def __init__(self,
num_in_ch,
num_out_ch,
kernel,
stride=1,
pad=0,
use_bias=True):
"""
引数
num_in_ch 入力チャンネル数
num_out_ch 出力チャンネル数
kernel カーネルサイズ
strid ストライドサイズ
pad ゼロパディング数
use_bias biasを使うかどうか
"""
super(Convolution2D, self).__init__()
self.kernel = kernel
self.stride = stride
self.pad = pad
self.use_bias = use_bias
self.parameters = {
"W":
node.Node(np.random.randn(num_out_ch, num_in_ch, kernel,
kernel).astype(np.float32),
name="W")
}
if use_bias:
self.parameters["b"] = node.Node(np.zeros(num_out_ch,
dtype=np.float32),
name="b")
def stack(mini_batch):
inputs, targets = [], []
for input, target in mini_batch:
inputs.append(input)
targets.append(target)
return (node.Node(np.array(inputs),
off=True), node.Node(np.array(targets), off=True))
def __init__(self, num_in_units, num_h_units):
super(Linear, self).__init__()
self.parameters = {
"W":
node.Node(np.random.randn(num_in_units,
num_h_units).astype(np.float32),
name="W"),
"b":
node.Node(np.zeros(num_h_units, dtype=np.float32), name="b")
}
def _construct(self):
"""
构建系统
:return:
"""
"""1)创建通道"""
for _ch in self.structure["channel"]:
_c = channel.Channel(_ch)
self.R.add_channel(_c)
"""2)创建节点"""
for _nd in self.structure["node"]:
_n = node.Node(_nd)
_n.add_function(self.structure["node"][_nd]["function"])
self.R.add_node(_n)
"""创建输入通道"""
if "input" in self.structure["node"][_nd]:
for _ch in self.structure["node"][_nd]["input"]:
_n.add_in_channel(_ch)
"""创建输出通道"""
if "output" in self.structure["node"][_nd]:
for _ch in self.structure["node"][_nd]["output"]:
_n.add_out_channel(_ch)
if "init" in self.structure["node"][_nd]:
_events = self.structure["node"][_nd]["init"]()
_q = self.R.get_channel(_ch)
for _e in _events:
_q.in_q(_e)
"""创建同步器"""
if "synchronizer" in self.structure["node"][_nd]:
if self.structure["node"][_nd]["synchronizer"]:
_sync = synchronizer.Synchronizer()
_n.add_synchronizer(_sync)
def __init__(self, num_in_units, alpha=0.9, eps=1e-5):
"""
引数
num_in_units ユニット数(入力が4Dの時はチャンネル数)
alpha 移動平均の更新率をコントロール
eps ゼロ除算を防ぐ
"""
super(BatchNormalization, self).__init__()
self.parameters = {
"W":
node.Node(np.random.randn(num_in_units).astype(np.float32),
name="W"),
"b":
node.Node(np.zeros(num_in_units, dtype=np.float32), name="b")
}
self.alpha = alpha
self.eps = eps
self.running_mu = node.Node(np.zeros(num_in_units, dtype=np.float32))
self.running_var = node.Node(np.ones(num_in_units, dtype=np.float32))
def create_node():
# Instantiate node
pin = node.Node()
# Set node params
pin.node_name = 'Current monitoring dashboard'
pin.node_description = 'Current trend monitoring system'
pin.node_number = 'N1_1507'
pin.host_ip = '127.0.0.1'
# pin.host_ip = '192.168.3.250'
pin.host_port = '502'
# Load register map
# map = pin.load_register_map()
# Connect with slave
pin.connect()
# Create and connect to db
engine = create_engine('sqlite:///database/current_data.db', echo=False)
# Initiate the basic page
return pin, map, engine
def set_up_nodes(self):
config_list = self.config_list
if not config_list:
config_name = utility.camel_to_snake(self.__class__.__name__)
try:
with open('./config_jsons/' + config_name +
'.json') as config_file:
self.config_list = json.load(config_file)
except FileNotFoundError:
raise FileNotFoundError(
'need a file named ' + config_name + '.json,' +
'or you can pass in your node configurations parameter when ELATestMetaClass is Initialized'
)
project_path = os.environ.get('GOPATH') + '/'.join(config.ELA_PATH)
print("source code path:", project_path)
node_path = "%s/elastos_test_runner_%s" % (
"./test", datetime.datetime.now().strftime("%Y%m%d_%H%M%S"))
os.makedirs(node_path)
print("Temp dir is:", node_path)
nodes_list = []
for index, item in enumerate(self.config_list):
name = item['name']
path = os.path.join(node_path, name + str(index))
os.makedirs(path)
shutil.copy(os.path.join(project_path, name), os.path.join(path))
configuration = item['config']
with open(path + '/config.json', 'w+') as f:
f.write(json.dumps(configuration, indent=4))
nodes_list.append(
node.Node(i=index,
dirname=node_path,
configuration=configuration['Configuration']))
return nodes_list
def __init__(self, num_in_ch, num_out_ch, kernel, stride=1, pad=0):
"""
引数
num_in_ch 上で示される関係性においてinputのチャンネル数
num_out_ch 上で示される関係性においてoutputのチャンネル数
kernel カーネルサイズ
stride ストライドサイズ
pad ゼロパディング数
"""
super(TransposedConvolution2D, self).__init__()
self.num_in_ch = num_in_ch
self.num_out_ch = num_out_ch
self.kernel = kernel
self.stride = stride
self.pad = pad
self.parameters = {
"W":
node.Node(np.random.randn(num_in_ch, num_out_ch, kernel,
kernel).astype(np.float32),
name="W")
}
print(str10)
print(perc_list[9])
print(str11)
print(perc_list[10])
print(str12)
print(perc_list[11])
print(str13)
print(perc_list[12])
print(str14)
print(perc_list[13])
print(str15)
print(perc_list[14])
print("creating Node_a....")
Node_a = nd.Node("juan0o_")
print("creating Node_b....")
Node_b = nd.Node("alejandrouribe718")
print("\nThis is the name to Node_a__________________________\n")
print(Node_a.get_name())
print("\nThis is the follow list Node_a__________________________\n")
print(Node_a.get_follow_list())
print("\nThis is the following list Node_a__________________________\n")
print(Node_a.get_following_list())
print(Node_a.get_biography())
print("\nThis is the biography Node_a__________________________\n")
print("\nThis is numLikes to Node_a__________________________\n")
print(Node_a.get_likes())
print(
"\nNow the realationship algorithm between Node_a and Node_b__________________________\n"
)