main.py
# -*- coding: utf-8 -*-
"""
Created on Wed Sep 26 08:43:37 2018
@author: yanghe
"""
# -*- coding: utf-8 -*-
"""
Created on Sat Apr 28 20:39:38 2018
@author: yanghe
"""
import tensorflow as tf
import numpy as np
from words_tool import *
X_train, Y_train = read_csv('data/train_emoji.csv')
X_test, Y_test = read_csv('data/tesss.csv')
word_to_index, index_to_word, word_to_vec_map = read_glove_vecs('data/glove.6B.50d.txt')
X_train_indices = sentences_to_indices(X_train, word_to_index, 10)
Y_train_oh = convert_to_one_hot(Y_train, C = 5)
X_test_indices = sentences_to_indices(X_test, word_to_index, 10)
Y_test_oh = convert_to_one_hot(Y_test, C = 5)
num_steps = 10
size = 50
n_hidden = 128
batch_size = 4
vocab_size = 400000
max_epoch = 10
learning_rate = 0.01
n_classes = 5
count = 0
input_data = tf.placeholder(tf.int32, [None, num_steps])
targets = tf.placeholder(tf.int32, [None, n_classes])
keep_prob = tf.placeholder(tf.float32)
def model(input_data,on_training):
with tf.variable_scope("embed") :
emb_matrix = np.zeros((vocab_size+1, size))
for word, index in word_to_index.items():
emb_matrix[index, :] = word_to_vec_map[word]
embedding = tf.get_variable("embedding", initializer=tf.constant(emb_matrix,tf.float32),trainable=False)
inputs = tf.nn.embedding_lookup(embedding , input_data)
with tf.variable_scope('Bi_RNN'):
inputs = tf.transpose(inputs, [1, 0, 2])
inputs = tf.reshape(inputs, [-1, size])
inputs = tf.split(inputs, num_steps)
lstm_fw_cell = tf.contrib.rnn.BasicLSTMCell(n_hidden, forget_bias=1.0)
lstm_bw_cell = tf.contrib.rnn.BasicLSTMCell(n_hidden, forget_bias=1.0)
if on_training:
lstm_fw_cell = tf.contrib.rnn.DropoutWrapper(lstm_fw_cell,output_keep_prob=keep_prob)
lstm_bw_cell = tf.contrib.rnn.DropoutWrapper(lstm_bw_cell,output_keep_prob=keep_prob)
outputs, _, _ = tf.contrib.rnn.static_bidirectional_rnn(lstm_fw_cell,
lstm_bw_cell,
inputs,
dtype=tf.float32)
with tf.name_scope('softmx'):
weights = tf.get_variable('weights', shape=[2*n_hidden, n_classes], initializer=tf.truncated_normal_initializer(stddev=0.1))
biases = tf.get_variable('biases', shape=[n_classes], initializer=tf.truncated_normal_initializer(stddev=0.1))
outputs = tf.matmul(outputs[-1], weights) + biases
return outputs
def train():
on_training = True
with tf.variable_scope("pred"):
pred = model(input_data,on_training)
saver = tf.train.Saver()
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=targets))
train_op = tf.train.AdamOptimizer(learning_rate).minimize(cost)
correct_pred = tf.equal(tf.argmax(targets, 1), tf.argmax(pred, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
with tf.Session() as sess :
tf.global_variables_initializer().run()
for i in range(max_epoch):
_= sess.run([train_op ],feed_dict={input_data: X_train_indices,
targets: Y_train_oh,
keep_prob:0.8})
if i % 5 == 0 :
accuracy_= sess.run(accuracy,feed_dict={input_data:X_test_indices,
targets:Y_test_oh,
keep_prob:1.0})
print("After %d , validation accuracy is %s " % (i,accuracy_))
saver.save(sess , 'saver/moedl_em.ckpt')
def predict():
global count
on_training = False
with tf.variable_scope("pred") as scope:
if count == 0:
pass
else:
scope.reuse_variables()
pred = model(input_data,on_training)
count += 1
saver = tf.train.Saver()
with tf.Session() as sess:
tf.global_variables_initializer().run()
saver.restore(sess,'./saver/moedl_em.ckpt')
# usr_input = input("Write the beginning of your want to say , but not benyond 10 words :")
X_test = sentences_to_indices(np.array([usr_input]), word_to_index, 10)
predict_= sess.run(pred,feed_dict={input_data:X_test,keep_prob:1.0})
print('you input is ',usr_input,'machine predict you want tp say:--->',label_to_emoji(np.argmax(predict_)))
#train()
predict()
#I love taking breaks
# This girl is messing with me
# she got me a nice present
# work is hard
words_tool.py
# -*- coding: utf-8 -*-
"""
Created on Sun Apr 29 09:48:38 2018
@author: yanghe
"""
import numpy as np
import csv
def convert_to_one_hot(Y, C):
Y = np.eye(C)[Y.reshape(-1)]
return Y
def read_glove_vecs(glove_file):
with open(glove_file, 'r',encoding='utf-8') as f:
words = set()
word_to_vec_map = {}
for line in f:
line = line.strip().split()
curr_word = line[0]
words.add(curr_word)
word_to_vec_map[curr_word] = np.array(line[1:], dtype=np.float64)
i = 1
words_to_index = {}
index_to_words = {}
for w in sorted(words):
words_to_index[w] = i
index_to_words[i] = w
i = i + 1
return words_to_index, index_to_words, word_to_vec_map
def random_mini_batches(x,y,mini_bath_size =64,seed =0):
np.random.seed(seed)
m = x.shape[0]
mini_batches = []
permutation = list(np.random.permutation(m))
shuffled_x = x[permutation]
shuffled_y = y[permutation]
num_complete_minibatches = int(np.floor(m/mini_bath_size))
for k in range(0,num_complete_minibatches):
mini_batch_x = shuffled_x[k*mini_bath_size:(k+1)*mini_bath_size,:,:]
mini_batch_y = shuffled_y[k*mini_bath_size:(k+1)*mini_bath_size,]
mini_batch = (mini_batch_x,mini_batch_y)
mini_batches.append(mini_batch)
if m % mini_bath_size != 0:
mini_batch_x = shuffled_x[(k+1)*mini_bath_size:,:,:]
mini_batch_y = shuffled_y[(k+1)*mini_bath_size:,:]
mini_batch = (mini_batch_x,mini_batch_y)
mini_batches.append(mini_batch)
return mini_batches
def sentence_to_avg(sentence, word_to_vec_map):
words = [i.lower() for i in sentence.split()]
avg = np.zeros((50,))
for w in words:
avg += word_to_vec_map[w]
avg = avg / len(words)
return avg
def sentences_to_indices(X, word_to_index, max_len):
m = X.shape[0]
X_indices = np.zeros((m, max_len))
for i in range(m):
sentence_words = [w.lower() for w in X[i].split()]
j = 0
for w in sentence_words:
X_indices[i, j] = word_to_index[w]
j += 1
return X_indices
def read_csv(filename = 'data/emojify_data.csv'):
phrase = []
emoji = []
with open (filename) as csvDataFile:
csvReader = csv.reader(csvDataFile)
for row in csvReader:
phrase.append(row[0])
emoji.append(row[1])
X = np.asarray(phrase)
Y = np.asarray(emoji, dtype=int)
return X, Y
emoji_dictionary = {"0": "heart,good", # :heart: prints a black instead of red heart depending on the font
"1": ":baseball:",
"2": ":simell",
"3": ":disappointed:",
"4": ":fork_and_knife:"}
def label_to_emoji(label):
return emoji_dictionary[str(label)]