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将训练得到的embedding可视化
# Step 6:Visualize the embeddings.
# pylint:disable=missing-docstring
# Function todraw visualization of distance between embeddings.
defplot_with_labels(low_dim_embs, labels, filename):
assert low_dim_embs.shape[0] >=len(labels), 'More labels than embeddings'
plt.figure(figsize=(18, 18)) # in inches
for i, label in enumerate(labels):
x, y = low_dim_embs[i, :]
plt.scatter(x, y)
plt.annotate(label,
xy=(x, y),
xytext=(5, 2),
textcoords='offset points',
ha='right',
va='bottom')
plt.savefig(filename)
try:
# pylint: disable=g-import-not-at-top
from sklearn.manifold import TSNE
import matplotlib.pyplot as plt
#指定默认字体
plt.rcParams['font.sans-serif'] =['SimHei']
plt.rcParams['font.family']='sans-serif'
#解决负号'-'显示为方块的问题
plt.rcParams['axes.unicode_minus'] = False
tsne = TSNE(perplexity=30, n_components=2,init='pca', n_iter=5000, method='exact')
plot_only = 500
low_dim_embs =tsne.fit_transform(final_embeddings[:plot_only, :])
labels = [reverse_dictionary[i] for i inxrange(plot_only)]
plot_with_labels(low_dim_embs, labels,os.path.join(gettempdir(), 'tsne.png'))
exceptImportError as ex:
print('Please install sklearn, matplotlib,and scipy to show embeddings.')
print(ex)
Linux上编译tensorflow的jar包和so文件
以下编译命令在tensorflow根目录执行，文件路径是相对于tensorflow根目录而言
编译so：
bazel build -c opt//tensorflow/contrib/android:libtensorflow_inference.so \
--crosstool_top=//external:android/crosstool \
--host_crosstool_top=@bazel_tools//tools/cpp:toolchain \
--cpu=armeabi-v7a
编译后文件位置：
bazel-bin/tensorflow/contrib/android/libtensorflow_inference.so
编译jar文件：
bazel build//tensorflow/contrib/android:android_tensorflow_inference_java
编译后文件位置：
bazel-bin/tensorflow/contrib/android/libandroid_tensorflow_inference_java.jar
作者：追逐丶
链接：https://www.jianshu.com/p/d3e2eced12b2
來源：简书
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