dbbf873d7657245fcc25702d3a75079202b0d5ac,train.py,,,#,134

Before Change

        total_loss = tf.reduce_sum(losses) / batch_size + L2

        global_step = tf.Variable(1, trainable=False)
        print("Config:", "n_epoch: ", n_epoch, "batch_size: ", batch_size, "base_lr: ", base_lr, "step_size: ",
              step_size)
        with tf.variable_scope("learning_rate"):
            lr_v = tf.Variable(base_lr, trainable=False)

        opt = tf.train.MomentumOptimizer(lr_v, 0.9)

After Change

        for _input, _target in zip(imgs_file_list, train_targets):
            yield _input.encode("utf-8"), cPickle.dumps(_target)

    n_epoch = math.ceil(n_step / len(imgs_file_list))
    dataset = tf.data.Dataset().from_generator(generator, output_types=(tf.string, tf.string))
    dataset = dataset.map(_map_fn, num_parallel_calls=8)
    // dataset = dataset.shuffle(buffer_size=2046)
    dataset = dataset.repeat(n_epoch)

Instances