# Example 1.1: Add scalars @cuda.jit def add_scalars(a, b, c): c[0] = a + b dev_c = cuda.device_array((1,), np.float32) add_scalars[1, 1](2.0, 7.0, dev_c) c = dev_c.copy_to_host() print(f"2.0 + 7.0 = {c[0]}") #
# Example 1.1: Add scalars @cuda.jit def add_scalars(a, b, c): c[0] = a + b dev_c = cuda.device_array((1,), np.float32) add_scalars[1, 1](2.0, 7.0, dev_c) c = dev_c.copy_to_host() print(f"2.0 + 7.0 = {c[0]}") # 2.0 + 7.0 = 9.0 因为GPU只能处理简单的工...
writer.add_scalars(main_tag="acc", tag_scalar_dict={'train':total_train_accuracy / train_data_size, 'test':total_accuracy / test_data_size}, global_step=i) writer.add_scalars(main_tag="loss", tag_scalar_dict={'train':total_train_loss / (326 / 10), 'test':total_test_loss/(175...
scalars(): print(sensordata_obj.pm25,sensordata_obj.timestamp) # join stmt = select(SensorData.id,SensorData.pm25, Sensor.location).join(SensorData.sensor).order_by(SensorData.pm25) for row in session.execute(stmt): print(row) # 依次连接多个数据库。从而验证代码的一致性 for dbname in ...
Similarly, you can combine linear expressions, variables, and scalars with the operators ==, <=, or >= to get instances of pulp.LpConstraint that represent the linear constraints of your model.Note: It’s also possible to build constraints with the rich comparison methods .__eq__(), ._...
py:181: RuntimeWarning: divide by zero encountered in double_scalars vif = 1. / (1. - r_squared_i) 独热编码后又导致了无穷大的多重共线性,这里需要进行处理,直接删除独热编码产生的一个类别(建议选择数量最多的那个),这样删除的好处是,当其他类型都为0的时候,实际上表示了这个被删除的特征。 # ...
import numpy as np #Let us take scalars first a = np.dot(8, 4) print("The dot Product of above given scalar values : ") print(a) # Now we will take 1-D arrays vect_a = 4 + 3j vect_b = 8 + 5j dot_product = np.dot(vect_a, vect_b) print("The Dot Product of two 1...
我们可以在 TensorBoard 的 SCALARS 选项卡下找到它们,如下面的截图所示。我们可以看到损失如何随迭代而减少,如下图所示: 下面的截图显示准确性随迭代次数增加的情况: 引入急切执行 TensorFlow 中的急切执行更符合 Python 风格,允许快速原型设计。与图模式不同,在图模式中,我们每次执行操作都需要构建一个图,而急切执行...
point_arrays['scalars'] = np.random.rand(globe.n_points) globe.set_active_scalars('scalars') plotter = pvqt.BackgroundPlotter() plotter.add_mesh(globe, lighting=False, show_edges=True, texture=True, scalars='scalars') plotter.view_isometric() # shrink globe in the background def shrink(...
One common application of dividing arrays by scalars is data normalization. Here’s a practical example using US housing prices: import numpy as np # Sample housing prices (in thousands of dollars) from different US cities housing_prices = np.array([ ...