Convolutional Neural Network for Text Classification in Tensorflow

Last update: Jan 02, 2023

Overview

This code belongs to the "Implementing a CNN for Text Classification in Tensorflow" blog post.

It is slightly simplified implementation of Kim's Convolutional Neural Networks for Sentence Classification paper in Tensorflow.

Requirements

Python 3
Tensorflow > 0.12
Numpy

Training

Print parameters:

./train.py --help

optional arguments:
  -h, --help            show this help message and exit
  --embedding_dim EMBEDDING_DIM
                        Dimensionality of character embedding (default: 128)
  --filter_sizes FILTER_SIZES
                        Comma-separated filter sizes (default: '3,4,5')
  --num_filters NUM_FILTERS
                        Number of filters per filter size (default: 128)
  --l2_reg_lambda L2_REG_LAMBDA
                        L2 regularizaion lambda (default: 0.0)
  --dropout_keep_prob DROPOUT_KEEP_PROB
                        Dropout keep probability (default: 0.5)
  --batch_size BATCH_SIZE
                        Batch Size (default: 64)
  --num_epochs NUM_EPOCHS
                        Number of training epochs (default: 100)
  --evaluate_every EVALUATE_EVERY
                        Evaluate model on dev set after this many steps
                        (default: 100)
  --checkpoint_every CHECKPOINT_EVERY
                        Save model after this many steps (default: 100)
  --allow_soft_placement ALLOW_SOFT_PLACEMENT
                        Allow device soft device placement
  --noallow_soft_placement
  --log_device_placement LOG_DEVICE_PLACEMENT
                        Log placement of ops on devices
  --nolog_device_placement

Train:

./train.py

Evaluating

./eval.py --eval_train --checkpoint_dir="./runs/1459637919/checkpoints/"

Replace the checkpoint dir with the output from the training. To use your own data, change the eval.py script to load your data.

Convolutional Neural Network for Text Classification in Tensorflow

Related tags

Overview

Requirements

Training

Evaluating

References

Owner

Denny Britz

Code for the Weighted, Accelerated and Restarted Primal-dual algorithm. This algorithm achieves stable linear convergence for reconstruction from undersampled noisy measurements under an approximate sharpness condition. See the paper for details.

A Python library for Deep Graph Networks

rliable is an open-source Python library for reliable evaluation, even with a handful of runs, on reinforcement learning and machine learnings benchmarks.

PHOTONAI is a high level python API for designing and optimizing machine learning pipelines.

The official repository for our paper "The Neural Data Router: Adaptive Control Flow in Transformers Improves Systematic Generalization".

Prompt-BERT: Prompt makes BERT Better at Sentence Embeddings

Replication package for the manuscript "Using Personality Detection Tools for Software Engineering Research: How Far Can We Go?" submitted to TOSEM

Software Platform for solving and manipulating multiparametric programs in Python

Brain tumor detection using CNN (InceptionResNetV2 Model)

A Multi-modal Perception Tracker (MPT) for speaker tracking using both audio and visual modalities

PassAPI is a password generator in hash format and fully developed in Python, with the aim of teaching how to handle and build

The code for our paper CrossFormer: A Versatile Vision Transformer Based on Cross-scale Attention.

pytorch, hand(object) detect ,yolo v5，手检测

Implementation of U-Net and SegNet for building segmentation

simple_pytorch_example project is a toy example of a python script that instantiates and trains a PyTorch neural network on the FashionMNIST dataset

Open-AI's DALL-E for large scale training in mesh-tensorflow.

A multi-mode modulator for multi-domain few-shot classification (ICCV)

Learning Saliency Propagation for Semi-supervised Instance Segmentation

An addon uses SMPL's poses and global translation to drive cartoon character in Blender.

OpenDelta - An Open-Source Framework for Paramter Efficient Tuning.