def printNet(self, f): # only complete if we've forwardt stride=1
print("ConvTranspose2D", file=f)
print(self.activation +
", filters={}, kernel_size={}, input_shape={}".format(
self.out_channels, list(self.kernel_size), list(self.prev)),
file=f)
print(h.printListsNumpy([[list(p) for p in l]
for l in self.weight.permute(2, 3, 1, 0).data
]),
file=f)
print(h.printNumpy(self.bias), file=f)
def printNet(self, f): # only complete if we've forwardt stride=1
print("Conv2D", file=f)
sz = list(self.prev)
print(
self.activation +
", filters={}, kernel_size={}, input_shape={}, stride={}, padding={}"
.format(self.out_channels, [self.kernel_size, self.kernel_size],
list(reversed(sz)), [self.stride, self.stride],
self.padding),
file=f)
print(h.printListsNumpy([[list(p) for p in l]
for l in self.weight.permute(2, 3, 1, 0).data
]),
file=f)
print(h.printNumpy(
self.bias if self.bias is not None else h.dten(self.out_channels)),
file=f)
def printNet(self, f):
print("EmbeddingWithSub(" + str(self.in_shape[0]) + ", " +
str(self.dim) + ")")
print(h.printNumpy(self.embed.weight), file=f)
def printNet(self, f):
print("Linear(" + str(self.out_neurons) + ")")
print(h.printListsNumpy(list(self.weight.transpose(1, 0).data)),
file=f)
print(h.printNumpy(self.bias), file=f)
def printNet(self, f):
print(h.printListsNumpy(list(self.weight.transpose(1, 0).data)),
file=f)
print(h.printNumpy(self.bias), file=f)
