a SicM@s.dZddlZddlmmZddlmmZ ddl m Z ddl m Z ddlmZddiZedejjjdddZedgd ejjjd?d"d#Zed$ejjjd%d&Zed'ejjjd(d)Zed*ejjjd+d,Zed-ejjjd.d/Zed0ejjjd1d2Z ed3ejjjd4d5Z!ej"j#Z#ej"j$Z$ej"j%Z%ej"j&Z&ed6ejjjd@d7d8Z'ed9ejjjd:d;Z(dS)AzBuilt-in activation functions.N)backend) generic_utils) keras_export softmax_v2softmaxzkeras.activations.softmaxcCst|jjdkr\t|tr(tjj||d}qjt|tj||dd}tj ||dd}||}nt d|||_ |S)aSoftmax converts a vector of values to a probability distribution. The elements of the output vector are in range (0, 1) and sum to 1. Each vector is handled independently. The `axis` argument sets which axis of the input the function is applied along. Softmax is often used as the activation for the last layer of a classification network because the result could be interpreted as a probability distribution. The softmax of each vector x is computed as `exp(x) / tf.reduce_sum(exp(x))`. The input values in are the log-odds of the resulting probability. Args: x : Input tensor. axis: Integer, axis along which the softmax normalization is applied. Returns: Tensor, output of softmax transformation (all values are non-negative and sum to 1). Examples: **Example 1: standalone usage** >>> inputs = tf.random.normal(shape=(32, 10)) >>> outputs = tf.keras.activations.softmax(inputs) >>> tf.reduce_sum(outputs[0, :]) # Each sample in the batch now sums to 1 **Example 2: usage in a `Dense` layer** >>> layer = tf.keras.layers.Dense(32, ... activation=tf.keras.activations.softmax) )axisT)r keepdimsz=Cannot apply softmax to a tensor that is 1D. Received input: ) shaperank isinstanceinttfnnrexp reduce_max reduce_sum ValueError _keras_logits)xr outputesrM/var/www/html/django/DPS/env/lib/python3.9/site-packages/keras/activations.pyr)s)   zkeras.activations.elu?cCs t||S)aExponential Linear Unit. The exponential linear unit (ELU) with `alpha > 0` is: `x` if `x > 0` and `alpha * (exp(x) - 1)` if `x < 0` The ELU hyperparameter `alpha` controls the value to which an ELU saturates for negative net inputs. ELUs diminish the vanishing gradient effect. ELUs have negative values which pushes the mean of the activations closer to zero. Mean activations that are closer to zero enable faster learning as they bring the gradient closer to the natural gradient. ELUs saturate to a negative value when the argument gets smaller. Saturation means a small derivative which decreases the variation and the information that is propagated to the next layer. Example Usage: >>> import tensorflow as tf >>> model = tf.keras.Sequential() >>> model.add(tf.keras.layers.Conv2D(32, (3, 3), activation='elu', ... input_shape=(28, 28, 1))) >>> model.add(tf.keras.layers.MaxPooling2D((2, 2))) >>> model.add(tf.keras.layers.Conv2D(64, (3, 3), activation='elu')) >>> model.add(tf.keras.layers.MaxPooling2D((2, 2))) >>> model.add(tf.keras.layers.Conv2D(64, (3, 3), activation='elu')) Args: x: Input tensor. alpha: A scalar, slope of negative section. `alpha` controls the value to which an ELU saturates for negative net inputs. Returns: The exponential linear unit (ELU) activation function: `x` if `x > 0` and `alpha * (exp(x) - 1)` if `x < 0`. Reference: [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) (Clevert et al, 2016)](https://arxiv.org/abs/1511.07289) )relu)ralpharrrres/rzkeras.activations.selucCs tj|S)aScaled Exponential Linear Unit (SELU). The Scaled Exponential Linear Unit (SELU) activation function is defined as: - `if x > 0: return scale * x` - `if x < 0: return scale * alpha * (exp(x) - 1)` where `alpha` and `scale` are pre-defined