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/external/tensorflow/tensorflow/python/kernel_tests/distributions/
Dcategorical_test.py30 from tensorflow.python.ops.distributions import categorical
39 return categorical.Categorical(logits, dtype=dtype)
47 dist = categorical.Categorical(probs=p)
56 dist = categorical.Categorical(logits=logits)
114 dist = categorical.Categorical(logits)
128 dist = categorical.Categorical(math_ops.log(histograms) - 50.)
135 dist = categorical.Categorical(math_ops.log(histograms) - 50.)
146 dist = categorical.Categorical(probs=histograms)
184 dist = categorical.Categorical(probs=histograms_ph)
196 dist = categorical.Categorical(probs=histograms)
[all …]
/external/tensorflow/tensorflow/python/keras/utils/
Dnp_utils.py74 categorical = np.zeros((n, num_classes), dtype=dtype)
75 categorical[np.arange(n), y] = 1
77 categorical = np.reshape(categorical, output_shape)
78 return categorical
/external/tensorflow/tensorflow/python/ops/distributions/
Ddistributions.py23 from tensorflow.python.ops.distributions.categorical import Categorical
/external/tensorflow/tensorflow/lite/testing/op_tests/
Dmultinomial.py48 out = tf.random.categorical(
/external/tensorflow/tensorflow/lite/tools/optimize/testdata/
DREADME.md30 of mapping categorical input to embeddings.
/external/tensorflow/tensorflow/tools/api/golden/v1/
Dtensorflow.distributions.-categorical.pbtxt3 is_instance: "<class \'tensorflow.python.ops.distributions.categorical.Categorical\'>"
Dtensorflow.random.pbtxt20 name: "categorical"
/external/tensorflow/tensorflow/tools/api/golden/v2/
Dtensorflow.random.pbtxt20 name: "categorical"
/external/tensorflow/tensorflow/python/ops/numpy_ops/g3doc/
DTensorFlow_NumPy_Text_Generation.ipynb725 "sampled_indices = tf.random.categorical(example_batch_predictions[0], num_samples=1)\n",
997 …"* Then, use a categorical distribution to calculate the index of the predicted character. Use thi…
1046 " # using a categorical distribution to predict the character returned by the model\n",
1048 " predicted_id = tf.random.categorical(predictions, num_samples=1)[-1,0].numpy()\n",
/external/tensorflow/tensorflow/python/ops/
Drandom_ops.py505 def categorical(logits, num_samples, dtype=None, seed=None, name=None): function
/external/tensorflow/tensorflow/python/ops/parallel_for/
Dcontrol_flow_ops_test.py896 return random_ops.categorical(logits=[[1., -1.]], num_samples=3)
905 return random_ops.categorical(logits_i, num_samples=3)
/external/tensorflow/
DRELEASE.md475 * `tf.random.categorical` op for output data type `tf.int64` on CPU.
557 categorical preprocessing layers now support `output_mode`.
5431 operations such as categorical feature encoding, text vectorization, data
5435 * Added **categorical data** processing layers:
5436 * `IntegerLookup` & `StringLookup`: build an index of categorical feature
5440 * `CategoryCrossing`: create new categorical features representing
5441 co-occurrences of previous categorical feature values
5442 * `Hashing`: the hashing trick, for large-vocabulary categorical features
5443 * `Discretization`: turn continuous numerical features into categorical
6879 * Add v2 sparse categorical crossentropy metric.
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/external/tensorflow/tensorflow/compiler/mlir/lite/ir/
Dtfl_ops.td5593 let summary = "Draws samples from a categorical distribution.";
5596 The generated values will have a categorical distribution based on the `logits`
/external/tensorflow/tensorflow/lite/g3doc/examples/on_device_training/
Doverview.ipynb297 "Convert the data labels to categorical values by performing one-hot encoding."
/external/cldr/tools/cldr-code/src/main/resources/org/unicode/cldr/util/data/transforms/
Dinternal_overrides.txt562 categorical → kˌætɪgˈorɪkəl
Dinternal_raw_IPA-old.txt32139 categorical %23809 -gɔr-, kˌætəgˈɑrɪkəl, kˌætəgˈɔrɪkəl, kˌætɪgˈorɪkəl
Dinternal_raw_IPA.txt27227 categorical %33785 kˌætəgˈɑrɪkəl, kˌætəgˈɔrɪkəl, kˌætɪgˈorɪkəl
/external/jline/src/src/test/resources/jline/example/
Denglish.gz
/external/icu/icu4j/perf-tests/data/collation/
Dulyss10.txt30389 light of reason, the incommensurable categorical intelligence situated in the