37 lines
1.3 KiB
Python
37 lines
1.3 KiB
Python
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import tensorflow as tf
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from tree.model import Model
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class Trainer(object):
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def __init__(self, config, model):
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assert isinstance(model, Model)
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self.config = config
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self.model = model
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self.opt = tf.train.AdagradOptimizer(config.init_lr)
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self.loss = model.get_loss()
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self.var_list = model.get_var_list()
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self.global_step = model.get_global_step()
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self.ema_op = model.ema_op
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self.summary = model.summary
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self.grads = self.opt.compute_gradients(self.loss, var_list=self.var_list)
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opt_op = self.opt.apply_gradients(self.grads, global_step=self.global_step)
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# Define train op
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with tf.control_dependencies([opt_op]):
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self.train_op = tf.group(self.ema_op)
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def get_train_op(self):
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return self.train_op
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def step(self, sess, batch, get_summary=False):
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assert isinstance(sess, tf.Session)
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feed_dict = self.model.get_feed_dict(batch, True)
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if get_summary:
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loss, summary, train_op = \
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sess.run([self.loss, self.summary, self.train_op], feed_dict=feed_dict)
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else:
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loss, train_op = sess.run([self.loss, self.train_op], feed_dict=feed_dict)
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summary = None
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return loss, summary, train_op
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