/external/tensorflow/tensorflow/contrib/gan/python/features/python/ |
H A D | conditioning_utils_test.py | 24 from tensorflow.python.ops import array_ops namespace 34 array_ops.placeholder(dtypes.float32, tensor_shape), 35 array_ops.placeholder(dtypes.float32, conditioning_shape)) 40 array_ops.placeholder(dtypes.float32, (4, 1)), 41 array_ops.placeholder(dtypes.float32, (5, 1))) 45 array_ops.placeholder(dtypes.float32, (5, None)), 46 array_ops.placeholder(dtypes.float32, (5, 1))) 50 array_ops.placeholder(dtypes.float32, (5, 2)), 51 array_ops.placeholder(dtypes.float32, (5))) 55 array_ops [all...] |
/external/tensorflow/tensorflow/examples/adding_an_op/ |
H A D | zero_out_grad_2.py | 23 from tensorflow.python.ops import array_ops namespace 40 shape = array_ops.shape(to_zero) 41 index = array_ops.zeros_like(shape) 42 first_grad = array_ops.reshape(grad, [-1])[0]
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/external/tensorflow/tensorflow/python/kernel_tests/ |
H A D | large_concat_op_test.py | 22 from tensorflow.python.ops import array_ops namespace 32 a = array_ops.ones([2**31 + 6], dtype=dtypes.int8) 33 b = array_ops.zeros([1024], dtype=dtypes.int8) 34 onezeros = array_ops.concat([a, b], 0)
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H A D | pad_op_test.py | 26 from tensorflow.python.ops import array_ops namespace 89 tf_val = array_ops.pad(np_inputs, paddings, mode=mode, 100 y = array_ops.pad(inx, ina, mode=mode, constant_values=constant_values) 122 array_ops.pad(array_ops.reshape( 124 array_ops.reshape( 130 array_ops.pad(array_ops.reshape( 132 array_ops.reshape( 138 array_ops [all...] |
/external/tensorflow/tensorflow/contrib/signal/python/ops/ |
H A D | shape_ops.py | 26 from tensorflow.python.ops import array_ops namespace 113 signal_rank = array_ops.rank(signal) 116 signal_shape = array_ops.shape(signal) 117 outer_dimensions, length_samples, inner_dimensions = array_ops.split( 119 length_samples = array_ops.reshape(length_samples, []) 120 num_outer_dimensions = array_ops.size(outer_dimensions) 121 num_inner_dimensions = array_ops.size(inner_dimensions) 137 paddings = array_ops.concat( 138 [array_ops.zeros([num_outer_dimensions, 2], dtype=pad_samples.dtype), 140 array_ops [all...] |
H A D | reconstruction_ops.py | 25 from tensorflow.python.ops import array_ops namespace 52 rank = array_ops.rank(input_tensor) 53 outer_indices, inner_indices = array_ops.split(math_ops.range(rank), 55 permutation = array_ops.concat([inner_indices, outer_indices], 0) 57 return array_ops.transpose(input_tensor, perm=permutation) 103 signal_shape = array_ops.shape(signal) 109 signal_rank = array_ops.rank(signal) 122 subframe_shape = array_ops.concat( 124 subframe_signal = array_ops.reshape(signal, subframe_shape) 133 segment_ids = array_ops [all...] |
/external/tensorflow/tensorflow/contrib/tensor_forest/hybrid/python/ops/ |
H A D | training_ops.py | 25 from tensorflow.python.ops import array_ops namespace 73 dl_du = array_ops.expand_dims(grad, 2) 81 du_df = array_ops.expand_dims( 95 df_dx = -array_ops.expand_dims(tree_weights_tensor, 0) 102 df_dt = -array_ops.expand_dims(input_data_tensor, 1) 108 df_db = array_ops.expand_dims( 109 array_ops.expand_dims(array_ops.ones_like(tree_thresholds_tensor), 0), 2) 115 dl_db = math_ops.reduce_mean(array_ops.squeeze(dl_du * du_df * df_db, [2]), 0) 159 dl_du = array_ops [all...] |
/external/tensorflow/tensorflow/contrib/bayesflow/python/ops/ |
H A D | custom_grad_impl.py | 26 from tensorflow.python.ops import array_ops namespace 84 gx = array_ops.identity(gx, name="gx") 101 fx = array_ops.stop_gradient(fx) 102 gx = array_ops.stop_gradient(gx) 110 return (sum_x - array_ops.stop_gradient(sum_x)) * gx + fx
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/external/tensorflow/tensorflow/contrib/gan/python/eval/python/ |
H A D | eval_utils_test.py | 22 from tensorflow.python.ops import array_ops namespace 30 input_tensor=array_ops.zeros([25, 32, 32, 3]), 36 images=array_ops.unstack(array_ops.zeros([25, 32, 32, 3])), 42 images=array_ops.zeros([25, 32, 32, 3]),
