Я прошел обучение для моей модели классификации текста для чат-бота, и теперь я хочу проверить эту модель и пообщаться с ней, что мне делать?
У меня есть модель классификации текста для чат-бота ион работает, и я обучил его после того, как были созданы 3 файла обучения:
model-15000.data-00000-of-00001 model-15000.index model-15000.meta
что теперьДолжен ли я поговорить с ботом, может ли кто-нибудь помочь мне с этим фрагментом кода и рассказать, как восстановить эти три файла и начать общаться с ботом
спасибо
вот поезд.py файл:
import tensorflow as tf
import numpy as np
import os
import datetime
import time
from rnn import RNN
import data_helpers
import sys
# Data loading params
tf.flags.DEFINE_string("pos_dir", "data/rt-polaritydata/rt-polarity.pos", "Path of positive data")
tf.flags.DEFINE_string("neg_dir", "data/rt-polaritydata/rt-polarity.neg", "Path of negative data")
tf.flags.DEFINE_float("dev_sample_percentage", .1, "Percentage of the training data to use for validation")
tf.flags.DEFINE_integer("max_sentence_length", 100, "Max sentence length in train/test data (Default: 100)")
# Model Hyperparameters
tf.flags.DEFINE_string("cell_type", "vanilla", "Type of rnn cell. Choose 'vanilla' or 'lstm' or 'gru' (Default: vanilla)")
tf.flags.DEFINE_string("word2vec", None, "Word2vec file with pre-trained embeddings")
tf.flags.DEFINE_integer("embedding_dim", 300, "Dimensionality of character embedding (Default: 300)")
tf.flags.DEFINE_integer("hidden_size", 128, "Dimensionality of character embedding (Default: 128)")
tf.flags.DEFINE_float("dropout_keep_prob", 0.5, "Dropout keep probability (Default: 0.5)")
tf.flags.DEFINE_float("l2_reg_lambda", 3.0, "L2 regularization lambda (Default: 3.0)")
# Training parameters
tf.flags.DEFINE_integer("batch_size", 64, "Batch Size (Default: 64)")
tf.flags.DEFINE_integer("num_epochs", 100, "Number of training epochs (Default: 100)")
tf.flags.DEFINE_integer("display_every", 10, "Number of iterations to display training info.")
tf.flags.DEFINE_integer("evaluate_every", 100, "Evaluate model on dev set after this many steps")
tf.flags.DEFINE_integer("checkpoint_every", 100, "Save model after this many steps")
tf.flags.DEFINE_integer("num_checkpoints", 5, "Number of checkpoints to store")
tf.flags.DEFINE_float("learning_rate", 1e-3, "Which learning rate to start with. (Default: 1e-3)")
# Misc Parameters
tf.flags.DEFINE_boolean("allow_soft_placement", True, "Allow device soft device placement")
tf.flags.DEFINE_boolean("log_device_placement", False, "Log placement of ops on devices")
FLAGS = tf.flags.FLAGS
FLAGS(sys.argv)
print("\nParameters:")
for attr, value in sorted(FLAGS.__flags.items()):
print("{} = {}".format(attr.upper(), value))
print("")
def train():
with tf.device('/cpu:0'):
x_text, y = data_helpers.load_data_and_labels(FLAGS.pos_dir, FLAGS.neg_dir)
text_vocab_processor = tf.contrib.learn.preprocessing.VocabularyProcessor(FLAGS.max_sentence_length)
x = np.array(list(text_vocab_processor.fit_transform(x_text)))
print("Text Vocabulary Size: {:d}".format(len(text_vocab_processor.vocabulary_)))
print("x = {0}".format(x.shape))
print("y = {0}".format(y.shape))
print("")
# Randomly shuffle data
np.random.seed(10)
shuffle_indices = np.random.permutation(np.arange(len(y)))
x_shuffled = x[shuffle_indices]
y_shuffled = y[shuffle_indices]
