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/external/tensorflow/tensorflow/python/keras/
Dlosses.py123 def __call__(self, y_true, y_pred, sample_weight=None): argument
152 y_true, y_pred, sample_weight)
158 losses = call_fn(y_true, y_pred)
180 def call(self, y_true, y_pred): argument
248 def call(self, y_true, y_pred): argument
258 if tensor_util.is_tf_type(y_pred) and tensor_util.is_tf_type(y_true):
259 y_pred, y_true = losses_utils.squeeze_or_expand_dimensions(y_pred, y_true)
262 return ag_fn(y_true, y_pred, **self._fn_kwargs)
1195 def mean_squared_error(y_true, y_pred): argument
1219 y_pred = ops.convert_to_tensor_v2_with_dispatch(y_pred)
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Dmetrics_functional_test.py44 y_pred = K.variable(np.random.random((6, 7)))
45 self.assertEqual(K.eval(metric(y_true, y_pred)).shape, (6,))
49 y_pred = K.variable([[0.8, 0.2], [0.6, 0.4], [0.7, 0.3], [0.9, 0.1]])
50 self.assertAllEqual(K.eval(metric(y_true, y_pred)), [0., 1., 1., 1.])
54 y_pred = K.variable([[0.8, 0.2], [0.6, 0.4], [0.7, 0.3], [0.9, 0.1]])
55 self.assertAllEqual(K.eval(metric(y_true, y_pred)), [0., 1., 1., 1.])
59 y_pred = K.variable(
63 self.assertAllEqual(K.eval(metric(y_true, y_pred)), [[1., 0.], [0., 1.]])
69 y_pred = K.variable(np.random.random((6, 7)))
70 self.assertEqual(K.eval(metric(y_true, y_pred)).shape, (6,))
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Dlosses_test.py184 y_pred = backend.variable(np.array([[0.3, 0.2, 0.1], [0.1, 0.2, 0.7]]))
187 loss = backend.eval(losses.categorical_hinge(y_true, y_pred))
198 y_pred = constant_op.constant([[4., 8.], [12., 3.]])
200 loss = mse_obj(y_true, y_pred, sample_weight=sample_weight)
212 def loss_fn(y_true, y_pred): argument
215 return mse_loss_fn(y_true, y_pred)
217 return mse_loss_fn(y_true, y_pred)
222 y_pred = constant_op.constant([[4., 8.], [12., 3.]])
226 def tf_functioned_loss_fn(y_true, y_pred, sample_weight=None): argument
227 return mse_obj(y_true, y_pred, sample_weight=sample_weight)
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Dmetrics_confusion_matrix_test.py61 y_pred = constant_op.constant(((0, 0, 1, 1, 0), (1, 1, 1, 1, 1),
64 update_op = fp_obj.update_state(y_true, y_pred)
74 y_pred = constant_op.constant(((0, 0, 1, 1, 0), (1, 1, 1, 1, 1),
77 result = fp_obj(y_true, y_pred, sample_weight=sample_weight)
84 y_pred = constant_op.constant(((0.9, 0.2, 0.8, 0.1), (0.2, 0.9, 0.7, 0.6),
89 update_op = fp_obj.update_state(y_true, y_pred)
98 y_pred = constant_op.constant(((0.9, 0.2, 0.8, 0.1), (0.2, 0.9, 0.7, 0.6),
105 result = fp_obj(y_true, y_pred, sample_weight=sample_weight)
141 y_pred = constant_op.constant(((0, 0, 1, 1, 0), (1, 1, 1, 1, 1),
144 update_op = fn_obj.update_state(y_true, y_pred)
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Dmetrics.py560 def update_state(self, y_true, y_pred, sample_weight=None): argument
574 y_pred = math_ops.cast(y_pred, self._dtype)
575 [y_pred, y_true], sample_weight = \
577 [y_pred, y_true], sample_weight)
578 y_pred, y_true = losses_utils.squeeze_or_expand_dimensions(
579 y_pred, y_true)
581 y_pred, self.normalizer = losses_utils.remove_squeezable_dimensions(
582 y_pred, self.normalizer)
583 y_pred.shape.assert_is_compatible_with(y_true.shape)
585 math_ops.abs(y_true - y_pred), self.normalizer)
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Dmetrics_test.py622 y_pred = self.l2_norm(self.np_y_pred, axis)
623 self.expected_loss = np.sum(np.multiply(y_true, y_pred), axis=(axis,))
626 self.y_pred = constant_op.constant(self.np_y_pred)
643 loss = cosine_obj(self.y_true, self.y_pred)
654 self.y_pred,
664 loss = cosine_obj(self.y_true, self.y_pred)
687 y_pred = constant_op.constant(((0, 0, 1, 1, 0), (1, 1, 1, 1, 1),
690 update_op = mae_obj.update_state(y_true, y_pred)
700 y_pred = constant_op.constant(((0, 0, 1, 1, 0), (1, 1, 1, 1, 1),
703 result = mae_obj(y_true, y_pred, sample_weight=sample_weight)
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/external/tensorflow/tensorflow/tools/api/golden/v2/
Dtensorflow.keras.losses.pbtxt73 argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
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\', \'from_logits\', \'label_smoothing\'], varargs=None, keywo…
97 …argspec: "args=[\'y_true\', \'y_pred\', \'from_logits\', \'label_smoothing\'], varargs=None, keywo…
101 argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
105 …argspec: "args=[\'y_true\', \'y_pred\', \'axis\'], varargs=None, keywords=None, defaults=[\'-1\'],…