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H A D | eval_utils_impl.py | 24 from tensorflow.python.ops import array_ops namespace 72 input_tensor = array_ops.reshape( 74 input_tensor = array_ops.transpose(input_tensor, [0, 1, 3, 2, 4]) 75 input_tensor = array_ops.reshape( 77 input_tensor = array_ops.transpose(input_tensor, [0, 2, 1, 3]) 78 input_tensor = array_ops.reshape( 113 images = array_ops.unstack(images) 126 rows[-1].extend([array_ops.zeros_like(images[-1])] * num_short) 129 rows = [array_ops.concat(row, 1) for row in rows] 132 img = array_ops [all...] |
/external/tensorflow/tensorflow/python/ops/ |
H A D | array_grad.py | 15 """Gradients for operators defined in array_ops.py.""" 29 from tensorflow.python.ops import array_ops namespace 39 return array_ops.unstack(grad, num=op.get_attr("N"), axis=op.get_attr("axis")) 45 return array_ops.stack(grads, axis=op.get_attr("axis")) 70 shape_of_shape = array_ops.shape(sizes[0]) 74 mask = array_ops.concat([ 75 array_ops.fill(array_ops.expand_dims(concat_dim, 0), 0), [1], 76 array_ops.fill(shape_of_shape - concat_dim - 1, 0) 78 begin = array_ops [all...] |
/external/tensorflow/tensorflow/compiler/tests/ |
H A D | slice_ops_test.py | 23 from tensorflow.python.ops import array_ops namespace 32 i = array_ops.placeholder(dtype, shape=[10]) 34 o = array_ops.slice(i, [2], [4]) 45 i = array_ops.placeholder(dtype, shape=[3, 3, 10]) 47 o = array_ops.slice(i, [1, 2, 2], [1, 1, 4]) 67 i = array_ops.placeholder(dtype, shape=[3, 3, 10]) 68 begin = array_ops.placeholder(dtypes.int32, shape=[3]) 70 o = array_ops.slice(i, begin, [1, 1, 4]) 91 i = array_ops.placeholder(dtype, shape=[3, 3, 10]) 92 begin = array_ops [all...] |
/external/tensorflow/tensorflow/contrib/metrics/python/metrics/ |
H A D | classification_test.py | 23 from tensorflow.python.ops import array_ops namespace 31 pred = array_ops.placeholder(dtypes.int32, shape=[None]) 32 labels = array_ops.placeholder(dtypes.int32, shape=[None]) 41 pred = array_ops.placeholder(dtypes.bool, shape=[None]) 42 labels = array_ops.placeholder(dtypes.bool, shape=[None]) 51 pred = array_ops.placeholder(dtypes.int64, shape=[None]) 52 labels = array_ops.placeholder(dtypes.int64, shape=[None]) 61 pred = array_ops.placeholder(dtypes.string, shape=[None]) 62 labels = array_ops.placeholder(dtypes.string, shape=[None]) 72 pred = array_ops [all...] |
/external/tensorflow/tensorflow/contrib/timeseries/python/timeseries/state_space_models/ |
H A D | periodic.py | 27 from tensorflow.python.ops import array_ops namespace 49 return self.transition_to_powers(array_ops.ones([], dtype=dtypes.int32)) 56 return array_ops.pad( 57 array_ops.ones([1], dtype=self.dtype), 75 range_shape_padded = array_ops.reshape( 77 array_ops.concat( 79 array_ops.ones([array_ops.rank(powers)], dtype=dtypes.int32), 84 row_indicator_shape = array_ops.shape(is_row_negative) 85 negative_row_indicator = array_ops [all...] |
/external/tensorflow/tensorflow/contrib/sparsemax/python/ops/ |
H A D | sparsemax.py | 23 from tensorflow.python.ops import array_ops namespace 49 obs = array_ops.shape(logits)[0] 50 dims = array_ops.shape(logits)[1] 52 z = logits - math_ops.reduce_mean(logits, axis=1)[:, array_ops.newaxis] 67 indices = array_ops.stack([math_ops.range(0, obs), k_z - 1], axis=1) 68 tau_sum = array_ops.gather_nd(z_cumsum, indices) 73 math_ops.cast(0, logits.dtype), z - tau_z[:, array_ops.newaxis])
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/external/tensorflow/tensorflow/contrib/timeseries/python/timeseries/ |
H A D | model_utils_test.py | 23 from tensorflow.python.ops import array_ops namespace 30 parameter = array_ops.constant(5) 31 overridden_parameter = array_ops.constant(3)
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/external/tensorflow/tensorflow/contrib/boosted_trees/lib/learner/batch/ |
H A D | categorical_split_handler.py | 26 from tensorflow.python.ops import array_ops namespace 120 example_indices, _ = array_ops.split( 122 example_indices = array_ops.squeeze(example_indices, [1]) 124 filtered_gradients = array_ops.gather(gradients, example_indices) 125 filtered_hessians = array_ops.gather(hessians, example_indices) 126 filtered_partition_ids = array_ops.gather(example_partition_ids, 128 unique_partitions, mapped_partitions = array_ops.unique( 136 gradients, mapped_partitions, array_ops.size(unique_partitions)) 138 hessians, mapped_partitions, array_ops.size(unique_partitions)) 146 bias_feature_ids = array_ops [all...] |
