"" return { "layer": "Flatten", "keep_dim": self.keep_dim, "optimizer": { "cache": self.optimizer.cache, "hyperparameters": self.optimizer.hyperparameters, }, } # 定义一个方法,用于计算单个小批量的层输出 def forward(self, X, retain_derived=True): # 如果需要保留在前向传播期间计算的...
act_fn = ELU(alpha=float(alpha))else:# 如果未识别出激活函数,则抛出异常raiseValueError("Unknown activation: {}".format(act_str))# 返回选择的激活函数对象returnact_fnclassSchedulerInitializer(object):# 初始化学习率调度器的类。有效的 `param` 值包括:# (a) `SchedulerBase` 实例的 `__str__` ...
topandas(array, flatten=True) except Exception as err: print(type(err), str(err)) # <class 'ValueError'> this array has more than one jagged array structure To describe data like these, you'd need two DataFrames, and any calculations involving both "a" and "b" would have to include...
390 390 is_dataarray = False 391 391 392 392 if flatten and is_dataarray: Diff for: requirements.txt +1-1 Original file line numberDiff line numberDiff line change @@ -1,4 +1,4 @@ 1 - numpy<1.24 1 + numpy>=1.23 2 2 pandas[excel] 3 3 parsimonious 4 4 xarray...
Awkward Array is a pure Python+Numpy library for manipulating complex data structures as you would Numpy arrays. Even if your data structures contain variable-length lists (jagged/ragged), are deeply nested (record structure), have different data types in the same list (heterogeneous), ...
Python does not necessarily mean slow. As long as the data blocks ("baskets") are large, this "array at a time" approach can even be faster than "event at a time" C++. Below, the rate of reading data into arrays with uproot is shown to be faster than C++ ROOT (left) and root_...