wide_deep | Wide and Deep Learning for CTR Prediction in tensorflow | Machine Learning library
kandi X-RAY | wide_deep Summary
kandi X-RAY | wide_deep Summary
A general Wide and Deep Joint Learning Framework. Deep part can be a simple Dnn, Dnn Variants(ResDnn, DenseDnn), MultiDnn or even combine with Cnn (Dnn-Cnn). Here, we use the wide and deep model to predict the click labels. The wide model is able to memorize interactions with data with a large number of features but not able to generalize these learned interactions on new data. The deep model generalizes well but is unable to learn exceptions within the data. The wide and deep model combines the two models and is able to generalize while learning exceptions. The code uses the high level tf.estimator.Estimator API. This API is great for fast iteration and quickly adapting models to your own datasets without major code overhauls. It allows you to move from single-worker training to distributed training, and it makes it easy to export model binaries for prediction.
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Top functions reviewed by kandi - BETA
- Generates a deep combined op
- Build logit function
- Build a logit_fn
- Logit function
- Builds the model columns
- Validate the cross feature conf
- Reads the cross feature configuration file
- Read the feature conf file
- Build an estimator
- Return the name of the activation function
- Construct input function
- Performs inference
- Forward pass through x
- Preprocess an image
- Prints tensors in a checkpoint file
- Prints the test results
- Build a custom Estimator
- Build a block of inputs
- Build a VGG model
- Get the name of the activation function
- Build the VGG model
- Prepare hdfs data preprocessing
- Train and evaluate a model
- Bottleneck block of inputs
- Bottleneck residual v2
- Residual layer
- Train a model
wide_deep Key Features
wide_deep Examples and Code Snippets
Community Discussions
Trending Discussions on wide_deep
QUESTION
I've found the information that so as to use Estimator Model on GPU I need the following code:
...ANSWER
Answered 2018-Sep-22 at 01:50You need to define the variables before you use them, as well as set the GPU count to a non-zero number.
QUESTION
I did build, owing a lot to the help I got on SO, a binary classifier basing on the the wide-and-deep Tensorflow tutorial (here is its "Main" file this question is referring to), used in "wide" only mode.
The function I use to extract the classification guess is:
...ANSWER
Answered 2018-May-14 at 16:19tf.estimator.LinearClassifier instances return a dictionary of values you can use. You're only using pred[ 'classes' ] in your code but you also have the probability values are in pred[ 'probabilities' ]. You can also just
QUESTION
I'm trying to understand the TensorFlow Wide & Deep Learning Tutorial. The census income dataset has two files for validation: adult.data and adult.test. After a certain number of epochs, it prints an evaluation (you can see the complete code here: https://github.com/tensorflow/models/blob/master/official/wide_deep/wide_deep.py). It uses "input_fn" to read input information from a csv file. It's used to read both files, adult.data and adult.test.
...ANSWER
Answered 2018-Feb-16 at 15:22Both training and testing require mini-batches of data, because both may lead to out-of-memory error (OOM) otherwise. You are right that the problem is more critical in training because backward pass effectively doubles memory consumption. But it doesn't mean OOM is impossible in inference.
Examples from my experience:
... and I'm sure there are many more examples that I haven't seen. Depending on your resources, 16281 might be small enough to fit into one batch, but in general it makes perfect sense to iterate in batches in inference and have a separate setting for this batch size, for instance if the model would ever run on another machine with fewer resources.
QUESTION
I am following the wide_deep tutorial but I am having a hard time reproducing the example of reading in a CSV properly.
Here is my code to generate a dummy CSV:
...ANSWER
Answered 2017-Dec-30 at 03:48The issue you are facing is due to the fact that the v.eval() will advance the iterator for all components. From the (DOCS):
Note that evaluating any of next1, next2, or next3 will advance the iterator for all components. A typical consumer of an iterator will include all components in a single expression.
One way to get what you are after is:
Code:QUESTION
I'm using the TensorFlow Dataset API to parse a CSV file and run a logistic regression. I'm following the example from the TF documentation here.
The following code snippet shows how I am setting up the model:
...ANSWER
Answered 2017-Dec-05 at 01:10The error is raised because the tf.feature_column methods expect the input to be batched, and
I think the cause is a simple typo, which is dropping out the Dataset.batch() transformation. Replace the dataset.batch(batch_size) with the following line:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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Install wide_deep
You can use wide_deep like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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