You can implement a tree-LSTM in Keras using the Subclassing API. This will allow you to define your own custom layers and models by subclassing the tf.keras.layers.Layer and tf.keras.Model classes, respectively.
To implement a tree-LSTM in the Subclassing API, you will need to define a custom layer that takes a tree-structured input and applies the LSTM operation to each node in the tree. Here is some pseudocode that outlines the steps you can follow:
class TreeLSTMLayer(tf.keras.layers.Layer):
def __init__(self, units, **kwargs):
super(TreeLSTMLayer, self).__init__(**kwargs)
self.units = units
def build(self, input_shape):
# Define the weight matrices and biases for the LSTM operation
# (e.g., self.W_i, self.W_f, self.W_o, self.W_c, self.b_i, etc.)
# based on the number of units in the layer
# (e.g., input_dim = units, output_dim = units)
# and the input shape of the tree (i.e., input_shape[0])
def call(self, inputs):
# Unpack the inputs into the tree structure and the initial states
# (e.g., tree, h_0, c_0 = inputs)
# Initialize a list to store the output states for each node in the tree
output_states = []
# Recursively traverse the tree and apply the LSTM operation
# at each node, updating the hidden and cell states as you go
# (e.g., h_t, c_t = lstm(x_t, h_t-1, c_t-1))
def traverse_tree(node, h_t, c_t):
# Apply the LSTM operation to the current node
# (e.g., i_t, f_t, o_t, g_t = lstm(x_t, h_t, c_t))
# Update the hidden and cell states
# (e.g., c_t = f_t * c_t + i_t * g_t, h_t = o_t * tf.tanh(c_t))
output_states.append((h_t, c_t))
# Recursively traverse the children of the current node
for child in node.children:
traverse_tree(child, h_t, c_t)
# Start the recursive traversal at the root of the tree
traverse_tree(tree.root, h_0, c_0)
# Return the output states for each node in the tree
return output_states
Once you have defined your custom TreeLSTMLayer, you can use it to build a tree-LSTM model by subclassing the tf.keras.Model class and using the TreeLSTMLayer as one of the layers in your model.
