| /external/tensorflow/tensorflow/contrib/gan/python/features/python/ |
| conditioning_utils_test.py | 24 from tensorflow.python.ops import array_ops
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.placeholder(dtypes.float32, (5, 4, 1))
[all...] |
| virtual_batchnorm_test.py | 29 from tensorflow.python.ops import array_ops
41 reference_batch = array_ops.zeros([5, 3, 16, 9, 15])
43 vbn(array_ops.ones([5, 7, 16, 9, 15]))
47 reference_batch = array_ops.zeros([5, 3, 16, 9, 15])
48 minibatch = array_ops.zeros([5, 3, 16, 3, 15])
76 full_batch = array_ops.concat([partial_batch, single_example], axis=0)
93 vb_mean = array_ops.squeeze(vb_mean, batch_axis)
94 vb_variance = array_ops.squeeze(vb_variance, batch_axis)
136 array_ops.stack(examples), training=True)
139 examples_except_i = array_ops.stack(examples[:i] + examples[i+1:]
[all...] |
| /external/tensorflow/tensorflow/contrib/gan/python/eval/python/ |
| eval_utils_test.py | 22 from tensorflow.python.ops import array_ops
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]),
|
| eval_utils_impl.py | 24 from tensorflow.python.ops import array_ops
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.concat(rows, 0
[all...] |
| /external/tensorflow/tensorflow/python/ops/ |
| array_grad.py | 15 """Gradients for operators defined in array_ops.py."""
29 from tensorflow.python.ops import array_ops
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.fill(shape_of_shape, 0
[all...] |
| nn_grad.py | 25 from tensorflow.python.ops import array_ops
48 array_ops.shape(op.inputs[1]),
70 array_ops.shape(op.inputs[0]),
94 array_ops.shape(op.inputs[0]),
102 array_ops.shape(op.inputs[1]),
117 array_ops.shape(op.inputs[1]),
136 array_ops.shape(op.inputs[0]),
154 array_ops.shape(op.inputs[0]),
164 return (array_ops.stop_gradient(op.inputs[0]),
187 return (array_ops.zeros
[all...] |
| spectral_grad.py | 24 from tensorflow.python.ops import array_ops
30 return math_ops.reduce_prod(array_ops.shape(grad)[-rank:])
77 input_shape = array_ops.shape(op.inputs[0])
81 expanded = array_ops.reshape(
83 array_ops.concat([
84 array_ops.ones([array_ops.rank(t) - 2], dtypes.int32),
85 array_ops.shape(matrix)
87 return array_ops.tile(
88 expanded, array_ops.concat([array_ops.shape(t)[:-2], [1, 1]], 0)
[all...] |
| sparse_grad.py | 23 from tensorflow.python.ops import array_ops
53 num_entries = array_ops.shape(input_indices)[0]
58 inverted_permutation = array_ops.invert_permutation(sp_ordered.values)
61 array_ops.gather(output_values_grad, inverted_permutation),
105 return (None, array_ops.gather_nd(out_grad, sp_indices), None, out_grad)
114 out_grad_reshaped = array_ops.reshape(out_grad, output_shape_kept_dims)
117 return (None, array_ops.gather_nd(out_grad_reshaped, sp_indices // scale),
157 b_grad = array_ops.transpose(b_grad)
168 parts_a = array_ops.gather(grad, rows if not adj_a else cols)
169 parts_b = array_ops.gather(b if not adj_b else array_ops.transpose(b)
[all...] |
| /external/tensorflow/tensorflow/compiler/tests/ |
| slice_ops_test.py | 23 from tensorflow.python.ops import array_ops
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.placeholder(dtypes.int32, shape=[3]
[all...] |
| /external/tensorflow/tensorflow/contrib/metrics/python/metrics/ |
| classification_test.py | 23 from tensorflow.python.ops import array_ops
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.placeholder(dtypes.int32, shape=[None]
[all...] |
| /external/tensorflow/tensorflow/contrib/timeseries/python/timeseries/state_space_models/ |
| periodic.py | 27 from tensorflow.python.ops import array_ops
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.where(is_row_negative, -array_ops.ones
[all...] |
| /external/tensorflow/tensorflow/contrib/crf/python/ops/ |
| crf.py | 57 from tensorflow.python.ops import array_ops
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.slice(inputs, [0, 1, 0], [-1, -1, -1]
[all...] |
| /external/tensorflow/tensorflow/contrib/losses/python/metric_learning/ |
