new gnn smoothing trial
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+5
-6
@@ -16,12 +16,11 @@ class Smoother(tf.keras.Model):
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def __init__(self, ):
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super().__init__()
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self.a = sp_matrix_to_sp_tensor(sio.loadmat("custom_utils/ssmpl0.mat")['L'].tocoo())
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self.conv1 = GeneralConv(channels=32, batch_norm=True, dropout=0.1, aggregate='mean', activation='prelu', use_bias=True)
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self.conv2 = GeneralConv(channels=128, batch_norm=True, dropout=0.1, aggregate='mean', activation='prelu', use_bias=True)
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self.conv3 = GeneralConv(channels=64, batch_norm=True, dropout=0.0, aggregate='mean', activation='prelu', use_bias=True)
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self.conv4 = GeneralConv(channels=16, batch_norm=True, dropout=0.0, aggregate='mean', activation='prelu', use_bias=True)
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self.conv5 = GCNConv(3, activation='tanh', use_bias=False)
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self.conv1 = GCNConv(64, activation='tanh', use_bias=False)
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self.conv2 = GCNConv(64, activation='tanh', use_bias=False)
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self.conv3 = GCNConv(64, activation='tanh', use_bias=False)
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self.conv4 = GCNConv(64, activation='tanh', use_bias=False)
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self.conv5 = GCNConv(64, activation='tanh', use_bias=False)
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self.conv6 = GCNConv(3, activation='tanh', use_bias=False)
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def call(self, inputs, training=None):
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