train.py 文件源码

def __init__(self, is_training=True):
        self.graph = tf.Graph()
        with self.graph.as_default():
            if is_training:
                self.x, self.y, self.num_batch = get_batch()
            else: # Evaluation
                self.x = tf.placeholder(tf.int32, shape=(None, hp.max_len,))
                self.y = tf.placeholder(tf.int32, shape=(None, hp.max_len,))

            # Character Embedding for x
            self.enc = embed(self.x, len(roma2idx), hp.embed_size, scope="emb_x")

            # Encoder
            self.memory = encode(self.enc, is_training=is_training)

            # Character Embedding for decoder_inputs
            self.decoder_inputs = shift_by_one(self.y)
            self.dec = embed(self.decoder_inputs, len(surf2idx), hp.embed_size, scope="emb_decoder_inputs")

            # Decoder
            self.outputs = decode(self.dec, self.memory, len(surf2idx), is_training=is_training) # (N, T', hp.n_mels*hp.r)
            self.logprobs = tf.log(tf.nn.softmax(self.outputs)+1e-10) 
            self.preds = tf.arg_max(self.outputs, dimension=-1)

            if is_training: 
                self.loss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=self.y, logits=self.outputs) 
                self.istarget = tf.to_float(tf.not_equal(self.y, tf.zeros_like(self.y))) # masking
                self.mean_loss = tf.reduce_sum(self.loss * self.istarget) / (tf.reduce_sum(self.istarget))

                # Training Scheme
                self.global_step = tf.Variable(0, name='global_step', trainable=False)
                self.optimizer = tf.train.AdamOptimizer(learning_rate=hp.lr)
                self.train_op = self.optimizer.minimize(self.mean_loss, global_step=self.global_step)

                # Summary 
                tf.summary.scalar('mean_loss', self.mean_loss)
                self.merged = tf.summary.merge_all()