p5_fastTextB_model.py 文件源码

def __init__(self, label_size, learning_rate, batch_size, decay_steps, decay_rate,num_sampled,sentence_len,vocab_size,embed_size,is_training):
        """init all hyperparameter here"""
        # set hyperparamter
        self.label_size = label_size
        self.batch_size = batch_size
        self.num_sampled = num_sampled
        self.sentence_len=sentence_len
        self.vocab_size=vocab_size
        self.embed_size=embed_size
        self.is_training=is_training
        self.learning_rate=learning_rate

        # add placeholder (X,label)
        self.sentence = tf.placeholder(tf.int32, [None, self.sentence_len], name="sentence")  # X
        self.labels = tf.placeholder(tf.int32, [None], name="Labels")  # y

        self.global_step = tf.Variable(0, trainable=False, name="Global_Step")
        self.epoch_step=tf.Variable(0,trainable=False,name="Epoch_Step")
        self.epoch_increment=tf.assign(self.epoch_step,tf.add(self.epoch_step,tf.constant(1)))
        self.decay_steps, self.decay_rate = decay_steps, decay_rate

        self.epoch_step = tf.Variable(0, trainable=False, name="Epoch_Step")
        self.instantiate_weights()
        self.logits = self.inference() #[None, self.label_size]
        if not is_training:
            return
        self.loss_val = self.loss()
        self.train_op = self.train()
        self.predictions = tf.argmax(self.logits, axis=1, name="predictions")  # shape:[None,]
        correct_prediction = tf.equal(tf.cast(self.predictions,tf.int32), self.labels) #tf.argmax(self.logits, 1)-->[batch_size]
        self.accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32), name="Accuracy") # shape=()