Numpy 形状不符合我的阵列的预期 您好,请考虑以下代码: # load the mnist training data CSV file into a list training_data_file = open("Training_Set/mnist_train_100.csv", 'r') training_data_list = training_data_file.readlines() training_data_file.close() caches_x = [] caches_y = [] for record in training_data_list: all_values = record.split(',') x_inputs = (np.asfarray(all_values[1:]) / 255.0 * 0.99) + 0.01 y_inputs = (np.asfarray(all_values[0])) cache_x = (x_inputs) caches_x.append(cache_x) cache_y = (y_inputs) caches_y.append(cache_y) train_x = np.array(caches_x).T print("shape of train_x = " + str(train_x.shape)) print("train_x =" + str(train_x)) train_y = np.array(caches_y).T print("shape of train_y = " + str(train_y.shape)) print("train_y =" + str(train_y))

Numpy 形状不符合我的阵列的预期 您好,请考虑以下代码: # load the mnist training data CSV file into a list training_data_file = open("Training_Set/mnist_train_100.csv", 'r') training_data_list = training_data_file.readlines() training_data_file.close() caches_x = [] caches_y = [] for record in training_data_list: all_values = record.split(',') x_inputs = (np.asfarray(all_values[1:]) / 255.0 * 0.99) + 0.01 y_inputs = (np.asfarray(all_values[0])) cache_x = (x_inputs) caches_x.append(cache_x) cache_y = (y_inputs) caches_y.append(cache_y) train_x = np.array(caches_x).T print("shape of train_x = " + str(train_x.shape)) print("train_x =" + str(train_x)) train_y = np.array(caches_y).T print("shape of train_y = " + str(train_y.shape)) print("train_y =" + str(train_y)),numpy,ipython,jupyter-notebook,mnist,Numpy,Ipython,Jupyter Notebook,Mnist,当我打印train_x的形状时(784103),这就是我所期望的 当我打印序列y的形状时,我有(103,) 我做错了什么?同样对于train_y,我希望(或希望)具有形状(1103) 有人能帮我吗?tnx.现金是一个数字列表np.数组([1,2,3,4])生成1d数组。如果你不明白,那么你需要做一些基本的numpy阅读。在这篇文章中,你还将学习如何重塑数组和添加维度。尝试重塑y_输入:y_输入。重塑(1,-1)这就可以了。注意:如果您有x.shape=784103,通常可以说您有784个不同的输

当我打印train_x的形状时(784103),这就是我所期望的

当我打印序列y的形状时,我有(103,)

我做错了什么?同样对于train_y,我希望(或希望)具有形状(1103)
有人能帮我吗?tnx.

现金是一个数字列表<代码>np.数组([1,2,3,4])
生成1d数组。如果你不明白,那么你需要做一些基本的numpy阅读。在这篇文章中,你还将学习如何重塑数组和添加维度。尝试重塑y_输入:y_输入。重塑(1,-1)这就可以了。注意:如果您有x.shape=784103,通常可以说您有784个不同的输入来训练w.r.t 103特性。所以你期望y是(784,1)每个输入的一个输出。在mnist情况下的分类中,y.shape是[输入的数量,类的数量]