Question regarding tracking Release v1.12 specific changes.
MADlib dev, While reviewing a PR, I noticed the doc top-level generated html page has a reference to previous releases ( https://madlib.incubator.apache.org/docs/latest/). I found the corresponding v1.11 changelist ( https://github.com/apache/incubator-madlib/commit/648b05798826956e9621027447af501c194392b8) which updated the previous release versions to include v1.10 (in addition to other v1.11 related changes). Aside from this PR, how does the project track these types of release changes (Jira, wiki, email, other). I could not find a reference to them in the project's wiki. It might be staring me in the face so I apologize if it is. Any guidance is greatly appreciated. -=e p.s. And yes, this is in preparation for my v1.12 release manager role. Gaining context comes at a price. -- *Ed Espino*
[GitHub] incubator-madlib pull request #149: MLP: Multilayer Perceptron
Github user edespino commented on a diff in the pull request: https://github.com/apache/incubator-madlib/pull/149#discussion_r127309270 --- Diff: src/ports/postgres/modules/convex/mlp.sql_in --- @@ -0,0 +1,744 @@ +/* --- *//** + * + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, + * software distributed under the License is distributed on an + * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + * KIND, either express or implied. See the License for the + * specific language governing permissions and limitations + * under the License. + * + * + * @file mlp.sql_in + * + * @brief SQL functions for multilayer perceptron + * @date June 2012 + * + * + *//* --- */ + +m4_include(`SQLCommon.m4') + +/** +@addtogroup grp_mlp + +Contents +Classification +Regression +Optimizer Parameters +Prediction Functions/a> +Examples +Technical Background +Literature +Related Topics + + +Multilayer Perceptron (MLP) is a model for regression and +classification + +Also called "vanilla neural networks", they consist of several +fully connected hidden layers with non-linear activation +functions. In the case of classification, the final layer of the +neural net has as many nodes as classes, and the output of the +neural net can be interpreted as the probability of a given input +feature belonging to a specific class. + + +@brief Solves classification and regression problems with several +fully connected layers and nonlinear activation functions. + +@anchor mlp_classification +@par Classification Training Function +The mlp classification training function has the following format: + +mlp_classification( +source_table, +output_table, +independent_varname, +dependent_varname, +hidden_layer_sizes, +optimizer_params, +activation +) + +\b Arguments + + source_table + TEXT. Name of the table containing the training data. + + model_table + TEXT. Name of the output table containing the model. Details of the output + tables are provided below. + + + independent_varname + TEXT. Expression list to evaluate for the +independent variables. An intercept variable should not be included as part +of this expression. Please note that expression should be able to be cast +to DOUBLE PRECISION[]. + + + dependent_varname + TEXT. Name of the dependent variable column. For classification, supported types are: + text, varchar, character varying, char, character + integer, smallint, bigint, and boolean. + + hidden_layer_sizes + INTEGER[], default: ARRAY[]. + The number of neurons in each hidden layer. The length of this array will + determine the number of hidden layers. Empty for no hidden layers. + + + + optimizer_params (optional) + TEXT, default: NULL. +Parameters for optimization in a comma-separated string +of key-value pairs. See the description below for details. + + + activation + TEXT, default: 'sigmoid'. +Activation function. Currently three functions are supported: 'sigmoid' (default), +'relu', and 'tanh'. The text can be any subset of the three +strings; for e.g., activation='s' will use the sigmoid activation. + + + +Output tables + +The model table produced by mlp contains the following columns: + + +coeffs +FLOAT8[]. Flat array containing the weights of the neural net + + +n_iterations +INTEGER. Number of iterations completed by stochastic gradient descent +algorithm. The algorithm either converged in this number of iterations +or hit the maximum number specified in the optimization parameters. + + +loss +FLOAT8. The cross entropy over the training data. +See Technical Background section below for more details. + + + + +A summ
[GitHub] incubator-madlib pull request #149: MLP: Multilayer Perceptron
