查数据的权威网站,平面设计培训班学费一般多少,合肥有做网站的吗,企业网站建设公司司今天在Anaconda运行Visualization of MLP weights on MNIST源码时出现了如图错误#xff1a; 提示无法导入fetch_openml#xff0c;查了一下是对应的sklearn软件包版本过低#xff0c;为0.17版。需要更新到0.20版。
1.打开Anaconda Prompt命令行
输入 conda list 命令 查看…今天在Anaconda运行Visualization of MLP weights on MNIST源码时出现了如图错误 提示无法导入fetch_openml查了一下是对应的sklearn软件包版本过低为0.17版。需要更新到0.20版。
1.打开Anaconda Prompt命令行
输入 conda list 命令 查看Anaconda中软件包的版本如图所示 2.执行 conda update --all命令 更新后可查看sklearn此时版本已经更新为0.20. 3. Visualization of MLP weights on MNIST源码为 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, version1, return_X_yTrue)
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_iter400, alpha1e-4,
# solversgd, verbose10, tol1e-4, random_state1)
mlp MLPClassifier(hidden_layer_sizes(50,), max_iter10, alpha1e-4,solversgd, verbose10, tol1e-4, random_state1,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), cmapplt.cm.gray, vmin.5 * vmin,vmax.5 * vmax)ax.set_xticks(())ax.set_yticks(())plt.show()
