| /external/tensorflow/tensorflow/contrib/slim/python/slim/nets/ |
| D | inception_v3_test.py | 39 batch_size = 5
43 inputs = random_ops.random_uniform((batch_size, height, width, 3))
47 [batch_size, num_classes])
50 [batch_size, num_classes])
53 batch_size = 5
56 inputs = random_ops.random_uniform((batch_size, height, width, 3))
60 [batch_size, 8, 8, 2048])
70 batch_size = 5
81 inputs = random_ops.random_uniform((batch_size, height, width, 3))
89 batch_size = 5
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| D | inception_v2_test.py | 39 batch_size = 5
43 inputs = random_ops.random_uniform((batch_size, height, width, 3))
47 [batch_size, num_classes])
50 [batch_size, num_classes])
53 batch_size = 5
56 inputs = random_ops.random_uniform((batch_size, height, width, 3))
60 [batch_size, 7, 7, 1024])
69 batch_size = 5
78 inputs = random_ops.random_uniform((batch_size, height, width, 3))
86 batch_size = 5
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| D | vgg_test.py | 34 batch_size = 5
38 inputs = random_ops.random_uniform((batch_size, height, width, 3))
42 [batch_size, num_classes])
45 batch_size = 1
49 inputs = random_ops.random_uniform((batch_size, height, width, 3))
53 [batch_size, 2, 2, num_classes])
56 batch_size = 5
61 inputs = random_ops.random_uniform((batch_size, height, width, 3))
73 batch_size = 5
77 inputs = random_ops.random_uniform((batch_size, height, width, 3))
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| D | inception_v1_test.py | 39 batch_size = 5
43 inputs = random_ops.random_uniform((batch_size, height, width, 3))
47 [batch_size, num_classes])
50 [batch_size, num_classes])
53 batch_size = 5
56 inputs = random_ops.random_uniform((batch_size, height, width, 3))
60 [batch_size, 7, 7, 1024])
70 batch_size = 5
80 inputs = random_ops.random_uniform((batch_size, height, width, 3))
88 batch_size = 5
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| D | overfeat_test.py | 33 batch_size = 5
37 inputs = random_ops.random_uniform((batch_size, height, width, 3))
41 [batch_size, num_classes])
44 batch_size = 1
48 inputs = random_ops.random_uniform((batch_size, height, width, 3))
52 [batch_size, 2, 2, num_classes])
55 batch_size = 5
59 inputs = random_ops.random_uniform((batch_size, height, width, 3))
70 batch_size = 5
74 inputs = random_ops.random_uniform((batch_size, height, width, 3))
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| D | alexnet_test.py | 33 batch_size = 5
37 inputs = random_ops.random_uniform((batch_size, height, width, 3))
41 [batch_size, num_classes])
44 batch_size = 1
48 inputs = random_ops.random_uniform((batch_size, height, width, 3))
52 [batch_size, 4, 7, num_classes])
55 batch_size = 5
59 inputs = random_ops.random_uniform((batch_size, height, width, 3))
70 batch_size = 5
74 inputs = random_ops.random_uniform((batch_size, height, width, 3))
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| /external/tensorflow/tensorflow/python/estimator/canned/ |
| D | dnn_test.py | 171 label_dimension, batch_size): argument
194 self.assertAllEqual((batch_size, label_dimension), predictions.shape)
207 batch_size = 10
208 data = np.linspace(0., 2., batch_size * label_dimension, dtype=np.float32)
209 data = data.reshape(batch_size, label_dimension)
214 batch_size=batch_size,
220 batch_size=batch_size,
224 batch_size=batch_size,
233 batch_size=batch_size)
240 batch_size = 10
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| D | dnn_linear_combined_test.py | 226 label_dimension, batch_size): argument
253 self.assertAllEqual((batch_size, label_dimension), predictions.shape)
266 batch_size = 10
267 data = np.linspace(0., 2., batch_size * label_dimension, dtype=np.float32)
268 data = data.reshape(batch_size, label_dimension)
273 batch_size=batch_size,
279 batch_size=batch_size,
283 batch_size=batch_size,
292 batch_size=batch_size)
299 batch_size = 10
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| /external/tensorflow/tensorflow/contrib/seq2seq/python/kernel_tests/ |
