ValueError: You selected an invalid strategy name: `strategy=None`. It must be either a string or an instance of `pytorch_lightning.strategies.Strategy`. Example choices: auto, ddp, ddp_spawn, deepspeed, ... Find a complete list of options in our documentation at https://lightning.ai

python3.7解决最小二乘遇到ValueError:Expected 2D array, got 1D array instead: array=[5.].关于reshape和predict

Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample. 翻译过来是： ValueError：预期为2D数组，改为获取1D数组： ...

【Python】绘图时报错ValueError: Invalid RGBA argument: (0.0, 0.5, 0.0, array([[0.42116073]]))

通常不会报出这种错误，但还是有奇人能搞出来。这种错误通过断点调试就可以发现错误在利用Python进行绘图时可能报出这种错误。 Traceback (most recent call last): File "E:\Anaconda3\envs\python27\Lib\lib-tk\...

def checkClicked(group, mouse_pos, group_type='NUMBER'): selected = [] # 数字卡片/运算符卡片 if group_type == GROUPTYPES[0] or group_type == GROUPTYPES[1]: max_selected = 2 if group_type == GROUPTYPES[0] else 1 num_selected = 0 for each in group: num_selected += int(each.is_selected) for each in group: if each.rect.collidepoint(mouse_pos): if each.is_selected: each.is_selected = not each.is_selected num_selected -= 1 each.select_order = None else: if num_selected < max_selected: each.is_selected = not each.is_selected num_selected += 1 each.select_order = str(num_selected) if each.is_selected: selected.append(each.attribute) # 按钮卡片 elif group_type == GROUPTYPES[2]: for each in group: if each.rect.collidepoint(mouse_pos): each.is_selected = True selected.append(each.attribute) # 抛出异常 else: raise ValueError('checkClicked.group_type unsupport <%s>, expect <%s>, <%s> or <%s>...' % (group_type, *GROUPTYPES)) return selected详细讲解这段代码

函数接受三个参数：group ...如果 group_type 不是数字卡片、运算符卡片或按钮卡片，则该函数会抛出一个 ValueError 异常，提示 group_type 的值不受支持。最后，该函数返回一个包含选中 sprite 属性的列表。

class SVDRecommender: def init(self, k=50, ncv=None, tol=0, which='LM', v0=None, maxiter=None, return_singular_vectors=True, solver='arpack'): self.k = k self.ncv = ncv self.tol = tol self.which = which self.v0 = v0 self.maxiter = maxiter self.return_singular_vectors = return_singular_vectors self.solver = solver def svds(self, A): if self.which == 'LM': largest = True elif self.which == 'SM': largest = False else: raise ValueError("which must be either 'LM' or 'SM'.") if not (isinstance(A, LinearOperator) or isspmatrix(A) or is_pydata_spmatrix(A)): A = np.asarray(A) n, m = A.shape if self.k <= 0 or self.k >= min(n, m): raise ValueError("k must be between 1 and min(A.shape), k=%d" % self.k) if isinstance(A, LinearOperator): if n > m: X_dot = A.matvec X_matmat = A.matmat XH_dot = A.rmatvec XH_mat = A.rmatmat else: X_dot = A.rmatvec X_matmat = A.rmatmat XH_dot = A.matvec XH_mat = A.matmat dtype = getattr(A, 'dtype', None) if dtype is None: dtype = A.dot(np.zeros([m, 1])).dtype else: if n > m: X_dot = X_matmat = A.dot XH_dot = XH_mat = _herm(A).dot else: XH_dot = XH_mat = A.dot X_dot = X_matmat = _herm(A).dot def matvec_XH_X(x): return XH_dot(X_dot(x)) def matmat_XH_X(x): return XH_mat(X_matmat(x)) XH_X = LinearOperator(matvec=matvec_XH_X, dtype=A.dtype, matmat=matmat_XH_X, shape=(min(A.shape), min(A.shape))) #获得隐式定义的格拉米矩阵的低秩近似。 eigvals, eigvec = eigsh(XH_X, k=self.k, tol=self.tol ** 2, maxiter=self.maxiter, ncv=self.ncv, which=self.which, v0=self.v0) #格拉米矩阵有实非负特征值。 eigvals = np.maximum(eigvals.real, 0) #使用来自pinvh的小特征值的复数检测。 t = eigvec.dtype.char.lower() factor = {'f': 1E3, 'd': 1E6} cond = factor[t] * np.finfo(t).eps cutoff = cond * np.max(eigvals) #获得一个指示哪些本征对不是简并微小的掩码， #并为阈值奇异值创建一个重新排序数组。 above_cutoff = (eigvals > cutoff) nlarge = above_cutoff.sum() nsmall = self.k - nlarge slarge = np.sqrt(eigvals[above_cutoff]) s = np.zeros_like(eigvals) s[:nlarge] = slarge if not self.return_singular_vectors: return np.sort(s) if n > m: vlarge = eigvec[:, above_cutoff] ularge = X_matmat(vlarge) / slarge if self.return_singular_vectors != 'vh' else None vhlarge = _herm(vlarge) else: ularge = eigvec[:, above_cutoff] vhlarge = _herm(X_matmat(ularge) / slarge) if self.return_singular_vectors != 'u' else None u = _augmented_orthonormal_cols(ularge, nsmall) if ularge is not None else None vh = _augmented_orthonormal_rows(vhlarge, nsmall) if vhlarge is not None else None indexes_sorted = np.argsort(s) s = s[indexes_sorted] if u is not None: u = u[:, indexes_sorted] if vh is not None: vh = vh[indexes_sorted] return u, s, vh def _augmented_orthonormal_cols(U, n): if U.shape[0] <= n: return U Q, R = np.linalg.qr(U) return Q[:, :n] def _augmented_orthonormal_rows(V, n): if V.shape[1] <= n: return V Q, R = np.linalg.qr(V.T) return Q[:, :n].T def _herm(x): return np.conjugate(x.T) 将上述代码修改为使用LM，迭代器使用arpack

