diff --git a/eval.py b/eval.py new file mode 100644 index 00000000..d5f88501 --- /dev/null +++ b/eval.py @@ -0,0 +1,47 @@ +from __future__ import print_function +import numpy as np +import tensorflow as tf + +import argparse +import codecs +import time +import os +from six.moves import cPickle + +from utils import TextLoader +from model import Model + +from six import text_type + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument('--save_dir', type=str, default='save', + help='model directory to store checkpointed models') + parser.add_argument('--text', type=str, + help='filename of text to evaluate on') + args = parser.parse_args() + eval(args) + +def eval(args): + with open(os.path.join(args.save_dir, 'config.pkl'), 'rb') as f: + saved_args = cPickle.load(f) + saved_args.batch_size = 1 + saved_args.seq_length = 200 + with open(os.path.join(args.save_dir, 'chars_vocab.pkl'), 'rb') as f: + chars, vocab = cPickle.load(f) + model = Model(saved_args, False) + + with codecs.open(args.text, 'r', encoding='utf-8') as f: + text = f.read() + + with tf.Session() as sess: + tf.initialize_all_variables().run() + saver = tf.train.Saver(tf.all_variables()) + ckpt = tf.train.get_checkpoint_state(args.save_dir) + if ckpt and ckpt.model_checkpoint_path: + saver.restore(sess, ckpt.model_checkpoint_path) + ppl = model.eval(sess, chars, vocab, text) + print('perplexity: {0}'.format(ppl)) + +if __name__ == '__main__': + main() diff --git a/model.py b/model.py index 5ea675f4..a6f25744 100644 --- a/model.py +++ b/model.py @@ -33,8 +33,8 @@ def __init__(self, args, infer=False): softmax_b = tf.get_variable("softmax_b", [args.vocab_size]) with tf.device("/cpu:0"): embedding = tf.get_variable("embedding", [args.vocab_size, args.rnn_size]) - inputs = tf.split(1, args.seq_length, tf.nn.embedding_lookup(embedding, self.input_data)) - inputs = [tf.squeeze(input_, [1]) for input_ in inputs] + input_embeddings = tf.nn.embedding_lookup(embedding, self.input_data) + inputs = tf.unpack(input_embeddings, axis=1) def loop(prev, _): prev = tf.matmul(prev, softmax_w) + softmax_b @@ -45,11 +45,11 @@ def loop(prev, _): output = tf.reshape(tf.concat(1, outputs), [-1, args.rnn_size]) self.logits = tf.matmul(output, softmax_w) + softmax_b self.probs = tf.nn.softmax(self.logits) - loss = seq2seq.sequence_loss_by_example([self.logits], + self.loss = seq2seq.sequence_loss_by_example([self.logits], [tf.reshape(self.targets, [-1])], [tf.ones([args.batch_size * args.seq_length])], args.vocab_size) - self.cost = tf.reduce_sum(loss) / args.batch_size / args.seq_length + self.cost = tf.reduce_sum(self.loss) / args.batch_size / args.seq_length self.final_state = last_state self.lr = tf.Variable(0.0, trainable=False) tvars = tf.trainable_variables() @@ -58,6 +58,29 @@ def loop(prev, _): optimizer = tf.train.AdamOptimizer(self.lr) self.train_op = optimizer.apply_gradients(zip(grads, tvars)) + def eval(self, sess, chars, vocab, text): + batch_size = 200 + state = sess.run(self.cell.zero_state(1, tf.float32)) + x = [vocab[c] if c in vocab else vocab['UNK'] for c in text] + x = [vocab['']] + x + [vocab['']] + total_len = len(x) - 1 + # pad x so the batch_size divides it + while len(x) % 200 != 1: + x.append(vocab[' ']) + y = np.array(x[1:]).reshape((-1, batch_size)) + x = np.array(x[:-1]).reshape((-1, batch_size)) + + total_loss = 0.0 + for i in range(x.shape[0]): + feed = {self.input_data: x[i:i+1, :], self.targets: y[i:i+1, :], + self.initial_state: state} + [state, loss] = sess.run([self.final_state, self.loss], feed) + total_loss += loss.sum() + # need to subtract off loss from padding tokens + total_loss -= loss[total_len % batch_size - batch_size:].sum() + avg_entropy = total_loss / len(text) + return np.exp(avg_entropy) # this is the perplexity + def sample(self, sess, chars, vocab, num=200, prime='The ', sampling_type=1): state = sess.run(self.cell.zero_state(1, tf.float32)) for char in prime[:-1]: diff --git a/model.py~ b/model.py~ new file mode 100644 index 00000000..526cd77a --- /dev/null +++ b/model.py~ @@ -0,0 +1,123 @@ +import tensorflow as tf +from tensorflow.python.ops import rnn_cell +from tensorflow.python.ops import seq2seq + +import numpy as np + +class Model(): + def __init__(self, args, infer=False): + self.args = args + if infer: + args.batch_size = 1 + args.seq_length = 1 + + if args.model == 'rnn': + cell_fn = rnn_cell.BasicRNNCell + elif args.model == 'gru': + cell_fn = rnn_cell.GRUCell + elif args.model == 'lstm': + cell_fn = rnn_cell.BasicLSTMCell + else: + raise Exception("model type not supported: {}".format(args.model)) + + cell = cell_fn(args.rnn_size, state_is_tuple=True) + + self.cell = cell = rnn_cell.MultiRNNCell([cell] * args.num_layers, state_is_tuple=True) + + self.input_data = tf.placeholder(tf.int32, [args.batch_size, args.seq_length]) + self.targets = tf.placeholder(tf.int32, [args.batch_size, args.seq_length]) + self.initial_state = cell.zero_state(args.batch_size, tf.float32) + + with tf.variable_scope('rnnlm'): + softmax_w = tf.get_variable("softmax_w", [args.rnn_size, args.vocab_size]) + softmax_b = tf.get_variable("softmax_b", [args.vocab_size]) + with tf.device("/cpu:0"): + embedding = tf.get_variable("embedding", [args.vocab_size, args.rnn_size]) + input_embeddings = tf.nn.embedding_lookup(embedding, self.input_data) + inputs = tf.unpack(input_embeddings, axis=1) + # inputs = tf.split(1, args.seq_length, tf.nn.embedding_lookup(embedding, self.input_data)) + # inputs = [tf.squeeze(input_, [1]) for input_ in inputs] + + def loop(prev, _): + prev = tf.matmul(prev, softmax_w) + softmax_b + prev_symbol = tf.stop_gradient(tf.argmax(prev, 1)) + return tf.nn.embedding_lookup(embedding, prev_symbol) + + outputs, last_state = seq2seq.rnn_decoder(inputs, self.initial_state, cell, loop_function=loop if infer else None, scope='rnnlm') + output = tf.reshape(tf.concat(1, outputs), [-1, args.rnn_size]) + self.logits = tf.matmul(output, softmax_w) + softmax_b + self.probs = tf.nn.softmax(self.logits) + self.loss = seq2seq.sequence_loss_by_example([self.logits], + [tf.reshape(self.targets, [-1])], + [tf.ones([args.batch_size * args.seq_length])], + args.vocab_size) + self.cost = tf.reduce_sum(self.loss) / args.batch_size / args.seq_length + self.final_state = last_state + self.lr = tf.Variable(0.0, trainable=False) + tvars = tf.trainable_variables() + grads, _ = tf.clip_by_global_norm(tf.gradients(self.cost, tvars), + args.grad_clip) + optimizer = tf.train.AdamOptimizer(self.lr) + self.train_op = optimizer.apply_gradients(zip(grads, tvars)) + + def eval(self, sess, chars, vocab, text): + batch_size = 200 + state = sess.run(self.cell.zero_state(1, tf.float32)) + x = [vocab[c] if c in vocab else vocab['UNK'] for c in text] + x = [vocab['']] + x + [vocab['']] + total_len = len(x) - 1 + # pad x so the batch_size divides it + while len(x) % 200 != 1: + x.append(vocab[' ']) + y = np.array(x[1:]).reshape((-1, batch_size)) + x = np.array(x[:-1]).reshape((-1, batch_size)) + + total_loss = 0.0 + for i in range(x.shape[0]): + feed = {self.input_data: x[i:i+1, :], self.targets: y[i:i+1, :], + self.initial_state: state} + [state, loss] = sess.run([self.final_state, self.loss], feed) + total_loss += loss.sum() + # need to subtract off loss from padding tokens + total_loss -= loss[total_len % batch_size - batch_size:].sum() + avg_entropy = total_loss / len(text) + return np.exp(avg_entropy) # this is the perplexity + + def sample(self, sess, chars, vocab, num=200, prime='The ', sampling_type=1): + state = sess.run(self.cell.zero_state(1, tf.float32)) + for char in prime[:-1]: + x = np.zeros((1, 1)) + x[0, 0] = vocab[char] + feed = {self.input_data: x, self.initial_state:state} + [state] = sess.run([self.final_state], feed) + + def weighted_pick(weights): + t = np.cumsum(weights) + s = np.sum(weights) + return(int(np.searchsorted(t, np.random.rand(1)*s))) + + ret = prime + char = prime[-1] + for n in range(num): + x = np.zeros((1, 1)) + x[0, 0] = vocab[char] + feed = {self.input_data: x, self.initial_state:state} + [probs, state] = sess.run([self.probs, self.final_state], feed) + p = probs[0] + + if sampling_type == 0: + sample = np.argmax(p) + elif sampling_type == 2: + if char == ' ': + sample = weighted_pick(p) + else: + sample = np.argmax(p) + else: # sampling_type == 1 default: + sample = weighted_pick(p) + + pred = chars[sample] + ret += pred + char = pred + return ret + + diff --git a/utils.py b/utils.py index 4df553ff..ddac5b91 100644 --- a/utils.py +++ b/utils.py @@ -4,6 +4,7 @@ from six.moves import cPickle import numpy as np + class TextLoader(): def __init__(self, data_dir, batch_size, seq_length, encoding='utf-8'): self.data_dir = data_dir @@ -28,13 +29,14 @@ def preprocess(self, input_file, vocab_file, tensor_file): with codecs.open(input_file, "r", encoding=self.encoding) as f: data = f.read() counter = collections.Counter(data) + counter.update(('', '', 'UNK')) # add tokens for start end and unk count_pairs = sorted(counter.items(), key=lambda x: -x[1]) self.chars, _ = zip(*count_pairs) self.vocab_size = len(self.chars) self.vocab = dict(zip(self.chars, range(len(self.chars)))) with open(vocab_file, 'wb') as f: cPickle.dump(self.chars, f) - self.tensor = np.array(list(map(self.vocab.get, data))) + self.tensor = np.array(list(map(self.vocab.get, [''] + list(data) + ['']))) np.save(tensor_file, self.tensor) def load_preprocessed(self, vocab_file, tensor_file):