for val in OrderedDict.fromkeys(data): TypeError: unhashable type: 'numpy.ndarray'

这个错误通常是由于试图将一个 NumPy 数组作为有序字典 (`OrderedDict`) 的键造成的。因为 NumPy 数组是可变的，所以它们不是可哈希的，不能用作字典的键，也不能用作有序字典的键。如果你需要使用 NumPy 数组作为键，可以考虑将它们转换为元组或其他可哈希的对象。例如，如果你有一个一维的 NumPy 数组 `a`，你可以将它转换为元组，然后用作有序字典的键，如下所示：

from collections import OrderedDict
a = np.array([1, 2, 3])
d = OrderedDict.fromkeys([(tuple(a),), 'some value'])

这样就可以避免 `TypeError: unhashable type: 'numpy.ndarray'` 错误。

相关问题

代码time_start = time.time() results = list() iterations = 2001 lr = 1e-2 model = func_critic_model(input_shape=(None, train_img.shape[1]), act_func='relu') loss_func = tf.keras.losses.MeanSquaredError() alg = "gd" # alg = "gd" for kk in range(iterations): with tf.GradientTape() as tape: predict_label = model(train_img) loss_val = loss_func(predict_label, train_lbl) grads = tape.gradient(loss_val, model.trainable_variables) overall_grad = tf.concat([tf.reshape(grad, -1) for grad in grads], 0) overall_model = tf.concat([tf.reshape(weight, -1) for weight in model.weights], 0) overall_grad = overall_grad + 0.001 * overall_model ## adding a regularization term results.append(loss_val.numpy()) if alg == 'gd': overall_model -= lr * overall_grad ### gradient descent elif alg == 'gdn': ## gradient descent with nestrov's momentum overall_vv_new = overall_model - lr * overall_grad overall_model = (1 + gamma) * oerall_vv_new - gamma * overall_vv overall_vv = overall_new pass model_start = 0 for idx, weight in enumerate(model.weights): model_end = model_start + tf.size(weight) weight.assign(tf.reshape()) for grad, ww in zip(grads, model.weights): ww.assign(ww - lr * grad) if kk % 100 == 0: print(f"Iter: {kk}, loss: {loss_val:.3f}, Duration: {time.time() - time_start:.3f} sec...") input_shape = train_img.shape[1] - 1 model = tf.keras.Sequential([ tf.keras.layers.Input(shape=(input_shape,)), tf.keras.layers.Dense(30, activation="relu"), tf.keras.layers.Dense(20, activation="relu"), tf.keras.layers.Dense(1) ]) n_epochs = 20 batch_size = 100 learning_rate = 0.01 momentum = 0.9 sgd_optimizer = tf.keras.optimizers.SGD(learning_rate=learning_rate, momentum=momentum) model.compile(loss="mean_squared_error", optimizer=sgd_optimizer) history = model.fit(train_img, train_lbl, epochs=n_epochs, batch_size=batch_size, validation_data=(test_img, test_lbl)) nag_optimizer = tf.keras.optimizers.SGD(learning_rate=learning_rate, momentum=momentum, nesterov=True) model.compile(loss="mean_squared_error", optimizer=nag_optimizer) history = model.fit(train_img, train_lbl, epochs=n_epochs, batch_size=batch_size, validation_data=(test_img, test_lbl))运行后报错TypeError: Missing required positional argument，如何改正

这个错误信息并没有指明是哪个函数缺少了必要的位置参数，因此需要仔细检查代码。根据代码的结构，可能是在使用`tf.reshape()`函数时缺少了必要的参数。

具体来说，下面这行代码中出现了这个错误：

weight.assign(tf.reshape())

`tf.reshape()`函数需要传入两个必要的参数：`tensor`和`shape`。因此，你需要指定这个函数的第一个参数`tensor`，也就是`weight`，以及希望将其reshape成的新形状`shape`。例如，如果你希望将`weight`变成一个形状为`(batch_size, input_shape)`的张量，可以这样写：

weight.assign(tf.reshape(weight, (batch_size, input_shape)))

请根据你的实际需求修改这行代码，确保`tf.reshape()`函数的两个参数都被正确地指定。

Traceback (most recent call last): File "c:\Users\裴沐阳\Desktop\裴沐阳毕设相关\毕设--图像分割\UNet\U-Net.py", line 347, in <module> history = fit(epoch, model, train_loader, val_loader, criterion, optimizer, sched) File "c:\Users\裴沐阳\Desktop\裴沐阳毕设相关\毕设--图像分割\UNet\U-Net.py", line 214, in fit for i, data in enumerate(tqdm(train_loader)): File "D:\python\python3.8\envs\pmyixq\lib\site-packages\tqdm\notebook.py", line 254, in __iter__ for obj in it: File "D:\python\python3.8\envs\pmyixq\lib\site-packages\tqdm\std.py", line 1178, in __iter__ for obj in iterable: File "D:\python\python3.8\envs\pmyixq\lib\site-packages\torch\utils\data\dataloader.py", line 681, in __next__ data = self._next_data() File "D:\python\python3.8\envs\pmyixq\lib\site-packages\torch\utils\data\dataloader.py", line 721, in _next_data data = self._dataset_fetcher.fetch(index) # may raise StopIteration File "D:\python\python3.8\envs\pmyixq\lib\site-packages\torch\utils\data\_utils\fetch.py", line 49, in fetch data = [self.dataset[idx] for idx in possibly_batched_index] File "D:\python\python3.8\envs\pmyixq\lib\site-packages\torch\utils\data\_utils\fetch.py", line 49, in <listcomp> data = [self.dataset[idx] for idx in possibly_batched_index] File "c:\Users\裴沐阳\Desktop\裴沐阳毕设相关\毕设--图像分割\UNet\U-Net.py", line 78, in __getitem__ aug = self.transform(image=img, mask=mask) File "D:\python\python3.8\envs\pmyixq\lib\site-packages\albumentations\core\composition.py", line 195, in __call__ self._check_args(**data) File "D:\python\python3.8\envs\pmyixq\lib\site-packages\albumentations\core\composition.py", line 275, in _check_args raise TypeError("{} must be numpy array type".format(data_name)) TypeError: mask must be numpy array type

根据您提供的错误信息，看起来出现了一个类型错误。错误消息指出了问题所在：遮罩(mask)必须是NumPy数组类型。这意味着在您的代码中，遮罩(mask)的数据类型不正确。

要解决这个问题，您可以尝试使用`np.array()`将遮罩数据转换为NumPy数组类型。例如，在加载数据时，您可以检查遮罩(mask)是否已经是NumPy数组类型，如果不是，则将其转换为NumPy数组类型。以下是一种可能的解决方案：

import numpy as np

# 在加载数据时，确保遮罩(mask)是NumPy数组类型
mask = np.array(mask)

# 继续进行后续操作

请确保在转换遮罩(mask)之前，您已经正确加载了图像和遮罩数据。希望这能帮助您解决问题！如果您有任何其他问题，请随时提问。