| /external/tensorflow/tensorflow/contrib/slim/python/slim/nets/ |
| D | vgg_test.py | 36 num_classes = 1000
39 logits, _ = vgg.vgg_a(inputs, num_classes)
42 [batch_size, num_classes])
47 num_classes = 1000
50 logits, _ = vgg.vgg_a(inputs, num_classes, spatial_squeeze=False)
53 [batch_size, 2, 2, num_classes])
58 num_classes = 1000
62 _, end_points = vgg.vgg_a(inputs, num_classes, is_training=is_training)
75 num_classes = 1000
78 vgg.vgg_a(inputs, num_classes)
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| D | inception_v3_test.py | 41 num_classes = 1000
44 logits, end_points = inception_v3.inception_v3(inputs, num_classes)
47 [batch_size, num_classes])
50 [batch_size, num_classes])
135 num_classes = 1000
138 _, end_points = inception_v3.inception_v3(inputs, num_classes)
142 [batch_size, num_classes])
146 [batch_size, num_classes])
159 num_classes = 1000
162 _, end_points = inception_v3.inception_v3(inputs, num_classes)
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| D | inception_v2_test.py | 41 num_classes = 1000
44 logits, end_points = inception_v2.inception_v2(inputs, num_classes)
47 [batch_size, num_classes])
50 [batch_size, num_classes])
129 num_classes = 1000
132 _, end_points = inception_v2.inception_v2(inputs, num_classes)
140 inputs, num_classes, scope='depth_multiplied_net', depth_multiplier=0.5)
150 num_classes = 1000
153 _, end_points = inception_v2.inception_v2(inputs, num_classes)
161 inputs, num_classes, scope='depth_multiplied_net', depth_multiplier=2.0)
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| D | overfeat_test.py | 35 num_classes = 1000
38 logits, _ = overfeat.overfeat(inputs, num_classes)
41 [batch_size, num_classes])
46 num_classes = 1000
49 logits, _ = overfeat.overfeat(inputs, num_classes, spatial_squeeze=False)
52 [batch_size, 2, 2, num_classes])
57 num_classes = 1000
60 _, end_points = overfeat.overfeat(inputs, num_classes)
72 num_classes = 1000
75 overfeat.overfeat(inputs, num_classes)
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| D | alexnet_test.py | 35 num_classes = 1000
38 logits, _ = alexnet.alexnet_v2(inputs, num_classes)
41 [batch_size, num_classes])
46 num_classes = 1000
49 logits, _ = alexnet.alexnet_v2(inputs, num_classes, spatial_squeeze=False)
52 [batch_size, 4, 7, num_classes])
57 num_classes = 1000
60 _, end_points = alexnet.alexnet_v2(inputs, num_classes)
72 num_classes = 1000
75 alexnet.alexnet_v2(inputs, num_classes)
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| D | inception_v1_test.py | 41 num_classes = 1000
44 logits, end_points = inception_v1.inception_v1(inputs, num_classes)
47 [batch_size, num_classes])
50 [batch_size, num_classes])
144 num_classes = 1000
149 logits, end_points = inception_v1.inception_v1(inputs, num_classes)
152 [batch_size, num_classes])
162 num_classes = 1000
165 logits, _ = inception_v1.inception_v1(inputs, num_classes)
167 self.assertListEqual(logits.get_shape().as_list(), [None, num_classes])
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| D | resnet_v1.py | 130 num_classes=None, argument
211 if num_classes is not None:
214 num_classes, [1, 1],
220 if num_classes is not None:
252 num_classes=None, argument
268 num_classes,
278 num_classes=None, argument
294 num_classes,
304 num_classes=None, argument
320 num_classes,
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| D | resnet_v2.py | 132 num_classes=None, argument
225 if num_classes is not None:
228 num_classes, [1, 1],
234 if num_classes is not None:
265 num_classes=None, argument
281 num_classes,
291 num_classes=None, argument
307 num_classes,
317 num_classes=None, argument
333 num_classes,
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| D | resnet_v1_test.py | 260 num_classes=None, argument