Github user edespino commented on a diff in the pull request: https://github.com/apache/incubator-madlib/pull/149#discussion_r127313158 --- Diff: src/ports/postgres/modules/convex/mlp.sql_in --- @@ -0,0 +1,744 @@ +/* --- *//** + * + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, + * software distributed under the License is distributed on an + * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + * KIND, either express or implied. See the License for the + * specific language governing permissions and limitations + * under the License. + * + * + * @file mlp.sql_in + * + * @brief SQL functions for multilayer perceptron + * @date June 2012 + * + * + *//* --- */ + +m4_include(`SQLCommon.m4') + +/** +@addtogroup grp_mlp + +Contents +Classification +Regression +Optimizer Parameters +Prediction Functions/a> +Examples +Technical Background +Literature +Related Topics + + +Multilayer Perceptron (MLP) is a model for regression and +classification + +Also called "vanilla neural networks", they consist of several +fully connected hidden layers with non-linear activation +functions. In the case of classification, the final layer of the +neural net has as many nodes as classes, and the output of the +neural net can be interpreted as the probability of a given input +feature belonging to a specific class. + + +@brief Solves classification and regression problems with several +fully connected layers and nonlinear activation functions. + +@anchor mlp_classification +@par Classification Training Function +The mlp classification training function has the following format: + +mlp_classification( +source_table, +output_table, +independent_varname, +dependent_varname, +hidden_layer_sizes, +optimizer_params, +activation +) + +\b Arguments + + source_table + TEXT. Name of the table containing the training data. + + model_table + TEXT. Name of the output table containing the model. Details of the output + tables are provided below. + + + independent_varname + TEXT. Expression list to evaluate for the +independent variables. An intercept variable should not be included as part +of this expression. Please note that expression should be able to be cast +to DOUBLE PRECISION[]. + + + dependent_varname + TEXT. Name of the dependent variable column. For classification, supported types are: + text, varchar, character varying, char, character + integer, smallint, bigint, and boolean. + + hidden_layer_sizes + INTEGER[], default: ARRAY[]. + The number of neurons in each hidden layer. The length of this array will + determine the number of hidden layers. Empty for no hidden layers. + + + + optimizer_params (optional) + TEXT, default: NULL. +Parameters for optimization in a comma-separated string +of key-value pairs. See the description below for details. + + + activation + TEXT, default: 'sigmoid'. +Activation function. Currently three functions are supported: 'sigmoid' (default), +'relu', and 'tanh'. The text can be any subset of the three +strings; for e.g., activation='s' will use the sigmoid activation. + + + +Output tables + +The model table produced by mlp contains the following columns: + + +coeffs +FLOAT8[]. Flat array containing the weights of the neural net + + +n_iterations +INTEGER. Number of iterations completed by stochastic gradient descent +algorithm. The algorithm either converged in this number of iterations +or hit the maximum number specified in the optimization parameters. + + +loss +FLOAT8. The cross entropy over the training data. +See Technical Background section below for more details. + + + + +A summ
[GitHub] incubator-madlib pull request #149: MLP: Multilayer Perceptron
Github user edespino commented on a diff in the pull request: https://github.com/apache/incubator-madlib/pull/149#discussion_r127314140 --- Diff: src/ports/postgres/modules/convex/mlp.sql_in --- @@ -0,0 +1,744 @@ +/* --- *//** + * + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, + * software distributed under the License is distributed on an + * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + * KIND, either express or implied. See the License for the + * specific language governing permissions and limitations + * under the License. + * + * + * @file mlp.sql_in + * + * @brief SQL functions for multilayer perceptron + * @date June 2012 + * + * + *//* --- */ + +m4_include(`SQLCommon.m4') + +/** +@addtogroup grp_mlp + +Contents +Classification +Regression +Optimizer Parameters +Prediction Functions/a> +Examples +Technical Background +Literature +Related Topics + + +Multilayer Perceptron (MLP) is a model for