wehub-resource-sync
94 lines
3.6 KiB
Python
94 lines
3.6 KiB
Python
'''
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code by Tae Hwan Jung(Jeff Jung) @graykode
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Reference : https://github.com/ioatr/textcnn
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'''
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import tensorflow as tf
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import numpy as np
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tf.reset_default_graph()
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# Text-CNN Parameter
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embedding_size = 2 # n-gram
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sequence_length = 3
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num_classes = 2 # 0 or 1
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filter_sizes = [2,2,2] # n-gram window
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num_filters = 3
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# 3 words sentences (=sequence_length is 3)
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sentences = ["i love you","he loves me", "she likes baseball", "i hate you","sorry for that", "this is awful"]
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labels = [1,1,1,0,0,0] # 1 is good, 0 is not good.
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word_list = " ".join(sentences).split()
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word_list = list(set(word_list))
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word_dict = {w: i for i, w in enumerate(word_list)}
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vocab_size = len(word_dict)
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inputs = []
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for sen in sentences:
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inputs.append(np.asarray([word_dict[n] for n in sen.split()]))
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outputs = []
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for out in labels:
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outputs.append(np.eye(num_classes)[out]) # ONE-HOT : To using Tensor Softmax Loss function
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# Model
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X = tf.placeholder(tf.int32, [None, sequence_length])
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Y = tf.placeholder(tf.int32, [None, num_classes])
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W = tf.Variable(tf.random_uniform([vocab_size, embedding_size], -1.0, 1.0))
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embedded_chars = tf.nn.embedding_lookup(W, X) # [batch_size, sequence_length, embedding_size]
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embedded_chars = tf.expand_dims(embedded_chars, -1) # add channel(=1) [batch_size, sequence_length, embedding_size, 1]
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pooled_outputs = []
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for i, filter_size in enumerate(filter_sizes):
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filter_shape = [filter_size, embedding_size, 1, num_filters]
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W = tf.Variable(tf.truncated_normal(filter_shape, stddev=0.1))
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b = tf.Variable(tf.constant(0.1, shape=[num_filters]))
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conv = tf.nn.conv2d(embedded_chars, # [batch_size, sequence_length, embedding_size, 1]
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W, # [filter_size(n-gram window), embedding_size, 1, num_filters(=3)]
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strides=[1, 1, 1, 1],
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padding='VALID')
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h = tf.nn.relu(tf.nn.bias_add(conv, b))
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pooled = tf.nn.max_pool(h,
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ksize=[1, sequence_length - filter_size + 1, 1, 1], # [batch_size, filter_height, filter_width, channel]
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strides=[1, 1, 1, 1],
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padding='VALID')
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pooled_outputs.append(pooled) # dim of pooled : [batch_size(=6), output_height(=1), output_width(=1), channel(=1)]
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num_filters_total = num_filters * len(filter_sizes)
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h_pool = tf.concat(pooled_outputs, num_filters) # h_pool : [batch_size(=6), output_height(=1), output_width(=1), channel(=1) * 3]
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h_pool_flat = tf.reshape(h_pool, [-1, num_filters_total]) # [batch_size, ]
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# Model-Training
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Weight = tf.get_variable('W', shape=[num_filters_total, num_classes],
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initializer=tf.contrib.layers.xavier_initializer())
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Bias = tf.Variable(tf.constant(0.1, shape=[num_classes]))
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model = tf.nn.xw_plus_b(h_pool_flat, Weight, Bias)
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cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=model, labels=Y))
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optimizer = tf.train.AdamOptimizer(0.001).minimize(cost)
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# Model-Predict
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hypothesis = tf.nn.softmax(model)
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predictions = tf.argmax(hypothesis, 1)
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# Training
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init = tf.global_variables_initializer()
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sess = tf.Session()
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sess.run(init)
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for epoch in range(5000):
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_, loss = sess.run([optimizer, cost], feed_dict={X: inputs, Y: outputs})
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if (epoch + 1)%1000 == 0:
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print('Epoch:', '%06d' % (epoch + 1), 'cost =', '{:.6f}'.format(loss))
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# Test
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test_text = 'sorry hate you'
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tests = []
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tests.append(np.asarray([word_dict[n] for n in test_text.split()]))
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predict = sess.run([predictions], feed_dict={X: tests})
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result = predict[0][0]
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if result == 0:
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print(test_text,"is Bad Mean...")
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else:
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print(test_text,"is Good Mean!!") |