275 return resnet_v1.resnet_v1(inputs, blocks, num_classes, is_training,
281 num_classes = 10
285 inputs, num_classes, global_pool=global_pool, scope='resnet')
287 self.assertListEqual(logits.get_shape().as_list(), [2, 1, 1, num_classes])
290 [2, 1, 1, num_classes])
294 num_classes = 10
298 inputs, num_classes, global_pool=global_pool, scope='resnet')
311 num_classes = 10
315 inputs, num_classes, global_pool=global_pool, scope='resnet')
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| D | resnet_v2_test.py | 264 num_classes=None, argument
279 return resnet_v2.resnet_v2(inputs, blocks, num_classes, is_training,
285 num_classes = 10
289 inputs, num_classes, global_pool=global_pool, scope='resnet')
291 self.assertListEqual(logits.get_shape().as_list(), [2, 1, 1, num_classes])
294 [2, 1, 1, num_classes])
298 num_classes = 10
302 inputs, num_classes, global_pool=global_pool, scope='resnet')
315 num_classes = 10
319 inputs, num_classes, global_pool=global_pool, scope='resnet')
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| /external/tensorflow/tensorflow/python/keras/ |
| D | model_subclassing_test.py | 49 def __init__(self, use_bn=False, use_dp=False, num_classes=10): argument
53 self.num_classes = num_classes
56 self.dense2 = keras.layers.Dense(num_classes, activation='softmax')
73 def __init__(self, num_classes=10): argument
75 self.num_classes = num_classes
79 self.dense1 = keras.layers.Dense(num_classes, activation='softmax')
89 def __init__(self, use_bn=False, use_dp=False, num_classes=(2, 3)): argument
93 self.num_classes = num_classes
96 self.dense2 = keras.layers.Dense(num_classes[0], activation='softmax')
97 self.dense3 = keras.layers.Dense(num_classes[1], activation='softmax')
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| D | testing_utils.py | 43 num_classes, argument
60 templates = 2 * num_classes * np.random.random((num_classes,) + input_shape)
61 y = np.random.randint(0, num_classes, size=(num_sample,))
288 def get_small_sequential_mlp(num_hidden, num_classes, input_dim=None): argument
295 activation = 'sigmoid' if num_classes == 1 else 'softmax'
296 model.add(keras.layers.Dense(num_classes, activation=activation))
300 def get_small_functional_mlp(num_hidden, num_classes, input_dim): argument
303 activation = 'sigmoid' if num_classes == 1 else 'softmax'
304 outputs = keras.layers.Dense(num_classes, activation=activation)(outputs)
311 def __init__(self, num_hidden, num_classes): argument
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| /external/tensorflow/tensorflow/contrib/tensor_forest/kernels/ |
| D | tree_utils_test.cc | 98 const int32 num_classes = 4; in TEST() local
102 {num_accumulators, num_classes}); in TEST()
108 {num_accumulators, num_splits, num_classes}); in TEST()
116 const int32 num_classes = 4; in TEST() local
121 {num_accumulators, num_classes}); in TEST()
127 {num_accumulators, num_splits, num_classes}); in TEST()
135 const int32 num_classes = 4; in TEST() local
139 {num_accumulators, num_classes}); in TEST()
143 {num_accumulators, num_classes}); in TEST()
150 {num_accumulators, num_splits, num_classes}); in TEST()
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| D | tree_utils.cc | 68 const Eigen::Tensor<float, 1, Eigen::RowMajor>& rights, int32 num_classes, in ClassificationSplitScore() argument
73 offsets[0] = i * (num_classes + 1) + 1; in ClassificationSplitScore()
75 extents[0] = num_classes; in ClassificationSplitScore()
86 const int32 num_classes = in GetTwoBestClassification() local
106 std::bind(ClassificationSplitScore, splits, rights, num_classes, in GetTwoBestClassification()
238 const int32 num_classes = in MakeBootstrapWeights() local