raise ValueError("which must be either 'LM' or 'SM'.") if not (isinstance(A, LinearOperator) or isspmatrix(A) or is_pydata_spmatrix(A)): A = np.asarray(A) n, m = A.shape if self.k <= 0 or ...

from scipy.sparse.linalg import eigsh, LinearOperator from scipy.sparse import isspmatrix, is_pydata_spmatrix class SVDRecommender: def init(self, k=50, ncv=None, tol=0, which='LM', v0=None, maxiter=None, return_singular_vectors=True, solver='arpack'): self.k = k self.ncv = ncv self.tol = tol self.which = which self.v0 = v0 self.maxiter = maxiter self.return_singular_vectors = return_singular_vectors self.solver = solver def svds(self, A): if self.which == 'LM': largest = True elif self.which == 'SM': largest = False else: raise ValueError("which must be either 'LM' or 'SM'.") if not (isinstance(A, LinearOperator) or isspmatrix(A) or is_pydata_spmatrix(A)): A = np.asarray(A) n, m = A.shape if self.k <= 0 or self.k >= min(n, m): raise ValueError("k must be between 1 and min(A.shape), k=%d" % self.k) if isinstance(A, LinearOperator): if n > m: X_dot = A.matvec X_matmat = A.matmat XH_dot = A.rmatvec XH_mat = A.rmatmat else: X_dot = A.rmatvec X_matmat = A.rmatmat XH_dot = A.matvec XH_mat = A.matmat dtype = getattr(A, 'dtype', None) if dtype is None: dtype = A.dot(np.zeros([m, 1])).dtype else: if n > m: X_dot = X_matmat = A.dot XH_dot = XH_mat = _herm(A).dot else: XH_dot = XH_mat = A.dot X_dot = X_matmat = _herm(A).dot def matvec_XH_X(x): return XH_dot(X_dot(x)) def matmat_XH_X(x): return XH_mat(X_matmat(x)) XH_X = LinearOperator(matvec=matvec_XH_X, dtype=A.dtype, matmat=matmat_XH_X, shape=(min(A.shape), min(A.shape))) #获得隐式定义的格拉米矩阵的低秩近似。 eigvals, eigvec = eigsh(XH_X, k=self.k, tol=self.tol ** 2, maxiter=self.maxiter, ncv=self.ncv, which=self.which, v0=self.v0) #格拉米矩阵有实非负特征值。 eigvals = np.maximum(eigvals.real, 0) #使用来自pinvh的小特征值的复数检测。 t = eigvec.dtype.char.lower() factor = {'f': 1E3, 'd': 1E6} cond = factor[t] * np.finfo(t).eps cutoff = cond * np.max(eigvals) #获得一个指示哪些本征对不是简并微小的掩码， #并为阈值奇异值创建一个重新排序数组。 above_cutoff = (eigvals > cutoff) nlarge = above_cutoff.sum() nsmall = self.k - nlarge slarge = np.sqrt(eigvals[above_cutoff]) s = np.zeros_like(eigvals) s[:nlarge] = slarge if not self.return_singular_vectors: return np.sort(s) if n > m: vlarge = eigvec[:, above_cutoff] ularge = X_matmat(vlarge) / slarge if self.return_singular_vectors != 'vh' else None vhlarge = _herm(vlarge) else: ularge = eigvec[:, above_cutoff] vhlarge = _herm(X_matmat(ularge) / slarge) if self.return_singular_vectors != 'u' else None u = _augmented_orthonormal_cols(ularge, nsmall) if ularge is not None else None vh = _augmented_orthonormal_rows(vhlarge, nsmall) if vhlarge is not None else None indexes_sorted = np.argsort(s) s = s[indexes_sorted] if u is not None: u = u[:, indexes_sorted] if vh is not None: vh = vh[indexes_sorted] return u, s, vh def _augmented_orthonormal_cols(U, n): if U.shape[0] <= n: return U Q, R = np.linalg.qr(U) return Q[:, :n] def _augmented_orthonormal_rows(V, n): if V.shape[1] <= n: return V Q, R = np.linalg.qr(V.T) return Q[:, :n].T def _herm(x): return np.conjugate(x.T)这段代码在largest = False处报错了，报错信息为：Local variable 'largest' is assigned to but never used (pyfLakes E)如何改正

