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modelHelpers.py
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modelHelpers.py
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# -*- coding: utf-8 -*-
"""WkiiQnA.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1byrfKZcTZKaIt_ELpt5jYoRlwj8wm3OC
"""
import tensorflow as tf
import tensorflow_datasets as tfds
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
from sklearn.model_selection import train_test_split
import unicodedata
import re
import numpy as np
import pandas as pd
import os
import io
import time
import pickle
#preprocessing
def preprocess_sentence(sentence):
sentence = sentence.lower().strip()
# creating a space between a word and the punctuation following it
# eg: "he is a boy." => "he is a boy ."
sentence = re.sub(r"([?.!,])", r" \1 ", sentence)
sentence = re.sub(r'[" "]+', " ", sentence)
# replacing everything with space except (a-z, A-Z, ".", "?", "!", ",")
sentence = re.sub(r"[^a-zA-Z?.!,]+", " ", sentence)
sentence = sentence.strip()
# adding a start and an end token to the sentence
# sentence = '<start> ' + sentence + ' <end>'
return sentence
with open('Wikiqatokenizer.pickle', 'rb') as f:
tokenizer = pickle.load(f)
# Define start and end token to indicate the start and end of a sentence
START_TOKEN, END_TOKEN = [tokenizer.vocab_size], [tokenizer.vocab_size + 1]
VOCAB_SIZE = tokenizer.vocab_size+2
#setting maximum length of a sentence
MAX_LENGTH=100
def attention(query,key,value,mask):
qk_matmul = tf.matmul(query,key,transpose_b=True)
depth= tf.cast(tf.shape(key)[-1],tf.float32)
logits = qk_matmul/tf.math.sqrt(depth)
if mask is not None:
logits += (mask *-1e9)
attention_weight = tf.nn.softmax(logits,axis=-1)
output = tf.matmul(attention_weight,value)
return output
class MultiHeadAttention(tf.keras.layers.Layer):
def __init__(self, d_model, num_heads, name="multi_head_attention"):
super(MultiHeadAttention, self).__init__(name=name)
self.num_heads = num_heads
self.d_model = d_model
assert d_model % self.num_heads == 0
self.depth = d_model // self.num_heads
self.query_dense = tf.keras.layers.Dense(units=d_model)
self.key_dense = tf.keras.layers.Dense(units=d_model)
self.value_dense = tf.keras.layers.Dense(units=d_model)
self.dense = tf.keras.layers.Dense(units=d_model)
def split_heads(self, inputs, batch_size):
inputs = tf.reshape(
inputs, shape=(batch_size, -1, self.num_heads, self.depth))
return tf.transpose(inputs, perm=[0, 2, 1, 3])
def call(self, inputs):
query, key, value, mask = inputs['query'], inputs['key'], inputs[
'value'], inputs['mask']
batch_size = tf.shape(query)[0]
# linear layers
query = self.query_dense(query)
key = self.key_dense(key)
value = self.value_dense(value)
# split heads
query = self.split_heads(query, batch_size)
key = self.split_heads(key, batch_size)
value = self.split_heads(value, batch_size)
# scaled dot-product attention
scaled_attention = attention(query, key, value, mask)
scaled_attention = tf.transpose(scaled_attention, perm=[0, 2, 1, 3])
# concatenation of heads
concat_attention = tf.reshape(scaled_attention,
(batch_size, -1, self.d_model))
# final linear layer
outputs = self.dense(concat_attention)
return outputs
def create_padding_mask(x):
mask = tf.cast(tf.math.equal(x, 0), tf.float32)
# (batch_size, 1, 1, sequence length)
return mask[:, tf.newaxis, tf.newaxis, :]
def create_look_ahead_mask(x):
seq_len = tf.shape(x)[1]
look_ahead_mask = 1 - tf.linalg.band_part(tf.ones((seq_len, seq_len)), -1, 0)
padding_mask = create_padding_mask(x)
return tf.maximum(look_ahead_mask, padding_mask)
#creating postional encoding for the layer to get the word postion
class PositionalEncoding(tf.keras.layers.Layer):
def __init__(self,postion,d_model):
super(PositionalEncoding,self).__init__()
self.pos_encoding = self.postional_encoding(postion,d_model)
def get_angles(self,postion,i,d_model):
angles = 1/tf.pow(10000,(2*(i//2))/tf.cast(d_model,dtype=tf.float32))
return postion*angles
def postional_encoding(self,postion,d_model):
angle_rads = self.get_angles(
postion = tf.range(postion,dtype=tf.float32)[:,tf.newaxis],
i = tf.range(d_model,dtype=tf.float32)[tf.newaxis,:],
d_model = d_model
)
# apply sin to even index in the array
sines = tf.math.sin(angle_rads[:, 0::2])
# apply cos to odd index in the array
cosines = tf.math.cos(angle_rads[:, 1::2])
pos_encoding = tf.concat([sines, cosines], axis=-1)
pos_encoding = pos_encoding[tf.newaxis, ...]
