chore: import upstream snapshot with attribution

2026-07-13 12:45:52 +08:00
commit aa6c9c0720
43 changed files with 5178 additions and 0 deletions
@@ -0,0 +1,94 @@
+'''
+  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!!")