constants (`alpha=1.67326324` and `scale=1.05070098`). Basically, the SELU activation function multiplies `scale` (> 1) with the output of the `tf.keras.activations.elu` function to ensure a slope larger than one for positive inputs. The values of `alpha` and `scale` are chosen so that the mean and variance of the inputs are preserved between two consecutive layers as long as the weights are initialized correctly (see `tf.keras.initializers.LecunNormal` initializer) and the number of input units is "large enough" (see reference paper for more information). Example Usage: >>> num_classes = 10 # 10-class problem >>> model = tf.keras.Sequential() >>> model.add(tf.keras.layers.Dense(64, kernel_initializer='lecun_normal', ... activation='selu')) >>> model.add(tf.keras.layers.Dense(32, kernel_initializer='lecun_normal', ... activation='selu')) >>> model.add(tf.keras.layers.Dense(16, kernel_initializer='lecun_normal', ... activation='selu')) >>> model.add(tf.keras.layers.Dense(num_classes, activation='softmax')) Args: x: A tensor or variable to compute the activation function for. Returns: The scaled exponential unit activation: `scale * elu(x, alpha)`. Notes: - To be used together with the `tf.keras.initializers.LecunNormal` initializer. - To be used together with the dropout variant `tf.keras.layers.AlphaDropout` (not regular dropout). References: - [Klambauer et al., 2017](https://arxiv.org/abs/1706.02515) )rrselurrrrrs3rzkeras.activations.softpluscCs tj|S)aSoftplus activation function, `softplus(x) = log(exp(x) + 1)`. Example Usage: >>> a = tf.constant([-20, -1.0, 0.0, 1.0, 20], dtype = tf.float32) >>> b = tf.keras.activations.softplus(a) >>> b.numpy() array([2.0611537e-09, 3.1326166e-01, 6.9314718e-01, 1.3132616e+00, 2.0000000e+01], dtype=float32) Args: x: Input tensor. Returns: The softplus activation: `log(exp(x) + 1)`. )rmathsoftplusr rrrr"sr"zkeras.activations.softsigncCs tj|S)ajSoftsign activation function, `softsign(x) = x / (abs(x) + 1)`. Example Usage: >>> a = tf.constant([-1.0, 0.0, 1.0], dtype = tf.float32) >>> b = tf.keras.activations.softsign(a) >>> b.numpy() array([-0.5, 0. , 0.5], dtype=float32) Args: x: Input tensor. Returns: The softsign activation: `x / (abs(x) + 1)`. )rr!softsignr rrrr#sr#zkeras.activations.swishcCs tj|S)aSwish activation function, `swish(x) = x * sigmoid(x)`. Swish activation function which returns `x*sigmoid(x)`. It is a smooth, non-monotonic function that consistently matches or outperforms ReLU on deep networks, it is unbounded above and bounded below. Example Usage: >>> a = tf.constant([-20, -1.0, 0.0, 1.0, 20], dtype = tf.float32) >>> b = tf.keras.activations.swish(a) >>> b.numpy() array([-4.1223075e-08, -2.6894143e-01, 0.0000000e+00, 7.3105860e-01, 2.0000000e+01], dtype=float32) Args: x: Input tensor. Returns: The swish activation applied to `x` (see reference paper for details). Reference: - [Ramachandran et al., 2017](https://arxiv.org/abs/1710.05941) )rrsilur rrrswishsr%zkeras.activations.relucCstj||||dS)aApplies the rectified linear unit activation function. With default values, this returns the standard ReLU activation: `max(x, 0)`, the element-wise maximum of 0 and the input tensor. Modifying default parameters allows you to use non-zero thresholds, change the max value of the activation, and to use a non-zero multiple of the input for values below the threshold. For example: >>> foo = tf.constant([-10, -5, 0.0, 5, 10], dtype = tf.float32) >>> tf.keras.activations.relu(foo).numpy() array([ 0., 0., 0., 5., 