I have the question on how to convert a Pytorch Neural Network to a Tensorflow Neural Network. The TF class as its written below does not work and I assume its die to the difference between nn.Sequential and tf.keras.Sequential.
class FullyConnected(nn.Sequential):
"""
Fully connected multi-layer network with ELU activations.
"""
def __init__(self, sizes, final_activation=None):
layers = []
for in_size, out_size in zip(sizes, sizes[1:]):
layers.append(nn.Linear(in_size, out_size))
layers.append(nn.ELU())
layers.pop(-1)
if final_activation is not None:
layers.append(final_activation)
super().__init__(*layers) here
class FullyConnected(tf.keras.Sequential):
"""
Fully connected multi-layer network with ELU activations.
"""
def __init__(self, sizes, final_activation=None):
layers = []
for out_size in sizes[1:-1]:
layers.append(Dense(units=out_size, activation='elu'))
if final_activation is not None:
layers.append(Dense(units=sizes[-1], activation='elu'))
else:
layers.append(Dense(units=sizes[-1]))
super().__init__(*layers)
If I try to initialize the network say by self.fc = FullyConnected(sizes=(sizes[:-1] + [self.dim * 2])) with sizes = [1, 128, 128, 128, 1] I get the Error: TypeError: object.__init__() takes exactly one argument (the instance to initialize) when using the TF network.
Can somebody help?
Many thanks in advance!!