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>>> def my_func(a):#定义了一个my_func()函数,接受一个array的参数 ... """Average first and last element of a 1-D array""" ... return (a[0] + a[-1]) * 0.5 #返回array的第一个元素和最后一个元素的平均值 >>> b = np.array([[1,2,3], [4,5,6], [7,8,9]]) >>> ...
(result3.shape())#维度为[3,3,2] #tensor([ [[1,12],[2,22],[3,33]], [[4,44],[5,55],[6,66]], [[7,77],[8,88],[9,99]] ]) # 若dim=3 result4 = torch.stack((A,B),dim=3) IndexError: Dimension out of range (expected to be in range of [-3,2], but got 3...
#二维填充import numpyasnp## ##(N, 8, 3) 变为齐次矩阵 # (N, 8, 4) 第0轴,第1轴,第2轴a = np.array([[[1,2,3],[3,4,5],[1,2,3],[3,4,5],[1,2,3],[3,4,5],[1,2,3],[3,4,5]], [[1,2,3],[3,4,5],[1,2,3],[3,4,5],[1,2,3],[3,4,5],[1,2...
importnumpyasnp a = np.array([ [0.1,0.2,0.3], [0.4,0.5,0.6] ])print(a.shape)print(a)print() b0 = np.expand_dims(a, axis=0)print(b0.shape)print(b0)print() b1 = np.expand_dims(a, axis=1)print(b1.shape)print(b1)print() ...
# print('The dimension(x.dim > 2) is too high to draw') # 梯度下降法只能求解def _gradient_descent(self, bs, lr, epoch): x = self.x # shuffle数据集没有必要 # np.random.shuffle(x) y = self.func(x) w = np.ones((self.dim + 1, 1), dtype=float) ...
read() image_np_expanded= np.expand_dims(image_np, axis=0) # Actual detection. output_dict= run_inference_for_single_image(image_np_expanded, detection_graph) # Visualization of the results of a detection. vis_util.visualize_boxes_and_labels_on_image_array( image_np, ...
def preprocess_image(img_path, model_image_size):image_type = imghdr.what(img_path)image = Image.open(img_path)resized_image = image.resize(tuple(reversed(model_image_size)), Image.BICUBIC)image_data = np.array(resized_image, dtype='float32')image_data /= 255.image_data = np.expand_...
[:, choose_samples] # choosing sample batch from classes chosen outputs 20X2X28X28X1 y_temp = np.arange(classes_per_set) # will return [0,1,2,3,...,19] support_set_x[i] = x_temp[:, :-1] support_set_y[i] = np.expand_dims(y_temp[:], axis=1) # expand dimension target...
# If a 1d array is added to a 2d array (or the other way), NumPy # chooses the array with smaller dimension and adds it to the one # with bigger dimension a = np.array([1, 2, 3]) b = np.array([(1, 2, 3), (4, 5, 6)]) print(np.add(a, b)) >>> [[2 4 6] ...