BaseModel.py 文件源码

def predict_from_placeholder(self,activation=None):
        self._build()
        self._get_summary()
        if activation is not None:
            self.logit = self._activate(self.logit,activation)
        with open(self.flags.pred_path,'w') as f:
            pass
        count = 0
        with tf.Session() as sess:
            self.sess = sess
            sess.run(tf.global_variables_initializer())
            sess.run(tf.local_variables_initializer())
            if self.flags.log_path and self.flags.visualize is not None:
                summary_writer = tf.summary.FileWriter(self.flags.log_path, sess.graph)
            for batch in self._batch_gen_test():
                x,_,epoch = batch
                if self.flags.log_path and self.flags.visualize is not None:
                    summary,pred = sess.run([self.summ_op,self.logit],feed_dict={self.inputs:x,self.is_training:0})
                    summary_writer.add_summary(summary, count)
                else:
                    pred = sess.run(self.logit,feed_dict={self.inputs:x,self.is_training:0})
                count+=1
                if count%self.flags.verbosity == 0:
                    print_mem_time("Epoch %d Batch %d "%(epoch,count))
                self.write_pred(pred)