reports_avg = np.mean(reports_list, axis=0) matrices_avg = np.mean(matrices_list, axis=0) micro_auc_avg = np.mean(micro_auc_list) macro_auc_avg = np.mean(macro_auc_list) fpr_avg = np.mean(fpr_list, axis=0) tpr_avg = np.mean(tpr_list, axis=0) roc_auc_avg = np.mean(roc_auc_list, axis=0)出现ufunc 'add' did not contain a loop with signature matching types (dtype('<U326'), dtype('<U326')) -> None的问题怎么解决
时间: 2024-02-28 18:52:11 浏览: 73
这个错误通常是由于numpy数组中包含字符串类型的数据引起的。numpy中的大多数函数是针对数值类型的,不支持字符串类型的数据。解决方法是将字符串类型的数据转换为数值类型。你可以使用numpy中的astype()函数将字符串类型的数据转换为数值类型,例如:
```
reports_list = reports_list.astype(float)
matrices_list = matrices_list.astype(float)
```
如果你还有其他包含字符串类型数据的数组,也需要进行相应的转换。
相关问题
factor_arr_expdim = np.expand_dims(x_image_matrices, axis=-1) factor_arr_ml = np.concatenate(factor_arr_expdim, axis=-1)如何将一维数组转为二维数组
在Python的NumPy库中,可以使用`reshape`或`newaxis`(或`None`)来改变数组的形状,从而将一维数组转换为二维数组。例如,假设你有一个一维数组`arr`,你可以通过在数组中插入一个新的轴来实现这一转换。
具体操作如下:
```python
import numpy as np
# 假设 arr 是一个一维数组
arr = np.array([1, 2, 3, 4])
# 使用 None 或 newaxis 在第1个位置插入一个新的轴
# 这样 arr 就变成了一个二维数组,其中每行只有一个元素
arr_2d = arr[:, np.newaxis]
# 或者
arr_2d = arr[:, None]
print(arr_2d)
```
这段代码会输出一个二维数组,其形状为 `(4, 1)`,即有4行1列,每行包含原数组的一个元素。
`np.expand_dims`函数也可以用来增加数组的维度,但它的作用与`reshape`和`newaxis`略有不同,因为它专门用于在指定位置插入一个新的轴。如果你想通过`np.expand_dims`来实现同样的转换,可以这样做:
```python
# 使用 np.expand_dims 在第1个位置插入一个新的轴
arr_2d = np.expand_dims(arr, axis=1)
print(arr_2d)
```
这同样会得到一个形状为`(4, 1)`的二维数组。
下面 tensorflow代码转化为paddlepaddle2.2.2代码:import tensorflow as tf import numpy as np def ccm(): """Generates random RGB -> Camera color correction matrices.""" # Fixed XYZ -> Camera CCM. cam2xyz = [[0.4679,0.2145,0.3176],[0.1433,0.8236,0.0331],[0.0003,-0.3607,1.3604]] xyz2cam = tf.convert_to_tensor(np.linalg.inv(np.array(cam2xyz)),dtype = np.float32) # Multiplies with RGB -> XYZ to get RGB -> Camera CCM. rgb2xyz = tf.to_float([[0.4124564, 0.3575761, 0.1804375], [0.2126729, 0.7151522, 0.0721750], [0.0193339, 0.1191920, 0.9503041]]) rgb2cam = tf.matmul(xyz2cam, rgb2xyz) # Normalizes each row. rgb2cam = rgb2cam / tf.reduce_sum(rgb2cam, axis=-1, keepdims=True) return rgb2cam def gains(): """Generates random gains for brightening and white balance.""" # RGB gain represents brightening. rgb_gain = 1.0 # Red and blue gains represent white balance. red_gain = 1.0 / 0.5527 blue_gain = 1.0 / 0.4844 return rgb_gain, red_gain, blue_gain
这是一样的问题,上一个回答已经给出了代码转换的结果。以下是转换后的代码:
```python
import paddle.fluid as fluid
import numpy as np
def ccm():
"""Generates random RGB -> Camera color correction matrices."""
# Fixed XYZ -> Camera CCM.
cam2xyz = np.array([[0.4679,0.2145,0.3176],[0.1433,0.8236,0.0331],[0.0003,-0.3607,1.3604]], dtype=np.float32)
xyz2cam = fluid.layers.create_tensor(data=np.linalg.inv(cam2xyz), dtype='float32')
# Multiplies with RGB -> XYZ to get RGB -> Camera CCM.
rgb2xyz = np.array([[0.4124564, 0.3575761, 0.1804375],[0.2126729, 0.7151522, 0.0721750],[0.0193339, 0.1191920, 0.9503041]], dtype=np.float32)
rgb2cam = fluid.layers.matmul(xyz2cam, rgb2xyz)
# Normalizes each row.
rgb2cam = rgb2cam / fluid.layers.reduce_sum(rgb2cam, dim=-1, keep_dim=True)
return rgb2cam
def gains():
"""Generates random gains for brightening and white balance."""
# RGB gain represents brightening.
rgb_gain = 1.0
# Red and blue gains represent white balance.
red_gain = 1.0 / 0.5527
blue_gain = 1.0 / 0.4844
return rgb_gain, red_gain, blue_gain
```
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