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from __future__ import print_function
import numpy as np
import tensorflow as tf
from tensorflow.contrib import rnn
import random
import collections
import time
import re
start_time = time.time()
def elapsed(sec):
if sec<60:
return str(sec) + " sec"
elif sec<(60*60):
return str(sec/60) + " min"
else:
return str(sec/(60*60)) + " hr"
# Target log path
logs_path = '/tmp/tensorflow/rnn_words'
writer = tf.summary.FileWriter(logs_path)
# Text file containing words for training
training_file = 'moby.txt'
def read_data(fname):
punct = re.compile(r'(\.|\,|\?|\--|\|)|\(|\!|\;|\_)')
with open(fname) as f:
content = f.readlines()
content = [x.strip() for x in content]
content = [x.lower() for x in content]
content = [punct.sub(r' \1 ', x) for x in content]
content = [word for i in range(len(content)) for word in content[i].split()]
content = np.array(content)
return content
training_data = read_data(training_file)
print("Loaded training data...")
def build_dataset(words):
count = collections.Counter(words).most_common()
dictionary = dict()
for word, _ in count:
dictionary[word] = len(dictionary)
reverse_dictionary = dict(zip(dictionary.values(), dictionary.keys()))
return dictionary, reverse_dictionary
dictionary, reverse_dictionary = build_dataset(training_data)
vocab_size = len(dictionary)
# Parameters
learning_rate = 0.001
training_iters = 50000
display_step = 1000
n_input = 4
# number of units in RNN cell
n_hidden = 512
# tf Graph input
x = tf.placeholder("float", [None, n_input, 1])
y = tf.placeholder("float", [None, vocab_size])
# RNN output node weights and biases
weights = {
'out': tf.Variable(tf.random_normal([n_hidden, vocab_size]))
}
biases = {
'out': tf.Variable(tf.random_normal([vocab_size]))
}
def RNN(x, weights, biases):
# reshape to [1, n_input]
x = tf.reshape(x, [-1, n_input])
# Generate a n_input-element sequence of inputs
# (eg. [had] [a] [general] -> [20] [6] [33])
x = tf.split(x,n_input,1)
# 2-layer LSTM, each layer has n_hidden units.
rnn_cell = rnn.MultiRNNCell([rnn.BasicLSTMCell(n_hidden),rnn.BasicLSTMCell(n_hidden)])
# generate prediction
outputs, states = rnn.static_rnn(rnn_cell, x, dtype=tf.float32)
# there are n_input outputs but
# we only want the last output
return tf.matmul(outputs[-1], weights['out']) + biases['out']
pred = RNN(x, weights, biases)
# Loss and optimizer
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=pred, labels=y))
optimizer = tf.train.RMSPropOptimizer(learning_rate=learning_rate).minimize(cost)
# Model evaluation
correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
# Initializing the variables
init = tf.global_variables_initializer()
# add ops to save and restore
saver = tf.train.Saver()
# Launch the graph
with tf.Session() as session:
session.run(init)
step = 0
offset = random.randint(0,n_input+1)
end_offset = n_input + 1
acc_total = 0
loss_total = 0
writer.add_graph(session.graph)
while step < training_iters:
# Generate a minibatch. Add some randomness on selection process.
if offset > (len(training_data)-end_offset):
offset = random.randint(0, n_input+1)
symbols_in_keys = [ [dictionary[ str(training_data[i])]] for i in range(offset, offset+n_input) ]
symbols_in_keys = np.reshape(np.array(symbols_in_keys), [-1, n_input, 1])
# We're only really saving the one output - this should change ideally
symbols_out_onehot = np.zeros([vocab_size], dtype=float)
symbols_out_onehot[dictionary[str(training_data[offset+n_input])]] = 1.0
symbols_out_onehot = np.reshape(symbols_out_onehot,[1,-1])
_, acc, loss, onehot_pred = session.run([optimizer, accuracy, cost, pred], \
feed_dict={x: symbols_in_keys, y: symbols_out_onehot})
loss_total += loss
acc_total += acc
if (step+1) % display_step == 0:
print("Iter= " + str(step+1) + ", Average Loss= " + \
"{:.6f}".format(loss_total/display_step) + ", Average Accuracy= " + \
"{:.2f}%".format(100*acc_total/display_step))
print("{:.2f}% Complete".format(100*(step/training_iters)))
acc_total = 0
loss_total = 0
symbols_in = [training_data[i] for i in range(offset, offset + n_input)]
symbols_out = training_data[offset + n_input]
symbols_out_pred = reverse_dictionary[int(tf.argmax(onehot_pred, 1).eval())]
print("%s - [%s] vs [%s]" % (symbols_in,symbols_out,symbols_out_pred))
step += 1
offset += (n_input+1)
print("Optimization Finished!")
print("Elapsed time: ", elapsed(time.time() - start_time))
print("Run on command line.")
print("\ttensorboard --logdir=%s" % (logs_path))
print("Point your web browser to: http://localhost:6006/")
save_path = saver.save(session, "/tmp/model.ckpt")
print("Model saved in path: %s" % save_path)
while True:
prompt = "%s words: " % n_input
sentence = input(prompt)
sentence = sentence.strip()
words = sentence.split(' ')
if len(words) != n_input:
continue
try:
symbols_in_keys = [dictionary[str(words[i])] for i in range(len(words))]
for i in range(32):
keys = np.reshape(np.array(symbols_in_keys), [-1, n_input, 1])
onehot_pred = session.run(pred, feed_dict={x: keys})
onehot_pred_index = int(tf.argmax(onehot_pred, 1).eval())
sentence = "%s %s" % (sentence,reverse_dictionary[onehot_pred_index])
symbols_in_keys = symbols_in_keys[1:]
symbols_in_keys.append(onehot_pred_index)
print(symbols_in_keys)
print(sentence)
except:
print("Word not in dictionary")