| /external/tensorflow/tensorflow/python/tpu/ |
| D | tpu_estimator_signals_test.py | 36 batch_size = params['batch_size']
42 dataset = dataset.batch(batch_size)
52 batch_size = params['batch_size']
58 dataset = dataset.batch(batch_size)
67 batch_size = 2
69 params = {'batch_size': batch_size}
81 self.assertAllEqual(a[:batch_size], result['a'])
82 self.assertAllEqual(b[:batch_size], result['b'])
86 self.assertAllEqual(a[batch_size:num_samples], result['a'])
87 self.assertAllEqual(b[batch_size:num_samples], result['b'])
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| /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))
[all …]
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| /external/tensorflow/tensorflow/lite/kernels/internal/ |
| D | kernel_utils.cc | 24 int input_size, int num_units, int batch_size, in RnnBatchStep() argument
32 batch_size, output_batch_leading_dim, activation, in RnnBatchStep()
41 int batch_size, int output_batch_leading_dim, in RnnBatchStep() argument
48 tensor_utils::VectorBatchVectorAssign(bias_ptr, num_units, batch_size, in RnnBatchStep()
53 input_weights_ptr, num_units, input_size, input_ptr_batch, batch_size, in RnnBatchStep()
60 batch_size, output_ptr_batch, /*result_stride=*/1); in RnnBatchStep()
66 batch_size, output_ptr_batch, /*result_stride=*/1); in RnnBatchStep()
70 output_ptr_batch, num_units * batch_size, activation, output_ptr_batch); in RnnBatchStep()
71 tensor_utils::CopyVector(output_ptr_batch, num_units * batch_size, in RnnBatchStep()
75 for (int k = 0; k < batch_size; k++) { in RnnBatchStep()
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| /external/tensorflow/tensorflow/python/data/experimental/kernel_tests/ |
| D | make_batched_features_dataset_test.py | 41 for batch_size in [1, 2]:
49 batch_size=batch_size))
51 batch_size, 0, num_epochs=num_epochs, label_key_provided=True)
61 batch_size=batch_size))
63 batch_size, 1, num_epochs=num_epochs, label_key_provided=True)
73 batch_size=batch_size))
75 batch_size, num_epochs=num_epochs, label_key_provided=True)
83 batch_size=batch_size))
84 self.verify_records(batch_size, num_epochs=num_epochs)
109 for batch_size in [1, 2]:
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| D | make_tf_record_dataset_test.py | 34 def _read_test(self, batch_size, num_epochs, file_index=None, argument
50 batch_size=batch_size,
57 batch_size,
67 for batch_size in [1, 2]:
70 self._read_test(batch_size, num_epochs, 0)
73 self._read_test(batch_size, num_epochs, 1)
76 self._read_test(batch_size, num_epochs)
79 self._read_test(batch_size, num_epochs, num_parallel_reads=8)
82 for batch_size in [1, 2, 10]:
85 self._read_test(batch_size, num_epochs, 0, drop_final_batch=True)
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| D | make_csv_dataset_test.py | 40 def _make_csv_dataset(self, filenames, batch_size, num_epochs=1, **kwargs): argument
42 filenames, batch_size=batch_size, num_epochs=num_epochs, **kwargs)
64 def _next_expected_batch(self, expected_output, expected_keys, batch_size, argument
71 if len(features[expected_keys[0]]) == batch_size:
80 batch_size, argument
91 batch_size,
112 batch_size=1, argument
122 batch_size=batch_size,
126 self._verify_output(dataset, batch_size, num_epochs, label_name,
153 batch_size=1,
[all …]
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| /external/tensorflow/tensorflow/contrib/seq2seq/python/kernel_tests/ |
| D | basic_decoder_test.py | 44 batch_size = 5
51 inputs = np.random.randn(batch_size, max_time,
66 dtype=dtypes.float32, batch_size=batch_size),
82 batch_size_t = my_decoder.batch_size
88 self.assertEqual((batch_size, expected_output_depth),
90 self.assertEqual((batch_size,), step_outputs[1].get_shape())
91 self.assertEqual((batch_size, cell_depth), first_state[0].get_shape())
92 self.assertEqual((batch_size, cell_depth), first_state[1].get_shape())
93 self.assertEqual((batch_size, cell_depth), step_state[0].get_shape())
94 self.assertEqual((batch_size, cell_depth), step_state[1].get_shape())
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| D | basic_decoder_v2_test.py | 47 batch_size = 5
54 inputs = np.random.randn(batch_size, max_time,
66 batch_size=batch_size)
90 batch_size_t = my_decoder.batch_size
96 self.assertEqual((batch_size, expected_output_depth),
