wehub-resource-sync
66 lines
2.1 KiB
Python
66 lines
2.1 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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char_arr = [c for c in 'abcdefghijklmnopqrstuvwxyz']
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word_dict = {n: i for i, n in enumerate(char_arr)}
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number_dict = {i: w for i, w in enumerate(char_arr)}
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n_class = len(word_dict) # number of class(=number of vocab)
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seq_data = ['make', 'need', 'coal', 'word', 'love', 'hate', 'live', 'home', 'hash', 'star']
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# TextLSTM Parameters
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n_step = 3
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n_hidden = 128
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def make_batch(seq_data):
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input_batch, target_batch = [], []
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for seq in seq_data:
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input = [word_dict[n] for n in seq[:-1]] # 'm', 'a' , 'k' is input
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target = word_dict[seq[-1]] # 'e' is target
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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.BasicLSTMCell(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(seq_data)
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for epoch in range(1000):
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_, loss = sess.run([optimizer, cost], feed_dict={X: input_batch, Y: target_batch})
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if (epoch + 1)%100 == 0:
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print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.6f}'.format(loss))
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inputs = [sen[:3] for sen in seq_data]
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predict = sess.run([prediction], feed_dict={X: input_batch})
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print(inputs, '->', [number_dict[n] for n in predict[0]]) |