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train_weight_saving.py
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train_weight_saving.py
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#packages
from random import randint
from numpy import array
from numpy import argmax
from numpy import array_equal
import numpy as np
import pandas as pd
import tensorflow as tf
import os
from tensorflow.python.keras.layers import Layer
from tensorflow.python.keras.models import load_model
from tensorflow.python.keras import backend as K
from tensorflow.python.keras.preprocessing.sequence import pad_sequences
from tensorflow.python.keras.layers import Input, GRU, Dense, Concatenate, TimeDistributed
from attention_keras_thushv import AttentionLayer
from tensorflow.python.keras.models import Model
import tensorflow.keras as keras
from tensorflow.python.keras.utils import to_categorical
import numpy as np
import os, sys
import pickle
import json
import time
#Reading & Processing data
spa = pd.read_csv('spa.txt', header=None, sep='\n')
spa.columns = ['Content']
text = spa['Content'].apply(lambda x: x[: x.find('CC-BY 2.0')])
text = text.str.strip()
spa['English'] = text.apply(lambda x: x.split('\t')[0])
spa['Spanish'] = text.apply(lambda x: x.split('\t')[1])
spa = spa[['English','Spanish']]
del(text)
spa = spa.sample(frac = 1)
# Dividing into train and test
train = spa.iloc[0 : int(0.9 * spa.shape[0]), ]
test = spa.iloc[int(0.9 * spa.shape[0]) : , ]
def sents2sequences(tokenizer, sentences, reverse=False, pad_length=None, padding_type='post'):
encoded_text = tokenizer.texts_to_sequences(sentences)
preproc_text = pad_sequences(encoded_text, padding=padding_type, maxlen=pad_length)
if reverse:
preproc_text = np.flip(preproc_text, axis=1)
return preproc_text
#
#
def define_nmt(hidden_size, batch_size, en_timesteps, en_vsize, sp_timesteps, sp_vsize):
""" Defining a NMT model """
# Define an input sequence and process it.
if batch_size:
encoder_inputs = Input(batch_shape=(batch_size, en_timesteps, en_vsize), name='encoder_inputs')
decoder_inputs = Input(batch_shape=(batch_size, sp_timesteps - 1, sp_vsize), name='decoder_inputs')
else:
encoder_inputs = Input(shape=(en_timesteps, en_vsize), name='encoder_inputs')
decoder_inputs = Input(shape=(sp_timesteps - 1, sp_vsize), name='decoder_inputs')
# Encoder GRU
encoder_gru = GRU(hidden_size, return_sequences=True, return_state=True, name='encoder_gru')
encoder_out, encoder_state = encoder_gru(encoder_inputs)
# Set up the decoder GRU, using `encoder_states` as initial state.
decoder_gru = GRU(hidden_size, return_sequences=True, return_state=True, name='decoder_gru')
decoder_out, decoder_state = decoder_gru(decoder_inputs, initial_state=encoder_state)
# Attention layer
attn_layer = AttentionLayer(name='attention_layer')
attn_out, attn_states = attn_layer([encoder_out, decoder_out])
# Concat attention input and decoder GRU output
decoder_concat_input = Concatenate(axis=-1, name='concat_layer')([decoder_out, attn_out])
# Dense layer
dense = Dense(sp_vsize, activation='softmax', name='softmax_layer')
dense_time = TimeDistributed(dense, name='time_distributed_layer')
decoder_pred = dense_time(decoder_concat_input)
# Full model
full_model = Model(inputs=[encoder_inputs, decoder_inputs], outputs=decoder_pred)
full_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics = ['accuracy'])
full_model.summary()
""" Inference model """
batch_size = 1
""" Encoder (Inference) model """
encoder_inf_inputs = Input(batch_shape=(batch_size, en_timesteps, en_vsize), name='encoder_inf_inputs')
encoder_inf_out, encoder_inf_state = encoder_gru(encoder_inf_inputs)
encoder_model = Model(inputs=encoder_inf_inputs, outputs=[encoder_inf_out, encoder_inf_state])
""" Decoder (Inference) model """
decoder_inf_inputs = Input(batch_shape=(batch_size, 1, sp_vsize), name='decoder_word_inputs')
encoder_inf_states = Input(batch_shape=(batch_size, en_timesteps, hidden_size), name='encoder_inf_states')
decoder_init_state = Input(batch_shape=(batch_size, hidden_size), name='decoder_init')
decoder_inf_out, decoder_inf_state = decoder_gru(decoder_inf_inputs, initial_state=decoder_init_state)