raise ValueError("which must be either 'LM' or 'SM'.") 改成： largest = self.which == 'LM' if not largest and self.which != 'SM': raise ValueError("which must be either 'LM' or 'SM'.") ...

class SVDRecommender: def init(self, k=50, ncv=None, tol=0, which='LM', v0=None, maxiter=None, return_singular_vectors=True, solver='arpack'): self.k = k self.ncv = ncv self.tol = tol self.which = which self.v0 = v0 self.maxiter = maxiter self.return_singular_vectors = return_singular_vectors self.solver = solver def svds(self, A): if which == 'LM': largest = True elif which == 'SM': largest = False else: raise ValueError("which must be either 'LM' or 'SM'.") if not (isinstance(A, LinearOperator) or isspmatrix(A) or is_pydata_spmatrix(A)): A = np.asarray(A) n, m = A.shape if k <= 0 or k >= min(n, m): raise ValueError("k must be between 1 and min(A.shape), k=%d" % k) if isinstance(A, LinearOperator): if n > m: X_dot = A.matvec X_matmat = A.matmat XH_dot = A.rmatvec XH_mat = A.rmatmat else: X_dot = A.rmatvec X_matmat = A.rmatmat XH_dot = A.matvec XH_mat = A.matmat dtype = getattr(A, 'dtype', None) if dtype is None: dtype = A.dot(np.zeros([m, 1])).dtype else: if n > m: X_dot = X_matmat = A.dot XH_dot = XH_mat = _herm(A).dot else: XH_dot = XH_mat = A.dot X_dot = X_matmat = _herm(A).dot def matvec_XH_X(x): return XH_dot(X_dot(x)) def matmat_XH_X(x): return XH_mat(X_matmat(x)) XH_X = LinearOperator(matvec=matvec_XH_X, dtype=A.dtype, matmat=matmat_XH_X, shape=(min(A.shape), min(A.shape))) # Get a low rank approximation of the implicitly defined gramian matrix. eigvals, eigvec = eigsh(XH_X, k=k, tol=tol ** 2, maxiter=maxiter, ncv=ncv, which=which, v0=v0) # Gramian matrix has real non-negative eigenvalues. eigvals = np.maximum(eigvals.real, 0) # Use complex detection of small eigenvalues from pinvh. t = eigvec.dtype.char.lower() factor = {'f': 1E3, 'd': 1E6} cond = factor[t] * np.finfo(t).eps cutoff = cond * np.max(eigvals) # Get a mask indicating which eigenpairs are not degenerate tiny, # and create a reordering array for thresholded singular values. above_cutoff = (eigvals > cutoff) nlarge = above_cutoff.sum() nsmall = k - nlarge slarge = np.sqrt(eigvals[above_cutoff]) s = np.zeros_like(eigvals) s[:nlarge] = slarge if not return_singular_vectors: return np.sort(s) if n > m: vlarge = eigvec[:, above_cutoff] ularge = X_matmat(vlarge) / slarge if return_singular_vectors != 'vh' else None vhlarge = _herm(vlarge) else: ularge = eigvec[:, above_cutoff] vhlarge = _herm(X_matmat(ularge) / slarge) if return_singular_vectors != 'u' else None u = _augmented_orthonormal_cols(ularge, nsmall) if ularge is not None else None vh = _augmented_orthonormal_rows(vhlarge, nsmall) if vhlarge is not None else None indexes_sorted = np.argsort(s) s = s[indexes_sorted] if u is not None: u = u[:, indexes_sorted] if vh is not None: vh = vh[indexes_sorted] return u, s, vh将这段代码放入一个.py文件中，用Spyder查看，有报错，可能是缩进有问题，无法被调用，根据这个问题，给出解决办法，给出改正后的完整代码

