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barely working decoder

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luni
2023-02-24 10:34:53 +00:00
parent c1113379ec
commit fb4e7dcc65
6 changed files with 92 additions and 30 deletions
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models/gnn.py
+20 -10
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@@ -34,29 +34,39 @@ class Smoother(tf.keras.Model):
x = self.conv6([x, self.a], training=training)
return x
import numpy as np
import scipy.sparse as sp
class GNNDecoder(tf.keras.Model):
def __init__(self, ):
super().__init__()
self.a = sp_matrix_to_sp_tensor(sio.loadmat("custom_utils/ssmpl0.mat")['L'].tocoo())
self.a_dense = sio.loadmat("custom_utils/ssmpl0.mat")['L'].todense()
# self.a_dense = sio.loadmat("custom_utils/ssmpl0.mat")['L'].todense()
# self.a_dense = np.ones((100,100), dtype=np.float32)
# self.a = sp_matrix_to_sp_tensor(sp.coo_matrix(self.a_dense))
self.flatten = Flatten()
self.conv1 = GATConv(channels=1024, attn_heads=4, concat_heads=True, dropout_rate=0.0, return_attn_coef=False, add_self_loops=True, activation=None, use_bias=True)
self.conv2 = GATConv(channels=512, attn_heads=4, concat_heads=True, dropout_rate=0.1, return_attn_coef=False, add_self_loops=True, activation=None, use_bias=True)
self.conv3 = GATConv(channels=256, attn_heads=4, concat_heads=True, dropout_rate=0.1, return_attn_coef=False, add_self_loops=True, activation=None, use_bias=True)
# self.conv1 = GATConv(channels=4, attn_heads=1, concat_heads=True, dropout_rate=0.0, return_attn_coef=False, add_self_loops=True, activation=None, use_bias=True)
# self.conv2 = GATConv(channels=4, attn_heads=1, concat_heads=True, dropout_rate=0.1, return_attn_coef=False, add_self_loops=True, activation=None, use_bias=True)
# self.conv3 = GATConv(channels=4, attn_heads=1, concat_heads=True, dropout_rate=0.1, return_attn_coef=False, add_self_loops=True, activation=None, use_bias=True)
self.dense1 = Dense(512)
self.dense2 = Dense(256)
self.conv1 = GCNConv(256, use_bias=True)
self.conv2 = GCNConv(256, use_bias=True)
self.conv3 = GeneralConv(channels=128, batch_norm=True, dropout=0.1, aggregate='sum', activation='prelu', use_bias=True)
self.conv4 = GeneralConv(channels=128, batch_norm=True, dropout=0.1, aggregate='sum', activation='prelu', use_bias=True)
self.conv5 = GeneralConv(channels=64, batch_norm=True, dropout=0.0, aggregate='sum', activation='prelu', use_bias=True)
self.conv6 = GeneralConv(channels=3, batch_norm=False, dropout=0.0, aggregate='sum', activation='tanh', use_bias=True)
self.conv6 = GCNConv(3, activation='tanh', use_bias=False)
def call(self, inputs, training=None):
x = inputs
x = self.flatten(x)
x = tf.tile(x[:,None,:], [1,14475,1])
x = self.conv1([x, self.a_dense], training=training)
x = self.conv2([x, self.a_dense], training=training)
x = self.conv3([x, self.a_dense], training=training)
x = self.dense1(x)
x = self.dense2(x)
x = self.conv1([x, self.a], training=training)
x = self.conv2([x, self.a], training=training)
x = self.conv3([x, self.a], training=training)
x = self.conv4([x, self.a], training=training)
x = self.conv5([x, self.a], training=training)
x = self.conv6([x, self.a], training=training)