wehub-resource-sync
70 lines
2.2 KiB
Python
70 lines
2.2 KiB
Python
'''
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code by Tae Hwan Jung(Jeff Jung) @graykode
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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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sentences = [ "i like dog", "i love coffee", "i hate milk"]
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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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number_dict = {i: w for i, w in enumerate(word_list)}
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n_class = len(word_dict)
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# TextRNN Parameter
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n_step = 2 # number of cells(= number of Step)
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n_hidden = 5 # number of hidden units in one cell
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def make_batch(sentences):
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input_batch = []
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target_batch = []
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for sen in sentences:
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word = sen.split()
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input = [word_dict[n] for n in word[:-1]]
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target = word_dict[word[-1]]
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input_batch.append(np.eye(n_class)[input])
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target_batch.append(np.eye(n_class)[target])
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return input_batch, target_batch
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# Model
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X = tf.placeholder(tf.float32, [None, n_step, n_class]) # [batch_size, n_step, n_class]
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Y = tf.placeholder(tf.float32, [None, n_class]) # [batch_size, n_class]
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W = tf.Variable(tf.random_normal([n_hidden, n_class]))
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b = tf.Variable(tf.random_normal([n_class]))
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cell = tf.nn.rnn_cell.BasicRNNCell(n_hidden)
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outputs, states = tf.nn.dynamic_rnn(cell, X, dtype=tf.float32)
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# outputs : [batch_size, n_step, n_hidden]
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outputs = tf.transpose(outputs, [1, 0, 2]) # [n_step, batch_size, n_hidden]
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outputs = outputs[-1] # [batch_size, n_hidden]
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model = tf.matmul(outputs, W) + b # model : [batch_size, n_class]
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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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prediction = tf.cast(tf.argmax(model, 1), tf.int32)
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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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input_batch, target_batch = make_batch(sentences)
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for epoch in range(5000):
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_, loss = sess.run([optimizer, cost], feed_dict={X: input_batch, Y: target_batch})
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if (epoch + 1)%1000 == 0:
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print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.6f}'.format(loss))
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input = [sen.split()[:2] for sen in sentences]
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predict = sess.run([prediction], feed_dict={X: input_batch})
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print([sen.split()[:2] for sen in sentences], '->', [number_dict[n] for n in predict[0]]) |