def __init__(self, in_channel, x):
super().__init__()
#self._save_graphs(save_graph_flag=True, save_graph_path=".")
self.biasadd = P.BiasAdd()
self.equal = P.Equal()
self.addn = P.AddN()
self.conv = Conv2d(in_channels=in_channel,
out_channels=in_channel,
kernel_size=1,
stride=1,
has_bias=False,
weight_init='ones',
pad_mode='same')
self.bn = BatchNorm2d(num_features=in_channel)
self.assignadd = P.AssignAdd()
self.assign = P.Assign()
self.relu = ReLU()
self.mean = P.ReduceMean(keep_dims=False)
self.bias = Parameter(Tensor(
np.random.randint(2, size=(3, )).astype((np.float32))),
name="bias")
self.bias2 = Parameter(Tensor(np.ones([3]).astype(np.float32)),
name="bias2")
self.parameterupdate = ParameterUpdate(self.bias)
self.value = Tensor(np.random.randn(*(3, )), ms.float32)
self.x = x
def __init__(self, c, weight, bias, moving_mean, moving_var_init):
super(Batchnorm_Net, self).__init__()
self.bn = BatchNorm2d(c,
eps=0.00001,
momentum=0.1,
beta_init=bias,
gamma_init=weight,
moving_mean_init=moving_mean,
moving_var_init=moving_var_init)
def __init__(self):
super().__init__()
self.bn1 = BatchNorm2d(num_features=4,
eps=1e-4,
momentum=0.9,
gamma_init=1,
beta_init=0,
moving_mean_init=0,
moving_var_init=1,
data_format="NHWC")
self.bn2 = BatchNorm2d(num_features=4,
eps=1e-4,
momentum=0.9,
gamma_init=1,
beta_init=0,
moving_mean_init=0,
moving_var_init=1,
data_format="NHWC")
self.add = P.Add()
self.relu = ReLU()
self.conv2d1 = Conv2d(in_channels=4,
out_channels=4,
kernel_size=2,
data_format="NHWC")
self.conv2d2 = Conv2d(in_channels=4,
out_channels=4,
kernel_size=2,
data_format="NHWC")
self.conv2d3 = Conv2d(in_channels=4,
out_channels=4,
kernel_size=2,
data_format="NHWC")
self.conv2d4 = Conv2d(in_channels=4,
out_channels=4,
kernel_size=2,
data_format="NHWC")
def __init__(self, in_channels, out_channels, kernel_size, vocab_size, embedding_size,
output_channels, target, sparse):
super().__init__()
set_seed(5)
self.relu = ReLU()
self.conv = Conv2d(in_channels=in_channels, out_channels=out_channels,
kernel_size=kernel_size, has_bias=True, weight_init='normal')
self.batchnorm = BatchNorm2d(num_features=out_channels)
self.embedding_lookup = EmbeddingLookup(vocab_size=vocab_size,
embedding_size=embedding_size,
param_init='normal', target=target, sparse=sparse)
self.flatten = Flatten()
self.cast = op.Cast()
self.bias = Parameter(Tensor(np.ones([output_channels]).astype(np.float32)), name='bias')
self.biasadd = op.BiasAdd()
self.type = mindspore.int32
def __init__(self, in_channel, out_channel):
super().__init__()
self.relu = PReLU(channel=in_channel, w=0.25)
self.bn = BatchNorm2d(num_features=in_channel)
self.conv = Conv2d(in_channels=in_channel,
out_channels=out_channel,
kernel_size=2,
stride=1,
has_bias=False,
weight_init='ones',
pad_mode='same')
self.mean = P.ReduceMean(keep_dims=False)
self.fc = Dense(in_channels=out_channel,
out_channels=out_channel,
weight_init='ones',
bias_init='zeros',
has_bias=True)