| D | basic_decoder_test.py | 47 batch_size = 5
54 inputs = np.random.randn(batch_size, max_time,
69 dtype=dtypes.float32, batch_size=batch_size),
85 batch_size_t = my_decoder.batch_size
91 self.assertEqual((batch_size, expected_output_depth),
93 self.assertEqual((batch_size,), step_outputs[1].get_shape())
94 self.assertEqual((batch_size, cell_depth), first_state[0].get_shape())
95 self.assertEqual((batch_size, cell_depth), first_state[1].get_shape())
96 self.assertEqual((batch_size, cell_depth), step_state[0].get_shape())
97 self.assertEqual((batch_size, cell_depth), step_state[1].get_shape())
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| D | beam_search_decoder_test.py | 111 self.batch_size = 2
118 dummy_cell_state = array_ops.zeros([self.batch_size, self.beam_width])
122 array_ops.ones([self.batch_size, self.beam_width])),
124 2, shape=[self.batch_size, self.beam_width], dtype=dtypes.int64),
126 [self.batch_size, self.beam_width], dtype=dtypes.bool))
128 logits_ = np.full([self.batch_size, self.beam_width, self.vocab_size],
146 batch_size=ops.convert_to_tensor(self.batch_size),
173 dummy_cell_state = array_ops.zeros([self.batch_size, self.beam_width])
177 array_ops.ones([self.batch_size, self.beam_width])),
183 logits_ = np.full([self.batch_size, self.beam_width, self.vocab_size],
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| /external/tensorflow/tensorflow/python/training/ |
| D | input_test.py | 444 batch_size = 10
448 counter = examples.count_up_to(num_batches * batch_size)
462 batch_size=batch_size)
466 [counter, sparse_counter, "string"], batch_size=batch_size)
475 np.arange(i * batch_size, (i + 1) * batch_size))
479 np.arange(2 * batch_size) // 2, # 0, 0, 1, 1, ...
480 [0, 1] * batch_size)).T)
482 expected = np.arange(2 * i * batch_size, 2 * (i + 1) * batch_size) // 2
483 expected *= ([1, -1] * batch_size) # mult by [1, -1, 1, -1, ...]
485 self.assertAllEqual(results[1].dense_shape, [batch_size, 2])
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| /external/tensorflow/tensorflow/contrib/slim/python/slim/data/ |
| D | prefetch_queue_test.py | 41 batch_size = 10
48 counter = examples.count_up_to(num_batches * batch_size)
55 [counter, image, label], batch_size=batch_size, num_threads=1)
65 np.arange(i * batch_size, (i + 1) * batch_size))
67 (batch_size, image_size, image_size, 3))
68 self.assertEquals(results[2].shape, (batch_size, 1))
78 batch_size = 10
85 counter = examples.count_up_to(num_batches * batch_size)
92 [counter, image, label], batch_size=batch_size, num_threads=4)
104 (batch_size, image_size, image_size, 3))
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| /external/tensorflow/tensorflow/core/util/ctc/ |
| D | ctc_beam_search_test.cc | 104 const int batch_size = 1; in TEST() local
119 int sequence_lengths[batch_size] = {timesteps}; in TEST()
120 float input_data_mat[timesteps][batch_size][num_classes] = { in TEST()
129 for (int b = 0; b < batch_size; ++b) { in TEST()
151 Eigen::Map<const Eigen::ArrayXi> seq_len(&sequence_lengths[0], batch_size); in TEST()
155 inputs.emplace_back(&input_data_mat[t][0][0], batch_size, num_classes); in TEST()
161 output.resize(batch_size); in TEST()
163 float score[batch_size][top_paths] = {{0.0}}; in TEST()
164 Eigen::Map<Eigen::MatrixXf> scores(&score[0][0], batch_size, top_paths); in TEST()
174 output.resize(batch_size); in TEST()
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| /external/tensorflow/tensorflow/contrib/eager/python/examples/resnet50/ |
| D | resnet50_graph_test.py | 35 def image_shape(batch_size): argument
37 return [batch_size, 3, 224, 224]
38 return [batch_size, 224, 224, 3]
41 def random_batch(batch_size): argument
42 images = np.random.rand(*image_shape(batch_size)).astype(np.float32)
45 low=0, high=num_classes, size=[batch_size]).astype(np.int32)
46 one_hot = np.zeros((batch_size, num_classes)).astype(np.float32)
47 one_hot[np.arange(batch_size), labels] = 1.
