def __init__(
self,
n_labels,
hidden_states=64,
activation='relu',
output_activation="softmax",
use_bias=False,
dropout_rate=0.5,
n_input_channels=None,
**kwargs,
):
super().__init__(**kwargs)
self.n_labels = n_labels
self.hidden_states = hidden_states
self.activation = activation
self.output_activation = output_activation
self.use_bias = use_bias
self.dropout_rate = dropout_rate
self.n_input_channels = n_input_channels
## need to add some regularization terms
self._d0 = Dropout(dropout_rate)
# this can hold a kernel_regularizer=reg which is yet to be implemented
self._gcn0 = GCNConv(hidden_states,
activation=activation,
use_bias=use_bias)
self.d1 = Dropout(dropout_rate)
self.gcn1 = GCNConv(n_labels,
activation=output_activation,
use_bias=use_bias)
def __init__(self, n_out = 7):
super().__init__()
# Define layers of the model
self.ECC1 = ECCConv(hidden_states, [hidden_states, hidden_states, hidden_states], n_out = hidden_states, activation = "relu")
self.GCN1 = GCNConv(hidden_states, activation = "relu")
self.GCN2 = GCNConv(hidden_states * 2, activation = "relu")
self.GCN3 = GCNConv(hidden_states * 4, activation = "relu")
self.GCN4 = GCNConv(hidden_states * 8, activation = "relu")
self.Pool1 = GlobalMaxPool()
self.Pool2 = GlobalAvgPool()
self.Pool3 = GlobalSumPool()
self.decode = [Dense(size * hidden_states) for size in [16, 8, 4, 2, 2]]
self.norm_layers = [BatchNormalization() for i in range(len(self.decode))]
self.d2 = Dense(n_out)
def __init__(self, n_out = 4, hidden_states=64, gat_layers=2, gat_activation='relu', decode_layers=3, decode_activation='relu', regularization=None, dropout=0.2, batch_norm=True, forward=True):
super().__init__()
self.n_out=n_out
self.hidden_states=hidden_states
self.gat_activation=conv_activation
self.forward=forward
self.dropout=dropout
self.gat_layers=gat_layers
self.regularize=regularization
if type(decode_activation)==str:
self.decode_activation=tf.keras.activations.get(decode_activation)
else:
self.decode_activation=decode_activation
self.batch_norm=batch_norm
# Define layers of the model
if self.edgeconv:
self.ECC1 = ECCConv(hidden_states, [hidden_states, hidden_states, hidden_states], n_out = hidden_states, activation = "relu", kernel_regularizer=self.regularize)
self.GCNs = [GCNConv(hidden_states*int(i), activation=self.conv_activation, kernel_regularizer=self.regularize) for i in 2**np.arange(self.conv_layers)]
self.Pool1 = GlobalMaxPool()
self.Pool2 = GlobalAvgPool()
self.Pool3 = GlobalSumPool()
self.decode = [Dense(i * hidden_states, activation=self.decode_activation) for i in 2**np.arange(decode_layers)]
self.dropout_layers = [Dropout(dropout) for i in range(len(self.decode))]
if self.batch_norm:
self.norm_layers = [BatchNormalization() for i in range(len(self.decode))]
else:
self.norm_layers = [no_norm for i in range(len(self.decode))]
self.final = Dense(n_out)
def __init__(self, n_out = 6, hidden_states = 64, forward = False, dropout = 0.5):
super().__init__()
self.forward = forward
# Define layers of the model
self.ECC1 = ECCConv(hidden_states, [hidden_states, hidden_states], n_out = hidden_states, activation = "relu")
self.GCN1 = GCNConv(hidden_states, activation = "relu")
self.GCN2 = GCNConv(hidden_states * 2, activation = "relu")
self.GCN3 = GCNConv(hidden_states * 4, activation = "relu")
self.GCN4 = GCNConv(hidden_states * 8, activation = "relu")
self.Pool1 = GlobalMaxPool()
self.Pool2 = GlobalAvgPool()
# self.Pool3 = GlobalSumPool()
self.decode = [Dense(size * hidden_states) for size in [16, 16, 8]]
self.drop_w = [Dropout(dropout) for _ in range(len(self.decode))]
self.norm_layers = [BatchNormalization() for _ in range(len(self.decode))]
self.angles = [Dense(hidden_states) for _ in range(2)]
self.angles_out = Dense(2)
self.sigs = [Dense(hidden_states) for _ in range(2)]
self.sigs_out = Dense(2)
def __init__(self,
n_out=4,
hidden_states=64,
n_GCN=2,
GCN_activation=LeakyReLU(alpha=0.2),
decode_activation=LeakyReLU(alpha=0.2),
regularize=None,
dropout=0.2,
forward=True,
ECC=True):
super().__init__()
self.n_out = n_out
self.hidden_states = hidden_states
self.conv_activation = GCN_activation
self.forward = forward
self.dropout = dropout
self.n_GCN = n_GCN
self.ECC = ECC
self.regularize = regularize
self.decode_activation = decode_activation
# Define layers of the model
self.ECC = ECC
if self.ECC:
self.ECC1 = ECCConv(hidden_states,
[hidden_states, hidden_states, hidden_states],
n_out=hidden_states,
activation="relu",
kernel_regularizer=self.regularize)
self.GCNs = [
GCNConv(hidden_states * int(i),
activation=GCN_activation,
kernel_regularizer=self.regularize)
for i in 2**np.arange(n_GCN)
]
self.Pool1 = GlobalMaxPool()
self.Pool2 = GlobalAvgPool()
self.Pool3 = GlobalSumPool()
self.decode = [Dense(i * hidden_states) for i in 2**np.arange(n_GCN)]
self.dropout_layers = [
Dropout(dropout) for i in range(len(self.decode))
]
self.norm_layers = [
BatchNormalization() for i in range(len(self.decode))
]
self.final = Dense(n_out)