98 self.assertEqual((batch_size,), step_outputs[1].get_shape())
99 self.assertEqual((batch_size, cell_depth), first_state[0].get_shape())
100 self.assertEqual((batch_size, cell_depth), first_state[1].get_shape())
101 self.assertEqual((batch_size, cell_depth), step_state[0].get_shape())
102 self.assertEqual((batch_size, cell_depth), step_state[1].get_shape())
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| D | beam_search_decoder_test.py | 99 batch_size = array_ops.shape(array)[1]
110 array, [max_time, batch_size * beam_width])
112 expected_array, [max_time, batch_size * beam_width])
184 batch_size, beam_width, is_valid=True): argument
189 batch_size = array_ops.constant(batch_size)
194 beam_search_decoder._check_batch_beam(t, batch_size, beam_width)
198 t, batch_size, beam_width)
258 self.batch_size = 2
266 dummy_cell_state = array_ops.zeros([self.batch_size, self.beam_width])
270 array_ops.ones([self.batch_size, self.beam_width])),
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| /external/tensorflow/tensorflow/python/training/ |
| D | input_test.py | 466 batch_size = 10
470 counter = examples.count_up_to(num_batches * batch_size)
484 batch_size=batch_size)
488 [counter, sparse_counter, "string"], batch_size=batch_size)
497 np.arange(i * batch_size, (i + 1) * batch_size))
501 np.arange(2 * batch_size) // 2, # 0, 0, 1, 1, ...
502 [0, 1] * batch_size)).T)
504 expected = np.arange(2 * i * batch_size, 2 * (i + 1) * batch_size) // 2
505 expected *= ([1, -1] * batch_size) # mult by [1, -1, 1, -1, ...]
507 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/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.
56 batch_size = 8
66 np_images, _ = random_batch(batch_size)
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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/densenet/ |
| D | densenet_graph_test.py | 32 def image_shape(batch_size): argument
34 return [batch_size, 3, 224, 224]
35 return [batch_size, 224, 224, 3]
38 def random_batch(batch_size): argument
39 images = np.random.rand(*image_shape(batch_size)).astype(np.float32)
42 low=0, high=num_classes, size=[batch_size]).astype(np.int32)
43 one_hot = np.zeros((batch_size, num_classes)).astype(np.float32)
44 one_hot[np.arange(batch_size), labels] = 1.
56 batch_size = 1
70 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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| /external/tensorflow/tensorflow/contrib/learn/python/learn/learn_io/ |
| D | graph_io.py | 50 batch_size, argument
103 batch_size=batch_size,
118 batch_size, argument
173 batch_size,
188 batch_size, argument
246 batch_size,
339 batch_size, argument
391 if (batch_size is None) or (
392 (not isinstance(batch_size, ops.Tensor)) and
393 (batch_size <= 0 or batch_size >= queue_capacity)):
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| /external/tensorflow/tensorflow/contrib/eager/python/examples/revnet/ |
| D | ops_test.py | 31 batch_size = 100
33 x = tf.random_normal(shape=[batch_size, 32, 32, 3])
36 self.assertEqual(y.shape, [batch_size, 32, 32, 5])
39 self.assertEqual(y.shape, [batch_size, 16, 16, 3])
42 self.assertEqual(y.shape, [batch_size, 16, 16, 5])
45 x = tf.random_normal(shape=[batch_size, 32, 32, 3])
50 dy = tf.random_normal(shape=[batch_size, 3, 32, 32])
56 x = tf.random_normal(shape=[batch_size, 3, 32, 32])
59 self.assertEqual(y.shape, [batch_size, 5, 32, 32])
62 self.assertEqual(y.shape, [batch_size, 3, 16, 16])
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| /external/tensorflow/tensorflow/contrib/learn/python/learn/estimators/ |
| D | debug.py | 96 batch_size = None
102 if batch_size is None:
103 batch_size = first_dim
105 size_checks.append(check_ops.assert_equal(batch_size, first_dim))
108 logits = array_ops.zeros([batch_size, head.logits_dimension])
209 def predict_classes(self, input_fn=None, batch_size=None): argument
222 input_fn=input_fn, batch_size=batch_size, outputs=[key])
227 batch_size=None): argument
240 batch_size=batch_size,
324 def predict_scores(self, input_fn=None, batch_size=None): argument
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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/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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