246 float denom = static_cast<float>(n) + static_cast<float>(num_classes); in MakeBootstrapWeights()
248 weights->resize(num_classes * 2); in MakeBootstrapWeights()
249 for (int i = 0; i < num_classes; i++) { in MakeBootstrapWeights()
255 (*weights)[num_classes + i] = (right_count + 1.0) / denom; in MakeBootstrapWeights()
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| /external/tensorflow/tensorflow/contrib/tensor_forest/kernels/v4/ |
| D | stat_utils.cc | 33 float GiniImpurity(const LeafStat& stats, int32 num_classes) { in GiniImpurity() argument
34 const float smoothed_sum = num_classes + stats.weight_sum(); in GiniImpurity()
36 2 * stats.weight_sum() + num_classes) / in GiniImpurity()
40 float WeightedGiniImpurity(const LeafStat& stats, int32 num_classes) { in WeightedGiniImpurity() argument
41 return stats.weight_sum() * GiniImpurity(stats, num_classes); in WeightedGiniImpurity()
74 float SmoothedGini(float sum, float square, int num_classes) { in SmoothedGini() argument
76 const float smoothed_sum = num_classes + sum; in SmoothedGini()
77 return 1.0 - (square + 2 * sum + num_classes) / (smoothed_sum * smoothed_sum); in SmoothedGini()
80 float WeightedSmoothedGini(float sum, float square, int num_classes) { in WeightedSmoothedGini() argument
81 return sum * SmoothedGini(sum, square, num_classes); in WeightedSmoothedGini()
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| /external/tensorflow/tensorflow/python/keras/utils/ |
| D | np_utils_test.py | 30 num_classes = 5
32 expected_shapes = [(1, num_classes),
33 (3, num_classes),
34 (4, 3, num_classes),
35 (5, 4, 3, num_classes),
36 (3, num_classes)]
37 labels = [np.random.randint(0, num_classes, shape) for shape in shapes]
39 keras.utils.to_categorical(label, num_classes) for label in labels]
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| /external/tensorflow/tensorflow/core/kernels/ |
| D | multinomial_op_gpu.cu.cc | 42 __global__ void MultinomialKernel(int32 nthreads, const int32 num_classes, in MultinomialKernel() argument
46 const int maxima_idx = index / num_classes; in MultinomialKernel()
50 static_cast<UnsignedOutputType>(index % num_classes)); in MultinomialKernel()
62 int num_classes, int num_samples, in operator ()()
74 bsc.set(2, num_classes); in operator ()()
78 boc.set(2, num_classes); in operator ()()
83 Eigen::array<int, 3> bsc{batch_size, num_samples, num_classes}; in operator ()()
84 Eigen::array<int, 3> boc{batch_size, 1, num_classes}; in operator ()()
106 /*in_dim1=*/num_classes, /*in_dim2=*/1, /*out_rank=*/1, in operator ()()
112 const int32 work_items = batch_size * num_samples * num_classes; in operator ()()
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| D | multinomial_op.cc | 51 int num_classes, int num_samples,
77 int num_classes, int num_samples, in operator ()()
86 auto DoWork = [ctx, num_samples, num_classes, &gen, &output, &logits]( in operator ()()
99 ctx->allocate_temp(DT_DOUBLE, TensorShape({num_classes}), in operator ()()
107 for (int64 j = 0; j < num_classes; ++j) { in operator ()()
119 for (int64 j = 0; j < num_classes; ++j) { in operator ()()
127 const double* cdf_end = cdf.data() + num_classes; in operator ()()
137 50 * (num_samples * std::log(num_classes) / std::log(2) + num_classes); in operator ()()
176 const int num_classes = static_cast<int>(logits_t.dim_size(1)); in DoCompute() local
177 OP_REQUIRES(ctx, num_classes > 0, in DoCompute()
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| /external/tensorflow/tensorflow/core/util/ctc/ |
| D | ctc_loss_calculator.h | 94 int num_classes, const Vector& seq_len,
123 auto num_classes = inputs[0].cols(); in CalculateLoss() local
