Merge pull request #32668 from asmitapoddar:patch-6
PiperOrigin-RevId: 271580224
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TensorFlower Gardener
commit
68c22de798
tensorflow
@ -42,12 +42,19 @@ _TF_ACTIVATIONS_V2 = {
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@keras_export('keras.activations.softmax')
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def softmax(x, axis=-1):
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"""The softmax activation function transforms the outputs so that all values are in
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"""Softmax converts a real vector to a vector of categorical probabilities.
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range (0, 1) and sum to 1. It is often used as the activation for the last
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The the elements of the output vector are in range (0, 1) and sum to 1.
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Each vector is handled independently. The `axis` argument sets which axis
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of the input the finction is applied along.
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Softmax is often used as the activation for the last
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layer of a classification network because the result could be interpreted as
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a probability distribution. The softmax of x is calculated by
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exp(x)/tf.reduce_sum(exp(x)).
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a probability distribution.
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The softmax of each vector x is calculated by `exp(x)/tf.reduce_sum(exp(x))`.
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The input values in are the log-odds of the resulting probability.
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Arguments:
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x : Input tensor.
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@ -118,15 +125,17 @@ def selu(x):
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https://towardsdatascience.com/selu-make-fnns-great-again-snn-8d61526802a9)
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Example Usage:
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```python3
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n_classes = 10 #10-class problem
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model = models.Sequential()
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model.add(Dense(64, kernel_initializer='lecun_normal', activation='selu',
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input_shape=(28, 28, 1))))
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model.add(Dense(32, kernel_initializer='lecun_normal', activation='selu'))
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model.add(Dense(16, kernel_initializer='lecun_normal', activation='selu'))
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model.add(Dense(n_classes, activation='softmax'))
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```
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>>> n_classes = 10 #10-class problem
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>>> from tensorflow.python.keras.layers import Dense
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>>> model = tf.keras.Sequential()
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>>> model.add(Dense(64, kernel_initializer='lecun_normal',
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... activation='selu', input_shape=(28, 28, 1)))
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>>> model.add(Dense(32, kernel_initializer='lecun_normal',
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... activation='selu'))
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>>> model.add(Dense(16, kernel_initializer='lecun_normal',
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... activation='selu'))
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>>> model.add(Dense(n_classes, activation='softmax'))
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Arguments:
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x: A tensor or variable to compute the activation function for.
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@ -200,48 +209,53 @@ def relu(x, alpha=0., max_value=None, threshold=0):
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@keras_export('keras.activations.tanh')
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def tanh(x):
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"""Hyperbolic Tangent (tanh) activation function.
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"""Hyperbolic tangent activation function.
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For example:
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```python
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# Constant 1-D tensor populated with value list.
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a = tf.constant([-3.0,-1.0, 0.0,1.0,3.0], dtype = tf.float32)
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b = tf.keras.activations.tanh(a) #[-0.9950547,-0.7615942,
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0.,0.7615942,0.9950547]
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```
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>>> a = tf.constant([-3.0,-1.0, 0.0,1.0,3.0], dtype = tf.float32)
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>>> b = tf.keras.activations.tanh(a)
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>>> b.numpy()
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array([-0.9950547, -0.7615942, 0. , 0.7615942, 0.9950547],
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dtype=float32)
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Arguments:
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x: Input tensor.
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Returns:
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A tensor of same shape and dtype of input `x`.
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The tanh activation: `tanh(x) = sinh(x)/cosh(x) = ((exp(x) -
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exp(-x))/(exp(x) + exp(-x)))`.
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Tensor of same shape and dtype of input `x`, with tanh activation:
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`tanh(x) = sinh(x)/cosh(x) = ((exp(x) - exp(-x))/(exp(x) + exp(-x)))`.
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"""
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return nn.tanh(x)
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@keras_export('keras.activations.sigmoid')
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def sigmoid(x):
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"""Sigmoid.
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"""Sigmoid activation function.
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Applies the sigmoid activation function. The sigmoid function is defined as
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1 divided by (1 + exp(-x)). It's curve is like an "S" and is like a smoothed
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version of the Heaviside (Unit Step Function) function. For small values
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(<-5) the sigmoid returns a value close to zero and for larger values (>5)
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the result of the function gets close to 1.
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Arguments:
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x: A tensor or variable.
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Returns:
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A tensor.
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Sigmoid activation function.
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Sigmoid is equivalent to a 2-element Softmax, where the second element is
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assumed to be zero.
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For example:
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>>> a = tf.constant([-20, -1.0, 0.0, 1.0, 20], dtype = tf.float32)
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>>> b = tf.keras.activations.sigmoid(a)
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>>> b.numpy()
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array([0. , 0.26894143, 0.5 , 0.7310586 , 1. ],
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dtype=float32)
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Arguments:
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x: Input tensor.
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Returns:
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The sigmoid activation: `(1.0 / (1.0 + exp(-x)))`.
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Tensor with the sigmoid activation: `(1.0 / (1.0 + exp(-x)))`.
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Tensor will be of same shape and dtype of input `x`.
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"""
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return nn.sigmoid(x)
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@ -250,11 +264,20 @@ def sigmoid(x):
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def exponential(x):
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"""Exponential activation function.