raise ValueError("which must be either 'LM' or 'SM'.") if not (isinstance(A, LinearOperator) or isspmatrix(A) or is_pydata_spmatrix(A)): A = np.asarray(A) n, m = A.shape if self.k <= 0 or self....

def new(cls, backend=None, prefix=None, kwargs): if backend is None and prefix is None: backend = 'disk' if backend is not None and backend not in cls._backends: raise ValueError( f'Backend {backend} is not supported. Currently supported ones' f' are {list(cls._backends.keys())}') if prefix is not None and prefix not in cls._prefix_to_backends: raise ValueError( f'prefix {prefix} is not supported. Currently supported ones ' f'are {list(cls._prefix_to_backends.keys())}') # concatenate the arguments to a unique key for determining whether # objects with the same arguments were created arg_key = f'{backend}:{prefix}' for key, value in kwargs.items(): arg_key += f':{key}:{value}' if arg_key in cls._instances: _instance = cls._instances[arg_key] else: # create a new object and put it to _instance _instance = super().new(cls) if backend is not None: _instance.client = cls._backends[backend](kwargs) else: _instance.client = cls._prefix_to_backends[prefix](**kwargs) cls._instances[arg_key] = _instance return _instance

如果指定的 backend 或 prefix 不在 _backends 或 _prefix_to_backends 中，则会引发一个 ValueError 异常。这个类会将所有的参数拼接成一个唯一的 key，用于判断是否已经创建了具有相同参数的对象。如果已经创建了...

解释一段python代码 class KalmanFilter(object): def init(self, dim_x, dim_z, dim_u=0): if dim_x < 1: raise ValueError('dim_x must be 1 or greater') if dim_z < 1: raise ValueError('dim_z must be 1 or greater') if dim_u < 0: raise ValueError('dim_u must be 0 or greater') self.dim_x = dim_x self.dim_z = dim_z self.dim_u = dim_u self.x = zeros((dim_x, 1)) # state self.P = eye(dim_x) # uncertainty covariance self.Q = eye(dim_x) # process uncertainty self.B = None # control transition matrix self.F = eye(dim_x) # state transition matrix self.H = zeros((dim_z, dim_x)) # Measurement function self.R = eye(dim_z) # state uncertainty self._alpha_sq = 1. # fading memory control self.M = np.zeros((dim_z, dim_z)) # process-measurement cross correlation self.z = np.array([[None]*self.dim_z]).T # gain and residual are computed during the innovation step. We # save them so that in case you want to inspect them for various # purposes self.K = np.zeros((dim_x, dim_z)) # kalman gain self.y = zeros((dim_z, 1)) self.S = np.zeros((dim_z, dim_z)) # system uncertainty self.SI = np.zeros((dim_z, dim_z)) # inverse system uncertainty # identity matrix. Do not alter this. self._I = np.eye(dim_x) # these will always be a copy of x,P after predict() is called self.x_prior = self.x.copy() self.P_prior = self.P.copy() # these will always be a copy of x,P after update() is called self.x_post = self.x.copy() self.P_post = self.P.copy() # Only computed only if requested via property self._log_likelihood = log(sys.float_info.min) self._likelihood = sys.float_info.min self._mahalanobis = None self.inv = np.linalg.inv

在这个方法中，首先会检查dim_x、dim_z和dim_u是否符合要求，如果不符合就会抛出ValueError异常。然后会根据参数的值初始化KalmanFilter对象的各个属性，包括状态量的维度dim_x、观测量的维度dim_z、控制量的维度dim...