regression and +classification + +Also called "vanilla neural networks", they consist of several +fully connected hidden layers with non-linear activation +functions. In the case of classification, the final layer of the +neural net has as many nodes as classes, and the output of the +neural net can be interpreted as the probability of a given input +feature belonging to a specific class. + + +@brief Solves classification and regression problems with several +fully connected layers and nonlinear activation functions. + +@anchor mlp_classification +@par Classification Training Function +The mlp classification training function has the following format: + +mlp_classification( +source_table, +output_table, +independent_varname, +dependent_varname, +hidden_layer_sizes, +optimizer_params, +activation +) + +\b Arguments + + source_table + TEXT. Name of the table containing the training data. + + model_table + TEXT. Name of the output table containing the model. Details of the output + tables are provided below. + + + independent_varname + TEXT. Expression list to evaluate for the +independent variables. An intercept variable should not be included as part +of this expression. Please note that expression should be able to be cast +to DOUBLE PRECISION[]. + + + dependent_varname + TEXT. Name of the dependent variable column. For classification, supported types are: + text, varchar, character varying, char, character + integer, smallint, bigint, and boolean. + + hidden_layer_sizes + INTEGER[], default: ARRAY[]. + The number of neurons in each hidden layer. The length of this array will + determine the number of hidden layers. Empty for no hidden layers. + + + + optimizer_params (optional) + TEXT, default: NULL. +Parameters for optimization in a comma-separated string +of key-value pairs. See the description below for details. + + + activation + TEXT, default: 'sigmoid'. +Activation function. Currently three functions are supported: 'sigmoid' (default), +'relu', and 'tanh'. The text can be any subset of the three +strings; for e.g., activation='s' will use the sigmoid activation. + + + +Output tables + +The model table produced by mlp contains the following columns: + + +coeffs +FLOAT8[]. Flat array containing the weights of the neural net + + +n_iterations +INTEGER. Number of iterations completed by stochastic gradient descent +algorithm. The algorithm either converged in this number of iterations +or hit the maximum number specified in the optimization parameters. + + +loss +FLOAT8. The cross entropy over the training data. +See Technical Background section below for more details. + + + + +A summ
[GitHub] incubator-madlib pull request #149: MLP: Multilayer Perceptron
Github user edespino commented on a diff in the pull request: https://github.com/apache/incubator-madlib/pull/149#discussion_r127314914 --- Diff: src/ports/postgres/modules/convex/mlp.sql_in --- @@ -0,0 +1,744 @@ +/* --- *//** + * + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, + * software distributed under the License is distributed on an + * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + * KIND, either express or implied. See the License for the + * specific language governing permissions and limitations + * under the License. + * + * + * @file mlp.sql_in + * + * @brief SQL functions for multilayer perceptron + * @date June 2012 + * + * + *//* --- */ + +m4_include(`SQLCommon.m4') + +/** +@addtogroup grp_mlp + +Contents +Classification +Regression +Optimizer Parameters +Prediction Functions/a> +Examples +Technical Background +Literature +Related Topics + + +Multilayer Perceptron (MLP) is a model for regression and +classification + +Also called "vanilla neural networks", they consist of several +fully connected hidden layers with non-linear activation +functions. In the case of classification, the final layer of the +neural net has as many nodes as classes, and the output of the +neural net can be interpreted as the probability of a given input +feature belonging to a specific class. + + +@brief Solves classification and regression problems with several +fully connected layers and nonlinear activation functions. + +@anchor mlp_classification +@par Classification Training Function +The mlp classification training function has the following format: + +mlp_classification( +source_table, +output_table, +independent_varname, +dependent_varname, +hidden_layer_sizes, +optimizer_params, +activation +) + +\b Arguments + + source_table + TEXT. Name of the table containing the training data. + + model_table + TEXT. Name of