This is an automated Markdown generation from the notebook 'Crepe-Gluon.ipynb'
Check the live demo here!
Slides available here
Recordings available here, part1, part2, part3.
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
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 -qThe 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 -nvHome_and_Kitchen
Books
CDs_and_Vinyl
Movies_and_TV
Cell_Phones_and_Accessories
Sports_and_Outdoors
Clothing_Shoes_and_Jewelry
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.
MAX_ITEMS_PER_CATEGORY = 250000Helper 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 = NoneIf 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
| X | Y | |
|---|---|---|
| 0 | Why didnt I find this sooner!!! | This product... | 0.0 |
| 1 | The only thing weighing it down is the second ... | 2.0 |
| 2 | Good | Works very good with a patch pulled or ... | 5.0 |
| 3 | Good mirror glasses | These are very reflectiv... | 6.0 |
| 4 | cute, cushy, too small :( | Well, here's anoth... | 6.0 |
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 batchAccording 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 encodedThe 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')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 GPUWe 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 weightsnet = 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 modelif load_params:
net.load_params('crepe_gluon_epoch6.params', ctx=ctx)
else:
net.collect_params().initialize(mx.init.Xavier(magnitude=2.24), ctx=ctx)if hybridize:
net.hybridize()We are in a multi-class classification problem, so we use the Softmax Cross entropy loss
softmax_cross_entropy = gluon.loss.SoftmaxCrossEntropyLoss()trainer = gluon.Trainer(net.collect_params(), 'sgd',
{'learning_rate': LEARNING_RATE,
'wd':WDECAY,
'momentum':MOMENTUM})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]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))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')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
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 %
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