class svd_recommender_py(): #svd矩阵推荐 def svds(A, ncv=None, tol=0, which='LM', v0=None, maxiter=None, return_singular_vectors=True, solver='arpack'): if which == 'LM': largest = True elif which == 'SM': largest = False else: raise ValueError("which must be either 'LM' or 'SM'.") if not (isinstance(A, LinearOperator) or isspmatrix(A) or is_pydata_spmatrix(A)): A = np.asarray(A) n, m = A.shape if k <= 0 or k >= min(n, m): raise ValueError("k must be between 1 and min(A.shape), k=%d" % k) if isinstance(A, LinearOperator): if n > m: X_dot = A.matvec X_matmat = A.matmat XH_dot = A.rmatvec XH_mat = A.rmatmat else: X_dot = A.rmatvec X_matmat = A.rmatmat XH_dot = A.matvec XH_mat = A.matmat dtype = getattr(A, 'dtype', None) if dtype is None: dtype = A.dot(np.zeros([m, 1])).dtype else: if n > m: X_dot = X_matmat = A.dot XH_dot = XH_mat = _herm(A).dot else: XH_dot = XH_mat = A.dot X_dot = X_matmat = _herm(A).dot def matvec_XH_X(x): return XH_dot(X_dot(x)) def matmat_XH_X(x): return XH_mat(X_matmat(x)) XH_X = LinearOperator(matvec=matvec_XH_X, dtype=A.dtype, matmat=matmat_XH_X, shape=(min(A.shape), min(A.shape))) # Get a low rank approximation of the implicitly defined gramian matrix. #获得隐式定义的格拉米矩阵的低秩近似。 #这不是解决问题的稳定方法。 solver == 'arpack' eigvals, eigvec = eigsh(XH_X, k=k, tol=tol ** 2, maxiter=maxiter, ncv=ncv, which=which, v0=v0) #格拉米矩阵具有实非负特征值。 eigvals = np.maximum(eigvals.real, 0) #使用来自pinvh的小特征值的复杂检测。 t = eigvec.dtype.char.lower() factor = {'f': 1E3, 'd': 1E6} cond = factor[t] * np.finfo(t).eps cutoff = cond * np.max(eigvals) #得到一个指示哪些本征对不是退化微小的掩码， #并创建阈值奇异值的重新排序数组。 above_cutoff = (eigvals > cutoff) nlarge = above_cutoff.sum() nsmall = k - nlarge slarge = np.sqrt(eigvals[above_cutoff]) s = np.zeros_like(eigvals) s[:nlarge] = slarge if not return_singular_vectors: return np.sort(s) if n > m: vlarge = eigvec[:, above_cutoff] ularge = X_matmat(vlarge) / slarge if return_singular_vectors != 'vh' else None vhlarge = _herm(vlarge) else: ularge = eigvec[:, above_cutoff] vhlarge = _herm(X_matmat(ularge) / slarge) if return_singular_vectors != 'u' else None u = _augmented_orthonormal_cols(ularge, nsmall) if ularge is not None else None vh = _augmented_orthonormal_rows(vhlarge, nsmall) if vhlarge is not None else None indexes_sorted = np.argsort(s) s = s[indexes_sorted] if u is not None: u = u[:, indexes_sorted] if vh is not None: vh = vh[indexes_sorted] return u, s, vh这段代码主要是为了将scipy包中的SVD计算方法封装成一个自定义类，是否封装合适？如果不合适，给出修改后的完整代码

raise ValueError("which must be either 'LM' or 'SM'.") if not (isinstance(A, LinearOperator) or isspmatrix(A) or is_pydata_spmatrix(A)): A = np.asarray(A) n, m = A.shape if k <= 0 or k >= min...

def merge(input_data_path): merged_data = pd.DataFrame() data_name_list = os.listdir(input_data_path) print(data_name_list) for data_name in data_name_list: # 读取csv文件 print('processing '+data_name+'……') src_file = os.path.join(input_data_path,data_name) data = pd.read_csv(src_file) # data['systemNo'] = data['iuId'].str[12:14] merged_data = pd.concat([merged_data, data]) del data # dst_file = os.path.join(history_data_path,data_name) # shutil.move(src_file,dst_file) # print('delete '+data_name+'……') # merged_data.to_csv(save_data_path,index=False) return merged_data合并后的数据进行data.loc[i, 'temp_range'] = data.loc[i, 'iu39Ti'] - data.loc[i, 'iu38To']报错ValueError: Must have equal len keys and value when setting with an iterable

这个错误通常是由于在设置DataFrame某一列的值时，传入的Iterable对象长度与DataFrame的长度不一致导致的。你可以在设置这一列的值之前，先检查一下这个Iterable对象的长度是否与DataFrame的长度相同。...