the output table containing the model. Details of the output + tables are provided below. + + + independent_varname + TEXT. Expression list to evaluate for the +independent variables. An intercept variable should not be included as part +of this expression. Please note that expression should be able to be cast +to DOUBLE PRECISION[]. + + + dependent_varname + TEXT. Name of the dependent variable column. For classification, supported types are: + text, varchar, character varying, char, character + integer, smallint, bigint, and boolean. + + hidden_layer_sizes + INTEGER[], default: ARRAY[]. + The number of neurons in each hidden layer. The length of this array will + determine the number of hidden layers. Empty for no hidden layers. + + + + optimizer_params (optional) + TEXT, default: NULL. +Parameters for optimization in a comma-separated string +of key-value pairs. See the description below for details. + + + activation + TEXT, default: 'sigmoid'. +Activation function. Currently three functions are supported: 'sigmoid' (default), +'relu', and 'tanh'. The text can be any subset of the three +strings; for e.g., activation='s' will use the sigmoid activation. + + + +Output tables + +The model table produced by mlp contains the following columns: + + +coeffs +FLOAT8[]. Flat array containing the weights of the neural net + + +n_iterations +INTEGER. Number of iterations completed by stochastic gradient descent +algorithm. The algorithm either converged in this number of iterations +or hit the maximum number specified in the optimization parameters. + + +loss +FLOAT8. The cross entropy over the training data. +See Technical Background section below for more details. + + + + +A summ
[GitHub] incubator-madlib pull request #149: MLP: Multilayer Perceptron
Github user edespino commented on a diff in the pull request: https://github.com/apache/incubator-madlib/pull/149#discussion_r127315428 --- Diff: src/ports/postgres/modules/convex/mlp.sql_in --- @@ -0,0 +1,744 @@ +/* --- *//** + * + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, + * software distributed under the License is distributed on an + * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + * KIND, either express or implied. See the License for the + * specific language governing permissions and limitations + * under the License. + * + * + * @file mlp.sql_in + * + * @brief SQL functions for multilayer perceptron + * @date June 2012 + * + * + *//* --- */ + +m4_include(`SQLCommon.m4') + +/** +@addtogroup grp_mlp + +Contents +Classification +Regression +Optimizer Parameters +Prediction Functions/a> +Examples +Technical Background +Literature +Related Topics + + +Multilayer Perceptron (MLP) is a model for regression and +classification + +Also called "vanilla neural networks", they consist of several +fully connected hidden layers with non-linear activation +functions. In the case of classification, the final layer of the +neural net has as many nodes as classes, and the output of the +neural net can be interpreted as the probability of a given input +feature belonging to a specific class. + + +@brief Solves classification and regression problems with several +fully connected layers and nonlinear activation functions. + +@anchor mlp_classification +@par Classification Training Function +The mlp classification training function has the following format: + +mlp_classification( +source_table, +output_table, +independent_varname, +dependent_varname, +hidden_layer_sizes, +optimizer_params, +activation +) + +\b Arguments + + source_table + TEXT. Name of the table containing the training data. + + model_table + TEXT. Name of the output table containing the model. Details of the output + tables are provided below. + + + independent_varname + TEXT. Expression list to evaluate for the +independent variables. An intercept variable should not be included as part +of this expression. Please note that expression should be able to be cast +to DOUBLE PRECISION[]. + + + dependent_varname + TEXT. Name of the dependent variable column. For classification, supported types are: + text, varchar, character varying, char, character + integer, smallint, bigint, and boolean. + + hidden_layer_sizes + INTEGER[], default: ARRAY[]. + The number of neurons in each hidden layer. The length of this array will + determine the number of hidden layers. Empty for no hidden layers. + + + + optimizer_params (optional) + TEXT, default: NULL. +Parameters for optimization in a comma-separated string +of key-value pairs. See the description below for details. + + + activation + TEXT, default: 'sigmoid'. +Activation function. Currently three functions are supported: 'sigmoid' (default), +'relu', and 'tanh'. The text can be any subset of the three +strings; for e.g., activation='s' will use the sigmoid activation. + + + +Output tables + +The model table produced by mlp contains the following columns: + + +coeffs +FLOAT8[]. Flat array containing the weights of the neural net + + +n_iterations +INTEGER. Number of iterations completed by stochastic gradient descent +algorithm. The algorithm either converged in this number of iterations +or hit the maximum number specified in the optimization parameters. + + +loss +FLOAT8. The cross entropy over the training data. +See Technical Background section below for more details. + + + + +A summ
[GitHub] incubator-madlib pull request #149: MLP: Multilayer Perceptron
Github user edespino commented on a diff in the pull request: https://github.com/apache/incubator-madlib/pull/149#discussion_r127317330 --- Diff: src/ports/postgres/modules/convex/mlp.sql_in --- @@ -0,0 +1,744 @@ +/* --- *//** + * + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, + * software distributed under the License is distributed on an + * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + * KIND, either express or implied. See the License for the + * specific language governing permissions and limitations + * under the License. + * + * + * @file mlp.sql_in + * + * @brief SQL functions for multilayer perceptron + * @date June 2012 + * + * + *//* --- */ + +m4_include(`SQLCommon.m4') + +/** +@addtogroup grp_mlp + +Contents +Classification +Regression +Optimizer Parameters +Prediction Functions/a> +Examples +Technical Background +Literature +Related Topics + + +Multilayer Perceptron (MLP) is a model for regression and +classification + +Also called "vanilla neural networks", they consist of several +fully connected hidden layers with non-linear activation +functions. In the case of classification, the final layer of the +neural net has as many nodes as classes, and the output of the +neural net can be interpreted as the probability of a given input +feature belonging to a specific class. + + +@brief Solves classification and regression problems with several +fully connected layers and nonlinear activation functions. + +@anchor mlp_classification +@par Classification Training Function +The mlp classification training function has the following format: + +mlp_classification( +source_table, +output_table, +independent_varname, +dependent_varname, +hidden_layer_sizes, +optimizer_params, +activation +) + +\b Arguments + + source_table + TEXT. Name of the table containing the training data. + + model_table + TEXT. Name of the output table containing the model. Details of the output + tables are provided below. + + + independent_varname + TEXT. Expression list to evaluate for the +independent variables. An intercept variable should not be included as part +of this expression. Please note that expression should be able to be cast +to DOUBLE PRECISION[]. + + + dependent_varname + TEXT. Name of the dependent variable column. For classification, supported types are: + text, varchar, character varying, char, character + integer, smallint, bigint, and boolean. + + hidden_layer_sizes + INTEGER[], default: ARRAY[]. + The number of neurons in each hidden layer. The length of this array will + determine the number of hidden layers. Empty for no hidden layers. + + + + optimizer_params (optional) + TEXT, default: NULL. +Parameters for optimization in a comma-separated string +of key-value pairs. See the description below for details. + + + activation + TEXT, default: 'sigmoid'. +Activation function. Currently three functions are supported: 'sigmoid' (default), +'relu', and 'tanh'. The text can be any subset of the three +strings; for e.g., activation='s' will use the sigmoid activation. + + + +Output tables + +The model table produced by mlp contains the following columns: + + +coeffs +FLOAT8[]. Flat array containing the weights of the neural net + + +n_iterations +INTEGER. Number of iterations completed by stochastic gradient descent +algorithm. The algorithm either converged in this number of iterations +or hit the maximum number specified in the optimization parameters. + + +loss +FLOAT8. The cross entropy over the training data. +See Technical Background section below for more details. + + + + +A summ
[GitHub] incubator-madlib pull request #149: MLP: Multilayer Perceptron