136 if (inputs[t].cols() != num_classes) { in CalculateLoss()
138 " to be: ", num_classes, in CalculateLoss()
161 batch_size, num_classes, seq_len, labels, &max_u_prime, &l_primes); in CalculateLoss()
167 auto ComputeLossAndGradients = [this, num_classes, &labels, &l_primes, in CalculateLoss()
196 Matrix y(num_classes, seq_len(b)); in CalculateLoss()
262 max_seq_len * num_classes * in CalculateLoss()
264 max_seq_len * 2 * (2 * num_classes + 1) * in CalculateLoss()
267 ((2 * num_classes + 1) * cost_log_sum_exp + in CalculateLoss()
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| D | ctc_beam_search_test.cc | 107 const int num_classes = 6; in TEST() local
111 CTCBeamSearchDecoder<> decoder(num_classes, 10 * top_paths, &default_scorer); in TEST()
116 num_classes, top_paths, &dictionary_scorer); in TEST()
120 float input_data_mat[timesteps][batch_size][num_classes] = { in TEST()
130 for (int c = 0; c < num_classes; ++c) { in TEST()
155 inputs.emplace_back(&input_data_mat[t][0][0], batch_size, num_classes); in TEST()
187 const int num_classes = 6; in TEST() local
191 CTCBeamSearchDecoder<> decoder(num_classes, top_paths, &default_scorer); in TEST()
195 float input_data_mat[timesteps][batch_size][num_classes] = { in TEST()
205 inputs.emplace_back(&input_data_mat[t][0][0], batch_size, num_classes); in TEST()
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| /external/tensorflow/tensorflow/contrib/nn/python/ops/ |
| D | sampling_ops.py | 116 num_classes, argument
209 if num_sampled > num_classes:
211 format(num_sampled, num_classes))
227 range_max=num_classes)
239 num_classes=num_classes,
252 num_classes, argument
327 num_classes=num_classes,
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| /external/tensorflow/tensorflow/python/ops/ |
| D | confusion_matrix.py | 96 num_classes=None, argument
151 (predictions, labels, num_classes, weights)) as name:
169 if num_classes is None:
170 num_classes = math_ops.maximum(math_ops.reduce_max(predictions),
173 num_classes_int64 = math_ops.cast(num_classes, dtypes.int64)
189 shape = array_ops.stack([num_classes, num_classes])
206 num_classes=None, argument
260 return confusion_matrix(labels, predictions, num_classes, weights, dtype,
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| /external/tensorflow/tensorflow/contrib/tensor_forest/python/ |
| D | tensor_forest_test.py | 36 num_classes=2,
41 self.assertEquals(2, hparams.num_classes)
50 num_classes=2,
59 num_classes=2,
73 num_classes=4,
89 num_classes=4,
105 num_classes=4,
123 num_classes=2,
166 num_classes=4,
189 num_classes=4,
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| /external/tensorflow/tensorflow/python/keras/engine/ |
| D | sequential_test.py | 67 num_classes = 2
70 num_hidden, num_classes, input_dim)
76 y = np.random.random((batch_size, num_classes))
105 num_classes = 2
107 model = testing_utils.get_small_sequential_mlp(num_hidden, num_classes)
118 y = np.random.random((batch_size, num_classes))
128 num_classes = 2
132 model = testing_utils.get_small_sequential_mlp(num_hidden, num_classes)
143 y = array_ops.zeros((num_samples, num_classes))
227 num_classes = 2
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| /external/tensorflow/tensorflow/lite/experimental/kernels/ |
| D | ctc_decoder.h | 43 CTCDecoder(int num_classes, int batch_size, bool merge_repeated) in CTCDecoder() argument
44 : num_classes_(num_classes), in CTCDecoder()
45 blank_index_(num_classes - 1), in CTCDecoder()
61 int num_classes() { return num_classes_; } in num_classes() function
74 CTCGreedyDecoder(int num_classes, int batch_size, bool merge_repeated) in CTCGreedyDecoder() argument
75 : CTCDecoder(num_classes, batch_size, merge_repeated) {} in CTCGreedyDecoder()
|