34 lines
1.6 KiB
Python
34 lines
1.6 KiB
Python
import os
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import sys
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import scipy.io as sio
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import tensorflow as tf
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from spektral.layers import GCNConv, GeneralConv
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from spektral.utils import sp_matrix_to_sp_tensor
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from tensorflow.keras.layers import (Conv2D, Conv2DTranspose, Dense, DepthwiseConv2D,
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GlobalAveragePooling2D, Layer,
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LayerNormalization, Flatten, Reshape, UpSampling2D)
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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class Smoother(tf.keras.Model):
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def __init__(self, features=64, activation_middle='tanh', activation_final='tanh', learn_weights=False):
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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 = GCNConv(features, activation=activation_middle, use_bias=False)
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self.conv2 = GCNConv(features, activation=activation_middle, use_bias=False)
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# self.conv3 = GCNConv(features, activation=activation_middle, 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 if not learn_weights else 24, activation=activation_final, use_bias=False)
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def call(self, inputs, training=None):
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x = inputs
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x = self.conv1([x, self.a], training=training)
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x = self.conv2([x, self.a], training=training)
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# x = self.conv3([x, self.a], training=training)
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# x = self.conv4([x, self.a], training=training)
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# x = self.conv5([x, self.a], training=training)
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x = self.conv6([x, self.a], training=training)
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return x |