Github user edespino commented on a diff in the pull request: https://github.com/apache/incubator-madlib/pull/149#discussion_r127317719 --- Diff: src/ports/postgres/modules/convex/mlp.sql_in --- @@ -0,0 +1,744 @@ +/* --- *//** + * + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, + * software distributed under the License is distributed on an + * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + * KIND, either express or implied. See the License for the + * specific language governing permissions and limitations + * under the License. + * + * + * @file mlp.sql_in + * + * @brief SQL functions for multilayer perceptron + * @date June 2012 + * + * + *//* --- */ + +m4_include(`SQLCommon.m4') + +/** +@addtogroup grp_mlp + +Contents +Classification +Regression +Optimizer Parameters +Prediction Functions/a> +Examples +Technical Background +Literature +Related Topics + + +Multilayer Perceptron (MLP) is a model for regression and +classification + +Also called "vanilla neural networks", they consist of several +fully connected hidden layers with non-linear activation +functions. In the case of classification, the final layer of the +neural net has as many nodes as classes, and the output of the +neural net can be interpreted as the probability of a given input +feature belonging to a specific class. + + +@brief Solves classification and regression problems with several +fully connected layers and nonlinear activation functions. + +@anchor mlp_classification +@par Classification Training Function +The mlp classification training function has the following format: + +mlp_classification( +source_table, +output_table, +independent_varname, +dependent_varname, +hidden_layer_sizes, +optimizer_params, +activation +) + +\b Arguments + + source_table + TEXT. Name of the table containing the training data. + + model_table + TEXT. Name of the output table containing the model. Details of the output + tables are provided below. + + + independent_varname + TEXT. Expression list to evaluate for the +independent variables. An intercept variable should not be included as part +of this expression. Please note that expression should be able to be cast +to DOUBLE PRECISION[]. + + + dependent_varname + TEXT. Name of the dependent variable column. For classification, supported types are: + text, varchar, character varying, char, character + integer, smallint, bigint, and boolean. + + hidden_layer_sizes + INTEGER[], default: ARRAY[]. + The number of neurons in each hidden layer. The length of this array will + determine the number of hidden layers. Empty for no hidden layers. + + + + optimizer_params (optional) + TEXT, default: NULL. +Parameters for optimization in a comma-separated string +of key-value pairs. See the description below for details. + + + activation + TEXT, default: 'sigmoid'. +Activation function. Currently three functions are supported: 'sigmoid' (default), +'relu', and 'tanh'. The text can be any subset of the three +strings; for e.g., activation='s' will use the sigmoid activation. + + + +Output tables + +The model table produced by mlp contains the following columns: + + +coeffs +FLOAT8[]. Flat array containing the weights of the neural net + + +n_iterations +INTEGER. Number of iterations completed by stochastic gradient descent +algorithm. The algorithm either converged in this number of iterations +or hit the maximum number specified in the optimization parameters. + + +loss +FLOAT8. The cross entropy over the training data. +See Technical Background section below for more details. + + + + +A summ
[GitHub] incubator-madlib pull request #149: MLP: Multilayer Perceptron
Github user edespino commented on a diff in the pull request: https://github.com/apache/incubator-madlib/pull/149#discussion_r127319839 --- Diff: src/ports/postgres/modules/convex/mlp.sql_in --- @@ -0,0 +1,744 @@ +/* --- *//** + * + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, + * software distributed under the License is distributed on an + * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + * KIND, either express or implied. See the License for the + * specific language governing permissions and limitations + * under the License. + * + * + * @file mlp.sql_in + * + * @brief SQL functions for multilayer perceptron + * @date June 2012 + * + * + *//* --- */ + +m4_include(`SQLCommon.m4') + +/** +@addtogroup grp_mlp + +Contents +Classification +Regression +Optimizer Parameters +Prediction Functions/a> +Examples +Technical Background +Literature +Related Topics + + +Multilayer Perceptron (MLP) is a model for regression and +classification + +Also called "vanilla neural networks", they consist of several +fully connected hidden layers with non-linear activation +functions. In the case of classification, the final layer of the +neural net has as many nodes as classes, and the output of the +neural net can be interpreted as the probability of a given input +feature belonging to a specific class. + + +@brief Solves classification and regression problems with several +fully connected layers and nonlinear activation functions. + +@anchor mlp_classification +@par Classification Training Function +The mlp classification training function has the following format: + +mlp_classification( +source_table, +output_table, +independent_varname, +dependent_varname, +hidden_layer_sizes, +optimizer_params, +activation +) + +\b Arguments + + source_table + TEXT. Name of the table containing the training data. + + model_table + TEXT. Name of the output table containing the model. Details of the output + tables are provided below. + + + independent_varname + TEXT. Expression list to evaluate for the +independent variables. An intercept variable should not be included as part +of this expression. Please note that expression should be able to be cast +to DOUBLE PRECISION[]. + + + dependent_varname + TEXT. Name of the dependent variable column. For classification, supported types are: + text, varchar, character varying, char, character + integer, smallint, bigint, and boolean. + + hidden_layer_sizes + INTEGER[], default: ARRAY[]. + The number of neurons in each hidden layer. The length of this array will + determine the number of hidden layers. Empty for no hidden layers. + + + + optimizer_params (optional) + TEXT, default: NULL. +Parameters for optimization in a comma-separated string +of key-value pairs. See the description below for details. + + + activation + TEXT, default: 'sigmoid'. +Activation function. Currently three functions are supported: 'sigmoid' (default), +'relu', and 'tanh'. The text can be any subset of the three +strings; for e.g., activation='s' will use the sigmoid activation. + + + +Output tables + +The model table produced by mlp contains the following columns: + + +coeffs +FLOAT8[]. Flat array containing the weights of the neural net + + +n_iterations +INTEGER. Number of iterations completed by stochastic gradient descent +algorithm. The algorithm either converged in this number of iterations +or hit the maximum number specified in the optimization parameters. + + +loss +FLOAT8. The cross entropy over the training data. +See Technical Background section below for more details. + + + + +A summ
[GitHub] incubator-madlib pull request #149: MLP: Multilayer Perceptron
Github user edespino commented on a diff in the pull request: https://github.com/apache/incubator-madlib/pull/149#discussion_r127324219 --- Diff: src/ports/postgres/modules/convex/test/mlp.sql_in --- @@ -0,0 +1,832 @@ +/* --- *//** + * + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, + * software distributed under the License is distributed on an + * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + * KIND, either express or implied. See the License for the + * specific language governing permissions and limitations + * under the License. + * + *//* --- */ + + + + +-- The data were collected by Anderson, Edgar (1935). +-- The irises of the Gaspe Peninsula, Bulletin of the American Iris Society, 59, 2â5. + +-- Classification + + +SELECT setseed(0.5); +DROP TABLE IF EXISTS iris_data, iris_test, mlp_class, mlp_class_summary CASCADE; +CREATE TABLE iris_data( +id integer, +attributes numeric[], +class_text varchar, +class integer +); + +INSERT INTO iris_data VALUES +(1,ARRAY[5.1,3.5,1.4,0.2],'Iris-setosa',1), +(2,ARRAY[4.9,3.0,1.4,0.2],'Iris-setosa',1), +(3,ARRAY[4.7,3.2,1.3,0.2],'Iris-setosa',1), +(4,ARRAY[4.6,3.1,1.5,0.2],'Iris-setosa',1), +(5,ARRAY[5.0,3.6,1.4,0.2],'Iris-setosa',1), +(6,ARRAY[5.4,3.9,1.7,0.4],'Iris-setosa',1), +(7,ARRAY[4.6,3.4,1.4,0.3],'Iris-setosa',1), +(8,ARRAY[5.0,3.4,1.5,0.2],'Iris-setosa',1), +(9,ARRAY[4.4,2.9,1.4,0.2],'Iris-setosa',1), +(10,ARRAY[4.9,3.1,1.5,0.1],'Iris-setosa',1), +(11,ARRAY[5.4,3.7,1.5,0.2],'Iris-setosa',1), +(12,ARRAY[4.8,3.4,1.6,0.2],'Iris-setosa',1), +(13,ARRAY[4.8,3.0,1.4,0.1],'Iris-setosa',1), +(14,ARRAY[4.3,3.0,1.1,0.1],'Iris-setosa',1), +(15,ARRAY[5.8,4.0,1.2,0.2],'Iris-setosa',1), +(16,ARRAY[5.7,4.4,1.5,0.4],'Iris-setosa',1), +(17,ARRAY[5.4,3.9,1.3,0.4],'Iris-setosa',1), +(18,ARRAY[5.1,3.5,1.4,0.3],'Iris-setosa',1), +(19,ARRAY[5.7,3.8,1.7,0.3],'Iris-setosa',1), +(20,ARRAY[5.1,3.8,1.5,0.3],'Iris-setosa',1), +(21,ARRAY[5.4,3.4,1.7,0.2],'Iris-setosa',1), +(22,ARRAY[5.1