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Character-level Convolutional Networks for text Classification

Crepe model implementation with MXNet/Gluon

This is an implementation of the crepe model, Character-level Convolutional Networks for Text Classification. That this is the paper we reference throughout the tutorial

We are going to perform a text classification task, trying to classify Amazon reviews according to the product category they belong to.

This work is inspired from a previous collaborative work with Ilia Karmanov and Miguel Fierro

Install Guide

You need to install Apache MXNet in order to run this tutorial. The following lines should work in most platform but checkout the Apache install guide for more info, especially if you plan to use GPU

# GPU install
!pip install mxnet-cu90 pandas -q
# CPU install
#!pip install mxnet pandas -q

Data download

The dataset has been made available on this website: http://jmcauley.ucsd.edu/data/amazon/, citation of relevant papers:

Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering R. He, J. McAuley WWW, 2016

Image-based recommendations on styles and substitutes J. McAuley, C. Targett, J. Shi, A. van den Hengel SIGIR, 2015

We are downloading a subset of the reviews, the k-core reviews, where k=5. That means that for each category, the dataset has been trimmed to only contain 5 reviews per individual product, and 5 reviews per user.

base_url = 'http://snap.stanford.edu/data/amazon/productGraph/categoryFiles/'
prefix = 'reviews_'
suffix = '_5.json.gz'
folder = 'data'
categories = [
    'Home_and_Kitchen', ""
    'Books', 
    'CDs_and_Vinyl', 
    'Movies_and_TV', 
    'Cell_Phones_and_Accessories',
    'Sports_and_Outdoors', 
    'Clothing_Shoes_and_Jewelry'
]
!mkdir -p $folder
for category in categories:
    print(category)
    url = base_url+prefix+category+suffix
    !wget -P $folder $url -nc -nv

Home_and_Kitchen
Books
CDs_and_Vinyl
Movies_and_TV
Cell_Phones_and_Accessories
Sports_and_Outdoors
Clothing_Shoes_and_Jewelry

Data Pre-processing

We need to perform some pre-processing steps in order to have the data in a format we can use for training (X,Y) In order to speed up training and balance the dataset we will only use a subset of reviews for each category.

Load the data in memory

MAX_ITEMS_PER_CATEGORY = 250000

Helper functions to read from the .json.gzip files

import pandas as pd
import gzip

def parse(path):
    g = gzip.open(path, 'rb')
    for line in g:
        yield eval(line)

def get_dataframe(path, num_lines):
    i = 0
    df = {}
    for d in parse(path):
        if i > num_lines:
            break
        df[i] = d
        i += 1

    return pd.DataFrame.from_dict(df, orient='index')

/home/ec2-user/anaconda3/lib/python3.6/site-packages/matplotlib/__init__.py:962: UserWarning: Duplicate key in file "/home/ec2-user/.config/matplotlib/matplotlibrc", line #2
  (fname, cnt))
/home/ec2-user/anaconda3/lib/python3.6/site-packages/matplotlib/__init__.py:962: UserWarning: Duplicate key in file "/home/ec2-user/.config/matplotlib/matplotlibrc", line #3
  (fname, cnt))

For each category we load MAX_ITEMS_PER_CATEGORY by randomly sampling the files and shuffling

# Loading data from file if exist
try:
    data = pd.read_pickle('pickleddata.pkl')
except:
    data = None

If the data is not available in the pickled file, we create it from scratch

if data is None:
    data = pd.DataFrame(data={'X':[],'Y':[]})
    for index, category in enumerate(categories):
        df = get_dataframe("{}/{}{}{}".format(folder, prefix, category, suffix), MAX_ITEMS_PER_CATEGORY)    
        # Each review's summary is prepended to the main review text
        df = pd.DataFrame(data={'X':(df['summary']+' | '+df['reviewText'])[:MAX_ITEMS_PER_CATEGORY],'Y':index})
        data = data.append(df)
        print('{}:{} reviews'.format(category, len(df)))