attn_inf_out, attn_inf_states = attn_layer([encoder_inf_states, decoder_inf_out])
decoder_inf_concat = Concatenate(axis=-1, name='concat')([decoder_inf_out, attn_inf_out])
decoder_inf_pred = TimeDistributed(dense)(decoder_inf_concat)
decoder_model = Model(inputs=[encoder_inf_states, decoder_init_state, decoder_inf_inputs],
outputs=[decoder_inf_pred, attn_inf_states, decoder_inf_state])
return full_model, encoder_model, decoder_model
#
#
batch_size = 128
hidden_size = 96
en_timesteps, sp_timesteps = 20, 20
#Storing as lists
tr_en_text, tr_sp_text, ts_en_text, ts_sp_text = train['English'].tolist(), train['Spanish'].tolist(), test['English'].tolist(), test['Spanish'].tolist()
#Appending start and end toekns to spanish sentences
tr_sp_text = ['sos ' + sent[:-1] + 'eos .' if sent.endswith('.') else 'sos ' + sent + ' eos .' for sent in tr_sp_text]
ts_sp_text = ['sos ' + sent[:-1] + 'eos .' if sent.endswith('.') else 'sos ' + sent + ' eos .' for sent in tr_sp_text]
def preprocess_data(en_tokenizer, sp_tokenizer, en_text, sp_text, en_timesteps, sp_timesteps):
""" Preprocessing data and getting a sequence of word indices """
en_seq = sents2sequences(en_tokenizer, en_text, reverse=False, padding_type='pre', pad_length=en_timesteps)
sp_seq = sents2sequences(sp_tokenizer, sp_text, pad_length=sp_timesteps)
return en_seq, sp_seq
#
#
def train(full_model, en_seq, sp_seq, batch_size, n_epochs=1):
""" Training the model """
for ep in range(n_epochs):
losses = []
for bi in range(0, en_seq.shape[0] - batch_size, batch_size):
try:
en_onehot_seq = to_categorical(en_seq[bi:bi + batch_size, :], num_classes=en_vsize)
sp_onehot_seq = to_categorical(sp_seq[bi:bi + batch_size, :], num_classes=sp_vsize)
full_model.train_on_batch([en_onehot_seq, sp_onehot_seq[:, :-1, :]], sp_onehot_seq[:, 1:, :])
l = full_model.evaluate([en_onehot_seq, sp_onehot_seq[:, :-1, :]], sp_onehot_seq[:, 1:, :],
batch_size=batch_size)
losses.append(l)
#Saving Weights
if bi%12800 == 0:
infer_dec_model.save_weights('decoder_weights_n.h5')
infer_enc_model.save_weights('encoder_weights_n.h5')
except:
continue
if (ep + 1) % 1 == 0:
print("Loss in epoch {}: {}".format(ep + 1, np.mean(losses)))
#
#
def infer_nmt(encoder_model, decoder_model, test_en_seq, en_vsize, sp_vsize):
"""
Infer logic
:param encoder_model: keras.Model
:param decoder_model: keras.Model
:param test_en_seq: sequence of word ids
:param en_vsize: int
:param sp_vsize: int
:return:
"""
test_sp_seq = sents2sequences(sp_tokenizer, ['sos'], sp_vsize)
test_en_onehot_seq = to_categorical(test_en_seq, num_classes=en_vsize)
test_sp_onehot_seq = np.expand_dims(to_categorical(test_sp_seq, num_classes=sp_vsize), 1)
enc_outs, enc_last_state = encoder_model.predict(test_en_onehot_seq)
dec_state = enc_last_state
attention_weights = []
sp_text = ''
for i in range(20):
dec_out, attention, dec_state = decoder_model.predict([enc_outs, dec_state, test_sp_onehot_seq])
dec_ind = np.argmax(dec_out, axis=-1)[0, 0]
#print('Decoder Output Top 10', dec_out[0,0,:10])
if dec_ind == 0:
break
test_sp_seq = sents2sequences(sp_tokenizer, [sp_tokenizer.index_word[dec_ind]], sp_vsize)
test_sp_onehot_seq = np.expand_dims(to_categorical(test_sp_seq, num_classes=sp_vsize), 1)
attention_weights.append((dec_ind, attention))
sp_text += sp_tokenizer.index_word[dec_ind] + ' '
return sp_text, attention_weights
#
#
""" Defining tokenizers """
en_tokenizer = keras.preprocessing.text.Tokenizer(oov_token='UNK')
en_tokenizer.fit_on_texts(tr_en_text)
sp_tokenizer = keras.preprocessing.text.Tokenizer(oov_token='UNK')
sp_tokenizer.fit_on_texts(tr_sp_text)
""" Getting preprocessed data """
en_seq, sp_seq = preprocess_data(en_tokenizer, sp_tokenizer, tr_en_text, tr_sp_text, en_timesteps, sp_timesteps)
en_vsize = 9513 #max(en_tokenizer.index_word.keys()) + 1
sp_vsize = 22259
""" Defining the full model """
full_model, infer_enc_model, infer_dec_model = define_nmt(hidden_size=hidden_size, batch_size=batch_size,
en_timesteps=en_timesteps, sp_timesteps=sp_timesteps,
en_vsize=en_vsize, sp_vsize=sp_vsize)
#
#
n_epochs = 10
#Training
train(full_model, en_seq, sp_seq, batch_size, n_epochs)
#Saving Weights
infer_dec_model.save_weights('decoder_weights_n.h5')
infer_enc_model.save_weights('encoder_weights_n.h5')