In the code, are the cell state and hidden state concatenated?

[zehuanzhang]

In the GeoMAN.py, Line 262 to Line 273:

multiply attention weights with the original input

local_x = local_attn * local_inp
global_x = global_attn * global_inp

Run the BasicLSTM with the newly input

cell_output, state = cell(tf.concat([local_x, global_x], axis=1), state)

Run the attention mechanism.

with tf.variable_scope('local_spatial_attn'):
local_attn = local_attention(state)
with tf.variable_scope('global_spatial_attn'):
global_attn = global_attention(state)
attn_weights.append((local_attn, global_attn))

Does this only consider cell state when calculating attention? In the paper Equation (1), cell state and hidden state are concatenated?

[CastleLiang]

The variable "state" actually contains both the cell state and the hidden state.