def alexnet(image, label, trainable=True):
conv1 = _conv2d_layer(
"conv1",
image,
filters=64,
kernel_size=11,
strides=4,
padding="VALID",
)
pool1 = flow.nn.avg_pool2d(conv1, 3, 2, "VALID", "NCHW", name="pool1")
conv2 = _conv2d_layer("conv2", pool1, filters=192, kernel_size=5)
pool2 = flow.nn.avg_pool2d(conv2, 3, 2, "VALID", "NCHW", name="pool2")
conv3 = _conv2d_layer("conv3", pool2, filters=384)
conv4 = _conv2d_layer("conv4", conv3, filters=384)
conv5 = _conv2d_layer("conv5", conv4, filters=256)
pool5 = flow.nn.avg_pool2d(conv5, 3, 2, "VALID", "NCHW", name="pool5")
if len(pool5.shape) > 2:
pool5 = flow.flatten(pool5, start_dim=1, end_dim=-1)
initializer = flow.truncated_normal_initializer(stddev=0.816496580927726)
fc1 = flow.layers.dense(
inputs=pool5,
units=4096,
activation=flow.math.relu,
use_bias=False,
kernel_initializer=initializer,
bias_initializer=False,
trainable=trainable,
name="fc1",
)
dropout1 = fc1
fc2 = flow.layers.dense(
inputs=dropout1,
units=4096,
activation=flow.math.relu,
use_bias=False,
kernel_initializer=initializer,
bias_initializer=False,
trainable=trainable,
name="fc2",
)
dropout2 = fc2
fc3 = flow.layers.dense(
inputs=dropout2,
units=1001,
activation=None,
use_bias=False,
kernel_initializer=initializer,
bias_initializer=False,
trainable=trainable,
name="fc3",
)
loss = flow.nn.sparse_softmax_cross_entropy_with_logits(
label, fc3, name="softmax_loss")
return loss
def _trunc_normal_(
self,
mean=0.0,
std=1.0,
a=-2.0,
b=2.0,
):
initializer_conf = flow.truncated_normal_initializer(mean=mean, stddev=std)
res = _init_by_initializer_conf(self, initializer_conf)
res = flow.clamp(res, min=a, max=b)
return res
def _build_dense(self, x, unit, name="dense_"):
"""
Build Dense Layer
:param x:
:return:
"""
self.init_range = 0.2
self.init = flow.truncated_normal_initializer(self.init_range)
self.reg = flow.regularizers.l2(0.01)
return flow.layers.dense(x,
units=unit,
kernel_initializer=self.init,
kernel_regularizer=self.reg,
use_bias=self.use_bias,
name=name + "w")
def test_float_initializer(test_case):
initializers = [
flow.random_normal_initializer(mean=3, stddev=4),
flow.random_uniform_initializer(minval=-6, maxval=18),
flow.truncated_normal_initializer(mean=-5, stddev=8),
flow.xavier_uniform_initializer(data_format="NCHW"),
flow.xavier_uniform_initializer(data_format="NHWC"),
flow.xavier_normal_initializer(data_format="NCHW"),
flow.xavier_normal_initializer(data_format="NHWC"),
flow.constant_initializer(value=4),
flow.ones_initializer(),
flow.zeros_initializer(),
]
kaiming_args = GenArgDict(
OrderedDict(
shape=[SHAPE],
mode=["fan_in", "fan_out", "fan_avg"],
distribution=["random_normal", "random_uniform"],
data_format=["NCHW", "NHWC"],
negative_slope=[0.5],
))
vs_args = GenArgDict(
OrderedDict(
scale=[3.4],
mode=["fan_in", "fan_out", "fan_avg"],
distribution=[
"truncated_normal", "random_normal", "random_uniform"
],
data_format=["NCHW", "NHWC"],
))
for args in kaiming_args:
initializers.append(flow.kaiming_initializer(**args))
for args in vs_args:
initializers.append(flow.variance_scaling_initializer(**args))
for initializer in initializers:
CompareTwoDistribution(test_case, flow.float32, initializer)