diff options
author | 2018-07-24 17:07:15 -0700 | |
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committer | 2018-07-24 17:11:24 -0700 | |
commit | 0cf2c612e5e6ff8c5026011e8186056801def747 (patch) | |
tree | 12793c95ad2aaa21bcddb466904ebd936feb326b /tensorflow/python/keras | |
parent | 4c161d7306eb934232e3fe65de2c31c3bb7cf875 (diff) |
Keras ReLU Consolidation
Consolidate functionality of ThresholdedReLU and LeakyReLU layers into ReLU
layer
PiperOrigin-RevId: 205917439
Diffstat (limited to 'tensorflow/python/keras')
-rw-r--r-- | tensorflow/python/keras/activations.py | 22 | ||||
-rw-r--r-- | tensorflow/python/keras/backend.py | 32 | ||||
-rw-r--r-- | tensorflow/python/keras/backend_test.py | 61 | ||||
-rw-r--r-- | tensorflow/python/keras/layers/advanced_activations.py | 37 | ||||
-rw-r--r-- | tensorflow/python/keras/layers/advanced_activations_test.py | 8 |
5 files changed, 141 insertions, 19 deletions
diff --git a/tensorflow/python/keras/activations.py b/tensorflow/python/keras/activations.py index f608dea430..99645de736 100644 --- a/tensorflow/python/keras/activations.py +++ b/tensorflow/python/keras/activations.py @@ -128,20 +128,26 @@ def softsign(x): @tf_export('keras.activations.relu') -def relu(x, alpha=0., max_value=None): +def relu(x, alpha=0., max_value=None, threshold=0): """Rectified Linear Unit. + With default values, it returns element-wise `max(x, 0)`. + + Otherwise, it follows: + `f(x) = max_value` for `x >= max_value`, + `f(x) = x` for `threshold <= x < max_value`, + `f(x) = alpha * (x - threshold)` otherwise. + Arguments: - x: Input tensor. - alpha: Slope of the negative part. Defaults to zero. - max_value: Maximum value for the output. + x: A tensor or variable. + alpha: A scalar, slope of negative section (default=`0.`). + max_value: float. Saturation threshold. + threshold: float. Threshold value for thresholded activation. Returns: - The (leaky) rectified linear unit activation: `x` if `x > 0`, - `alpha * x` if `x < 0`. If `max_value` is defined, the result - is truncated to this value. + A tensor. """ - return K.relu(x, alpha=alpha, max_value=max_value) + return K.relu(x, alpha=alpha, max_value=max_value, threshold=threshold) @tf_export('keras.activations.tanh') diff --git a/tensorflow/python/keras/backend.py b/tensorflow/python/keras/backend.py index 333f927d2f..38794f1612 100644 --- a/tensorflow/python/keras/backend.py +++ b/tensorflow/python/keras/backend.py @@ -3372,26 +3372,48 @@ def in_test_phase(x, alt, training=None): @tf_export('keras.backend.relu') -def relu(x, alpha=0., max_value=None): +def relu(x, alpha=0., max_value=None, threshold=0): """Rectified linear unit. With default values, it returns element-wise `max(x, 0)`. + Otherwise, it follows: + `f(x) = max_value` for `x >= max_value`, + `f(x) = x` for `threshold <= x < max_value`, + `f(x) = alpha * (x - threshold)` otherwise. + Arguments: x: A tensor or variable. alpha: A scalar, slope of negative section (default=`0.`). - max_value: Saturation threshold. + max_value: float. Saturation threshold. + threshold: float. Threshold value for thresholded activation. Returns: A tensor. """ + clip_max = max_value is not None + if alpha != 0.: - negative_part = nn.relu(-x) - x = nn.relu(x) - if max_value is not None: + if threshold != 0: + negative_part = nn.relu(-x + threshold) + else: + negative_part = nn.relu(-x) + + if threshold != 0: + # computes x for x > threshold else 0 + x = x * math_ops.cast(math_ops.greater(x, threshold), floatx()) + elif max_value == 6: + # if no threshold, then can use nn.relu6 native TF op for performance + x = nn.relu6(x) + clip_max = False + else: + x = nn.relu(x) + + if clip_max: max_value = _to_tensor(max_value, x.dtype.base_dtype) zero = _to_tensor(0., x.dtype.base_dtype) x = clip_ops.clip_by_value(x, zero, max_value) + if alpha != 0.: alpha = _to_tensor(alpha, x.dtype.base_dtype) x -= alpha * negative_part diff --git a/tensorflow/python/keras/backend_test.py b/tensorflow/python/keras/backend_test.py index 36478ea089..40e7910061 100644 --- a/tensorflow/python/keras/backend_test.py +++ b/tensorflow/python/keras/backend_test.py @@ -23,6 +23,7 @@ import scipy.sparse from tensorflow.python import keras from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops from tensorflow.python.framework import sparse_tensor from tensorflow.python.ops import variables from tensorflow.python.platform import test @@ -490,6 +491,66 @@ class BackendLinearAlgebraTest(test.TestCase): input_shape_a=(4, 7), input_shape_b=(4, 7)) + def test_relu(self): + x = ops.convert_to_tensor([[-4, 0], [2, 7]], 'float32') + with self.test_session(): + # standard relu + relu_op = keras.backend.relu(x) + self.assertAllClose(keras.backend.eval(relu_op), [[0, 0], [2, 7]]) + + # alpha + relu_op = keras.backend.relu(x, alpha=0.5) + self.assertAllClose(keras.backend.eval(relu_op), [[-2, 0], [2, 7]]) + + # max_value < some elements + relu_op = keras.backend.relu(x, max_value=5) + self.assertAllClose(keras.backend.eval(relu_op), [[0, 0], [2, 5]]) + + # nn.relu6 used + relu_op = keras.backend.relu(x, max_value=6) + self.assertTrue('Relu6' in relu_op.name) # uses tf.nn.relu6 + self.assertAllClose(keras.backend.eval(relu_op), [[0, 0], [2, 6]]) + + # max value > 6 + relu_op = keras.backend.relu(x, max_value=10) + self.assertAllClose(keras.backend.eval(relu_op), [[0, 0], [2, 7]]) + + # max value is float + relu_op = keras.backend.relu(x, max_value=4.3) + self.assertAllClose(keras.backend.eval(relu_op), [[0, 0], [2, 4.3]]) + + # max value == 0 + relu_op = keras.backend.relu(x, max_value=0) + self.assertAllClose(keras.backend.eval(relu_op), [[0, 0], [0, 0]]) + + # alpha and max_value + relu_op = keras.backend.relu(x, alpha=0.25, max_value=3) + self.assertAllClose(keras.backend.eval(relu_op), [[-1, 0], [2, 3]]) + + # threshold + relu_op = keras.backend.relu(x, threshold=3) + self.assertAllClose(keras.backend.eval(relu_op), [[0, 0], [0, 7]]) + + # threshold is float + relu_op = keras.backend.relu(x, threshold=1.5) + self.assertAllClose(keras.backend.eval(relu_op), [[0, 0], [2, 7]]) + + # threshold is negative + relu_op = keras.backend.relu(x, threshold=-5) + self.assertAllClose(keras.backend.eval(relu_op), [[-4, 0], [2, 7]]) + + # threshold and max_value + relu_op = keras.backend.relu(x, threshold=3, max_value=5) + self.assertAllClose(keras.backend.eval(relu_op), [[0, 0], [0, 5]]) + + # threshold and alpha + relu_op = keras.backend.relu(x, alpha=0.25, threshold=4) + self.assertAllClose(keras.backend.eval(relu_op), [[-2, -1], [-0.5, 7]]) + + # threshold, alpha, and max_value + relu_op = keras.backend.relu(x, alpha=0.25, threshold=4, max_value=5) + self.assertAllClose(keras.backend.eval(relu_op), [[-2, -1], [-0.5, 5]]) + class BackendShapeOpsTest(test.TestCase): diff --git a/tensorflow/python/keras/layers/advanced_activations.py b/tensorflow/python/keras/layers/advanced_activations.py index eba10da6f3..61ab69c16f 100644 --- a/tensorflow/python/keras/layers/advanced_activations.py +++ b/tensorflow/python/keras/layers/advanced_activations.py @@ -284,6 +284,13 @@ class Softmax(Layer): class ReLU(Layer): """Rectified Linear Unit activation function. + With default values, it returns element-wise `max(x, 0)`. + + Otherwise, it follows: + `f(x) = max_value` for `x >= max_value`, + `f(x) = x` for `threshold <= x < max_value`, + `f(x) = negative_slope * (x - threshold)` otherwise. + Input shape: Arbitrary. Use the keyword argument `input_shape` (tuple of integers, does not include the samples axis) @@ -294,21 +301,39 @@ class ReLU(Layer): Arguments: max_value: float >= 0. Maximum activation value. + negative_slope: float >= 0. Negative slope coefficient. + threshold: float. Threshold value for thresholded activation. """ - def __init__(self, max_value=None, **kwargs): + def __init__(self, max_value=None, negative_slope=0, threshold=0, **kwargs): super(ReLU, self).__init__(**kwargs) - self.support_masking = True - self.max_value = K.cast_to_floatx(max_value) - if self.max_value < 0.: + if max_value is not None and max_value < 0.: raise ValueError('max_value of Relu layer ' 'cannot be negative value: ' + str(max_value)) + if negative_slope < 0.: + raise ValueError('negative_slope of Relu layer ' + 'cannot be negative value: ' + str(negative_slope)) + + self.support_masking = True + self.max_value = K.cast_to_floatx(max_value) + self.negative_slope = K.cast_to_floatx(negative_slope) + self.threshold = K.cast_to_floatx(threshold) def call(self, inputs): - return activations.relu(inputs, max_value=self.max_value) + # alpha is used for leaky relu slope in activations instead of + # negative_slope. + return activations.relu( + inputs, + alpha=self.negative_slope, + max_value=self.max_value, + threshold=self.threshold) def get_config(self): - config = {'max_value': self.max_value} + config = { + 'max_value': self.max_value, + 'negative_slope': self.negative_slope, + 'threshold': self.threshold + } base_config = super(ReLU, self).get_config() return dict(list(base_config.items()) + list(config.items())) diff --git a/tensorflow/python/keras/layers/advanced_activations_test.py b/tensorflow/python/keras/layers/advanced_activations_test.py index 9e1f15b1bc..53c1baa2bb 100644 --- a/tensorflow/python/keras/layers/advanced_activations_test.py +++ b/tensorflow/python/keras/layers/advanced_activations_test.py @@ -75,6 +75,14 @@ class AdvancedActivationsTest(test.TestCase): testing_utils.layer_test(keras.layers.ReLU, kwargs={'max_value': -10}, input_shape=(2, 3, 4)) + with self.assertRaisesRegexp( + ValueError, + 'negative_slope of Relu layer cannot be negative value: -2'): + with self.test_session(): + testing_utils.layer_test( + keras.layers.ReLU, + kwargs={'negative_slope': -2}, + input_shape=(2, 3, 4)) if __name__ == '__main__': |