Args: nb_kernels: int, the channel of output, also is the number of filters alpha (float): Scalar multiplier for the product of input tensors A * B. beta (float): Scalar multiplier for input tensor C. ransA (bool): Whether A sh
(self,
nb_kernels,
alpha=1.0,
beta=1.0,
transA=False,
transB=True,
bias=True,
bias_shape=None)
| 369 | """ |
| 370 | |
| 371 | def __init__(self, |
| 372 | nb_kernels, |
| 373 | alpha=1.0, |
| 374 | beta=1.0, |
| 375 | transA=False, |
| 376 | transB=True, |
| 377 | bias=True, |
| 378 | bias_shape=None): |
| 379 | """ |
| 380 | Args: |
| 381 | nb_kernels: int, the channel of output, also is the number of |
| 382 | filters |
| 383 | alpha (float): Scalar multiplier for the product of input tensors A * B. |
| 384 | beta (float): Scalar multiplier for input tensor C. |
| 385 | ransA (bool): Whether A should be transposed |
| 386 | transB (bool): Whether B should be transposed |
| 387 | bias: bool |
| 388 | """ |
| 389 | super(Gemm, self).__init__() |
| 390 | self.nb_kernels = nb_kernels |
| 391 | self.alpha = alpha |
| 392 | self.beta = beta |
| 393 | self.transA = 1 if transA else 0 |
| 394 | self.transB = 1 if transB else 0 |
| 395 | self.bias = bias |
| 396 | self.bias_shape = bias_shape |
| 397 | |
| 398 | def initialize(self, x): |
| 399 | if self.transA == 0: |