def dense_layer(self, x, f, act="relu", n=None):
dense_l = G_layers.dense(f, act=None, name=n + "_dense")
out = dense_l(x)
self.layers[n+"_dense"] = dense_l
if act:
act_l = G_layers.relu(n + "_relu")
self.layers[n+"_relu"] = act_l
out = act_l(out)
return out
def conv_bn_act(self, x, f, n, s, k=None, p="same", rank=2, act=True, trans=False, out_p="auto"):
if trans:
c_layer = G_layers.conv_transpose
else:
c_layer = G_layers.conv
if k:
conv_l = c_layer(f=f, p=p, k=k, s=s, out_p=out_p, rank=rank, dilation_rate=(1,1), name=n+"_conv")
else:
conv_l = c_layer(f=f, p=p, rank=rank, dilation_rate=(1,1), name=n+"_conv")
out = conv_l(x)
norm_l = G_layers.batch_norm(name=n + "_norm")
out = norm_l(out)
self.layers[n+"_conv"] = conv_l
self.layers[n+"_norm"] = norm_l
if act:
act_l = G_layers.relu(name=n + "_relu")
self.layers[n + "_relu"] = act_l
out = act_l(out)
return out