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For example:
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>>> a = tf.constant([-3.0,-1.0, 0.0,1.0,3.0], dtype = tf.float32)
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>>> b = tf.keras.activations.exponential(a)
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>>> b.numpy()
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array([ 0.04978707, 0.36787945, 1. , 2.7182817 , 20.085537 ],
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dtype=float32)
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Arguments:
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x: Input tensor.
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Returns:
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The exponential activation: `exp(x)`.
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Tensor with exponential activation: `exp(x)`. Tensor will be of same
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shape and dtype of input `x`.
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"""
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return math_ops.exp(x)
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@ -265,11 +288,19 @@ def hard_sigmoid(x):
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Faster to compute than sigmoid activation.
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For example:
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>>> a = tf.constant([-3.0,-1.0, 0.0,1.0,3.0], dtype = tf.float32)
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>>> b = tf.keras.activations.hard_sigmoid(a)
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>>> b.numpy()
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array([0. , 0.3, 0.5, 0.7, 1. ], dtype=float32)
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Arguments:
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x: Input tensor.
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Returns:
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Hard sigmoid activation:
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The hard sigmoid activation:
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- `0` if `x < -2.5`
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- `1` if `x > 2.5`
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- `0.2 * x + 0.5` if `-2.5 <= x <= 2.5`.
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@ -281,17 +312,46 @@ def hard_sigmoid(x):
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def linear(x):
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"""Linear activation function.
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For example:
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>>> a = tf.constant([-3.0,-1.0, 0.0,1.0,3.0], dtype = tf.float32)
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>>> b = tf.keras.activations.linear(a)
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>>> b.numpy()
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array([-3., -1., 0., 1., 3.], dtype=float32)
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Arguments:
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x: Input tensor.
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Returns:
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The linear activation: `x`.
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the input unmodified.
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"""
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return x
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@keras_export('keras.activations.serialize')
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def serialize(activation):
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"""Returns name attribute (`__name__`) of function.
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Arguments:
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activation : Function
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Returns:
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String denoting the name attribute of the input function
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For example:
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>>> tf.keras.activations.serialize(tf.keras.activations.tanh)
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'tanh'
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>>> tf.keras.activations.serialize(tf.keras.activations.sigmoid)
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'sigmoid'
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>>> tf.keras.activations.serialize('abcd')
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Traceback (most recent call last):
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...
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ValueError: ('Cannot serialize', 'abcd')
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Raises:
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ValueError: The input function is not a valid one.
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"""
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if (hasattr(activation, '__name__') and
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activation.__name__ in _TF_ACTIVATIONS_V2):
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return _TF_ACTIVATIONS_V2[activation.__name__]
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@ -300,6 +360,33 @@ def serialize(activation):
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@keras_export('keras.activations.deserialize')
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def deserialize(name, custom_objects=None):
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"""Returns activation function denoted by input string.
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Arguments:
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x : String
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Returns:
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Tensorlow Activation function denoted by input string.
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For example:
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>>> tf.keras.activations.deserialize('linear')
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<function linear at 0x1239596a8>
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>>> tf.keras.activations.deserialize('sigmoid')
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<function sigmoid at 0x123959510>
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>>> tf.keras.activations.deserialize('abcd')
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Traceback (most recent call last):
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...
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ValueError: Unknown activation function:abcd
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Args:
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name: The name of the actiuvation function.
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custom_objects: A {name:value} dictionary for activations not build into
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keras.
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Raises:
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ValueError: `Unknown activation function` if the input string does not
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denote any defined Tensorflow activation function.
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"""
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return deserialize_keras_object(
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name,
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module_objects=globals(),
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@ -309,6 +396,35 @@ def deserialize(name, custom_objects=None):
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@keras_export('keras.activations.get')
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def get(identifier):
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"""Returns function.
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Arguments:
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identifier: Function or string
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Returns:
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Activation function denoted by input:
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- `Linear activation function` if input is `None`.
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- Function corresponding to the input string or input function.
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For example:
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>>> tf.keras.activations.get('softmax')
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<function softmax at 0x1222a3d90>
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>>> tf.keras.activations.get(tf.keras.activations.softmax)
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<function softmax at 0x1222a3d90>
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>>> tf.keras.activations.get(None)
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<function linear at 0x1239596a8>
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>>> tf.keras.activations.get(abs)
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<built-in function abs>
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>>> tf.keras.activations.get('abcd')
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Traceback (most recent call last):
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...
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ValueError: Unknown activation function:abcd
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Raises:
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ValueError: Input is an unknown function or string, i.e., the input does
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not denote any defined function.
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"""
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if identifier is None:
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return linear
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if isinstance(identifier, six.string_types):
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@ -121,12 +121,15 @@ class CustomOutputChecker(doctest.OutputChecker):
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This allows it to be customized before they are compared.
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"""
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ID_RE = re.compile(r'\bid=(\d+)\b')
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ADDRESS_RE = re.compile(r'\bat 0x[0-9a-f]*?>')
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def check_output(self, want, got, optionflags):
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# Replace tf.Tensor's id with ellipsis(...) because tensor's id can change
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# on each execution. Users may forget to use ellipsis while writing
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# examples in docstrings, so replacing the id with `...` makes it safe.
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want = re.sub(r'\bid=(\d+)\b', r'id=...', want)
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want = self.ID_RE.sub('id=...', want)
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want = self.ADDRESS_RE.sub('at ...>', want)
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return doctest.OutputChecker.check_output(self, want, got, optionflags)
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_MESSAGE = textwrap.dedent("""\n
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