| /external/tensorflow/tensorflow/python/keras/ |
| D | losses.py | 68 def __call__(self, y_true, y_pred, sample_weight=None): argument
95 (y_pred, y_true, sample_weight)):
96 losses = self.call(y_true, y_pred)
117 def call(self, y_true, y_pred): argument
148 def call(self, y_true, y_pred): argument
158 return self.fn(y_true, y_pred, **self._fn_kwargs)
669 def mean_squared_error(y_true, y_pred): argument
670 y_pred = ops.convert_to_tensor(y_pred)
671 y_true = math_ops.cast(y_true, y_pred.dtype)
672 return K.mean(math_ops.squared_difference(y_pred, y_true), axis=-1)
[all …]
|
| D | metrics_functional_test.py | 43 y_pred = K.variable(np.random.random((6, 7)))
44 self.assertEqual(K.eval(metric(y_true, y_pred)).shape, (6,))
48 y_pred = K.variable([[0.8, 0.2], [0.6, 0.4], [0.7, 0.3], [0.9, 0.1]])
49 print(K.eval(metric(y_true, y_pred)))
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 print(K.eval(metric(y_true, y_pred)))
56 self.assertAllEqual(K.eval(metric(y_true, y_pred)), [0., 1., 1., 1.])
62 y_pred = K.variable(np.random.random((6, 7)))
63 self.assertEqual(K.eval(metric(y_true, y_pred)).shape, (6,))
[all …]
|
| D | losses_test.py | 59 def __call__(self, y_true, y_pred, sample_weight=None): argument
60 return (self.mse_fraction * keras.losses.mse(y_true, y_pred) +
61 (1 - self.mse_fraction) * keras.losses.mae(y_true, y_pred))
143 y_pred = keras.backend.variable(np.array([[0.3, 0.2, 0.1],
147 loss = keras.backend.eval(keras.losses.categorical_hinge(y_true, y_pred))
192 y_pred = constant_op.constant([[4., 8.], [12., 3.]])
194 loss = mse_obj(y_true, y_pred, sample_weight=sample_weight)
221 y_pred = constant_op.constant([4, 8, 12, 8, 1, 3],
224 loss = mse_obj(y_true, y_pred)
230 y_pred = constant_op.constant([4, 8, 12, 8, 1, 3],
[all …]
|
| D | metrics_confusion_matrix_test.py | 58 y_pred = constant_op.constant(((0, 0, 1, 1, 0), (1, 1, 1, 1, 1),
61 update_op = fp_obj.update_state(y_true, y_pred)
71 y_pred = constant_op.constant(((0, 0, 1, 1, 0), (1, 1, 1, 1, 1),
74 result = fp_obj(y_true, y_pred, sample_weight=sample_weight)
81 y_pred = constant_op.constant(((0.9, 0.2, 0.8, 0.1), (0.2, 0.9, 0.7, 0.6),
86 update_op = fp_obj.update_state(y_true, y_pred)
95 y_pred = constant_op.constant(((0.9, 0.2, 0.8, 0.1), (0.2, 0.9, 0.7, 0.6),
102 result = fp_obj(y_true, y_pred, sample_weight=sample_weight)
138 y_pred = constant_op.constant(((0, 0, 1, 1, 0), (1, 1, 1, 1, 1),
141 update_op = fn_obj.update_state(y_true, y_pred)
[all …]
|
| D | metrics.py | 479 def update_state(self, y_true, y_pred, sample_weight=None): argument
493 y_pred = math_ops.cast(y_pred, self._dtype)
494 y_pred, y_true, sample_weight = squeeze_or_expand_dimensions(
495 y_pred, y_true, sample_weight)
497 y_pred, self.normalizer = confusion_matrix.remove_squeezable_dimensions(
498 y_pred, self.normalizer)
499 y_pred.shape.assert_is_compatible_with(y_true.shape)
501 math_ops.abs(y_true - y_pred), self.normalizer)
530 def update_state(self, y_true, y_pred, sample_weight=None): argument
546 y_pred = math_ops.cast(y_pred, self._dtype)
[all …]
|
| D | metrics_test.py | 470 y_pred = self.l2_norm(self.np_y_pred, axis)
471 self.expected_loss = np.sum(np.multiply(y_true, y_pred), axis=(axis,))
474 self.y_pred = constant_op.constant(self.np_y_pred)
491 loss = cosine_obj(self.y_true, self.y_pred)
502 self.y_pred,
512 loss = cosine_obj(self.y_true, self.y_pred)
535 y_pred = constant_op.constant(((0, 0, 1, 1, 0), (1, 1, 1, 1, 1),
538 update_op = mae_obj.update_state(y_true, y_pred)
548 y_pred = constant_op.constant(((0, 0, 1, 1, 0), (1, 1, 1, 1, 1),
551 result = mae_obj(y_true, y_pred, sample_weight=sample_weight)
[all …]
|
| /external/tensorflow/tensorflow/tools/api/golden/v1/ |
| D | tensorflow.keras.losses.pbtxt | 69 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\'],…
[all …]
|
| D | tensorflow.keras.metrics.pbtxt | 149 argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
153 argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
157 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\', \'threshold\'], varargs=None, keywords=None, defaults=[\'0…
173 …argspec: "args=[\'y_true\', \'y_pred\', \'from_logits\', \'label_smoothing\'], varargs=None, keywo…
177 argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
181 …argspec: "args=[\'y_true\', \'y_pred\', \'from_logits\', \'label_smoothing\'], varargs=None, keywo…
185 …argspec: "args=[\'y_true\', \'y_pred\', \'axis\'], varargs=None, keywords=None, defaults=[\'-1\'],…
[all …]
|
| D | tensorflow.keras.losses.-loss.pbtxt | 11 argspec: "args=[\'self\', \'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
|
| /external/tensorflow/tensorflow/tools/api/golden/v2/ |
| D | tensorflow.losses.pbtxt | 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\'], 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"
