[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-179251-105":59,"doc-detail-179251-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","momm-tensorflow","Momm TensorFlow","","This document excerpt, likely from a presentation or tutorial slide, outlines the foundational steps for building a machine learning model using TensorFlow. It details the process of assembling a computational graph, starting with data input and progressing through the creation of placeholders for inputs and labels. The guide further explains the declaration of variables for weights and biases, followed by constructing the predictive model equation (Y_predicted = X * w + b). Key components also covered include specifying the loss function, such as mean squared error (tf.square(Y-Y_predicted, name=\"loss\")), and defining an optimizer like Gradient Descent (tf.train.GradientDescentOptimizer). The document emphasizes the creation of a TensorFlow session as the environment for executing the graph and running operations like printing variable values. This structured approach is crucial for understanding and implementing deep learning models within the TensorFlow framework, facilitating efficient model training and evaluation.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":30,"@type":76,"position":81},"https://docshare.wps.com/document/technology/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/momm-tensorflow/179251/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/momm-tensorflow/179251.png","ImageObject",300,407,{"name":92,"@type":93},"\tCallum ","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-09","2026-09-02",true,{"@type":102,"interactionType":103,"userInteractionCount":34},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the role of tf.Session() in TensorFlow?","Question",{"text":112,"@type":113},"A tf.Session() in TensorFlow creates an environment for executing the computational graph and running operations.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How are inputs and labels handled in a TensorFlow model?",{"text":117,"@type":113},"Inputs and labels are handled using placeholders, defined with tf.placeholder(), which allow data to be fed into the graph during runtime.",{"name":119,"@type":110,"acceptedAnswer":120},"What is the purpose of tf.Variable() in TensorFlow?",{"text":121,"@type":113},"tf.Variable() is used to create trainable parameters, such as weights and biases, that are essential for building machine learning models.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},179251,1788336085,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":29,"category_name":30,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":34,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":8,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":129,"read_time":81},137451211410,"https://ap-avatar.wpscdn.com/avatar/2000bb0a9246f588df?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786362646172706240","| import tensorflow as tf |\n| --- |\n| placeholders |\n| tf. placeholder(dtype , shape=None , name=None) |\n| Variables |\n| tf.Variable(initial_value=None , trainable‐=True , collections=None , name=None , dtype=None , ...) |\n| Show Variable |\n|  |\n| with tf.Session() as sess: [print sess.run](print sess.run)(x) |\n| Phase 1: Assemble graph |\n| Step 1 : Read in data\u003Cbr>Step 2: Create placeholders for inputs and labels\u003Cbr>tf. placeholder(dtype , shape=None , name=None)\u003Cbr>Step 3: Create weight and bias\u003Cbr>tf.Variable(initial_value=None , trainable‐=True , collections=None , name=None , dtype=None , ...)\u003Cbr>Step 4: Build model to predict YY_predicted = X * w + b\u003Cbr>Step 5: Specify loss function\u003Cbr>tf.square(Y-Y_predicted , name=\"loss\") Step 6: Create optimizer\u003Cbr>tf.train.GradientDescentOptimizer(learning‐_rate=0.001) . minimize(loss) |\n|  |","cbCaimysS2HU0Ayk","https://ap.wps.com/l/cbCaimysS2HU0Ayk","pdf",32992,"English","# Phase 1: Assemble graph\n## Step 1 : Read in data\n## Step 2: Create placeholders for inputs and labels\n## Step 3: Create weight and bias\n## Step 4: Build model to predict YY_predicted = X * w + b\n## Step 5: Specify loss function\n## Step 6: Create optimizer","[{\"question\":\"What is the role of tf.Session() in TensorFlow?\",\"answer\":\"A tf.Session() in TensorFlow creates an environment for executing the computational graph and running operations.\"},{\"question\":\"How are inputs and labels handled in a TensorFlow model?\",\"answer\":\"Inputs and labels are handled using placeholders, defined with tf.placeholder(), which allow data to be fed into the graph during runtime.\"},{\"question\":\"What is the purpose of tf.Variable() in TensorFlow?\",\"answer\":\"tf.Variable() is used to create trainable parameters, such as weights and biases, that are essential for building machine learning models.\"}]","Momm TensorFlow | PDF"]