AmrutaKoshe

#pre-processing of data to remove special characters, hashtags, urls etc
import re
def cleanResume(resumeText):
    resumeText = re.sub('http\S+\s*', ' ', resumeText)  # remove URLs
    resumeText = re.sub('RT|cc', ' ', resumeText)  # remove RT and cc
    resumeText = re.sub('#\S+', '', resumeText)  # remove hashtags
    resumeText = re.sub('@\S+', '  ', resumeText)  # remove mentions
    resumeText = re.sub('[%s]' % re.escape("""!"#$%&'()*+,-./:;<=>[email protected][\]^_`{|}~"""), ' ', resumeText)  # remove punctuations
    resumeText = re.sub(r'[^\x00-\x7f]',r' ', resumeText) 
    resumeText = re.sub('\s+', ' ', resumeText)  # remove extra whitespace

AmrutaKoshe

#tokenize features and labels

import tensorflow as tf
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.preprocessing.text import Tokenizer

# Tokenize feature data
vocab_size = 6000
oov_tok = '<>'

AmrutaKoshe

# Tokenize label data 
label_tokenizer = Tokenizer(lower=True)
label_tokenizer.fit_on_texts(labels)

label_index = label_tokenizer.word_index
print(dict(list(label_index.items())))

# Print example label encodings from train and test datasets
train_label_sequences = label_tokenizer.texts_to_sequences(train_labels)

AmrutaKoshe

#Train a sequential model

# Define the neural network
embedding_dim = 64

model = tf.keras.Sequential([
  # Add an Embedding layer expecting input vocab of size 6000, and output embedding dimension of size 64 we set at the top
  tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length=1),
  tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(embedding_dim)),
  #tf.keras.layers.Dense(embedding_dim, activation='relu'),

AmrutaKoshe

# let's create an array containing the previous three examples to predict and use our model to get predictions

to_predict = [test_feature_padded[3],test_feature_padded[8],test_feature_padded[17]]
prediction = model.predict_classes(np.array(to_predict))

AmrutaKoshe

print(test_labels[3])
print(test_labels[8])
print(test_labels[17])

AmrutaKoshe

import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import wget
import os

AmrutaKoshe

!wget -N "https://cainvas-static.s3.amazonaws.com/media/user_data/AmrutaKoshe/dog_photos.zip"
!unzip -qo dog_photos.zip

AmrutaKoshe

Name=[]
for file in os.listdir(directory):
    Name+=[file]
print(Name)
print(len(Name))

AmrutaKoshe

Breed = 'dog breed/Akita dog'
import os 
sub_class = os.listdir(Breed)

fig = plt.figure(figsize=(10,5))
for e in range(len(sub_class[:10])):
    plt.subplot(2,5,e+1)
    img = plt.imread(os.path.join(Breed,sub_class[e]))
    plt.imshow(img, cmap=plt.get_cmap('gray'))
    plt.axis('off')