cnn.py 文件源码

def train(self):

        data = Data(self.train_dat, self.train_lab)
        batch_num = self.length/self.batch_size if self.length%self.batch_size == 0 else self.length/self.batch_size + 1

        model = self.add_model()

        with self.sess as sess:

            tf.initialize_all_variables().run()

            for ite in range(self.iterations):
                print "Iteration {}".format(ite)
                cost = 0.
                pbar = pb.ProgressBar(widgets=[pb.Percentage(), pb.Bar(), pb.ETA()], maxval=batch_num).start()
                for i in range(batch_num):
                    batch_x, batch_y = data.next_batch(self.batch_size)

                    c, _ = self.sess.run([model['loss'], model['optimizer']], feed_dict={model['train_x']:batch_x, model['train_y']:batch_y, model['p_keep_dens']:0.75})

                    cost += c / batch_num
                    pbar.update(i+1)
                pbar.finish()

                print ">>cost: {}".format(cost)

                t_acc, d_acc = self.eval(model, 3000)
                # early stop
                if t_acc >= 0.995 and d_acc >= 0.995:
                    break

            self.predict(model)