return tf.cast(pos_encoding, tf.float32)
def call(self,inputs):
return inputs + self.pos_encoding[:, :tf.shape(inputs)[1], :]
def get_config(self):
config = super().get_config().copy()
config.update({
"pos_encoding":self.pos_encoding
})
return config
def encoder_layer(units, d_model, num_heads, dropout, name="encoder_layer"):
inputs = tf.keras.Input(shape=(None, d_model), name="inputs")
padding_mask = tf.keras.Input(shape=(1, 1, None), name="padding_mask")
attention = MultiHeadAttention(
d_model, num_heads, name="attention")({
'query': inputs,
'key': inputs,
'value': inputs,
'mask': padding_mask
})
attention = tf.keras.layers.Dropout(rate=0.3)(attention)
attention = tf.keras.layers.LayerNormalization(
epsilon=1e-6)(inputs + attention)
outputs = tf.keras.layers.Dense(units=units, activation='relu')(attention)
outputs = tf.keras.layers.Dense(units=d_model)(outputs)
outputs = tf.keras.layers.Dropout(rate=dropout)(outputs)
outputs = tf.keras.layers.LayerNormalization(
epsilon=1e-6)(attention + outputs)
return tf.keras.Model(
inputs=[inputs, padding_mask], outputs=outputs, name=name)
def encoder(vocab_size,num_layers,units,d_model,num_heads,dropout,name='encoder'):
inputs = tf.keras.Input(shape=(None,),name='inputs')
padding_mask = tf.keras.Input(shape=(1,1,None),name='padding_mask')
embeddings = tf.keras.layers.Embedding(vocab_size,d_model)(inputs)
embeddings *= tf.math.sqrt(tf.cast(d_model,tf.float32))
embeddings = PositionalEncoding(vocab_size,d_model)(embeddings)
outputs = tf.keras.layers.Dropout(rate=dropout)(embeddings)
for i in range(num_layers):
outputs = encoder_layer(
units=units,
d_model=d_model,
num_heads=num_heads,
dropout=dropout,
name="encoder_layer_{}".format(i),
)([outputs, padding_mask])
return tf.keras.Model(
inputs=[inputs,padding_mask],outputs=outputs,name=name
)
def decoder_layer(units, d_model, num_heads, dropout, name="decoder_layer"):
inputs = tf.keras.Input(shape=(None, d_model), name="inputs")
enc_outputs = tf.keras.Input(shape=(None, d_model), name="encoder_outputs")
look_ahead_mask = tf.keras.Input(
shape=(1, None, None), name="look_ahead_mask")
padding_mask = tf.keras.Input(shape=(1, 1, None), name='padding_mask')
attention1 = MultiHeadAttention(
d_model, num_heads, name="attention_1")(inputs={
'query': inputs,
'key': inputs,
'value': inputs,
'mask': look_ahead_mask
})
attention1 = tf.keras.layers.LayerNormalization(
epsilon=1e-6)(attention1 + inputs)
attention2 = MultiHeadAttention(
d_model, num_heads, name="attention_2")(inputs={
'query': attention1,
'key': enc_outputs,
'value': enc_outputs,
'mask': padding_mask
})
attention2 = tf.keras.layers.Dropout(rate=dropout)(attention2)
attention2 = tf.keras.layers.LayerNormalization(
epsilon=1e-6)(attention2 + attention1)
outputs = tf.keras.layers.Dense(units=units, activation='relu')(attention2)
outputs = tf.keras.layers.Dense(units=d_model)(outputs)
outputs = tf.keras.layers.Dropout(rate=dropout)(outputs)
outputs = tf.keras.layers.LayerNormalization(
epsilon=1e-6)(outputs + attention2)
return tf.keras.Model(
inputs=[inputs, enc_outputs, look_ahead_mask, padding_mask],
outputs=outputs,
name=name)
def decoder(vocab_size,
num_layers,
units,
d_model,
num_heads,
dropout,
name='decoder'):
inputs = tf.keras.Input(shape=(None,), name='inputs')
enc_outputs = tf.keras.Input(shape=(None, d_model), name='encoder_outputs')
look_ahead_mask = tf.keras.Input(
shape=(1, None, None), name='look_ahead_mask')
padding_mask = tf.keras.Input(shape=(1, 1, None), name='padding_mask')
embeddings = tf.keras.layers.Embedding(vocab_size, d_model)(inputs)
embeddings *= tf.math.sqrt(tf.cast(d_model, tf.float32))
embeddings = PositionalEncoding(vocab_size, d_model)(embeddings)
outputs = tf.keras.layers.Dropout(rate=dropout)(embeddings)
for i in range(num_layers):
outputs = decoder_layer(
units=units,
d_model=d_model,
num_heads=num_heads,
dropout=dropout,
name='decoder_layer_{}'.format(i),
)(inputs=[outputs, enc_outputs, look_ahead_mask, padding_mask])
return tf.keras.Model(
