expand_dims(tf.squeeze([[1, 2, 3]], axis=[0]), dim=0).eval(), a)
132 tf.squeeze(tf.expand_dims([[1, 2, 3]], dim=1), axis=[1]).eval(), a)
135 tf.squeeze(tf.expand_dims([[1, 2, 3]], dim=1), axis=[1]).eval(), a)
166 batched_mat = tf.expand_dims(mat, [0])
169 self.assertAllEqual(result_batched, np.expand_dims(result, 0))
/external/tensorflow/tensorflow/python/keras/utils/
Dkernelized_utils.py32 return array_ops.expand_dims(u, 0)
48 array_ops.expand_dims(x_matrix, 1), [1, y_shape[0], 1])
50 array_ops.expand_dims(y_matrix, 0), [x_shape[0], 1, 1])
Dmetrics_utils.py373 predictions_extra_dim = array_ops.expand_dims(y_pred, 0)
374 labels_extra_dim = array_ops.expand_dims(
414 label_weights = array_ops.expand_dims(label_weights, 0)
522 mask = array_ops.expand_dims(mask.flat_values, -1)
528 flat_values.append(array_ops.expand_dims(value.flat_values, -1))
/external/tensorflow/tensorflow/python/ops/
Dctc_ops.py499 array_ops.expand_dims(indices, 0), [batch_size, 1, 1])
500 batch_idx = array_ops.expand_dims(math_ops.range(batch_size), 1) * [1, 0, 0]
501 indices += array_ops.expand_dims(batch_idx, 1)
507 return array_ops.expand_dims(trans, 0) + label_to_label
557 one_hot = array_ops.expand_dims(one_hot, axis=0)
558 ilabel_log_probs = array_ops.expand_dims(ilabel_log_probs, axis=2)
577 one_hot = array_ops.expand_dims(one_hot, axis=0)
578 label_states = array_ops.expand_dims(label_states, axis=3)
602 indices = unique_y + array_ops.expand_dims(batch_offset, axis=-1)
683 logit_mask = array_ops.expand_dims(logit_mask, axis=2)
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Dimage_ops_impl.py1057 image = array_ops.expand_dims(image, 0)
1060 image = array_ops.expand_dims(image, 0)
1143 image = array_ops.expand_dims(image, 0)
1146 image = array_ops.expand_dims(image, 0)
1225 image = array_ops.expand_dims(image, 0)
1228 image = array_ops.expand_dims(image, 0)
1342 images = array_ops.expand_dims(images, 0)
1683 image = array_ops.expand_dims(image, 0)
1686 image = array_ops.expand_dims(image, 0)
2444 gray_float = array_ops.expand_dims(gray_float, -1)
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Dinit_ops.py654 array_ops.expand_dims(q, 0), shape)
658 array_ops.expand_dims(q, 0), shape)
662 array_ops.expand_dims(q, 0), shape)
865 return array_ops.expand_dims(array_ops.expand_dims(orth, 0), 0)
993 return array_ops.expand_dims(orth, 0)
1149 return array_ops.expand_dims(
1150 array_ops.expand_dims(array_ops.expand_dims(orth, 0), 0), 0)
Dnn_grad.py514 vec = array_ops.expand_dims(vec, -1)
536 array_ops.expand_dims(grad_grad, 1),
537 array_ops.expand_dims(softmax, 2)),
562 array_ops.expand_dims(grad_grad, 1),
563 array_ops.expand_dims(softmax, 2)),
1128 array_ops.expand_dims(
1138 array_ops.expand_dims(ind, -1), array_ops.reshape(grad, [-1]),
1163 math_ops.equal(array_ops.expand_dims(output, -1), input), grad.dtype)
1165 grad = array_ops.expand_dims(grad, -1)
1166 num_selected = array_ops.expand_dims(math_ops.reduce_sum(indicators, -1), -1)
Dweights_broadcast_ops.py37 values_shape_2d = array_ops.expand_dims(values_shape, -1)
40 weights_shape_2d = array_ops.expand_dims(weights_shape, -1)