# Split train/test set
# TODO: This is very crude, should use cross-validation
dev_sample_index = -1 * int(FLAGS.dev_sample_percentage * float(len(y)))
x_train, x_dev = x_shuffled[:dev_sample_index], x_shuffled[dev_sample_index:]
y_train, y_dev = y_shuffled[:dev_sample_index], y_shuffled[dev_sample_index:]
print("Train/Dev split: {:d}/{:d}\n".format(len(y_train), len(y_dev)))
with tf.Graph().as_default():
session_conf = tf.ConfigProto(
allow_soft_placement=FLAGS.allow_soft_placement,
log_device_placement=FLAGS.log_device_placement)
sess = tf.Session(config=session_conf)
with sess.as_default():
rnn = RNN(
sequence_length=x_train.shape[1],
num_classes=y_train.shape[1],
vocab_size=len(text_vocab_processor.vocabulary_),
embedding_size=FLAGS.embedding_dim,
cell_type=FLAGS.cell_type,
hidden_size=FLAGS.hidden_size,
l2_reg_lambda=FLAGS.l2_reg_lambda
)
# Define Training procedure
global_step = tf.Variable(0, name="global_step", trainable=False)
train_op = tf.train.AdamOptimizer(FLAGS.learning_rate).minimize(rnn.loss, global_step=global_step)
# Output directory for models and summaries
timestamp = str(int(time.time()))
out_dir = os.path.abspath(os.path.join(os.path.curdir, "runs", timestamp))
print("Writing to {}\n".format(out_dir))
# Summaries for loss and accuracy
loss_summary = tf.summary.scalar("loss", rnn.loss)
acc_summary = tf.summary.scalar("accuracy", rnn.accuracy)
# Train Summaries
train_summary_op = tf.summary.merge([loss_summary, acc_summary])
train_summary_dir = os.path.join(out_dir, "summaries", "train")
train_summary_writer = tf.summary.FileWriter(train_summary_dir, sess.graph)
# Dev summaries
dev_summary_op = tf.summary.merge([loss_summary, acc_summary])
dev_summary_dir = os.path.join(out_dir, "summaries", "dev")
dev_summary_writer = tf.summary.FileWriter(dev_summary_dir, sess.graph)
# Checkpoint directory. Tensorflow assumes this directory already exists so we need to create it
checkpoint_dir = os.path.abspath(os.path.join(out_dir, "checkpoints"))
checkpoint_prefix = os.path.join(checkpoint_dir, "model")
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
saver = tf.train.Saver(tf.global_variables(), max_to_keep=FLAGS.num_checkpoints)
# Write vocabulary
text_vocab_processor.save(os.path.join(out_dir, "text_vocab"))
# Initialize all variables
sess.run(tf.global_variables_initializer())
# Pre-trained word2vec
if FLAGS.word2vec:
# initial matrix with random uniform
initW = np.random.uniform(-0.25, 0.25, (len(text_vocab_processor.vocabulary_), FLAGS.embedding_dim))
# load any vectors from the word2vec
print("Load word2vec file {0}".format(FLAGS.word2vec))
with open(FLAGS.word2vec, "rb") as f:
header = f.readline()
vocab_size, layer1_size = map(int, header.split())
binary_len = np.dtype('float32').itemsize * layer1_size
for line in range(vocab_size):
word = []
while True:
ch = f.read(1).decode('latin-1')
if ch == ' ':
word = ''.join(word)
break
if ch != '\n':
word.append(ch)
idx = text_vocab_processor.vocabulary_.get(word)
if idx != 0:
initW[idx] = np.fromstring(f.read(binary_len), dtype='float32')
else:
f.read(binary_len)
sess.run(rnn.W_text.assign(initW))
print("Success to load pre-trained word2vec model!\n")
# Generate batches
batches = data_helpers.batch_iter(
list(zip(x_train, y_train)), FLAGS.batch_size, FLAGS.num_epochs)