inputs=[inputs, enc_outputs, look_ahead_mask, padding_mask],
outputs=outputs,
name=name)
def transformer(vocab_size,
num_layers,
units,
d_model,
num_heads,
dropout,
name="transformer"):
inputs = tf.keras.Input(shape=(None,), name="inputs")
dec_inputs = tf.keras.Input(shape=(None,), name="dec_inputs")
enc_padding_mask = tf.keras.layers.Lambda(
create_padding_mask, output_shape=(1, 1, None),
name='enc_padding_mask')(inputs)
# mask the future tokens for decoder inputs at the 1st attention block
look_ahead_mask = tf.keras.layers.Lambda(
create_look_ahead_mask,
output_shape=(1, None, None),
name='look_ahead_mask')(dec_inputs)
# mask the encoder outputs for the 2nd attention block
dec_padding_mask = tf.keras.layers.Lambda(
create_padding_mask, output_shape=(1, 1, None),
name='dec_padding_mask')(inputs)
enc_outputs = encoder(
vocab_size=vocab_size,
num_layers=num_layers,
units=units,
d_model=d_model,
num_heads=num_heads,
dropout=dropout,
)(inputs=[inputs, enc_padding_mask])
dec_outputs = decoder(
vocab_size=vocab_size,
num_layers=num_layers,
units=units,
d_model=d_model,
num_heads=num_heads,
dropout=dropout,
)(inputs=[dec_inputs, enc_outputs, look_ahead_mask, dec_padding_mask])
outputs = tf.keras.layers.Dense(units=vocab_size, name="outputs")(dec_outputs)
return tf.keras.Model(inputs=[inputs, dec_inputs], outputs=outputs, name=name)
#choosing hyperparameter
NUM_LAYERS=2
D_MODEL=256
NUM_HEADS=8
UNITS = 512
DROPOUT=0.1
model = transformer(vocab_size=VOCAB_SIZE,num_layers=NUM_LAYERS,units=UNITS,d_model=D_MODEL,num_heads=NUM_HEADS,dropout=DROPOUT)
def loss_function(y_true, y_pred):
y_true = tf.reshape(y_true, shape=(-1, MAX_LENGTH - 1))
loss = tf.keras.losses.SparseCategoricalCrossentropy(
from_logits=True, reduction='none')(y_true, y_pred)
mask = tf.cast(tf.not_equal(y_true, 0), tf.float32)
loss = tf.multiply(loss, mask)
return tf.reduce_mean(loss)
class CustomSchedule(tf.keras.optimizers.schedules.LearningRateSchedule):
def __init__(self, d_model, warmup_steps=4000):
super(CustomSchedule, self).__init__()
self.d_model = d_model
self.d_model = tf.cast(self.d_model, tf.float32)
self.warmup_steps = warmup_steps
def __call__(self, step):
arg1 = tf.math.rsqrt(step)
arg2 = step * (self.warmup_steps**-1.5)
return tf.math.rsqrt(self.d_model) * tf.math.minimum(arg1, arg2)
learning_rate = CustomSchedule(D_MODEL)
optimizer = tf.keras.optimizers.Adam(
learning_rate, beta_1=0.9, beta_2=0.98, epsilon=1e-9)
def accuracy(y_true, y_pred):
# ensure labels have shape (batch_size, MAX_LENGTH - 1)
y_true = tf.reshape(y_true, shape=(-1, MAX_LENGTH - 1))
return tf.keras.metrics.sparse_categorical_accuracy(y_true, y_pred)
def evaluate(sentence,model):
sentence = preprocess_sentence(sentence)
sentence = tf.expand_dims(
START_TOKEN + tokenizer.encode(sentence) + END_TOKEN, axis=0)
output = tf.expand_dims(START_TOKEN, 0)
for i in range(MAX_LENGTH):
predictions = model(inputs=[sentence, output], training=False)
# select the last word from the seq_len dimension
predictions = predictions[:, -1:, :]
predicted_id = tf.cast(tf.argmax(predictions, axis=-1), tf.int32)
# return the result if the predicted_id is equal to the end token
if tf.equal(predicted_id, END_TOKEN[0]):
break
# concatenated the predicted_id to the output which is given to the decoder
# as its input.
output = tf.concat([output, predicted_id], axis=-1)
return tf.squeeze(output, axis=0)
def predict(sentence,model):
prediction = evaluate(sentence,model)
predicted_sentence = tokenizer.decode(
[i for i in prediction if i < tokenizer.vocab_size])
print('Input: {}'.format(sentence))
print('Output: {}'.format(predicted_sentence))
return predicted_sentence
if __name__ == '__main__':
##path to weights folder switch to conversational one if you want that
model.load_weights("model_weight_WkiiQNa\model3.h5")
model.compile(optimizer=optimizer, loss=loss_function, metrics=[accuracy])
model.summary()
output=predict("What did movie theaters do for sound before synchronized sound was introduced into film",model)