    # Shuffle the samples
    data = data.sample(frac=1)
    data.reset_index(drop=True, inplace=True)
    # Saving the data in a pickled file
    pd.to_pickle(data, 'pickleddata.pkl')

Let's visualize the data:

print('Value counts:\n',data['Y'].value_counts())
for i,cat in enumerate(categories):
    print(i, cat)
data.head()

Value counts:
 1.0    250000
6.0    250000
5.0    250000
3.0    250000
2.0    250000
0.0    250000
4.0    194439
Name: Y, dtype: int64
0 Home_and_Kitchen
1 Books
2 CDs_and_Vinyl
3 Movies_and_TV
4 Cell_Phones_and_Accessories
5 Sports_and_Outdoors
6 Clothing_Shoes_and_Jewelry

Creating the dataset

import mxnet as mx
from mxnet import nd, autograd, gluon
from mxnet.gluon.data import ArrayDataset
from mxnet.gluon.data import DataLoader
import numpy as np
import multiprocessing

/home/ec2-user/anaconda3/lib/python3.6/site-packages/urllib3/contrib/pyopenssl.py:46: DeprecationWarning: OpenSSL.rand is deprecated - you should use os.urandom instead
  import OpenSSL.SSL

Setting up the parameters for the network

ALPHABET = list("abcdefghijklmnopqrstuvwxyz0123456789-,;.!?:'\"/\\|_@#$%^&*~`+ =<>()[]{}") # The 69 characters as specified in the paper
ALPHABET_INDEX = {letter: index for index, letter in enumerate(ALPHABET)} # { a: 0, b: 1, etc}
FEATURE_LEN = 1014 # max-length in characters for one document
NUM_WORKERS = multiprocessing.cpu_count() # number of workers used in the data loading
BATCH_SIZE = 128 # number of documents per batch

According to the paper, each document needs to be encoded in the following manner: - Truncate to 1014 characters - Reverse the string - One-hot encode based on the alphabet

The following encode function does this for us

def encode(text):
    encoded = np.zeros([len(ALPHABET), FEATURE_LEN], dtype='float32')
    review = text.lower()[:FEATURE_LEN-1:-1]
    i = 0
    for letter in text:
        if i >= FEATURE_LEN:
            break;
        if letter in ALPHABET_INDEX:
            encoded[ALPHABET_INDEX[letter]][i] = 1
        i += 1
    return encoded

The MXNet DataSet and DataLoader API lets you create different worker to pre-fetch the data and encode it the way you want, in order to prevent your GPU from starving

class AmazonDataSet(ArrayDataset):
    # We pre-process the documents on the fly
    def __getitem__(self, idx):
        return encode(self._data[0][idx]), self._data[1][idx]

We split our data into a training and a testing dataset

split = 0.8
split_index = int(split*len(data))
train_data_X = data['X'][:split_index].as_matrix()
train_data_Y = data['Y'][:split_index].as_matrix()
test_data_X = data['X'][split_index:].as_matrix()
test_data_Y = data['Y'][split_index:].as_matrix()
train_dataset = AmazonDataSet(train_data_X, train_data_Y)
test_dataset = AmazonDataSet(test_data_X, test_data_Y)

Creating the training and testing dataloader, with NUM_WORKERS set to the number of CPU core

train_dataloader = DataLoader(train_dataset, shuffle=True, batch_size=BATCH_SIZE, num_workers=NUM_WORKERS, last_batch='discard')

test_dataloader = DataLoader(test_dataset, shuffle=True, batch_size=BATCH_SIZE, num_workers=NUM_WORKERS, last_batch='discard')

Creation of the network

The context will define where the training takes place, on the CPU or on the GPU

# ctx = mx.cpu()
ctx = mx.gpu() # to run on GPU

We create the network following the instructions describe in the paper, using the small feature and small output units configuration

Based on the paper we set the following parameters:

