今天在Anaconda運行Visualization of MLP weights on MNIST源碼時出現了如圖錯誤：

提示無法導入fetch_openml，查了一下是對應的sklearn軟件包版本過低，為0.17版。需要更新到0.20版。

1.打開Anaconda Prompt命令行

輸入?conda list 命令?查看Anaconda中軟件包的版本，如圖所示：

2.執行 conda update --all命令

更新后可查看sklearn此時版本已經更新為0.20.

3. Visualization of MLP weights on MNIST源碼為：

""" ===================================== Visualization of MLP weights on MNIST =====================================Sometimes looking at the learned coefficients of a neural network can provide insight into the learning behavior. For example if weights look unstructured, maybe some were not used at all, or if very large coefficients exist, maybe regularization was too low or the learning rate too high.This example shows how to plot some of the first layer weights in a MLPClassifier trained on the MNIST dataset.The input data consists of 28x28 pixel handwritten digits, leading to 784 features in the dataset. Therefore the first layer weight matrix have the shape (784, hidden_layer_sizes[0]). We can therefore visualize a single column of the weight matrix as a 28x28 pixel image.To make the example run faster, we use very few hidden units, and train only for a very short time. Training longer would result in weights with a much smoother spatial appearance. """ import matplotlib.pyplot as plt from sklearn.datasets import fetch_openml from sklearn.neural_network import MLPClassifierprint(__doc__)# Load data from https://www.openml.org/d/554 X, y = fetch_openml('mnist_784', version=1, return_X_y=True) X = X / 255.# rescale the data, use the traditional train/test split X_train, X_test = X[:60000], X[60000:] y_train, y_test = y[:60000], y[60000:]# mlp = MLPClassifier(hidden_layer_sizes=(100, 100), max_iter=400, alpha=1e-4, # solver='sgd', verbose=10, tol=1e-4, random_state=1) mlp = MLPClassifier(hidden_layer_sizes=(50,), max_iter=10, alpha=1e-4,solver='sgd', verbose=10, tol=1e-4, random_state=1,learning_rate_init=.1)mlp.fit(X_train, y_train) print("Training set score: %f" % mlp.score(X_train, y_train)) print("Test set score: %f" % mlp.score(X_test, y_test))fig, axes = plt.subplots(4, 4) # use global min / max to ensure all weights are shown on the same scale vmin, vmax = mlp.coefs_[0].min(), mlp.coefs_[0].max() for coef, ax in zip(mlp.coefs_[0].T, axes.ravel()):ax.matshow(coef.reshape(28, 28), cmap=plt.cm.gray, vmin=.5 * vmin,vmax=.5 * vmax)ax.set_xticks(())ax.set_yticks(())plt.show()

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