自然语言处理2------CNN(Convolutional Neural Network）

CNN(Convolutional Neural Network）

• 2-1. TextCNN - Binary Sentiment Classification

• Paper下载：

TextCNN-Tensor.py

'''
  code by Tae Hwan Jung(Jeff Jung) @graykode
  Reference : https://github.com/ioatr/textcnn
'''
import tensorflow as tf
import numpy as np

tf.reset_default_graph()

# Text-CNN Parameter
embedding_size = 2 # n-gram
sequence_length = 3
num_classes = 2 # 0 or 1
filter_sizes = [2,2,2] # n-gram window
num_filters = 3

# 3 words sentences (=sequence_length is 3)
sentences = ["i love you","he loves me", "she likes baseball", "i hate you","sorry for that", "this is awful"]
labels = [1,1,1,0,0,0] # 1 is good, 0 is not good.

word_list = " ".join(sentences).split()
word_list = list(set(word_list))
word_dict = {w: i for i, w in enumerate(word_list)}
vocab_size = len(word_dict)

inputs = []
for sen in sentences:
    inputs.append(np.asarray([word_dict[n] for n in sen.split()]))

outputs = []
for out in labels:
    outputs.append(np.eye(num_classes)[out]) # ONE-HOT : To using Tensor Softmax Loss function

# Model
X = tf.placeholder(tf.int32, [None, sequence_length])
Y = tf.placeholder(tf.int32, [None, num_classes])

W = tf.Variable(tf.random_uniform([vocab_size, embedding_size], -1.0, 1.0))
embedded_chars = tf.nn.embedding_lookup(W, X) # [batch_size, sequence_length, embedding_size]
embedded_chars = tf.expand_dims(embedded_chars, -1) # add channel(=1) [batch_size, sequence_length, embedding_size, 1]

pooled_outputs = []
for i, filter_size in enumerate(filter_sizes):
    filter_shape = [filter_size, embedding_size, 1, num_filters]
    W = tf.Variable(tf.truncated_normal(filter_shape, stddev=0.1))
    b = tf.Variable(tf.constant(0.1, shape=[num_filters]))

    conv = tf.nn.conv2d(embedded_chars, # [batch_size, sequence_length, embedding_size, 1]
                        W,              # [filter_size(n-gram window), embedding_size, 1, num_filters(=3)]
                        strides=[1, 1, 1, 1],
                        padding='VALID')
    h = tf.nn.relu(tf.nn.bias_add(conv, b))
    pooled = tf.nn.max_pool(h,
                            ksize=[1, sequence_length - filter_size + 1, 1, 1], # [batch_size, filter_height, filter_width, channel]
                            strides=[1, 1, 1, 1],
                            padding='VALID')
    pooled_outputs.append(pooled) # dim of pooled : [batch_size(=6), output_height(=1), output_width(=1), channel(=1)]

num_filters_total = num_filters * len(filter_sizes)
h_pool = tf.concat(pooled_outputs, num_filters) # h_pool : [batch_size(=6), output_height(=1), output_width(=1), channel(=1) * 3]
h_pool_flat = tf.reshape(h_pool, [-1, num_filters_total]) # [batch_size, ]

# Model-Training
Weight = tf.get_variable('W', shape=[num_filters_total, num_classes], 
                    initializer=tf.contrib.layers.xavier_initializer())
Bias = tf.Variable(tf.constant(0.1, shape=[num_classes]))
model = tf.nn.xw_plus_b(h_pool_flat, Weight, Bias)  
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=model, labels=Y))
optimizer = tf.train.AdamOptimizer(0.001).minimize(cost)

# Model-Predict
hypothesis = tf.nn.softmax(model)
predictions = tf.argmax(hypothesis, 1)
# Training
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)

for epoch in range(5000):
    _, loss = sess.run([optimizer, cost], feed_dict={X: inputs, Y: outputs})
    if (epoch + 1)%1000 == 0:
        print('Epoch:', '%06d' % (epoch + 1), 'cost =', '{:.6f}'.format(loss))

# Test
test_text = 'sorry hate you'
tests = []
tests.append(np.asarray([word_dict[n] for n in test_text.split()]))

predict = sess.run([predictions], feed_dict={X: tests})
result = predict[0][0]
if result == 0:
    print(test_text,"is Bad Mean...")
else:
    print(test_text,"is Good Mean!!")

