Source code for stellargraph.layer.sort_pooling

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import tensorflow as tf
from tensorflow.keras.layers import Layer
from ..core.experimental import experimental
from ..core.validation import require_integer_in_range


[docs]class SortPooling(Layer): """ Sort Pooling Keras layer. Note that sorting is performed using only the last column of the input tensor as stated in [1], "For convenience, we set the last graph convolution to have one channel and only used this single channel for sorting." [1] An End-to-End Deep Learning Atchitecture for Graph Classification, M. Zhang, Z. Cui, M. Neumann, and Y. Chen, AAAI-18, https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/viewPaper/17146 Args: k (int): The number of rows of output tensor. flatten_output (bool): If True then the output tensor is reshaped to vector for each element in the batch. """ def __init__(self, k, flatten_output=False): super().__init__() require_integer_in_range(k, "k", min_val=1) self.trainable = False self.k = k self.flatten_output = flatten_output
[docs] def get_config(self): """ Gets class configuration for Keras serialization. Used by keras model serialization. Returns: A dictionary that contains the config of the layer """ return {"k": self.k, "flatten_output": self.flatten_output}
[docs] def compute_output_shape(self, input_shapes): """ Computes the output shape of the layer. Assumes the following inputs: Args: input_shapes (tuple of ints) Shape tuples can include None for free dimensions, instead of an integer. Returns: An input shape tuple. """ if self.flatten_output: return input_shapes[0], self.k * input_shapes[2], 1 else: return input_shapes[0], self.k, input_shapes[2]
def _sort_tensor_with_mask(self, inputs): embeddings, mask = inputs[0], inputs[1] masked_sorted_embeddings = tf.gather( embeddings, tf.argsort( tf.boolean_mask(embeddings, mask)[..., -1], axis=0, direction="DESCENDING", ), ) embeddings = tf.pad( masked_sorted_embeddings, [ [0, (tf.shape(embeddings)[0] - tf.shape(masked_sorted_embeddings)[0])], [0, 0], ], ) return embeddings
[docs] def call(self, embeddings, mask): """ Applies the layer. Args: embeddings (tensor): the node features (size B x N x Sum F_i) where B is the batch size, N is the number of nodes in the largest graph in the batch, and F_i is the dimensionality of node features output from the i-th convolutional layer. mask (tensor): a boolean mask (size B x N) Returns: Keras Tensor that represents the output of the layer. """ outputs = tf.map_fn( self._sort_tensor_with_mask, (embeddings, mask), dtype=embeddings.dtype ) # padding or truncation based on the value of self.k and the graph size (number of nodes) outputs_shape = tf.shape(outputs) outputs = tf.cond( tf.math.less(outputs_shape, self.k)[1], true_fn=lambda: tf.pad( outputs, [[0, 0], [0, (self.k - outputs_shape)[1]], [0, 0]] ), false_fn=lambda: outputs[:, : self.k, :], ) if self.flatten_output: outputs = tf.reshape( outputs, [outputs_shape[0], embeddings.shape[-1] * self.k, 1] ) return outputs