| /external/tensorflow/tensorflow/python/keras/ |
| D | losses.py | 120 def __call__(self, y_true, y_pred, sample_weight=None): argument
149 y_true, y_pred, sample_weight)
155 losses = call_fn(y_true, y_pred)
177 def call(self, y_true, y_pred): argument
245 def call(self, y_true, y_pred): argument
255 if tensor_util.is_tf_type(y_pred) and tensor_util.is_tf_type(y_true):
256 y_pred, y_true = losses_utils.squeeze_or_expand_dimensions(y_pred, y_true)
259 return ag_fn(y_true, y_pred, **self._fn_kwargs)
1192 def mean_squared_error(y_true, y_pred): argument
1217 y_true = math_ops.cast(y_true, y_pred.dtype)
[all …]
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| D | metrics.py | 575 def update_state(self, y_true, y_pred, sample_weight=None): argument
588 y_true = math_ops.cast(y_true, self._dtype)
590 [y_pred, y_true], sample_weight = \
592 [y_pred, y_true], sample_weight)
593 y_pred, y_true = losses_utils.squeeze_or_expand_dimensions(
594 y_pred, y_true)
598 y_pred.shape.assert_is_compatible_with(y_true.shape)
600 math_ops.abs(y_true - y_pred), self.normalizer)
645 def update_state(self, y_true, y_pred, sample_weight=None): argument
666 y_true = math_ops.cast(y_true, self._dtype)
[all …]
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| /external/tensorflow/tensorflow/tools/api/golden/v2/ |
| D | tensorflow.losses.pbtxt | 77 argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
81 argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
85 argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
89 argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
93 argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
97 …argspec: "args=[\'y_true\', \'y_pred\', \'from_logits\', \'label_smoothing\', \'axis\'], varargs=N…
101 …argspec: "args=[\'y_true\', \'y_pred\', \'apply_class_balancing\', \'alpha\', \'gamma\', \'from_lo…
105 …argspec: "args=[\'y_true\', \'y_pred\', \'from_logits\', \'label_smoothing\', \'axis\'], varargs=N…
109 argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
113 …argspec: "args=[\'y_true\', \'y_pred\', \'axis\'], varargs=None, keywords=None, defaults=[\'-1\'],…
[all …]
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| D | tensorflow.metrics.pbtxt | 177 argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
181 argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
185 argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
189 argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
193 argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
197 …argspec: "args=[\'y_true\', \'y_pred\', \'threshold\'], varargs=None, keywords=None, defaults=[\'0…
201 …argspec: "args=[\'y_true\', \'y_pred\', \'from_logits\', \'label_smoothing\', \'axis\'], varargs=N…
205 …argspec: "args=[\'y_true\', \'y_pred\', \'apply_class_balancing\', \'alpha\', \'gamma\', \'from_lo…
209 argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
213 …argspec: "args=[\'y_true\', \'y_pred\', \'from_logits\', \'label_smoothing\', \'axis\'], varargs=N…
[all …]
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| D | tensorflow.losses.-loss.pbtxt | 11 argspec: "args=[\'self\', \'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
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| D | tensorflow.losses.-cosine-similarity.pbtxt | 13 argspec: "args=[\'self\', \'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
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| D | tensorflow.losses.-squared-hinge.pbtxt | 13 argspec: "args=[\'self\', \'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
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| D | tensorflow.losses.-mean-absolute-percentage-error.pbtxt | 13 argspec: "args=[\'self\', \'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
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| D | tensorflow.losses.-huber.pbtxt | 13 argspec: "args=[\'self\', \'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
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| D | tensorflow.losses.-hinge.pbtxt | 13 argspec: "args=[\'self\', \'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
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| D | tensorflow.losses.-mean-absolute-error.pbtxt | 13 argspec: "args=[\'self\', \'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
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| D | tensorflow.losses.-log-cosh.pbtxt | 13 argspec: "args=[\'self\', \'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
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| D | tensorflow.losses.-mean-squared-logarithmic-error.pbtxt | 13 argspec: "args=[\'self\', \'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
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| D | tensorflow.losses.-poisson.pbtxt | 13 argspec: "args=[\'self\', \'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
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| /external/rnnoise/training/ |
| D | rnn_train.py | 31 def my_crossentropy(y_true, y_pred): argument
32 return K.mean(2*K.abs(y_true-0.5) * K.binary_crossentropy(y_pred, y_true), axis=-1)
34 def mymask(y_true): argument
35 return K.minimum(y_true+1., 1.)