NUM_FILTERS = 256 # number of convolutional filters per convolutional layer
NUM_OUTPUTS = len(categories) # number of classes
FULLY_CONNECTED = 1024 # number of unit in the fully connected dense layer
DROPOUT_RATE = 0.5 # probability of node drop out
LEARNING_RATE = 0.01 # learning rate of the gradient
MOMENTUM = 0.9 # momentum of the gradient
WDECAY = 0.00001 # regularization term to limit size of weights

net = gluon.nn.HybridSequential()
with net.name_scope():
    net.add(gluon.nn.Conv1D(channels=NUM_FILTERS, kernel_size=7, activation='relu'))
    net.add(gluon.nn.MaxPool1D(pool_size=3, strides=3))
    net.add(gluon.nn.Conv1D(channels=NUM_FILTERS, kernel_size=7, activation='relu'))
    net.add(gluon.nn.MaxPool1D(pool_size=3, strides=3))
    net.add(gluon.nn.Conv1D(channels=NUM_FILTERS, kernel_size=3, activation='relu'))
    net.add(gluon.nn.Conv1D(channels=NUM_FILTERS, kernel_size=3, activation='relu'))
    net.add(gluon.nn.Conv1D(channels=NUM_FILTERS, kernel_size=3, activation='relu'))
    net.add(gluon.nn.Conv1D(channels=NUM_FILTERS, kernel_size=3, activation='relu'))
    net.add(gluon.nn.MaxPool1D(pool_size=3, strides=3))
    net.add(gluon.nn.Flatten())
    net.add(gluon.nn.Dense(FULLY_CONNECTED, activation='relu'))
    net.add(gluon.nn.Dropout(DROPOUT_RATE))
    net.add(gluon.nn.Dense(FULLY_CONNECTED, activation='relu'))
    net.add(gluon.nn.Dropout(DROPOUT_RATE))
    net.add(gluon.nn.Dense(NUM_OUTPUTS))

print(net)

HybridSequential(
  (0): Conv1D(None -> 256, kernel_size=(7,), stride=(1,))
  (1): MaxPool1D(size=(3,), stride=(3,), padding=(0,), ceil_mode=False)
  (2): Conv1D(None -> 256, kernel_size=(7,), stride=(1,))
  (3): MaxPool1D(size=(3,), stride=(3,), padding=(0,), ceil_mode=False)
  (4): Conv1D(None -> 256, kernel_size=(3,), stride=(1,))
  (5): Conv1D(None -> 256, kernel_size=(3,), stride=(1,))
  (6): Conv1D(None -> 256, kernel_size=(3,), stride=(1,))
  (7): Conv1D(None -> 256, kernel_size=(3,), stride=(1,))
  (8): MaxPool1D(size=(3,), stride=(3,), padding=(0,), ceil_mode=False)
  (9): Flatten
  (10): Dense(None -> 1024, Activation(relu))
  (11): Dropout(p = 0.5)
  (12): Dense(None -> 1024, Activation(relu))
  (13): Dropout(p = 0.5)
  (14): Dense(None -> 7, linear)
)

Here we define whether we load a pre-trained version of the model and hybridize the network for speed improvements

hybridize = True # for speed improvement, compile the network but no in-depth debugging possible
load_params = True # Load pre-trained model

Parameter initialization

if load_params:
    net.load_params('crepe_gluon_epoch6.params', ctx=ctx)
else:
    net.collect_params().initialize(mx.init.Xavier(magnitude=2.24), ctx=ctx)

Hybridization

if hybridize:
    net.hybridize()

Softmax cross-entropy Loss

We are in a multi-class classification problem, so we use the Softmax Cross entropy loss

softmax_cross_entropy = gluon.loss.SoftmaxCrossEntropyLoss()

Optimizer

trainer = gluon.Trainer(net.collect_params(), 'sgd', 
                        {'learning_rate': LEARNING_RATE, 
                         'wd':WDECAY, 
                         'momentum':MOMENTUM})