/external/tensorflow/tensorflow/python/ops/structured/
Dstructured_array_ops.py30 @dispatch.dispatch_for_types(array_ops.expand_dims, StructuredTensor)
32 def expand_dims(input, axis=None, name=None, dim=None): # pylint: disable=redefined-builtin function
126 k: array_ops.expand_dims(v, axis)
/external/tensorflow/tensorflow/lite/kernels/
Dexpand_dims.cc27 namespace expand_dims { namespace
116 static TfLiteRegistration r = {nullptr, nullptr, expand_dims::Prepare, in Register_EXPAND_DIMS()
117 expand_dims::Eval}; in Register_EXPAND_DIMS()
/external/tensorflow/tensorflow/python/keras/integration_test/
Dvectorized_map_test.py32 inp = tf.expand_dims(inp, 0)
33 label = tf.expand_dims(label, 0)
/external/tensorflow/tensorflow/python/keras/layers/
Ddense_attention.py160 v_mask = array_ops.expand_dims(v_mask, axis=-2)
178 q_mask = array_ops.expand_dims(q_mask, axis=-1)
496 q_reshaped = array_ops.expand_dims(query, axis=-2)
498 k_reshaped = array_ops.expand_dims(key, axis=-3)
/external/tensorflow/tensorflow/examples/wav_to_spectrogram/
Dwav_to_spectrogram.cc69 Output expand_dims = in WavToSpectrogram() local
71 Output squeeze = Squeeze(root.WithOpName("squeeze"), expand_dims, in WavToSpectrogram()
/external/tensorflow/tensorflow/python/keras/layers/preprocessing/
Ddiscretization.py71 return np.hstack((np.expand_dims(np.sort(values), 1), np.ones((n, 1))))
77 return np.hstack((np.expand_dims(part, 1),
108 return np.hstack((np.expand_dims(new_bins, 1),
109 np.expand_dims(new_weights, 1)))
/external/tensorflow/tensorflow/compiler/tests/
Dmatrix_inverse_op_test.py57 [np.expand_dims(matrix1, 0),
58 np.expand_dims(matrix2, 0)])
/external/tensorflow/tensorflow/lite/testing/nnapi_tflite_zip_tests/
Dnot_supported.txt133 expand_dims/expand_dims_axis_value=0,constant_axis=True,input_shape=[5,4],input_type=tf.float32
134 expand_dims/expand_dims_axis_value=1,constant_axis=True,input_shape=[5,4],input_type=tf.float32
135 expand_dims/expand_dims_axis_value=2,constant_axis=True,input_shape=[5,4],input_type=tf.float32
136 expand_dims/expand_dims_axis_value=-1,constant_axis=True,input_shape=[5,4],input_type=tf.float32
137 expand_dims/expand_dims_axis_value=-2,constant_axis=True,input_shape=[5,4],input_type=tf.float32
138 expand_dims/expand_dims_axis_value=-3,constant_axis=True,input_shape=[5,4],input_type=tf.float32
139 expand_dims/expand_dims_axis_value=0,constant_axis=False,input_shape=[5,4],input_type=tf.float32
140 expand_dims/expand_dims_axis_value=1,constant_axis=False,input_shape=[5,4],input_type=tf.float32
141 expand_dims/expand_dims_axis_value=2,constant_axis=False,input_shape=[5,4],input_type=tf.float32
142 expand_dims/expand_dims_axis_value=-1,constant_axis=False,input_shape=[5,4],input_type=tf.float32
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/external/tensorflow/tensorflow/python/keras/layers/preprocessing/benchmarks/
Ddiscretization_adapt_benchmark.py63 lambda x: array_ops.expand_dims(math_ops.cast(x, dtypes.float32), -1))
90 lambda x: array_ops.expand_dims(math_ops.cast(x, dtypes.float32), -1))

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