本文整理汇总了Python中sklearn.manifold.TSNE.evaluate方法的典型用法代码示例。如果您正苦于以下问题：Python TSNE.evaluate方法的具体用法？Python TSNE.evaluate怎么用？Python TSNE.evaluate使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类sklearn.manifold.TSNE的用法示例。
在下文中一共展示了TSNE.evaluate方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: print
# 需要导入模块: from sklearn.manifold import TSNE [as 别名]
# 或者: from sklearn.manifold.TSNE import evaluate [as 别名]
print("prefilter_train: ", prefilter_train.shape)
print("prefilter_test: ", prefilter_test.shape)
print("Performing PCA")
X_pca = pca(prefilter_train)
plotScatter(X_pca, y_train, title="6_PCA reduction (2d) of auto-encoded data (%dd)" % prefilter_train.shape[1])
print("Performing TSNE")
model = TSNE(n_components=2, random_state=0, init="pca")
toPlot = model.fit_transform(prefilter_train[:1000])
plotTSNE(toPlot, y_train[:1000], nb_classes, "7_t-SNE embedding of auto-encoded data ")
print("Classifying and comparing")
# Classify results from Autoencoder
print("Building classical fully connected layer for classification")
model = Sequential()
model.add(Dense(prefilter_train.shape[1], nb_classes, activation=activation))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam')
model.fit(prefilter_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch, show_accuracy=False, verbose=0, validation_data=(prefilter_test, Y_test))
score = model.evaluate(prefilter_test, Y_test, verbose=0, show_accuracy=True)
print('\nscore:', score)
print('Loss change:', 100*(score[0] - classical_score[0])/classical_score[0], '%')
print('Accuracy change:', 100*(score[1] - classical_score[1])/classical_score[1], '%')