AI News, harvardnlp/sent-conv-torch

harvardnlp/sent-conv-torch

This code implements Kim (2014) sentence convolution code in Torch with GPUs.

To make data in hdf5 format, run the following (with word2vec .bin path and choice of dataset): To run training with GPUs: Results are timestamped and saved to the results/ directory.

The training pipeline requires Python hdf5 (the h5py module) and the following lua packages: Training on word2vec architecture models requires downloading word2vec and unzipping.

The data takes word2vec embeddings, processes the vocabulary, and outputs a data matrix of vocabulary indices for each sentence.

To create the hdf5 file, run the following with DATASET as one of the described datasets: The script outputs: We allow training on arbitrary text datasets.

Training hyperparameters: Model hyperparameters: The following results were collected with the same training setup as in Kim (2014) (same parameters, 10-fold cross validation if data has no test set, 25 epochs).

With 1 highway layer, SST1 achieves a mean score of mean 47.8, stddev 0.857, over 5 trials, and with 2 highway layers, mean 47.1, stddev 1.47, over 10 trials.

ILSVRC2012 task 1who is the best in ILSVRC2012 task 1 ?

It can be seen as similar in flavor to MNIST(e.g., the images are of small cropped digits), but incorporates an order of magnitude more labeled data (over 600,000 digit images) and comes from a significantly harder, unsolved, real world problem (recognizing digits and numbers in natural scene images).

How To Build a Machine Learning Classifier in Python with Scikit-learn

Machine learning is a research field in computer science, artificial intelligence, and statistics.

Banks use machine learning to detect fraudulent activity in credit card transactions, and healthcare companies are beginning to use machine learning to monitor, assess, and diagnose patients.

Make sure you’re in the directory where your environment is located, and run the following command: With our programming environment activated, check to see if the Sckikit-learn module is already installed: If sklearn is installed, this command will complete with no error.

If it is not installed, you will see the following error message: The error message indicates that sklearn is not installed, so download the library using pip: Once the installation completes, launch Jupyter Notebook: In Jupyter, create a new Python Notebook called ML Tutorial.

The dataset has 569 instances, or data, on 569 tumors and includes information on 30 attributes, or features, such as the radius of the tumor, texture, smoothness, and area.

The important dictionary keys to consider are the classification label names (target_names), the actual labels (target), the attribute/feature names (feature_names), and the attributes (data).

Given the label we are trying to predict (malignant versus benign tumor), possible useful attributes include the size, radius, and texture of the tumor.

To get a better understanding of our dataset, let's take a look at our data by printing our class labels, the first data instance's label, our feature names, and the feature values for the first data instance: You'll see the following results if you run the code:

As the image shows, our class names are malignant and benign, which are then mapped to binary values of 0 and 1, where 0 represents malignant tumors and 1 represents benign tumors.

Then initialize the model with the GaussianNB() function, then train the model by fitting it to the data using gnb.fit(): After we train the model, we can then use the trained model to make predictions on our test set, which we do using the predict() function.

As you see in the Jupyter Notebook output, the predict() function returned an array of 0s and 1s which represent our predicted values for the tumor class (malignant vs.

Using the array of true class labels, we can evaluate the accuracy of our model's predicted values by comparing the two arrays (test_labels vs.

Predicting Income with the Census Income Dataset

The Census Income Data Set contains over 48,000 samples with attributes including age, occupation, education, and income (a binary label, either >50K or <=50K).

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 wide and deep model truly shines on larger data sets with high-cardinality features, where each feature has millions/billions of unique possible values (which is the specialty of the wide model).

It allows you to move from single-worker training to distributed training, and it makes it easy to export model binaries for prediction.

You can run the code locally as follows: The model is saved to /tmp/census_model by default, which can be changed using the --model_dir flag.

You can also experiment with -inter and -intra flag to explore inter/intra op parallelism for potential better performance as follows: Please note the above optional inter/intra op does not affect model accuracy.

You can export the model into Tensorflow SavedModel format by using the argument --export_dir: After the model finishes training, use saved_model_cli to inspect and execute the SavedModel.

You can also run this model on Cloud ML Engine, which provides hyperparameter tuning to maximize your model's results and enables deploying your model for prediction.

Training and Testing Data Sets

Typically, when you separate a data set into a training set and testing set, most of the data is used for training, and a smaller portion of the data is used for testing.

The information about the size of the training and testing data sets, and which row belongs to which set, is stored with the structure, and all the models that are based on that structure can use the sets for training and testing.

By default, after you have defined the data sources for a mining structure, the Data Mining Wizard will divide the data into two sets: one with 70 percent of the source data, for training the model, and one with 30 percent of the source data, for testing the model.

You can also configure the wizard to set a maximum number of training cases, or you can combine the limits to allow a maximum percentage of cases up to a specified maximum number of cases.

For example, if you specify 30 percent holdout for the testing cases, and the maximum number of test cases as 1000, the size of the test set will never exceed 1000 cases.

If you use the same data source view for different mining structures, and want to ensure that the data is divided in roughly the same way for all mining structures and their models, you should specify the seed that is used to initialize random sampling.

If you want to determine the number of cases used for training or for testing, or if you want to find additional details about the cases included in the training and test sets, you can query the model structure by creating a DMX query.

TensorFlow-Slim image classification model library

to train models from scratch or fine-tune them from pre-trained network weights.

It also contains code for downloading standard image datasets, converting

In this section, we describe the steps required to install the appropriate prerequisite

repository as follows: This will put the TF-Slim image models library in $HOME/workspace/models/research/slim. (It

you can safely ignore this.) To verify that this has worked, execute the following commands;

As part of this library, we've included scripts to download several popular image

datasets (listed below) and convert them to slim format.

For each dataset, we'll need to download the raw data and convert it to TensorFlow's

When the script finishes you will find several TFRecord files created: These represent the training and validation data, sharded over 5 files each. You

stores pointers to the data file, as well as various other pieces of metadata,

example of how to load data using a TF-Slim dataset descriptor using a TF-Slim DatasetDataProvider is

The TFRecord format consists of a set of sharded files where each entry is a serialized tf.Example proto.

Each tf.Example proto contains the ImageNet image (JPEG encoded) as well as metadata such as label and bounding box information.

the data may take several hours (up to half a day) depending on your

that your hard disk has at least 500 GB of free space for downloading and storing

The final line of the output script should read: When the script finishes you will find 1024 and 128 training and validation files

model file, the link to the model checkpoint, and the top 1 and top 5 accuracy

Some academic papers report higher accuracy by using multiple crops

ResNet V2 models use Inception pre-processing and input image size of 299 (use --preprocessing_name

(#) More information and details about the NASNet architectures are available at this README All 16 float MobileNet V1 models reported in the MobileNet Paper and all 16

We provide an easy way to train a model from scratch using any TF-Slim dataset. The

indicate a checkpoint from which to fine-tune, we'll call training with the

of output labels, we wont be able restore the final logits (classifier) layer.

new model will have a final 'logits' layer whose dimensions differ from the pre-trained

the pre-trained logits layer will have dimensions [2048 x 1001] but our

specify which subsets of layers should trained, the rest would remain frozen.

Below we give an example of downloading the pretrained inception model and evaluating

an example of how to evaluate a model at multiple checkpoints during or after the training.

To use it with a model name defined by slim, run: If you then want to use the resulting model with your own or pretrained checkpoints

with the variables inlined as constants using: The output node names will vary depending on the model, but you can inspect and estimate

them using the summarize_graph tool: To run the resulting graph in C++, you can look at the label_image sample code:

ImageNet labels being shifted down by one: The preprocessing functions all take height and width as parameters.

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