54 batch_size = 64
64 np_images, _ = random_batch(batch_size)
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| /external/tensorflow/tensorflow/contrib/rnn/python/kernel_tests/ |
| D | gru_ops_test.py | 45 batch_size = 4
57 feed[x] = np.random.randn(num_steps, batch_size, input_size)
62 batch_size = 4
70 x = array_ops.zeros([batch_size, input_size])
71 h = array_ops.zeros([batch_size, cell_size])
74 x_value = np.random.rand(batch_size, input_size)
75 h_value = np.random.rand(batch_size, cell_size)
95 batch_size = 2
107 dtypes.float32, shape=(time_steps, batch_size, input_size))
108 h = array_ops.zeros([batch_size, cell_size])
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| D | rnn_cell_test.py | 53 batch_size = 3
65 x = array_ops.zeros([batch_size, input_size])
66 m = array_ops.zeros([batch_size, state_size])
76 0.1 * np.ones((batch_size, state_size))
87 batch_size = 3
94 x = array_ops.zeros([batch_size, input_size])
95 m = array_ops.zeros([batch_size, state_size * num_shifts])
108 0.1 * np.ones((batch_size, int(state_size * (num_shifts))))
113 self.assertEqual(res[0].shape, (batch_size, num_units * num_shifts))
114 self.assertEqual(res[1].shape, (batch_size, state_size * num_shifts))
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| /external/tensorflow/tensorflow/contrib/learn/python/learn/learn_io/ |
| D | graph_io.py | 43 batch_size, argument
96 batch_size=batch_size,
110 batch_size, argument
165 batch_size,
179 batch_size, argument
237 batch_size,
330 batch_size, argument
382 if (batch_size is None) or (
383 (not isinstance(batch_size, ops.Tensor)) and
384 (batch_size <= 0 or batch_size >= queue_capacity)):
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| /external/tensorflow/tensorflow/contrib/learn/python/learn/estimators/ |
| D | debug.py | 92 batch_size = None
98 if batch_size is None:
99 batch_size = first_dim
101 size_checks.append(check_ops.assert_equal(batch_size, first_dim))
104 logits = array_ops.zeros([batch_size, head.logits_dimension])
201 def predict_classes(self, input_fn=None, batch_size=None): argument
214 input_fn=input_fn, batch_size=batch_size, outputs=[key])
219 batch_size=None): argument
232 batch_size=batch_size,
312 def predict_scores(self, input_fn=None, batch_size=None): argument
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| D | dynamic_rnn_estimator_test.py | 357 batch_size = 11
363 def get_shift_input_fn(batch_size, sequence_length, seed=None): argument
367 [batch_size, sequence_length + 1],
373 [batch_size, sequence_length])
377 [batch_size, sequence_length])), 2)
380 [batch_size, cell_size], seed=((i + 1) * seed))
402 train_input_fn = get_shift_input_fn(batch_size, sequence_length, seed=12321)
403 eval_input_fn = get_shift_input_fn(batch_size, sequence_length, seed=32123)
411 self.assertListEqual(list(state_piece.shape), [batch_size, state_size])
416 batch_size = 11
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| /external/tensorflow/tensorflow/contrib/training/python/training/ |
| D | sampling_ops_test.py | 47 batch_size = 16
55 batch_size,
63 batch_size,
71 probs, batch_size, init_probs)
76 array_ops.zeros([1, 3]), label, probs, batch_size, init_probs)
84 batch_size,
94 batch_size,
100 sampling_ops.stratified_sample(val, label, 1, batch_size, init_probs)
109 batch_size,
116 val, label, [.1] * 10, batch_size, init_probs=[.2] * 5)
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| /external/tensorflow/tensorflow/contrib/lite/kernels/ |
| D | softmax_test.cc | 80 const int batch_size = 2; in TEST() local
88 SoftmaxOpModel m(batch_size, input_size, beta); in TEST()
90 m.SetInput(0, input_buffer, input_buffer + input_size * batch_size); in TEST()
