Minor correction in K3IMDB3

c39243e6 · Jean-Luc Parouty · 0ad729fb · c39243e6
Commit c39243e6 authored 1 year ago by Jean-Luc Parouty
--- a/Embedding.Keras3/03-Prediction.ipynb
+++ b/Embedding.Keras3/03-Prediction.ipynb
@@ -229,7 +229,7 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "y_pred   = model.predict(x_data, verbose=fit_verbosity)"
+    "y_pred   = model.predict(x_data, verbose=0)"
   ]
  },
  {

 %% Cell type:markdown id: tags:

 <img width="800px" src="../fidle/img/header.svg"></img>

 # <!-- TITLE --> [K3IMDB3] - Reload and reuse a saved model
 <!-- DESC --> Retrieving a saved model to perform a sentiment analysis (movie review), using Keras 3 and PyTorch
 <!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->

 ## Objectives :
 - The objective is to guess whether our personal film reviews are **positive or negative** based on the analysis of the text.
 - For this, we will use our **previously saved model**.

 ## What we're going to do :

 - Preparing our data
 - Retrieve our saved model
 - Evaluate the result

 %% Cell type:markdown id: tags:

 ## Step 1 - Init python stuff

 %% Cell type:code id: tags:

 ``` python
 import os
 os.environ['KERAS_BACKEND'] = 'torch'

 import keras

 import json,re
 import numpy as np

 import fidle

 # Init Fidle environment
 run_id, run_dir, datasets_dir = fidle.init('K3IMDB3')
 ```

 %% Cell type:markdown id: tags:

 ### 1.2 - Parameters
 The words in the vocabulary are classified from the most frequent to the rarest.
 `vocab_size` is the number of words we will remember in our vocabulary (the other words will be considered as unknown).
 `review_len` is the review length
 `saved_models` where our models were previously saved
 `dictionaries_dir` is where we will go to save our dictionaries. (./data is a good choice)

 %% Cell type:code id: tags:

 ``` python
 vocab_size           = 10000
 review_len           = 256

 saved_models         = './run/K3IMDB2'
 dictionaries_dir     = './data'
 ```

 %% Cell type:markdown id: tags:

 Override parameters (batch mode) - Just forget this cell

 %% Cell type:code id: tags:

 ``` python
 fidle.override('vocab_size', 'review_len', 'saved_models', 'dictionaries_dir')
 ```

 %% Cell type:markdown id: tags:

 ## Step 2 : Preparing the data
 ### 2.1 - Our reviews :

 %% Cell type:code id: tags:

 ``` python
 reviews = [ "This film is particularly nice, a must see.",
             "This film is a great classic that cannot be ignored.",
             "I don't remember ever having seen such a movie...",
             "This movie is just abominable and doesn't deserve to be seen!"]
 ```

 %% Cell type:markdown id: tags:

 ### 2.2 - Retrieve dictionaries
 Note : This dictionary is generated by [02-Embedding-Keras](02-Keras-embedding.ipynb) notebook.

 %% Cell type:code id: tags:

 ``` python
 with open(f'{dictionaries_dir}/word_index.json', 'r') as fp:
    word_index = json.load(fp)
    index_word = { i:w      for w,i in word_index.items() }
    print('Dictionaries loaded. ', len(word_index), 'entries' )
 ```

 %% Cell type:markdown id: tags:

 ### 2.3 - Clean, index and padd
 Phases are split into words, punctuation is removed, sentence length is limited and padding is added...
 **Note** : 1 is "Start" and 2 is "unknown"

 %% Cell type:code id: tags:

 ``` python
 start_char = 1      # Start of a sequence (padding is 0)
 oov_char   = 2      # Out-of-vocabulary
 index_from = 3      # First word id

 nb_reviews = len(reviews)
 x_data     = []

 # ---- For all reviews
 for review in reviews:
    print('Words are : ', end='')
    # ---- First index must be <start>
    index_review=[start_char]
    print(f'{start_char} ', end='')
    # ---- For all words
    for w in review.split(' '):
        # ---- Clean it
        w_clean = re.sub(r"[^a-zA-Z0-9]", "", w)
        # ---- Not empty ?
        if len(w_clean)>0:
            # ---- Get the index - must be inside dict or is out of vocab (oov)
            w_index = word_index.get(w, oov_char)
            if w_index>vocab_size : w_index=oov_char
            # ---- Add the index if < vocab_size
            index_review.append(w_index)
            print(f'{w_index} ', end='')
    # ---- Add the indexed review
    x_data.append(index_review)
    print()

 # ---- Padding
 x_data = keras.preprocessing.sequence.pad_sequences(x_data, value   = 0, padding = 'post', maxlen  = review_len)
 ```

 %% Cell type:markdown id: tags:

 ### 2.4 - Have a look

 %% Cell type:code id: tags:

 ``` python
 def translate(x):
    return ' '.join( [index_word.get(i,'?') for i in x] )

 for i in range(nb_reviews):
    imax=np.where(x_data[i]==0)[0][0]+5
    print(f'\nText review {i}  :',    reviews[i])
    print(f'tokens vector  :', list(x_data[i][:imax]), '(...)')
    print('Translation    :', translate(x_data[i][:imax]), '(...)')
 ```

 %% Cell type:markdown id: tags:

 ## Step 3 - Bring back the model

 %% Cell type:code id: tags:

 ``` python
 model = keras.models.load_model(f'{saved_models}/models/best_model.keras')
 ```

 %% Cell type:markdown id: tags:

 ## Step 4 - Predict

 %% Cell type:code id: tags:

 ``` python
-y_pred   = model.predict(x_data, verbose=fit_verbosity)
+y_pred   = model.predict(x_data, verbose=0)
 ```

 %% Cell type:markdown id: tags:

 #### And the winner is :

 %% Cell type:code id: tags:

 ``` python
 for i,review in enumerate(reviews):
    rate    = y_pred[i][0]
    opinion =  'NEGATIVE :-(' if rate<0.5 else 'POSITIVE :-)'
    print(f'{review:<70} => {rate:.2f} - {opinion}')
 ```

 %% Cell type:code id: tags:

 ``` python
 fidle.end()
 ```

 %% Cell type:markdown id: tags:

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