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import tensorflow as tf |
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from tensorflow.python.ops import rnn_cell |
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from tensorflow.python.ops import seq2seq |
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import random |
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import numpy as np |
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class Model(): |
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def __init__(self, args, infer=False): |
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self.args = args |
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if infer: |
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args.batch_size = 1 |
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args.seq_length = 1 |
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if args.model == 'rnn': |
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cell_fn = rnn_cell.BasicRNNCell |
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elif args.model == 'gru': |
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cell_fn = rnn_cell.GRUCell |
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elif args.model == 'lstm': |
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cell_fn = rnn_cell.BasicLSTMCell |
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else: |
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raise Exception("model type not supported: {}".format(args.model)) |
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cell = cell_fn(args.rnn_size) |
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self.cell = cell = rnn_cell.MultiRNNCell([cell] * args.num_layers) |
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self.input_data = tf.placeholder(tf.int32, [args.batch_size, args.seq_length]) |
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self.targets = tf.placeholder(tf.int32, [args.batch_size, args.seq_length]) |
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self.initial_state = cell.zero_state(args.batch_size, tf.float32) |
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with tf.variable_scope('rnnlm'): |
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softmax_w = tf.get_variable("softmax_w", [args.rnn_size, args.vocab_size]) |
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softmax_b = tf.get_variable("softmax_b", [args.vocab_size]) |
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with tf.device("/cpu:0"): |
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embedding = tf.get_variable("embedding", [args.vocab_size, args.rnn_size]) |
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inputs = tf.split(1, args.seq_length, tf.nn.embedding_lookup(embedding, self.input_data)) |
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inputs = [tf.squeeze(input_, [1]) for input_ in inputs] |
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def loop(prev, _): |
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prev = tf.matmul(prev, softmax_w) + softmax_b |
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prev_symbol = tf.stop_gradient(tf.argmax(prev, 1)) |
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return tf.nn.embedding_lookup(embedding, prev_symbol) |
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outputs, last_state = seq2seq.rnn_decoder(inputs, self.initial_state, cell, loop_function=loop if infer else None, scope='rnnlm') |
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output = tf.reshape(tf.concat(1, outputs), [-1, args.rnn_size]) |
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self.logits = tf.matmul(output, softmax_w) + softmax_b |
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self.probs = tf.nn.softmax(self.logits) |
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loss = seq2seq.sequence_loss_by_example([self.logits], |
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[tf.reshape(self.targets, [-1])], |
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[tf.ones([args.batch_size * args.seq_length])], |
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args.vocab_size) |
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self.cost = tf.reduce_sum(loss) / args.batch_size / args.seq_length |
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self.final_state = last_state |
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self.lr = tf.Variable(0.0, trainable=False) |
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tvars = tf.trainable_variables() |
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grads, _ = tf.clip_by_global_norm(tf.gradients(self.cost, tvars), |
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args.grad_clip) |
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optimizer = tf.train.AdamOptimizer(self.lr) |
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self.train_op = optimizer.apply_gradients(zip(grads, tvars)) |
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def sample(self, sess, words, vocab, num=200, prime='first all', sampling_type=1): |
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state = sess.run(self.cell.zero_state(1, tf.float32)) |
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if not len(prime) or prime == " ": |
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prime = random.choice(list(vocab.keys())) |
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print (prime) |
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for word in prime.split()[:-1]: |
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print (word) |
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x = np.zeros((1, 1)) |
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x[0, 0] = vocab.get(word,0) |
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feed = {self.input_data: x, self.initial_state:state} |
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[state] = sess.run([self.final_state], feed) |
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def weighted_pick(weights): |
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t = np.cumsum(weights) |
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s = np.sum(weights) |
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return(int(np.searchsorted(t, np.random.rand(1)*s))) |
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ret = prime |
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word = prime.split()[-1] |
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for n in range(num): |
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x = np.zeros((1, 1)) |
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x[0, 0] = vocab.get(word,0) |
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feed = {self.input_data: x, self.initial_state:state} |
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[probs, state] = sess.run([self.probs, self.final_state], feed) |
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p = probs[0] |
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if sampling_type == 0: |
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sample = np.argmax(p) |
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elif sampling_type == 2: |
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if word == '\n': |
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sample = weighted_pick(p) |
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else: |
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sample = np.argmax(p) |
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else: |
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sample = weighted_pick(p) |
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pred = words[sample] |
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ret += ' ' + pred |
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word = pred |
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return ret |
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