86edb86bfadfaea0f7925a09e4473258ae82f9f7,txtgen/modules/decoders/rnn_decoders.py,AttentionRNNDecoder,__init__,#AttentionRNNDecoder#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#,268

Before Change

        //TODO(zhiting)
        att_params = hparams["attention"]
        attention_class = hparams["attention"]["class"] //LuongAttention
        attention_kwargs = hparams["attention"]["params"]
        attention_kwargs["num_units"] = n_hidden
        attention_kwargs["memory_sequence_length"] = attention_values_length
        attention_kwargs["memory"] = attention_keys
        attention_modules = ["txtgen.custom", "tensorflow.contrib.seq2seq"]

After Change

        attn_modules = ["txtgen.custom", "tensorflow.contrib.seq2seq"]
        // Use variable_scope to ensure all trainable variables created in
        // the attention mechanism  are collected
        with tf.variable_scope(self.variable_scope):
            attention_mechanism = get_instance(
                attn_hparams["type"], attn_kwargs, attn_modules)

        atten_cell_kwargs = {
            "attention_layer_size": attn_hparams["attention_layer_size"],
            "alignment_history": attn_hparams["alignment_history"],
            "output_attention": attn_hparams["output_attention"],

Instances