my IA Challenge.md

title	tags	description
my IA Challenge	django, keras	View the slide with "Slide Mode".

Andres Bernardo Vargas Rodriguez

github.com/zodman

• I'm Python lover 🐍
• Fullstack developer
• Linux user expert 🐧

---

I want to said. My expertise in IA, machine learning, deep learning, and neurons is very rusty.

👴

---

What data.json contains?

text and topics

vagrant@ubuntu-xenial:/vagrant$ cat data.json | tail -n 2 | jq
{
  "count": 0,
  "text": "lol",
  "topics": [
    {
      "topic": 0,
      "relevance": 32,
      "confidence": 224
    }
  ],
  "player": "4446",
  "flags": 4247631,
  "client_id": 999,
  "filtered": 0,
  "simplified": "lol"
}

---

Keras

---

My plans

Text Classification 👊

• Using keras

Web development 🐍

• Using django

---

Text Classification 👊

• Obtain train data and test data.
• Define the best Keras Model for the case.
• Pass the train data to a Keras model compiled and then evaluate the test data.
• Check if train data works as expected

---

First, try

model = Sequential()
model.add(Dense(512, input_dim=vocab_size))
model.add(Activation('relu'))
  
model.add(Dense(9))
model.add(Activation('softmax'))


model.compile(loss='categorical_crossentropy', 
              optimizer='rmsprop', 
              metrics=['accuracy'])

            
Test score: 2.1609163373932803
Test accuracy: 38.34586441516876
            

---

---

Keras.Sequential Keras.Dense model.compile

---

Second try:

model = Sequential()
model.add(Dense(10, input_dim=vocab_size, activation="relu"))
model.add(Dense(len(num_tops), activation="sigmoid"))


## Compile the models
model.compile(loss='binary_crossentropy', 
              optimizer='adam', 
              metrics=['accuracy'])

Test score: 0.14842960193641203
Test accuracy: 96.06708884239197

👍

---

check training =====
###
text: her asdvice
Actual topics: [0]
Predicted topics: [0] **YEAH!*
###
text: so they took lyk 3 pics
Actual topics: [0, 3, 4]
Predicted topics: [0] **YEAH!*
###
text: ohhhh
Actual topics: [0]
Predicted topics: [0] **YEAH!*
###
text: so i was like ARGHHH
Actual topics: [0]
Predicted topics: [0] **YEAH!*
###
text: dnt laf ab god
Actual topics: [0, 3, 11, 12]
Predicted topics: [0] **YEAH!*

👍 👍 👍 👍 👍 👍

---

Text classification ✅

Next ?

let's do some Web development.

---

With:

• Django
• Celery
• django-restframework
• vuejs (parceljs)

Simple SPA for send new text and expect to use my new script to classify new text.

---

Demo time.

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My Conclusion

• This challenge was FUN ❗
• With the better understanding about the models and compile options you can obtain a better results.
• I missing the unittest ✋