• TextCNN-Torch.py

'''
  code by Tae Hwan Jung(Jeff Jung) @graykode
'''
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
import torch.nn.functional as F

dtype = torch.FloatTensor

# Text-CNN Parameter
embedding_size = 2 # n-gram
sequence_length = 3
num_classes = 2  # 0 or 1
filter_sizes = [2, 2, 2] # n-gram window
num_filters = 3

# 3 words sentences (=sequence_length is 3)
sentences = ["i love you", "he loves me", "she likes baseball", "i hate you", "sorry for that", "this is awful"]
labels = [1, 1, 1, 0, 0, 0]  # 1 is good, 0 is not good.

word_list = " ".join(sentences).split()
word_list = list(set(word_list))
word_dict = {w: i for i, w in enumerate(word_list)}
vocab_size = len(word_dict)

inputs = []
for sen in sentences:
    inputs.append(np.asarray([word_dict[n] for n in sen.split()]))

targets = []
for out in labels:
    targets.append(out) # To using Torch Softmax Loss function

input_batch = Variable(torch.LongTensor(inputs))
target_batch = Variable(torch.LongTensor(targets))


class TextCNN(nn.Module):
    def __init__(self):
        super(TextCNN, self).__init__()

        self.num_filters_total = num_filters * len(filter_sizes)
        self.W = nn.Parameter(torch.empty(vocab_size, embedding_size).uniform_(-1, 1)).type(dtype)
        self.Weight = nn.Parameter(torch.empty(self.num_filters_total, num_classes).uniform_(-1, 1)).type(dtype)
        self.Bias = nn.Parameter(0.1 * torch.ones([num_classes])).type(dtype)

    def forward(self, X):
        embedded_chars = self.W[X] # [batch_size, sequence_length, sequence_length]
        embedded_chars = embedded_chars.unsqueeze(1) # add channel(=1) [batch, channel(=1), sequence_length, embedding_size]

        pooled_outputs = []
        for filter_size in filter_sizes:
            # conv : [input_channel(=1), output_channel(=3), (filter_height, filter_width), bias_option]
            conv = nn.Conv2d(1, num_filters, (filter_size, embedding_size), bias=True)(embedded_chars)
            h = F.relu(conv)
            # mp : ((filter_height, filter_width))
            mp = nn.MaxPool2d((sequence_length - filter_size + 1, 1))
            # pooled : [batch_size(=6), output_height(=1), output_width(=1), output_channel(=3)]
            pooled = mp(h).permute(0, 3, 2, 1)
            pooled_outputs.append(pooled)

        h_pool = torch.cat(pooled_outputs, len(filter_sizes)) # [batch_size(=6), output_height(=1), output_width(=1), output_channel(=3) * 3]
        h_pool_flat = torch.reshape(h_pool, [-1, self.num_filters_total]) # [batch_size(=6), output_height * output_width * (output_channel * 3)]

        model = torch.mm(h_pool_flat, self.Weight) + self.Bias # [batch_size, num_classes]
        return model

model = TextCNN()

criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# Training
for epoch in range(5000):
    optimizer.zero_grad()
    output = model(input_batch)

    # output : [batch_size, num_classes], target_batch : [batch_size] (LongTensor, not one-hot)
    loss = criterion(output, target_batch)
    if (epoch + 1) % 1000 == 0:
        print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.6f}'.format(loss))

    loss.backward()
    optimizer.step()

# Test
test_text = 'sorry hate you'
tests = [np.asarray([word_dict[n] for n in test_text.split()])]
test_batch = Variable(torch.LongTensor(tests))

# Predict
predict = model(test_batch).data.max(1, keepdim=True)[1]
if predict[0][0] == 0:
    print(test_text,"is Bad Mean...")
else:
    print(test_text,"is Good Mean!!")

• 2-2. DCNN(Dynamic Convolutional Neural Network)

等待上传loading