# Training loop. For each batch...
for batch in batches:
x_batch, y_batch = zip(*batch)
# Train
feed_dict = {
rnn.input_text: x_batch,
rnn.input_y: y_batch,
rnn.dropout_keep_prob: FLAGS.dropout_keep_prob
}
_, step, summaries, loss, accuracy = sess.run(
[train_op, global_step, train_summary_op, rnn.loss, rnn.accuracy], feed_dict)
train_summary_writer.add_summary(summaries, step)
# Training log display
if step % FLAGS.display_every == 0:
time_str = datetime.datetime.now().isoformat()
print("{}: step {}, loss {:g}, acc {:g}".format(time_str, step, loss, accuracy))
# Evaluation
if step % FLAGS.evaluate_every == 0:
print("\nEvaluation:")
feed_dict_dev = {
rnn.input_text: x_dev,
rnn.input_y: y_dev,
rnn.dropout_keep_prob: 1.0
}
summaries_dev, loss, accuracy = sess.run(
[dev_summary_op, rnn.loss, rnn.accuracy], feed_dict_dev)
dev_summary_writer.add_summary(summaries_dev, step)
time_str = datetime.datetime.now().isoformat()
print("{}: step {}, loss {:g}, acc {:g}\n".format(time_str, step, loss, accuracy))
# Model checkpoint
if step % FLAGS.checkpoint_every == 0:
path = saver.save(sess, checkpoint_prefix, global_step=step)
print("Saved model checkpoint to {}\n".format(path))
def main(_):
train()
if __name__ == "__main__":
tf.app.run()
##################rnn.py file #################
import tensorflow as tf
class RNN:
def __init__(self, sequence_length, num_classes, vocab_size, embedding_size,
cell_type, hidden_size, l2_reg_lambda=0.0):
# Placeholders for input, output and dropout
self.input_text = tf.placeholder(tf.int32, shape=[None, sequence_length], name='input_text')
self.input_y = tf.placeholder(tf.float32, shape=[None, num_classes], name='input_y')
self.dropout_keep_prob = tf.placeholder(tf.float32, name='dropout_keep_prob')
l2_loss = tf.constant(0.0)
text_length = self._length(self.input_text)
# Embedding layer
with tf.device('/cpu:0'), tf.name_scope("text-embedding"):
self.W_text = tf.Variable(tf.random_uniform([vocab_size, embedding_size], -1.0, 1.0), name="W_text")
self.embedded_chars = tf.nn.embedding_lookup(self.W_text, self.input_text)
# Recurrent Neural Network
with tf.name_scope("rnn"):
cell = self._get_cell(hidden_size, cell_type)
cell = tf.nn.rnn_cell.DropoutWrapper(cell, output_keep_prob=self.dropout_keep_prob)
all_outputs, _ = tf.nn.dynamic_rnn(cell=cell,
inputs=self.embedded_chars,
sequence_length=text_length,
dtype=tf.float32)
self.h_outputs = self.last_relevant(all_outputs, text_length)
# Final scores and predictions
with tf.name_scope("output"):
W = tf.get_variable("W", shape=[hidden_size, num_classes], initializer=tf.contrib.layers.xavier_initializer())
b = tf.Variable(tf.constant(0.1, shape=[num_classes]), name="b")
l2_loss += tf.nn.l2_loss(W)
l2_loss += tf.nn.l2_loss(b)
self.logits = tf.nn.xw_plus_b(self.h_outputs, W, b, name="logits")
self.predictions = tf.argmax(self.logits, 1, name="predictions")
# Calculate mean cross-entropy loss
with tf.name_scope("loss"):
losses = tf.nn.softmax_cross_entropy_with_logits(logits=self.logits, labels=self.input_y)
self.loss = tf.reduce_mean(losses) + l2_reg_lambda * l2_loss
# Accuracy
with tf.name_scope("accuracy"):
correct_predictions = tf.equal(self.predictions, tf.argmax(self.input_y, axis=1))
self.accuracy = tf.reduce_mean(tf.cast(correct_predictions, tf.float32), name="accuracy")
@staticmethod
def _get_cell(hidden_size, cell_type):
if cell_type == "vanilla":
return tf.nn.rnn_cell.BasicRNNCell(hidden_size)
elif cell_type == "lstm":
return tf.nn.rnn_cell.BasicLSTMCell(hidden_size)
elif cell_type == "gru":
return tf.nn.rnn_cell.GRUCell(hidden_size)
else:
print("ERROR: '" + cell_type + "' is a wrong cell type !!!")
return None
# Length of the sequence data
@staticmethod
def _length(seq):
relevant = tf.sign(tf.abs(seq))
length = tf.reduce_sum(relevant, reduction_indices=1)
length = tf.cast(length, tf.int32)
return length
# Extract the output of last cell of each sequence
# Ex) The movie is good -> length = 4
# output = [ [1.314, -3.32, ..., 0.98]
# [0.287, -0.50, ..., 1.55]
# [2.194, -2.12, ..., 0.63]
# [1.938, -1.88, ..., 1.31]
# [ 0.0, 0.0, ..., 0.0]
# ...
# [ 0.0, 0.0, ..., 0.0] ]
# The output we need is 4th output of cell, so extract it.
@staticmethod
def last_relevant(seq, length):
batch_size = tf.shape(seq)[0]
max_length = int(seq.get_shape()[1])
input_size = int(seq.get_shape()[2])
index = tf.range(0, batch_size) * max_length + (length - 1)
flat = tf.reshape(seq, [-1, input_size])
return tf.gather(flat, index)