wehub-resource-sync
73 lines
2.4 KiB
Python
73 lines
2.4 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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sentence = (
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'Lorem ipsum dolor sit amet consectetur adipisicing elit '
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'sed do eiusmod tempor incididunt ut labore et dolore magna '
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'aliqua Ut enim ad minim veniam quis nostrud exercitation'
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)
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word_dict = {w: i for i, w in enumerate(list(set(sentence.split())))}
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number_dict = {i: w for i, w in enumerate(list(set(sentence.split())))}
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n_class = len(word_dict)
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n_step = len(sentence.split())
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n_hidden = 5
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def make_batch(sentence):
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input_batch = []
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target_batch = []
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words = sentence.split()
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for i, word in enumerate(words[:-1]):
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input = [word_dict[n] for n in words[:(i + 1)]]
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input = input + [0] * (n_step - len(input))
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target = word_dict[words[i + 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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# Bi-LSTM Model
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X = tf.placeholder(tf.float32, [None, n_step, n_class])
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Y = tf.placeholder(tf.float32, [None, n_class])
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W = tf.Variable(tf.random_normal([n_hidden * 2, n_class]))
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b = tf.Variable(tf.random_normal([n_class]))
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lstm_fw_cell = tf.nn.rnn_cell.LSTMCell(n_hidden)
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lstm_bw_cell = tf.nn.rnn_cell.LSTMCell(n_hidden)
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# outputs : [batch_size, len_seq, n_hidden], states : [batch_size, n_hidden]
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outputs, _ = tf.nn.bidirectional_dynamic_rnn(lstm_fw_cell,lstm_bw_cell, X, dtype=tf.float32)
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outputs = tf.concat([outputs[0], outputs[1]], 2) # output[0] : lstm_fw, output[1] : lstm_bw
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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
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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(sentence)
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for epoch in range(10000):
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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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predict = sess.run([prediction], feed_dict={X: input_batch})
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print(sentence)
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print([number_dict[n] for n in [pre for pre in predict[0]]]) |