| metric_loss_ops.py | 24 from tensorflow.python.ops import array_ops
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.transpose(labels)
[all...] |
| /external/tensorflow/tensorflow/examples/adding_an_op/ |
| zero_out_grad_2.py | 23 from tensorflow.python.ops import array_ops
40 shape = array_ops.shape(to_zero)
41 index = array_ops.zeros_like(shape)
42 first_grad = array_ops.reshape(grad, [-1])[0]
|
| /external/tensorflow/tensorflow/python/kernel_tests/ |
| large_concat_op_test.py | 22 from tensorflow.python.ops import array_ops
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)
|
| pad_op_test.py | 26 from tensorflow.python.ops import array_ops
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.pad(array_ops.reshape
[all...] |
| /external/tensorflow/tensorflow/python/summary/ |
| text_summary_test.py | 21 from tensorflow.python.ops import array_ops
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"))
|
| /external/tensorflow/tensorflow/python/ops/distributions/ |
| multinomial.py | 23 from tensorflow.python.ops import array_ops
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.newaxis, ...], n_draw
[all...] |
| /external/tensorflow/tensorflow/contrib/timeseries/python/timeseries/ |
| math_utils.py | 33 from tensorflow.python.ops import array_ops
62 diagonal = array_ops.matrix_diag_part(covariance_matrix)
66 return array_ops.matrix_set_diag(
93 matrix_shape = array_ops.shape(matrix)
99 matrix_shape = array_ops.shape(matrix)
104 array_ops.pad(
106 paddings=array_ops.concat(
108 array_ops.zeros(
109 [array_ops.rank(matrix) - 1, 2], dtype=dtypes.int32), [(
113 blocked = array_ops.concat(row_blocks, -2, name=name
[all...] |
| /external/tensorflow/tensorflow/contrib/signal/python/ops/ |
| shape_ops.py | 26 from tensorflow.python.ops import array_ops
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.zeros([num_inner_dimensions, 2], dtype=pad_samples.dtype)]
[all...] |
| /external/tensorflow/tensorflow/contrib/tensor_forest/hybrid/python/ops/ |
| training_ops.py | 25 from tensorflow.python.ops import array_ops
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.expand_dims(unpack_path_op(path_tensor, routing_grad), 2
[all...] |
| /external/tensorflow/tensorflow/contrib/layers/python/ops/ |
| sparse_ops.py | 24 from tensorflow.python.ops import array_ops
73 indices = array_ops.where(
77 values=array_ops.gather_nd(dense_tensor, indices, name="values"),
78 dense_shape=array_ops.shape(
152 zeros_like_indicators = array_ops.zeros_like(
154 binary_indicators = array_ops.where(
156 array_ops.ones_like(indicators, dtype=dtypes.int64, name="ones"),
164 row_index_indicators = array_ops.where(
167 result_last_dim = array_ops.reshape(
178 indices=array_ops.concat(
[all...] |
| /external/tensorflow/tensorflow/contrib/layers/python/layers/ |
| embedding_ops.py | 30 from tensorflow.python.ops import array_ops
129 array_ops.size(original_shape)
134 array_ops.slice(original_shape, [0], [original_rank - 1])),
135 array_ops.gather(original_shape, original_rank - 1)])
163 is_row_empty = array_ops.tile(
164 array_ops.reshape(is_row_empty, [-1, 1]),
165 array_ops.stack([1, array_ops.shape(result)[1]]))
167 result = array_ops.where(is_row_empty,
168 array_ops.zeros_like(result)
[all...] |
| /external/tensorflow/tensorflow/contrib/sparsemax/python/ops/ |
| sparsemax.py | 23 from tensorflow.python.ops import array_ops
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])
|
| /external/tensorflow/tensorflow/contrib/linear_optimizer/python/ |
| sdca_optimizer.py | 23 from tensorflow.python.ops import array_ops
111 sparse_indices = array_ops.where(
114 sparse_values = array_ops.gather_nd(dense_tensor, sparse_indices)
118 array_ops.reshape(
119 array_ops.split(
121 array_ops.reshape(
122 array_ops.split(
124 array_ops.reshape(math_ops.to_float(sparse_values), [-1]))
146 math_ops.less_equal(array_ops.rank(transformed_tensor), 2),
150 transformed_tensor = array_ops.reshape(transformed_tensor,
[all...] |
