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lambdaMXnet.py
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lambdaMXnet.py
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import ctypes
import os
from six.moves import urllib
import zipfile
import stat
import logging
import json
import numpy as np
from one_hot import one_hot
logging.basicConfig(level=logging.DEBUG)
print "logging"
def response(status_code, response_body):
return {
'statusCode': status_code,
'body': str(response_body),
'headers': {
'Content-Type': 'application/json',
},
}
def vectorize_sequences(sequences, dimension):
results = np.zeros((len(sequences), dimension))
for i, sequence in enumerate(sequences):
results[i, sequence] = 1.
return results
def one_hot_encode(messages, dimension):
data = []
for msg in messages:
temp = one_hot(msg, vocabulary_lenght)
data.append(temp)
return data
for d, _, files in os.walk('lib'):
for f in files:
if f.endswith('.a') or f.endswith('.settings'):
continue
print('loading %s...' % f)
ctypes.cdll.LoadLibrary(os.path.join(d, f))
path_to_model = "./"
vocabulary_lenght = 9013
import mxnet as mx
model_dir = "./"
vocabulary_lenght = 9013
symbol = mx.sym.load('%s/model.json' % model_dir)
outputs = mx.symbol.softmax(data=symbol, name='softmax_label')
inputs = mx.sym.var('data')
param_dict = mx.gluon.ParameterDict('model_')
net = mx.gluon.SymbolBlock(outputs, inputs, param_dict)
net.load_params('%s/model.params' % model_dir, ctx=mx.cpu())
def handler(event, context):
print event
#params = event['queryStringParameters']
sms = event['body']
print sms
test_messages = [sms.encode('ascii','ignore')]
one_hot_test_messages = one_hot_encode(test_messages, vocabulary_lenght)
encoded_test_messages = vectorize_sequences(one_hot_test_messages, vocabulary_lenght)
encoded_test_messages = mx.nd.array(encoded_test_messages)
output = net(encoded_test_messages)
predictions = np.argmax(output, axis=1)
print(predictions[0])
result = predictions[0]
return response(200, result)