Evaluate Accuracy

def evaluate_accuracy(data_iterator, net):
    acc = mx.metric.Accuracy()
    for i, (data, label) in enumerate(data_iterator):
        data = data.as_in_context(ctx)
        label = label.as_in_context(ctx)
        output = net(data)
        prediction = nd.argmax(output, axis=1)

        if (i%50 == 0):
            print("Samples {}".format(i*len(data)))
        acc.update(preds=prediction, labels=label)
    return acc.get()[1]

Training Loop

We loop through the batches given by the data_loader. These batches have been asynchronously fetched by the workers.

After an epoch, we measure the test_accuracy and save the parameters of the model

start_epoch = 6
number_epochs = 7
smoothing_constant = .01
for e in range(start_epoch, number_epochs):
    for i, (review, label) in enumerate(train_dataloader):
        review = review.as_in_context(ctx)
        label = label.as_in_context(ctx)
        with autograd.record():
            output = net(review)
            loss = softmax_cross_entropy(output, label)
        loss.backward()
        trainer.step(review.shape[0])
        
        # moving average of the loss
        curr_loss = nd.mean(loss).asscalar()
        moving_loss = (curr_loss if (i == 0) 
                       else (1 - smoothing_constant) * moving_loss + (smoothing_constant) * curr_loss)

        if (i%50 == 0):
            nd.waitall()
            print('Batch {}:{},{}'.format(i,curr_loss,moving_loss))

    test_accuracy = evaluate_accuracy(test_dataloader, net)
    #Save the model using the gluon params format
    net.save_params('crepe_epoch_{}_test_acc_{}.params'.format(e,int(test_accuracy*10000)/100))
    print("Epoch %s. Loss: %s, Test_acc %s" % (e, moving_loss, test_accuracy))

Export to the symbolic format

The save_params() method works for models trained in Gluon.

However the export() function, exports it to a format usable in the symbolic API. We need the symbolic API in order to make it compatible with the current version of MXNet Model Server, for deployment purposes

net.export('model/crepe')

Random testing

Let's randomly pick a few reviews and see how the classifier does!

import random
index = random.randint(1, len(data))
review = data['X'][index]
label = categories[int(data['Y'][index])]
print(review)
print('\nCategory: {}\n'.format(label))
encoded = nd.array([encode(review)], ctx=ctx)
output = net(encoded)
predicted = categories[np.argmax(output[0].asnumpy())]
if predicted == label:
      print('Correct')
else:
      print('Incorrectly predicted {}'.format(predicted))

Fine Breadmaker | We have used this mainly for the standard and whole wheat modes.  Their recipes work fine; also fine with Pamela's bread mix.

Category: Home_and_Kitchen

Correct

Manual Testing

We can also write our own reviews, encode them and see what the model predicts

review_title = "Good stuff"
review = "This album is definitely better than the previous one"

print(review_title)
print(review + '\n')
encoded = nd.array([encode(review + " | " + review_title)], ctx=ctx)
output = net(encoded)
softmax = nd.exp(output) / nd.sum(nd.exp(output))[0]
predicted = categories[np.argmax(output[0].asnumpy())]
print('Predicted: {}\n'.format(predicted))
for i, val in enumerate(categories):
    print(val, float(int(softmax[0][i].asnumpy()*1000)/10), '%')

Good stuff
This album is definitely better than the previous one

Predicted: CDs_and_Vinyl

Home_and_Kitchen 0.0 %
Books 0.0 %
CDs_and_Vinyl 98.7 %
Movies_and_TV 0.8 %
Cell_Phones_and_Accessories 0.2 %
Sports_and_Outdoors 0.1 %
Clothing_Shoes_and_Jewelry 0.0 %

Model Deployment

Head over to the model/ folder and have a look at the README.md to learn how you can deploy this pre-trained model to MXNet Model Server. You can then package the API in a docker container for cloud deployment!

An interactive live demo is available here