Adilmar

import tensorflow as tf
import sys

# change this as you see fit
#image_path = sys.argv[1]
image_path = 'teste2.jpg'

# read in the image_data
image_data = tf.gfile.FastGFile(image_path, 'rb').read()

Adilmar

python retrain.py --bottleneck_dir=tf_files/bottlenecks --how_many_training_steps 4000 --model_dir=tf_files/inception --output_graph=tf_files/retrained_graph.pb --output_labels=tf_files/retrained_labels.txt --image_dir tf_files/images

Adilmar

context = {}

ERROR_THRESHOLD = 0.25
def classify(sentence):
    results = model.predict([bow(sentence, words)])[0]
    results = [[i,r] for i,r in enumerate(results) if r>ERROR_THRESHOLD]
    results.sort(key=lambda x: x[1], reverse=True)
    return_list = []
    for r in results:
        return_list.append((classes[r[0]], r[1]))

Adilmar

def clean_up_sentence(sentence):
    # tokenizando frases
    sentence_words = nltk.word_tokenize(sentence)
    # stem 
    sentence_words = [stemmer.stem(word.lower()) for word in sentence_words]
    return sentence_words

def bow(sentence, words, show_details=False):
    sentence_words = clean_up_sentence(sentence)
    bag = [0]*len(words)  

Adilmar

# NLP
import nltk
from nltk.stem.lancaster import LancasterStemmer
stemmer = LancasterStemmer()

# importando bibliotecas
import numpy as np
import tflearn
import tensorflow as tf
import random

Adilmar

def clean_up_sentence(sentence):
    # tokenize the pattern
    sentence_words = nltk.word_tokenize(sentence)
    # stem each word
    sentence_words = [stemmer.stem(word.lower()) for word in sentence_words]
    return sentence_words

# return bag of words array: 0 or 1 for each word in the bag that exists in the sentence
def bow(sentence, words, show_details=False):
    # tokenize the pattern

Adilmar

# save all of our data structures
import pickle
pickle.dump( {'words':words, 'classes':classes, 'train_x':train_x, 'train_y':train_y}, open( "training_data", "wb" ) )

Adilmar

# reset underlying graph data
tf.reset_default_graph()
# Build neural network
net = tflearn.input_data(shape=[None, len(train_x[0])])
net = tflearn.fully_connected(net, 8)
net = tflearn.fully_connected(net, 8)
net = tflearn.fully_connected(net, len(train_y[0]), activation='softmax')
net = tflearn.regression(net)

# Define model and setup tensorboard

Adilmar

# create our training data
training = []
output = []
# create an empty array for our output
output_empty = [0] * len(classes)

# training set, bag of words for each sentence
for doc in documents:
    # initialize our bag of words
    bag = []

Adilmar

words = []
classes = []
documents = []
ignore_words = ['?']
# loop through each sentence in our intents patterns
for intent in intents['intents']:
    for pattern in intent['patterns']:
        # tokenize each word in the sentence
        w = nltk.word_tokenize(pattern)
        # add to our words list