10.], dtype=float32) >>> tf.keras.activations.relu(foo, alpha=0.5).numpy() array([-5. , -2.5, 0. , 5. , 10. ], dtype=float32) >>> tf.keras.activations.relu(foo, max_value=5.).numpy() array([0., 0., 0., 5., 5.], dtype=float32) >>> tf.keras.activations.relu(foo, threshold=5.).numpy() array([-0., -0., 0., 0., 10.], dtype=float32) Args: x: Input `tensor` or `variable`. alpha: A `float` that governs the slope for values lower than the threshold. max_value: A `float` that sets the saturation threshold (the largest value the function will return). threshold: A `float` giving the threshold value of the activation function below which values will be damped or set to zero. Returns: A `Tensor` representing the input tensor, transformed by the relu activation function. Tensor will be of the same shape and dtype of input `x`. )r max_value threshold)rrelu)rrr'r(rrrr)s&r)zkeras.activations.gelu)v1FcCstj||S)aApplies the Gaussian error linear unit (GELU) activation function. Gaussian error linear unit (GELU) computes `x * P(X <= x)`, where `P(X) ~ N(0, 1)`. The (GELU) nonlinearity weights inputs by their value, rather than gates inputs by their sign as in ReLU. For example: >>> x = tf.constant([-3.0, -1.0, 0.0, 1.0, 3.0], dtype=tf.float32) >>> y = tf.keras.activations.gelu(x) >>> y.numpy() array([-0.00404951, -0.15865529, 0. , 0.8413447 , 2.9959507 ], dtype=float32) >>> y = tf.keras.activations.gelu(x, approximate=True) >>> y.numpy() array([-0.00363752, -0.15880796, 0. , 0.841192 , 2.9963627 ], dtype=float32) Args: x: Input tensor. approximate: A `bool`, whether to enable approximation. Returns: The gaussian error linear activation: `0.5 * x * (1 + tanh(sqrt(2 / pi) * (x + 0.044715 * x^3)))` if `approximate` is `True` or `x * P(X <= x) = 0.5 * x * (1 + erf(x / sqrt(2)))`, where `P(X) ~ N(0, 1)`, if `approximate` is `False`. Reference: - [Gaussian Error Linear Units (GELUs)](https://arxiv.org/abs/1606.08415) )rrgelu)r approximaterrrr+Bs%r+zkeras.activations.tanhcCs t|S)aHyperbolic tangent activation function. For example: >>> a = tf.constant([-3.0,-1.0, 0.0,1.0,3.0], dtype = tf.float32) >>> b = tf.keras.activations.tanh(a) >>> b.numpy() array([-0.9950547, -0.7615942, 0., 0.7615942, 0.9950547], dtype=float32) Args: x: Input tensor. Returns: Tensor of same shape and dtype of input `x`, with tanh activation: `tanh(x) = sinh(x)/cosh(x) = ((exp(x) - exp(-x))/(exp(x) + exp(-x)))`. )rtanhr rrrr-jsr-zkeras.activations.sigmoidcCst|}||_|S)a&Sigmoid activation function, `sigmoid(x) = 1 / (1 + exp(-x))`. Applies the sigmoid activation function. For small values (<-5), `sigmoid` returns a value close to zero, and for large values (>5) the result of the function gets close to 1. Sigmoid is equivalent to a 2-element Softmax, where the second element is assumed to be zero. The sigmoid function always returns a value between 0 and 1. For example: >>> a = tf.constant([-20, -1.0, 0.0, 1.0, 20], dtype = tf.float32) >>> b = tf.keras.activations.sigmoid(a) >>> b.numpy() array([2.0611537e-09, 2.6894143e-01, 5.0000000e-01, 7.3105860e-01, 1.0000000e+00], dtype=float32) Args: x: Input tensor. Returns: Tensor with the sigmoid activation: `1 / (1 + exp(-x))`. )rsigmoidr)rrrrrr.s r.zkeras.activations.exponentialcCs t|S)ayExponential activation function. For example: >>> a = tf.constant([-3.0,-1.0, 0.0,1.0,3.0], dtype = tf.float32) >>> b = tf.keras.activations.exponential(a) >>> b.numpy() array([0.04978707, 0.36787945, 1., 2.7182817 , 20.085537], dtype=float32) Args: x: Input tensor. Returns: Tensor with