94 std::unique_ptr<float[]> output_buffer(new float[input_size * batch_size]); in TEST()
95 static tflite::Dims<4> input_dims = {{input_size, 1, 1, batch_size}, in TEST()
102 output_buffer.get() + input_size * batch_size); in TEST()
108 const int batch_size = 2; in TEST() local
116 SoftmaxOpModel m(batch_size, input_size, beta); in TEST()
118 m.SetInput(0, input_buffer, input_buffer + input_size * batch_size); in TEST()
122 std::unique_ptr<float[]> output_buffer(new float[input_size * batch_size]); in TEST()
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| /external/tensorflow/tensorflow/contrib/rnn/kernels/ |
| D | gru_ops.cc | 52 const int64 batch_size = x_tensor->dim_size(0); in Compute() local
59 OP_REQUIRES(ctx, h_prev_tensor->dim_size(0) == batch_size, in Compute()
62 batch_size)); in Compute()
111 ctx, ctx->allocate_output("r", TensorShape({batch_size, cell_size}), in Compute()
116 ctx, ctx->allocate_output("u", TensorShape({batch_size, cell_size}), in Compute()
121 ctx, ctx->allocate_output("c", TensorShape({batch_size, cell_size}), in Compute()
127 TensorShape({batch_size, cell_size}), &h_tensor)); in Compute()
133 TensorShape({batch_size, input_size + cell_size}), in Compute()
139 TensorShape({batch_size, input_size + cell_size}), in Compute()
145 TensorShape({batch_size, 2 * cell_size}), in Compute()
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| /external/tensorflow/tensorflow/contrib/eager/python/examples/gan/ |
| D | mnist_graph_test.py | 41 def _create_graph(self, batch_size): argument
43 images_data = np.random.randn(batch_size, 784).astype(np.float32)
57 shape=[batch_size, NOISE_DIM])
77 def _report(self, test_name, start, num_iters, batch_size): argument
80 name = 'graph_%s_%s_batch_%d_%s' % (test_name, dev, batch_size,
82 extras = {'examples_per_sec': batch_size / avg_time}
87 for batch_size in [64, 128, 256]:
96 ) = self._create_graph(batch_size)
106 noise = np.random.uniform(-1.0, 1.0, size=[batch_size, NOISE_DIM])
117 noise = np.random.uniform(-1.0, 1.0, size=[batch_size, NOISE_DIM])
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| /external/tensorflow/tensorflow/python/kernel_tests/ |
| D | rnn_test.py | 77 def zero_state(self, batch_size, dtype): argument
95 def zero_state(self, batch_size, dtype): argument
277 batch_size = 512
282 sequence_length = np.random.randint(0, max_time, size=batch_size)
284 np.random.randn(batch_size, num_units).astype(np.float32)
324 def static_vs_dynamic_rnn_benchmark(batch_size, max_time, num_units, use_gpu): argument
330 sequence_length = np.random.randint(0, max_time, size=batch_size)
332 np.random.randn(batch_size, num_units).astype(np.float32)
358 (batch_size, max_time, num_units, use_gpu, delta_static, delta_dynamic,
386 def half_seq_len_vs_unroll_half_rnn_benchmark(batch_size, max_time, num_units, argument
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| /external/tensorflow/tensorflow/core/kernels/ |
| D | matrix_inverse_op.cc | 149 const int64 batch_size = input_copy_reshaped.dimension(0); in ComputeAsync() local
155 TensorShape{batch_size, n}, &pivots), in ComputeAsync()
159 sizeof(Scalar*) * batch_size, "input_copy_ptr_array", in ComputeAsync()
162 sizeof(Scalar*) * batch_size, "output_copy_ptr_array", in ComputeAsync()
166 if (n < 32 || batch_size > n) { in ComputeAsync()
176 for (int batch = 0; batch < batch_size; ++batch) { in ComputeAsync()
184 solver->GetDeviceLapackInfo(batch_size, "MatInvBatched")); in ComputeAsync()
189 batch_size), in ComputeAsync()
196 solver->GetDeviceLapackInfo(batch_size, "GetrfBatched")); in ComputeAsync()
200 &dev_info.back(), batch_size), in ComputeAsync()
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