37 def msse(y_true, y_pred): argument
38 return K.mean(mymask(y_true) * K.square(K.sqrt(y_pred) - K.sqrt(y_true)), axis=-1)
40 def mycost(y_true, y_pred): argument
41 …y_true) * (10*K.square(K.square(K.sqrt(y_pred) - K.sqrt(y_true))) + K.square(K.sqrt(y_pred) - K.sq…
43 def my_accuracy(y_true, y_pred): argument
44 return K.mean(2*K.abs(y_true-0.5) * K.equal(y_true, K.round(y_pred)), axis=-1)
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| D | dump_rnn.py | 74 def mean_squared_sqrt_error(y_true, y_pred): argument
75 return K.mean(K.square(K.sqrt(y_pred) - K.sqrt(y_true)), axis=-1)
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| /external/tensorflow/tensorflow/python/ops/losses/ |
| D | util.py | 30 def squeeze_or_expand_dimensions(y_pred, y_true=None, sample_weight=None): argument
56 if y_true is not None:
62 y_true_shape = y_true.shape
67 y_true, y_pred = confusion_matrix.remove_squeezable_dimensions(
68 y_true, y_pred)
71 rank_diff = array_ops.rank(y_pred) - array_ops.rank(y_true)
73 y_true, y_pred)
76 is_last_dim_1, squeeze_dims, lambda: (y_true, y_pred))
77 y_true, y_pred = control_flow_ops.cond(
81 return y_pred, y_true
[all …]
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| /external/tensorflow/tensorflow/python/keras/utils/ |
| D | losses_utils.py | 154 def squeeze_or_expand_dimensions(y_pred, y_true=None, sample_weight=None): argument
180 if y_true is not None:
186 y_true_shape = y_true.shape
191 y_true, y_pred = remove_squeezable_dimensions(
192 y_true, y_pred)
195 rank_diff = array_ops.rank(y_pred) - array_ops.rank(y_true)
197 y_true, y_pred)
200 is_last_dim_1, squeeze_dims, lambda: (y_true, y_pred))
201 y_true, y_pred = control_flow_ops.cond(
205 return y_pred, y_true
[all …]
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| D | metrics_utils.py | 282 y_true, argument
401 y_true = math_ops.cast(math_ops.cast(y_true, dtypes.bool), y_true.dtype)
403 y_true = array_ops.reshape(y_true, [-1])
406 true_labels = math_ops.multiply(y_true, weights)
407 false_labels = math_ops.multiply((1.0 - y_true), weights)
506 y_true, argument
585 y_true = math_ops.cast(y_true, dtype=variable_dtype)
608 y_true], _ = ragged_assert_compatible_and_get_flat_values([y_pred, y_true],
631 y_pred, y_true = losses_utils.squeeze_or_expand_dimensions(
632 y_pred, y_true)
[all …]
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| /external/tensorflow/tensorflow/python/keras/engine/ |
| D | compile_utils.py | 165 y_true, argument
183 y_true = self._conform_to_outputs(y_pred, y_true)
190 y_true = nest.flatten(y_true)
196 zip_args = (y_true, y_pred, sample_weight, self._losses, self._loss_weights,
341 def build(self, y_pred, y_true): argument
355 y_true = nest.list_to_tuple(y_true)
362 self._metrics, y_true, y_pred)
366 y_true, y_pred)
433 def update_state(self, y_true, y_pred, sample_weight=None): argument
435 y_true = self._conform_to_outputs(y_pred, y_true)
[all …]
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| /external/libopus/training/ |
| D | rnn_train.py | 28 def binary_crossentrop2(y_true, y_pred): argument
29 return K.mean(2*K.abs(y_true-0.5) * K.binary_crossentropy(y_true, y_pred), axis=-1)
31 def binary_accuracy2(y_true, y_pred): argument
32 …return K.mean(K.cast(K.equal(y_true, K.round(y_pred)), 'float32') + K.cast(K.equal(y_true, 0.5), '…
|
| D | rnn_dump.py | 35 def binary_crossentrop2(y_true, y_pred): argument
36 return K.mean(2*K.abs(y_true-0.5) * K.binary_crossentropy(y_pred, y_true), axis=-1)
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| /external/tensorflow/tensorflow/python/keras/benchmarks/ |
| D | metrics_memory_benchmark_test.py | 38 self.y_true = np.random.randint(2, size=(1024, 1024))
57 auc(self.y_true, self.y_pred)
69 auc(self.y_true, self.y_pred)
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| /external/libopus/scripts/ |
| D | dump_rnn.py | 32 def binary_crossentrop2(y_true, y_pred): argument
33 return K.mean(2*K.abs(y_true-0.5) * K.binary_crossentropy(y_pred, y_true), axis=-1)
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| D | rnn_train.py | 19 def binary_crossentrop2(y_true, y_pred): argument
20 return K.mean(2*K.abs(y_true-0.5) * K.binary_crossentropy(y_pred, y_true), axis=-1)
|