exponential activation: `exp(x)`. )rrr rrr exponentialsr/zkeras.activations.hard_sigmoidcCs t|S)azHard sigmoid activation function. A faster approximation of the sigmoid activation. Piecewise linear approximation of the sigmoid function. Ref: 'https://en.wikipedia.org/wiki/Hard_sigmoid' For example: >>> a = tf.constant([-3.0,-1.0, 0.0,1.0,3.0], dtype = tf.float32) >>> b = tf.keras.activations.hard_sigmoid(a) >>> b.numpy() array([0. , 0.3, 0.5, 0.7, 1. ], dtype=float32) Args: x: Input tensor. Returns: The hard sigmoid activation, defined as: - `if x < -2.5: return 0` - `if x > 2.5: return 1` - `if -2.5 <= x <= 2.5: return 0.2 * x + 0.5` )r hard_sigmoidr rrrr0sr0zkeras.activations.linearcCs|S)aJLinear activation function (pass-through). For example: >>> a = tf.constant([-3.0,-1.0, 0.0,1.0,3.0], dtype = tf.float32) >>> b = tf.keras.activations.linear(a) >>> b.numpy() array([-3., -1., 0., 1., 3.], dtype=float32) Args: x: Input tensor. Returns: The input, unmodified. rr rrrlinearsr1zkeras.activations.serializecCs(t|dr|jtvrt|jSt|S)aCReturns the string identifier of an activation function. Args: activation : Function object. Returns: String denoting the name attribute of the input function For example: >>> tf.keras.activations.serialize(tf.keras.activations.tanh) 'tanh' >>> tf.keras.activations.serialize(tf.keras.activations.sigmoid) 'sigmoid' >>> tf.keras.activations.serialize('abcd') Traceback (most recent call last): ... ValueError: ('Cannot serialize', 'abcd') Raises: ValueError: The input function is not a valid one. __name__)hasattrr2_TF_ACTIVATIONS_V2rserialize_keras_object) activationrrr serializes  r7zkeras.activations.deserializecCs4i}tjt}tj|t|ftdtj|||ddS)aReturns activation function given a string identifier. Args: name: The name of the activation function. custom_objects: Optional `{function_name: function_obj}` dictionary listing user-provided activation functions. Returns: Corresponding activation function. For example: >>> tf.keras.activations.deserialize('linear') >>> tf.keras.activations.deserialize('sigmoid') >>> tf.keras.activations.deserialize('abcd') Traceback (most recent call last): ... ValueError: Unknown activation function:abcd Raises: ValueError: `Unknown activation function` if the input string does not denote any defined Tensorflow activation function. ) obj_filterzactivation function)module_objectscustom_objectsprintable_module_name)sysmodulesr2r!populate_dict_with_module_objectsactivation_layerscallabledeserialize_keras_object)namer:Zactivation_functionsZcurrent_modulerrr deserializes rCzkeras.activations.getcCs@|dur tSt|ttfr"t|St|r.|Std|dS)aReturns function. Args: identifier: Function or string Returns: Function corresponding to the input string or input function. For example: >>> tf.keras.activations.get('softmax') >>> tf.keras.activations.get(tf.keras.activations.softmax) >>> tf.keras.activations.get(None) >>> tf.keras.activations.get(abs) >>> tf.keras.activations.get('abcd') Traceback (most recent call last): ... ValueError: Unknown activation function:abcd Raises: ValueError: Input is an unknown function or string, i.e., the input does not denote any defined function. Nz4Could not interpret activation function identifier: )r1r strdictrCr@ TypeError) identifierrrrget@srH)r)r)r&Nr&)F)N))__doc__r<tensorflow.compat.v2compatv2rZkeras.layers.activationlayersr6r?kerasr keras.utilsr tensorflow.python.util.tf_exportrr4 __internal__dispatchadd_dispatch_supportrrrr"r#r%r)r+r-r.r/r0r1r7r leaky_relu log_softmaxrelu6r$rCrHrrrrsx   :0 4   ) &      !-