[all …]
|
| D | tensorflow.keras.losses.pbtxt | 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\'], 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"
[all …]
|
| D | tensorflow.keras.metrics.pbtxt | 149 argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
153 argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
157 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\', \'threshold\'], varargs=None, keywords=None, defaults=[\'0…
173 …argspec: "args=[\'y_true\', \'y_pred\', \'from_logits\', \'label_smoothing\'], varargs=None, keywo…
177 argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
181 …argspec: "args=[\'y_true\', \'y_pred\', \'from_logits\', \'label_smoothing\'], varargs=None, keywo…
193 argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
[all …]
|
| D | tensorflow.metrics.pbtxt | 149 argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
153 argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
157 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\', \'threshold\'], varargs=None, keywords=None, defaults=[\'0…
173 …argspec: "args=[\'y_true\', \'y_pred\', \'from_logits\', \'label_smoothing\'], varargs=None, keywo…
177 argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
181 …argspec: "args=[\'y_true\', \'y_pred\', \'from_logits\', \'label_smoothing\'], varargs=None, keywo…
193 argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
[all …]
|
| D | tensorflow.losses.-loss.pbtxt | 11 argspec: "args=[\'self\', \'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
|
| D | tensorflow.keras.losses.-loss.pbtxt | 11 argspec: "args=[\'self\', \'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None"
|
| /external/tensorflow/tensorflow/python/keras/utils/ |
| D | metrics_utils.py | 214 y_pred, argument
264 y_pred = math_ops.cast(y_pred, dtype=dtypes.float32)
265 y_pred.shape.assert_is_compatible_with(y_true.shape)
285 y_pred,
286 math_ops.cast(0.0, dtype=y_pred.dtype),
289 y_pred,
290 math_ops.cast(1.0, dtype=y_pred.dtype),
293 y_pred, y_true, sample_weight = squeeze_or_expand_dimensions(
294 y_pred, y_true, sample_weight)
297 y_pred = _filter_top_k(y_pred, top_k)
[all …]
|
| D | losses_utils.py | 60 def squeeze_or_expand_dimensions(y_pred, y_true, sample_weight): argument
83 y_pred_shape = y_pred.get_shape()
96 y_true, y_pred = confusion_matrix.remove_squeezable_dimensions(
97 y_true, y_pred)
100 rank_diff = array_ops.rank(y_pred) - array_ops.rank(y_true)
102 y_true, y_pred)
103 is_last_dim_1 = math_ops.equal(1, array_ops.shape(y_pred)[-1])
105 is_last_dim_1, squeeze_dims, lambda: (y_true, y_pred))
106 y_true, y_pred = control_flow_ops.cond(
110 return y_pred, y_true, None
[all …]
|
| D | tf_utils_test.py | 137 def custom_loss(y_obs, y_pred): argument
139 obtained_prediction_box[0] = y_pred
140 return y_pred
|
| /external/tensorflow/tensorflow/contrib/learn/python/learn/estimators/ |
| D | _sklearn.py | 157 def _accuracy_score(y_true, y_pred): argument
158 score = y_true == y_pred
162 def _mean_squared_error(y_true, y_pred): argument
165 if len(y_pred.shape) > 1:
166 y_pred = np.squeeze(y_pred)
167 return np.average((y_true - y_pred)**2)
|
| /external/tensorflow/tensorflow/python/keras/engine/ |
| D | training_gpu_test.py | 47 … loss = lambda y_true, y_pred: K.sparse_categorical_crossentropy( # pylint: disable=g-long-lambda argument
48 y_true, y_pred, axis=axis)
52 loss = lambda y_true, y_pred: K.categorical_crossentropy( # pylint: disable=g-long-lambda argument
53 y_true, y_pred, axis=axis)
57 …loss = lambda y_true, y_pred: K.binary_crossentropy(y_true, y_pred) # pylint: disable=unnecessary… argument
|
| /external/tensorflow/tensorflow/contrib/losses/python/metric_learning/ |
| D | metric_loss_ops_test.py | 431 y_pred = cluster_ics
433 if sum(y_pred == cluster_idx) == 0:
439 pdists[medoid_ics[cluster_idx], y_pred == cluster_idx]) +
441 y_gt, y_pred)))
443 pdist_in = pdists[y_pred == cluster_idx, :]
444 pdist_in = pdist_in[:, y_pred == cluster_idx]
448 for i in range(y_pred.size):
449 if y_pred[i] != cluster_idx:
465 y_pred == cluster_idx)[0][max_score_idx]
|
| /external/tensorflow/tensorflow/contrib/seq2seq/python/ops/ |
| D | loss.py | 160 def __call__(self, y_true, y_pred, sample_weight=None): argument
162 return sequence_loss(y_pred, y_true, sample_weight,
170 def call(self, y_true, y_pred): argument
|
| /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)
|
| 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)
|
| /external/tensorflow/tensorflow/python/keras/mixed_precision/experimental/ |
| D | keras_test.py | 280 def loss_fn(y_true, y_pred): argument
282 return math_ops.reduce_mean(y_pred)
358 def loss_fn(y_true, y_pred): argument
360 self.assertEqual(y_pred.dtype, dtypes.float32)
361 return math_ops.reduce_mean(y_pred)
|