paddle.v2.fluid.optimizer.Optimizer(global_step=None, regularization=None)paddle.v2.fluid.optimizer.Optimizer(learning_rate, global_step=None, regularization=None)
Optimizer Base class.
Define the common interface of an optimizer. User should not use this class directly, but need to use one of it’s implementation.
+global_learning_rateget global decayed learning rate +:return:
+create_optimization_pass(parameters_and_grads, loss, startup_program=None)For users in China, we provide a faster mirror:
++
Then, you would back and paste the address into the local browser:
+http://localhost:8888/ diff --git a/develop/doc/operators.json b/develop/doc/operators.json index c56cbd101ad8ece5d0626d49b00415a060313215..6adc19462372aeee62c5b1c0e0129c5831a05cc7 100644 --- a/develop/doc/operators.json +++ b/develop/doc/operators.json @@ -1334,6 +1334,24 @@ "intermediate" : 0 } ], "attrs" : [ ] +},{ + "type" : "abs", + "comment" : "\nAbs Activation Operator.\n\n$out = |x|$\n\n", + "inputs" : [ + { + "name" : "X", + "comment" : "Input of Abs operator", + "duplicable" : 0, + "intermediate" : 0 + } ], + "outputs" : [ + { + "name" : "Out", + "comment" : "Output of Abs operator", + "duplicable" : 0, + "intermediate" : 0 + } ], + "attrs" : [ ] },{ "type" : "softmax", "comment" : "\nSoftmax Operator.\n\nThe input of the softmax operator is a 2-D tensor with shape N x K (N is the\nbatch_size, K is the dimension of input feature). The output tensor has the\nsame shape as the input tensor.\n\nFor each row of the input tensor, the softmax operator squashes the\nK-dimensional vector of arbitrary real values to a K-dimensional vector of real\nvalues in the range [0, 1] that add up to 1.\nIt computes the exponential of the given dimension and the sum of exponential\nvalues of all the other dimensions in the K-dimensional vector input.\nThen the ratio of the exponential of the given dimension and the sum of\nexponential values of all the other dimensions is the output of the softmax\noperator.\n\nFor each row $i$ and each column $j$ in Input(X), we have:\n $$Out[i, j] = \\frac{\\exp(X[i, j])}{\\sum_j(exp(X[i, j])}$$\n\n", @@ -1575,6 +1593,35 @@ "intermediate" : 0 } ], "attrs" : [ ] +},{ + "type" : "elementwise_pow", + "comment" : "\nLimited Elementwise Pow Operator.\n\nThe equation is:\n\n$$Out = X ^ Y$$\n\n$X$ is a tensor of any dimension and the dimensions of tensor $Y$ must be\nsmaller than or equal to the dimensions of $X$.\n\nThere are two cases for this operator:\n1. The shape of $Y$ is same with $X$;\n2. The shape of $Y$ is a subset of $X$.\n\nFor case 2:\n$Y$ will be broadcasted to match the shape of $X$ and axis should be\nset to index of the start dimension to broadcast $Y$ onto $X$.\n\nFor example\n .. code-block:: python\n\n shape(X) = (2, 3, 4, 5), shape(Y) = (,)\n shape(X) = (2, 3, 4, 5), shape(Y) = (5,)\n shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)\n shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1\n shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0\n\nEither of the inputs $X$ and $Y$ or none can carry the LoD (Level of Details)\ninformation. However, the output only shares the LoD information with input $X$.\n\n", + "inputs" : [ + { + "name" : "X", + "comment" : "(Tensor), The first input tensor of elementwise op.", + "duplicable" : 0, + "intermediate" : 0 + }, { + "name" : "Y", + "comment" : "(Tensor), The second input tensor of elementwise op.", + "duplicable" : 0, + "intermediate" : 0 + } ], + "outputs" : [ + { + "name" : "Out", + "comment" : "The output of elementwise op.", + "duplicable" : 0, + "intermediate" : 0 + } ], + "attrs" : [ + { + "name" : "axis", + "type" : "int", + "comment" : "(int, default -1). The start dimension index for broadcasting Y onto X.", + "generated" : 0 + } ] },{ "type" : "proximal_gd", "comment" : "\nProximalGD Operator.