encoding_embedding_size = 64
decoding_embedding_size = 64
# RNN Size
rnn_size = 64
# Number of Layers
num_layers = 2
# Learning Rate
learning_rate = 0.0001
# Dropout Keep Probability
keep_probability = 0.8
display_step = 10
save_path = 'checkpoints/sup/actor/dev'
save_path_critic = 'checkpoints/sup/critic/dev'
(source_int_text, target_int_text), (source_vocab_to_int, target_vocab_to_int), (source_int_to_vocab, target_int_to_vocab) = util.load_preprocess('preprocess.p')
load_path_actor = util.load_params('params_actor_sup.p')
source_train = util.read_list('source_train.npy')
target_train = util.read_list('target_train.npy')
valid_size = batch_size * 10
train_source = source_train[valid_size:]
train_target = target_train[valid_size:]
valid_source = source_train[:valid_size]
valid_target = target_train[:valid_size]
test_acc_list = []
train_graph = tf.Graph()
critic_graph = tf.Graph()
actor_graph = tf.Graph()
import tensorflow as tf
import numpy as np
import util
_, (source_vocab_to_int,
target_vocab_to_int), (source_int_to_vocab,
target_int_to_vocab) = util.load_preprocess()
load_path = util.load_params()
# To feed a sentence into the model for translation, you first need to preprocess it
def sentence_to_seq(sentence, vocab_to_int):
"""
Convert a sentence to a sequence of ids
:param sentence: String
:param vocab_to_int: Dictionary to go from the words to an id
:return: List of word ids
"""
sentence = sentence.lower()
sentence_list = sentence.split()
word_ids = []
for val in sentence_list:
word_ids.append(vocab_to_int.get(val, vocab_to_int['<UNK>']))
return word_ids
# Translate
translate_sentence = 'he saw a old yellow truck .'
translate_sentence = sentence_to_seq(translate_sentence, source_vocab_to_int)
import tensorflow as tf
import numpy as np
import util
_, vocab_to_int, int_to_vocab, token_dict = util.load_preprocess()
seq_length, load_dir = util.load_params()
def get_tensors(loaded_graph):
"""
Get input, initial state, final state, and probabilities tensor from <loaded_graph>
:param loaded_graph: TensorFlow graph loaded from file
:return: Tuple (InputTensor, InitialStateTensor, FinalStateTensor, ProbsTensor)
"""
with loaded_graph.as_default():
inputs = tf.get_default_graph().get_tensor_by_name('input:0')
initial_state = tf.get_default_graph().get_tensor_by_name(
'initial_state:0')
fianl_state = tf.get_default_graph().get_tensor_by_name(
'final_state:0')
prob = tf.get_default_graph().get_tensor_by_name('probs:0')
return inputs, initial_state, fianl_state, prob
def pick_word(probabilities, int_to_vocab):
"""
Pick the next word in the generated text
:param probabilities: Probabilites of the next word
:param int_to_vocab: Dictionary of word ids as the keys and words as the values
:return: String of the predicted word
"""
# Number of Layers
num_layers = args.num_layers
# Embedding Size
encoding_embedding_size = args.encoding_embedding_size
decoding_embedding_size = args.decoding_embedding_size
# Learning Rate
learning_rate = args.learning_rate
# Dropout Keep Probability
keep_probability = args.keep_prob
display_step = args.display_step
# build the graph
save_path = args.save_path
(source_int_text,
target_int_text), (source_vocab_to_int,
target_vocab_to_int), _ = util.load_preprocess()
max_target_sentence_length = max(
[len(sentence) for sentence in source_int_text])
model = models.SeqToSeq(rnn_size, num_layers, len(target_vocab_to_int),
len(source_vocab_to_int), batch_size,
encoding_embedding_size, decoding_embedding_size,
keep_prob)
train_graph = tf.Graph()
with train_graph.as_default():
input_data, targets, lr, keep_prob, target_sequence_length, max_target_sequence_length, source_sequence_length = model.model_inputs(
)
#sequence_length = tf.placeholder_with_default(max_target_sentence_length, None, name='sequence_length')
input_shape = tf.shape(input_data)