/external/tensorflow/tensorflow/contrib/crf/python/ops/ |
H A D | crf.py | 57 from tensorflow.python.ops import array_ops namespace 90 batch_size = array_ops.shape(inputs, out_type=tag_indices.dtype)[0] 91 example_inds = array_ops.reshape( 93 return array_ops.gather_nd( 94 array_ops.squeeze(inputs, [1]), 95 array_ops.concat([example_inds, tag_indices], axis=1)) 106 pred=math_ops.equal(inputs.shape[1].value or array_ops.shape(inputs)[1], 125 first_input = array_ops.slice(inputs, [0, 0, 0], [-1, 1, -1]) 126 first_input = array_ops.squeeze(first_input, [1]) 135 rest_of_input = array_ops [all...] |
/external/tensorflow/tensorflow/contrib/losses/python/metric_learning/ |
H A D | metric_loss_ops.py | 24 from tensorflow.python.ops import array_ops namespace 59 array_ops.transpose(feature)), 62 feature, array_ops.transpose(feature)) 80 num_data = array_ops.shape(feature)[0] 82 mask_offdiagonals = array_ops.ones_like(pairwise_distances) - array_ops.diag( 83 array_ops.ones([num_data])) 182 lshape = array_ops.shape(labels) 184 labels = array_ops.reshape(labels, [lshape[0], 1]) 189 adjacency = math_ops.equal(labels, array_ops [all...] |
/external/tensorflow/tensorflow/python/ops/distributions/ |
H A D | student_t.py | 27 from tensorflow.python.ops import array_ops namespace 164 self._df = array_ops.identity(df, name="df") 165 self._loc = array_ops.identity(loc, name="loc") 166 self._scale = array_ops.identity(scale, name="scale") 201 return array_ops.broadcast_dynamic_shape( 202 array_ops.shape(self.df), 203 array_ops.broadcast_dynamic_shape( 204 array_ops.shape(self.loc), array_ops.shape(self.scale))) 207 return array_ops [all...] |
H A D | multinomial.py | 23 from tensorflow.python.ops import array_ops namespace 198 self._mean_val = self._total_count[..., array_ops.newaxis] * self._probs 226 return array_ops.shape(self._mean_val)[:-1] 232 return array_ops.shape(self._mean_val)[-1:] 242 n_draws = array_ops.ones_like( 244 logits = array_ops.ones_like( 245 n_draws[..., array_ops.newaxis], dtype=self.logits.dtype) * self.logits 248 flat_logits = array_ops.reshape(logits, [-1, k]) # [B1B2...Bm, k] 249 flat_ndraws = n * array_ops.reshape(n_draws, [-1]) # [B1B2...Bm] 254 x = random_ops.multinomial(logits[array_ops [all...] |
/external/tensorflow/tensorflow/python/summary/ |
H A D | text_summary_test.py | 21 from tensorflow.python.ops import array_ops namespace 39 num = array_ops.constant(1) 43 arr = array_ops.constant(["one", "two", "three"]) 48 summ = text_summary.text_summary("foo", array_ops.constant("one"))
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/external/tensorflow/tensorflow/contrib/py2tf/utils/ |
H A D | misc.py | 22 from tensorflow.python.ops import array_ops namespace 41 return array_ops.identity(a) if isinstance(a, ops.Tensor) else a
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/external/tensorflow/tensorflow/contrib/seq2seq/python/ops/ |
H A D | loss.py | 23 from tensorflow.python.ops import array_ops namespace 87 num_classes = array_ops.shape(logits)[2] 88 logits_flat = array_ops.reshape(logits, [-1, num_classes]) 89 targets = array_ops.reshape(targets, [-1]) 95 crossent *= array_ops.reshape(weights, [-1]) 102 batch_size = array_ops.shape(logits)[0] 103 sequence_length = array_ops.shape(logits)[1] 104 crossent = array_ops.reshape(crossent, [batch_size, sequence_length])
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/external/tensorflow/tensorflow/python/ops/linalg/ |
H A D | linalg_impl.py | 22 from tensorflow.python.ops import array_ops namespace 30 band_part = array_ops.matrix_band_part 37 diag = array_ops.matrix_diag 38 diag_part = array_ops.matrix_diag_part 49 set_diag = array_ops.matrix_set_diag 54 transpose = array_ops.matrix_transpose 87 math_ops.log(math_ops.real(array_ops.matrix_diag_part(chol))), 115 return array_ops.matrix_transpose(matrix, conjugate=True)
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