117 argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
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Dtensorflow.losses.pbtxt73 argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
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\', \'from_logits\', \'label_smoothing\'], varargs=None, keywo…
97 …argspec: "args=[\'y_true\', \'y_pred\', \'from_logits\', \'label_smoothing\'], varargs=None, keywo…
101 argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
105 …argspec: "args=[\'y_true\', \'y_pred\', \'axis\'], varargs=None, keywords=None, defaults=[\'-1\'],…
117 argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
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Dtensorflow.metrics.pbtxt157 argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
161 argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
165 argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
169 argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
173 argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
177 …argspec: "args=[\'y_true\', \'y_pred\', \'threshold\'], varargs=None, keywords=None, defaults=[\'0…
181 …argspec: "args=[\'y_true\', \'y_pred\', \'from_logits\', \'label_smoothing\'], varargs=None, keywo…
185 argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
189 …argspec: "args=[\'y_true\', \'y_pred\', \'from_logits\', \'label_smoothing\'], varargs=None, keywo…
201 argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
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Dtensorflow.keras.metrics.pbtxt157 argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
161 argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
165 argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
169 argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
173 argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
177 …argspec: "args=[\'y_true\', \'y_pred\', \'threshold\'], varargs=None, keywords=None, defaults=[\'0…
181 …argspec: "args=[\'y_true\', \'y_pred\', \'from_logits\', \'label_smoothing\'], varargs=None, keywo…
185 argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
189 …argspec: "args=[\'y_true\', \'y_pred\', \'from_logits\', \'label_smoothing\'], varargs=None, keywo…
201 argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
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/external/tensorflow/tensorflow/tools/api/golden/v1/
Dtensorflow.keras.losses.pbtxt69 argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
73 argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
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\', \'from_logits\', \'label_smoothing\'], varargs=None, keywo…
93 …argspec: "args=[\'y_true\', \'y_pred\', \'from_logits\', \'label_smoothing\'], varargs=None, keywo…
97 argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
101 …argspec: "args=[\'y_true\', \'y_pred\', \'axis\'], varargs=None, keywords=None, defaults=[\'-1\'],…
105 …argspec: "args=[\'y_true\', \'y_pred\', \'axis\'], varargs=None, keywords=None, defaults=[\'-1\'],…
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Dtensorflow.keras.metrics.pbtxt157 argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
161 argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
165 argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
169 argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
173 argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
177 …argspec: "args=[\'y_true\', \'y_pred\', \'threshold\'], varargs=None, keywords=None, defaults=[\'0…
181 …argspec: "args=[\'y_true\', \'y_pred\', \'from_logits\', \'label_smoothing\'], varargs=None, keywo…
185 argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
189 …argspec: "args=[\'y_true\', \'y_pred\', \'from_logits\', \'label_smoothing\'], varargs=None, keywo…
193 …argspec: "args=[\'y_true\', \'y_pred\', \'axis\'], varargs=None, keywords=None, defaults=[\'-1\'],…
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/external/tensorflow/tensorflow/python/keras/engine/
Dcompile_utils.py41 def build(self, y_pred): argument
45 self._output_names = create_pseudo_output_names(y_pred)
135 def build(self, y_pred): argument
137 super(LossesContainer, self).build(y_pred)
139 self._losses = self._maybe_broadcast_to_outputs(y_pred, self._losses)
140 self._losses = self._conform_to_outputs(y_pred, self._losses)
145 y_pred, self._loss_weights)
146 self._loss_weights = self._conform_to_outputs(y_pred, self._loss_weights)