,3.7,1.5,0.4],'Iris-setosa',1), +(23,ARRAY[4.6,3.6,1.0,0.2],'Iris-setosa',1), +(24,ARRAY[5.1,3.3,1.7,0.5],'Iris-setosa',1), +(25,ARRAY[4.8,3.4,1.9,0.2],'Iris-setosa',1), +(26,ARRAY[5.0,3.0,1.6,0.2],'Iris-setosa',1), +(27,ARRAY[5.0,3.4,1.6,0.4],'Iris-setosa',1), +(28,ARRAY[5.2,3.5,1.5,0.2],'Iris-setosa',1), +(29,ARRAY[5.2,3.4,1.4,0.2],'Iris-setosa',1), +(30,ARRAY[4.7,3.2,1.6,0.2],'Iris-setosa',1), +(31,ARRAY[4.8,3.1,1.6,0.2],'Iris-setosa',1), +(32,ARRAY[5.4,3.4,1.5,0.4],'Iris-setosa',1), +(33,ARRAY[5.2,4.1,1.5,0.1],'Iris-setosa',1), +(34,ARRAY[5.5,4.2,1.4,0.2],'Iris-setosa',1), +(35,ARRAY[4.9,3.1,1.5,0.1],'Iris-setosa',1), +(36,ARRAY[5.0,3.2,1.2,0.2],'Iris-setosa',1), +(37,ARRAY[5.5,3.5,1.3,0.2],'Iris-setosa',1), +(38,ARRAY[4.9,3.1,1.5,0.1],'Iris-setosa',1), +(39,ARRAY[4.4,3.0,1.3,0.2],'Iris-setosa',1), +(40,ARRAY[5.1,3.4,1.5,0.2],'Iris-setosa',1), +(41,ARRAY[5.0,3.5,1.3,0.3],'Iris-setosa',1), +(42,ARRAY[4.5,2.3,1.3,0.3],'Iris-setosa',1), +(43,ARRAY[4.4,3.2,1.3,0.2],'Iris-setosa',1), +(44,ARRAY[5.0,3.5,1.6,0.6],'Iris-setosa',1), +(45,ARRAY[5.1,3.8,1.9,0.4],'Iris-setosa',1), +(46,ARRAY[4.8,3.0,1.4,0.3],'Iris-setosa',1), +(47,ARRAY[5.1,3.8,1.6,0.2],'Iris-setosa',1), +(48,ARRAY[4.6,3.2,1.4,0.2],'Iris-setosa',1), +(49,ARRAY[5.3,3.7,1.5,0.2],'Iris-setosa',1), +(50,ARRAY[5.0,3.3,1.4,0.2],'Iris-setosa',1), +(51,ARRAY[7.0,3.2,4.7,1.4],'Iris-versicolor',2), +(52,ARRAY[6.4,3.2,4.5,1.5],'Iris-versicolor',2), +(53,ARRAY[6.9,3.1,4.9,1.5],'Iris-versicolor',2), +(54,ARRAY[5.5,2.3,4.0,1.3],'Iris-versicolor',2), +(55,ARRAY[6.5,2.8,4.6,1.5],'Iris-versicolor',2), +(56,ARRAY[5.7,2.8,4.5,1.3],'Iris-versicolor',2), +(57,ARRAY[6.3,3.3,4.7,1.6],'Iris-versicolor',2), +(58,ARRAY[4.9,2.4,3.3,1.0],'Iris-versicolor',2), +(59,ARRAY[6.6,2.9,4.6,1.3],'Iris-versicolor',2), +(60,ARRAY[5.2,2.7,3.9,1.4],'Iris-versicolor',2), +(61,ARRAY[5.0,2.0,3.5,1.0],'Iris-versicolor',2), +(62,ARRAY[5.9,3.0,4.2,1.5],'Iris-versicolor',2), +(63,ARRAY[6.0,2.2,4.0,1.0],'Iris-versi
External references to MADlib incubator project content
When MADlib graduates, will the previous incubator links redirect to the TLP location? I noticed the following MADlib incubator references in the Pivotal Greenplum DB docs:: source page: Greenplum MADlib Extension for Analytics https://gpdb.docs.pivotal.io/4390/ref_guide/extensions/madlib.html#topic9 link references: MADlib web site is at http://madlib.incubator.apache.org/ MADlib documentation is at http://madlib.incubator.apache.org/documentation.html -=e -- *Ed Espino*
Re: External references to MADlib incubator project content
Yes it will re-direct to the TLP location. On Thu, Jul 13, 2017 at 3:42 PM, Ed Espino wrote: > When MADlib graduates, will the previous incubator links redirect to the > TLP location? I noticed the following MADlib incubator references in the > Pivotal Greenplum DB docs:: > > source page: > Greenplum MADlib Extension for Analytics > https://gpdb.docs.pivotal.io/4390/ref_guide/extensions/madlib.html#topic9 > > link references: > MADlib web site is at http://madlib.incubator.apache.org/ > MADlib documentation is at > http://madlib.incubator.apache.org/documentation.html > > -=e > -- > *Ed Espino* >
Re: Question regarding tracking Release v1.12 specific changes.
Hi Ed, We have two sql files in the src/madpack folder to track the changes: diff_udf and diff_udt. To use them, you install the old version of madlib in the schema "madlib_old_vers" and the new version in the default schema "madlib". With this setup, running these sql files automatically give you the changed UDTs and UDFs. The rest is handled manually. I hope it helps, please let me know if there is anything else I can help with. Thanks, Orhan On Thu, Jul 13, 2017 at 12:14 PM, Ed Espino wrote: > MADlib dev, > > While reviewing a PR, I noticed the doc top-level generated html page has a > reference to previous releases ( > https://madlib.incubator.apache.org/docs/latest/). I found the > corresponding v1.11 changelist ( > https://github.com/apache/incubator-madlib/commit/ > 648b05798826956e9621027447af501c194392b8) > which updated the previous release versions to include v1.10 (in addition > to other v1.11 related changes). Aside from this PR, how does the project > track these types of release changes (Jira, wiki, email, other). I could > not find a reference to them in the project's wiki. It might be staring me > in the face so I apologize if it is. Any guidance is greatly appreciated. > > -=e > > p.s. And yes, this is in preparation for my v1.12 release manager role. > Gaining context comes at a price. > > -- > *Ed Espino* >