\n\nOptimizer that implements the proximal gradient descent algorithm:\n\n$$\nprox\\_param = param - learning\\_rate * grad \\\\\nparam = sign(prox\\_param) / (1 + learning\\_rate * l2) *\n \\max(|prox\\_param| - learning\\_rate * l1, 0)\n$$ \n\nThe paper that proposed Proximal Gradient Descent:\n(http://papers.nips.cc/paper/3793-efficient-learning-using-forward-backward-splitting.pdf)\n\n", @@ -3192,24 +3239,6 @@ "intermediate" : 0 } ], "attrs" : [ ] -},{ - "type" : "abs", - "comment" : "\nAbs Activation Operator.\n\n$out = |x|$\n\n", - "inputs" : [ - { - "name" : "X", - "comment" : "Input of Abs operator", - "duplicable" : 0, - "intermediate" : 0 - } ], - "outputs" : [ - { - "name" : "Out", - "comment" : "Output of Abs operator", - "duplicable" : 0, - "intermediate" : 0 - } ], - "attrs" : [ ] },{ "type" : "softplus", "comment" : "\nSoftplus Activation Operator.\n\n$out = \\ln(1 + e^{x})$\n\n", diff --git a/develop/doc/searchindex.js b/develop/doc/searchindex.js index 6467d04d9791ee3924efb0db80a671b43c8df15d..f07fd8d18313041dac1a7702d60fa1d972287206 100644 --- a/develop/doc/searchindex.js +++ b/develop/doc/searchindex.js @@ -1 +1 @@ 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\ No newline at end of file diff --git a/develop/doc_cn/_sources/getstarted/build_and_install/docker_install_cn.rst.txt b/develop/doc_cn/_sources/getstarted/build_and_install/docker_install_cn.rst.txt index 98fada7bdb46f4dd2927d6f93bcbcebbe7d18604..79d214635a069a739060e0b79424729f6ff90387 100644 --- a/develop/doc_cn/_sources/getstarted/build_and_install/docker_install_cn.rst.txt +++ b/develop/doc_cn/_sources/getstarted/build_and_install/docker_install_cn.rst.txt @@ -95,6 +95,12 @@ PaddlePaddle Book是为用户和开发者制作的一个交互式的Jupyter Note docker run -p 8888:8888 paddlepaddle/book +国内用户可以使用下面的镜像源来加速访问： + + .. code-block: bash + + docker run -p 8888:8888 docker.paddlepaddlehub.com/book + 然后在浏览器中输入以下网址： .. code-block:: text diff --git a/develop/doc_cn/api/v2/fluid/optimizer.html b/develop/doc_cn/api/v2/fluid/optimizer.html index 61295441576cedb6f16cba2a8d6e280c4b730f20..f3d46b5af6eaeac98cdf4e8a06e87a40772989c0 100644 --- a/develop/doc_cn/api/v2/fluid/optimizer.html +++ b/develop/doc_cn/api/v2/fluid/optimizer.html @@ -241,11 +241,18 @@Optimizer¶
- -class
+classpaddle.v2.fluid.optimizer.Optimizer(global_step=None, regularization=None)paddle.v2.fluid.optimizer.Optimizer(learning_rate, global_step=None, regularization=None)Optimizer Base class.
Define the common interface of an optimizer. User should not use this class directly, but need to use one of it’s implementation.
++
+- +
+global_learning_rateget global decayed learning rate +:return:
+
- diff --git a/develop/doc_cn/getstarted/build_and_install/docker_install_cn.html b/develop/doc_cn/getstarted/build_and_install/docker_install_cn.html index 83e4d487dd26bddd7aa8043473c58a1802a578d0..2a7f71bcefc90cde1d58ee473b63d613e6a61397 100644 --- a/develop/doc_cn/getstarted/build_and_install/docker_install_cn.html +++ b/develop/doc_cn/getstarted/build_and_install/docker_install_cn.html @@ -314,6 +314,9 @@ PaddlePaddle Book是为用户和开发者制作的一个交互式的Jupyter Note
create_optimization_pass(parameters_and_grads, loss, startup_program=None)
国内用户可以使用下面的镜像源来加速访问：
++
然后在浏览器中输入以下网址：
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