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evaluating_model.py
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evaluating_model.py
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from pickle import load
from numpy import array
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.models import load_model
#loading the clean dataset
def load_dataset(filename):
return load(open(filename,'rb'))
def create_tokenizer(lines):
tokenizer = Tokenizer()
tokenizer.fit_on_texts(lines)
return tokenizer
def max_length(lines):
return max([len(s.split()) for s in lines])
def encode_text(tokenizer,lines,length):
encoded = tokenizer.texts_to_sequences(lines)
padded = pad_sequences(encoded,maxlen=length,padding='post')
return padded
def main():
trainLines,trainLabels = load_dataset('train.pkl')
testLines,testLabels = load_dataset('test.pkl')
tokenizer = create_tokenizer(trainLines)
length = max_length(trainLines)
vocab_size = len(tokenizer.word_index) + 1
print('Max document length: %d' % length)
print('Vocabulary size: %d' % vocab_size)
trainX = encode_text(tokenizer,trainLines,length)
testX = encode_text(tokenizer,testLines,length)
print(trainX.shape,testX.shape)
#Evaluating on models of different batches
model = load_model('model_batch8.h5')
loss,acc = model.evaluate([trainX,trainX,trainX],array(trainLabels),verbose = 0)
print('Train Accuracy: %f' %(acc*100))
loss,acc = model.evaluate([testX,testX,testX],array(testLabels),verbose = 0)
print('Test Accuracy: %f' %(acc*100))
model = load_model('model_batch16.h5')
loss,acc = model.evaluate([trainX,trainX,trainX],array(trainLabels),verbose = 0)
print('Train Accuracy: %f' %(acc*100))
loss,acc = model.evaluate([testX,testX,testX],array(testLabels),verbose = 0)
print('Test Accuracy: %f' %(acc*100))
model = load_model('model_batch32.h5')
loss,acc = model.evaluate([trainX,trainX,trainX],array(trainLabels),verbose = 0)
print('Train Accuracy: %f' %(acc*100))
loss,acc = model.evaluate([testX,testX,testX],array(testLabels),verbose = 0)
print('Test Accuracy: %f' %(acc*100))
main()