embedding

问题 I am trying to embed python script in a c++ application. To try out the integration, I made a pilot code: // c++ code int main(int argc, char *argv[]) { PyObject *pName, *pModule, *pDict, *pFunc, *pValue; if (argc < 3) { printf("Usage: exe_name python_source function_name\n"); return 1; } // Initialize the Python Interpreter Py_Initialize(); // Build the name object pName = PyBytes_FromString(argv[1]); //std::to_string(argv[1]).encode( // Load the module object pModule = PyImport_Import(pName

问题 I have 3 word embeddings : embedding#1 : [w11, w12, w13, w14] embedding#2 : [w21, w22, w23, w24] embedding#3 : [w31, w32, w33, w34] Is there a way to get a fourth embedding by adding all three vectors, with the trainable weights from all of them, like: embedding#4 : [w11 + w21 + w31, w12 + w22 + w32, w13 + w23 + w33, w14 + w24 + w34] ? Is there a way to do this in a keras layer? Problem I want to learn the word embeddings for Indonesian language. I plan to do this by training a sequence

问题 I have trained a word2vec model in tensorflow. But when I save the session, it only outputted model.ckpt.data / .index / .meta files. I was thinking of implementing KNN method in retrieving nearest words. I saw answers of using gensim, but how can I save my tensorflow word2vec model into .txt first? 回答1: Simply evaluate the embeddings matrix into a numpy array and write it to the file along with resolved words. Sample code: vocabulary_size = 50000 embedding_size = 128 # Assume your word to

问题 I have created a simple React component. I want that component to use as a plugin or widget. I have taken help from Writing embeddable Javascript plugin with React & Webpack and manage to set my webpack as below: output: { path: __dirname + '/dist', filename: './app/components/FirstWidgetIndex.js', publicPath: '/', library: 'tracking', libraryTarget: 'umd', umdNamedDefine: true, } And my component is as below: import React, { Component, PropTypes } from 'react'; export default class

问题 I don't know about you but I'm having a really hard time getting my head around TLF. I'm using Flash CS5. I've got an embedded font loaded from an external SWF. As far as I can tell, it's embedded properly - it used to work before I switched from the classic TextField - but I needed to switch because I need the advanced ligature support. My code is: //setting up... var text:TFLTextField = new TLFTextField(); text.width = 530; text.height = 330; text.type = TextFieldType.INPUT; text.text =

问题 I am building TensorFlow model for NLP task, and I am using pretrained Glove 300d word-vector/embedding dataset. Obviously some tokens can't be resolved as embeddings, because were not included into training dataset for word vector embedding model, e.g. rare names. I can replace those tokens with vectors of 0s, but rather than dropping this information on the floor, I prefer to encode it somehow and include to my training data. Say, I have 'raijin' word, which can't be resolved as embedding

问题 I have anaconda Python first on my path, but a simple Python embedding example shows my Mac system python version instead, even though ProgramFullPath correctly points to anaconda python. Is there a way to correctly find / use anaconda python? Minimal example: #include <Python.h> #include <stdio.h> int main(void) { Py_Initialize(); printf("Python version:\n%s\n", Py_GetVersion()); printf("Python Program Full Path:\n%s\n", Py_GetProgramFullPath()); Py_Finalize(); return 0; } I compile with,

问题 With pretrained embeddings, we can specify them as weights in keras' embedding layer. To use multiple embeddings, would specifying multiple embedding layer be suitable? i.e. embedding_layer1 = Embedding(len(word_index) + 1, EMBEDDING_DIM, weights=[embedding_matrix_1], input_length=MAX_SEQUENCE_LENGTH, trainable=False) embedding_layer2 = Embedding(len(word_index) + 1, EMBEDDING_DIM, weights=[embedding_matrix_2], input_length=MAX_SEQUENCE_LENGTH, trainable=False) model.add(embedding_layer1)

问题 I created a embedded derby in JDBC mode but when I try to access it in java class it gives me error: java.sql.SQLException: Failed to create database 'myDB', see the next exception for details. at org.apache.derby.impl.jdbc.SQLExceptionFactory40.getSQLException(Unknown Source) at org.apache.derby.impl.jdbc.Util.newEmbedSQLException(Unknown Source) at org.apache.derby.impl.jdbc.Util.seeNextException(Unknown Source) at org.apache.derby.impl.jdbc.EmbedConnection.createDatabase(Unknown Source) at

问题 Is it possible to embed Lua into Python 3.x? If so how could I run and execute Lua scripts within my Python program? How good would the interaction be between the Languages for instance would Lua have access to all of Python's variables and classes and would Python have access to all of Lua's information? 回答1: Try Lunatic Python, a two-way bridge between Python and Lua. 回答2: In addition to Lunatic Python, there is Lupa, powered by LuaJIT: http://pypi.python.org/pypi/lupa/0.9 See the link