167 y_pred, argument
184 y_true = self._conform_to_outputs(y_pred, y_true)
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Dtraining_gpu_test.py49 … loss = lambda y_true, y_pred: K.sparse_categorical_crossentropy( # pylint: disable=g-long-lambda argument
50 y_true, y_pred, axis=axis)
54 loss = lambda y_true, y_pred: K.categorical_crossentropy( # pylint: disable=g-long-lambda argument
55 y_true, y_pred, axis=axis)
59 …loss = lambda y_true, y_pred: K.binary_crossentropy(y_true, y_pred) # pylint: disable=unnecessary… argument
Dcompile_utils_test.py345 def custom_loss_fn(y_true, y_pred): argument
346 return math_ops.reduce_sum(y_true - y_pred)
350 def __call__(self, y_true, y_pred): argument
351 return math_ops.reduce_sum(y_true - y_pred)
364 def custom_loss_fn(y_true, y_pred): argument
366 return losses_mod.mse(y_true, y_pred)
371 def call(self, y_true, y_pred): argument
373 math_ops.squared_difference, y_true, y_pred)
751 def custom_metric_fn(y_true, y_pred): argument
752 return math_ops.reduce_sum(y_true - y_pred)
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/external/tensorflow/tensorflow/python/ops/losses/
Dutil.py34 def squeeze_or_expand_dimensions(y_pred, y_true=None, sample_weight=None): argument
58 y_pred_shape = y_pred.shape
71 y_true, y_pred = confusion_matrix.remove_squeezable_dimensions(
72 y_true, y_pred)
75 rank_diff = array_ops.rank(y_pred) - array_ops.rank(y_true)
77 y_true, y_pred)
78 is_last_dim_1 = math_ops.equal(1, array_ops.shape(y_pred)[-1])
80 is_last_dim_1, squeeze_dims, lambda: (y_true, y_pred))
81 y_true, y_pred = control_flow_ops.cond(
85 return y_pred, y_true
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/external/tensorflow/tensorflow/python/keras/utils/
Dmetrics_utils.py238 y_pred, argument
312 y_pred = math_ops.cast(y_pred, dtype=variable_dtype)
322 [y_pred,
323 y_true], _ = ragged_assert_compatible_and_get_flat_values([y_pred, y_true],
337 y_pred,
338 math_ops.cast(0.0, dtype=y_pred.dtype),
341 y_pred,
342 math_ops.cast(1.0, dtype=y_pred.dtype),
346 y_pred, y_true = losses_utils.squeeze_or_expand_dimensions(
347 y_pred, y_true)
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Dlosses_utils.py146 def squeeze_or_expand_dimensions(y_pred, y_true=None, sample_weight=None): argument
170 y_pred_shape = y_pred.shape
183 y_true, y_pred = remove_squeezable_dimensions(
184 y_true, y_pred)
187 rank_diff = array_ops.rank(y_pred) - array_ops.rank(y_true)
189 y_true, y_pred)
190 is_last_dim_1 = math_ops.equal(1, array_ops.shape(y_pred)[-1])
192 is_last_dim_1, squeeze_dims, lambda: (y_true, y_pred))
193 y_true, y_pred = control_flow_ops.cond(
197 return y_pred, y_true
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/external/tensorflow/tensorflow/lite/micro/examples/hello_world/
Dhello_world_test.cc102 float y_pred = (y_pred_quantized - output_zero_point) * output_scale; in TF_LITE_MICRO_TEST() local
106 TF_LITE_MICRO_EXPECT_NEAR(y_true, y_pred, epsilon); in TF_LITE_MICRO_TEST()
113 y_pred = (output->data.int8[0] - output_zero_point) * output_scale; in TF_LITE_MICRO_TEST()
114 TF_LITE_MICRO_EXPECT_NEAR(y_true, y_pred, epsilon); in TF_LITE_MICRO_TEST()
120 y_pred = (output->data.int8[0] - output_zero_point) * output_scale; in TF_LITE_MICRO_TEST()
121 TF_LITE_MICRO_EXPECT_NEAR(y_true, y_pred, epsilon); in TF_LITE_MICRO_TEST()
127 y_pred = (output->data.int8[0] - output_zero_point) * output_scale; in TF_LITE_MICRO_TEST()
128 TF_LITE_MICRO_EXPECT_NEAR(y_true, y_pred, epsilon); in TF_LITE_MICRO_TEST()
/external/rnnoise/training/
Drnn_train.py31 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)
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 …square(K.square(K.sqrt(y_pred) - K.sqrt(y_true))) + K.square(K.sqrt(y_pred) - K.sqrt(y_true)) + 0.…
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)
/external/libopus/training/
Drnn_train.py28 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), '…
Drnn_dump.py35 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)
/external/libopus/scripts/
Ddump_rnn.py32 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)
Drnn_train.py19 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)
/external/tensorflow/tensorflow/python/keras/tests/
Dcustom_training_loop_test.py102 y_pred = model(x, training=True)
103 loss = keras.losses.binary_crossentropy(y, y_pred)
127 y_pred = y_pred_1 + y_pred_2
128 loss = keras.losses.mean_squared_error(y, y_pred)

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