请仿照这一段代码，if self.type == "csv": try: self.pd_data = pd.read_csv(self.file_path) except: raise ValueError('csv路径错误') elif self.type == "txt": self.pd_data = pd.DataFrame( pd.read_table(self.file_path, sep=' ', header=None, engine='python', names=self.names)) elif self.type == "pmlb": try: self.pd_data = fetch_data(str(self.file_path), local_cache_dir='./datasets', return_X_y=False) self.x, self.y = fetch_data(str(self.file_path), local_cache_dir='./datasets', return_X_y=True) except: raise ValueError('pmlb数据集名称错误') elif self.type == "numpy": try: self.pd_data = pd.DataFrame(self.file_path) self.x, self.y = pd.DataFrame(self.file_path[:, :-1]), pd.DataFrame(self.file_path[:, -1]) except: raise ValueError('numpy数据集设置失败')

def __init__(self, file_path, file_type, names=None): self.file_path = file_path self.file_type = file_type self.names = names self.pd_data = None self.x = None self.y = None def read_data...

browser = webdriver.Chrome()报错ValueError: Timeout value connect was , but it must be an int, float or None.

这个错误是因为 webdriver.Chrome() 方法的 timeout 参数需要传入一个整数、浮点数或 None，但是你传入了一个空值或非数字类型的值。你可以尝试修改代码为： from selenium import webdriver browser = ...

import numpy as np from scipy.optimize import linprog def integer_cutting_plane(c, A, b, bounds): relaxed_A = A relaxed_b = b while True: res = linprog(c=c, A_ub=relaxed_A, b_ub=relaxed_b, bounds=bounds) x = res.x if all(int(val) == val for val in x): return x.astype(int) new_constraint = (relaxed_A @ x <= relaxed_b) relaxed_A = np.vstack((relaxed_A, new_constraint)) def get_bounds(): return [(0, None), (0, None)] def get_c(): return np.array([40, 90]) def get_A(): return np.array([[-9, -7], [-7, -20]]) def get_b(): return np.array([-56, -70]) if name == 'main': bounds = get_bounds() integer_cutting_plane(get_c(), get_A(), get_b(), bounds)以上代码运行报错ValueError: Invalid input for linprog: b_ub must be a 1-D array; b_ub must not have more than one non-singleton dimension and the number of rows in A_ub must equal the number of values in b_ub 请解决

relaxed_A = np.vstack((relaxed_A, new_constraint)) def get_bounds(): return [(0, None), (0, None)] def get_c(): return np.array([40, 90]) def get_A(): return np.array([[-9, -7], [-7, -20]]) ...

def call(self, input, index=None): if input is None: raise ValueError("Input cannot be None.") output = self.forward_static(input) if index is None: index = np.argmax(output.cpu().data.numpy()) one_hot = np.zeros((1, output.size()[-1]), dtype=np.float32) one_hot[0][index] = 1 one_hot = Variable(torch.from_numpy(one_hot), requires_grad=True) if self.cuda: one_hot = torch.sum(one_hot.cuda() * output) else: one_hot = torch.sum(one_hot * output) one_hot.backward() output = input.grad.cpu().data.numpy() output = output[0, :, :, :] return output 代码中执行output = input.grad.cpu().data.numpy() 时报错'NoneType' object has no attribute 'cpu' 如何解决

raise ValueError("Input cannot be None.") output = self.forward_static(input) if index is None: index = np.argmax(output.cpu().data.numpy()) one_hot = np.zeros((1, output.size()[-1]), dtype=...

ValueError Traceback (most recent call last) Cell In[17], line 5 3 model.load_weights("unet_membrane.hdf5") 4 #results = model.predict_generator(testGene,67,verbose=1) ----> 5 results = model.predict(testGene,verbose=1) 6 saveResult("data/results",results) File d:\Software\Anaconda\Ana_qinghua\lib\site-packages\keras\utils\traceback_utils.py:70, in filter_traceback..error_handler(*args, **kwargs) 67 filtered_tb = _process_traceback_frames(e.traceback) 68 # To get the full stack trace, call: 69 # tf.debugging.disable_traceback_filtering() ---> 70 raise e.with_traceback(filtered_tb) from None 71 finally: 72 del filtered_tb File ~\AppData\Local\Temp\__autograph_generated_file8h3jf8qv.py:15, in outer_factory..inner_factory..tf__predict_function(iterator) 13 try: 14 do_return = True ---> 15 retval_ = ag__.converted_call(ag__.ld(step_function), (ag.ld(self), ag.ld(iterator)), None, fscope) 16 except: 17 do_return = False ValueError: in user code: ... Call arguments received by layer 'model_7' (type Functional): • inputs=tf.Tensor(shape=(None, None, None), dtype=float32) • training=False • mask=None Output is truncated. View as a scrollable element or open in a text editor. Adjust cell output settings...

该层要求接收一个形状为 (None, None, None) 的 float32 类型的输入张量，但实际上传入的输入数据与其形状不匹配，导致了该错误的发生。可能的原因是输入数据的形状与模型的输入形状不匹配或者数据类型不正确。需要...

ValueError: name for name_scope must be a string.

If you are passing a variable or an object, convert it to a string before passing it. For example: import tensorflow as tf # define a variable var = tf.Variable(0, name='my_var') # create a ...

class MyDataSet(Dataset): """自定义数据集""" def init(self, images_path: list, images_class: list, transform=None): self.images_path = images_path self.images_class = images_class self.transform = transform def len(self): return len(self.images_path) def getitem(self, item): img = Image.open(self.images_path[item]) # RGB为彩色图片，L为灰度图片 if img.mode != 'RGB': raise ValueError("image: {} isn't RGB mode.".format(self.images_path[item])) label = self.images_class[item] if self.transform is not None: img = self.transform(img) print(label) return img, label @staticmethod def collate_fn(batch): # 官方实现的default_collate可以参考 # https://github.com/pytorch/pytorch/blob/67b7e751e6b5931a9f45274653f4f653a4e6cdf6/torch/utils/data/_utils/collate.py images, labels = tuple(zip(*batch)) images = torch.stack(images, dim=0) labels = torch.as_tensor(np.array(labels[:, 0])) return images, labels

这段代码是一个自定义的 PyTorch 数据集类 MyDataSet，它继承自 PyTorch 内置的 Dataset 类。在 __init__ 方法中，该类接收两个列表 images_path 和 images_class，分别代表图片的路径和对应的类别。还...

c++class Student: def init(self): self._age = None self._gender = None self._scores = {'Chinese': None, 'Math': None, 'English': None} @property def age(self): return self._age @age.setter def age(self, value): if not isinstance(value, int) or value < 0: raise ValueError('Invalid age') self._age = value @property def gender(self): return self._gender @gender.setter def gender(self, value): if value not in ['male', 'female']: raise ValueError('Invalid gender') self._gender = value @property def scores(self): return self._scores def set_score(self, subject, score): if subject not in self._scores: raise ValueError('Invalid subject') self._scores[subject] = score def input_score(subject): while True: try: score = int(input(f'请输入{subject}成绩：')) if score < 0 or score > 100: print('分数必须在0-100之间') continue return score except ValueError: print('请输入数字') # 主函数 try: s = Student() s.age = int(input('请输入学生年龄：')) s.gender = input('请输入学生性别（male或female）：') for subject in ['Chinese', 'Math', 'English']: score = input_score(subject) s.set_score(subject, score) print('学生信息设置成功！') except ValueError as e: print('学生信息设置失败：', e)

这是一个使用 Python 实现的学生类，包含年龄、性别和成绩等属性。其中，年龄和性别使用了装饰器 @property 和 @setter 实现了属性的访问和修改限制。成绩可以通过方法 set_score() 进行设置，输入成绩时使用了一个...

ValueError: You selected an invalid strategy name: `strategy=None`. It must be either a string or an instance of `pytorch_lightning.strategies.Strategy`. Example choices: auto, ddp, ddp_spawn, deepspeed, ... Find a complete list of options in our documentation at https://lightning.ai

ValueError: You selected an invalid strategy name: `strategy=None`. It must be either a string or an instance of `pytorch_lightning.strategies.Strategy`. Example choices: auto, ddp, ddp_spawn, deepspeed,

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