API - 可视化¶
TensorFlow 提供了可视化模型和激活输出等的工具 TensorBoard。
在这里，我们进一步提供一些可视化模型参数和数据的函数。
read_image(image[, path]) |
Read one image. |
read_images(img_list[, path, n_threads, ...]) |
Returns all images in list by given path and name of each image file. |
save_image(image[, image_path]) |
Save one image. |
save_images(images, size[, image_path]) |
Save mutiple images into one single image. |
draw_boxes_and_labels_to_image(image[, ...]) |
Draw bboxes and class labels on image. |
W([W, second, saveable, shape, name, fig_idx]) |
Visualize every columns of the weight matrix to a group of Greyscale img. |
CNN2d([CNN, second, saveable, name, fig_idx]) |
Display a group of RGB or Greyscale CNN masks. |
frame([I, second, saveable, name, cmap, fig_idx]) |
Display a frame(image). |
images2d([images, second, saveable, name, ...]) |
Display a group of RGB or Greyscale images. |
tsne_embedding(embeddings, reverse_dictionary) |
Visualize the embeddings by using t-SNE. |
读取与保存图片¶
读取单个图片¶
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tensorlayer.visualize.read_image(image, path='')[源代码]¶ -
Read one image.
Parameters: images : string, file name.
path : string, path.
读取多个图片¶
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tensorlayer.visualize.read_images(img_list, path='', n_threads=10, printable=True)[源代码]¶ -
Returns all images in list by given path and name of each image file.
Parameters: img_list : list of string, the image file names.
path : string, image folder path.
n_threads : int, number of thread to read image.
printable : bool, print infomation when reading images, default is True.
保存单个图片¶
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tensorlayer.visualize.save_image(image, image_path='')[源代码]¶ -
Save one image.
Parameters: images : numpy array [w, h, c]
image_path : string.
保存多个图片¶
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tensorlayer.visualize.save_images(images, size, image_path='')[源代码]¶ -
Save mutiple images into one single image.
Parameters: images : numpy array [batch, w, h, c]
size : list of two int, row and column number.
number of images should be equal or less than size[0] * size[1]
image_path : string.
Examples
>>> images = np.random.rand(64, 100, 100, 3) >>> tl.visualize.save_images(images, [8, 8], 'temp.png')
保存目标检测图片¶
tensorlayer.visualize.draw_boxes_and_labels_to_image(image, classes=[], coords=[], scores=[], classes_list=[], is_center=True, is_rescale=True, save_name=None)[源代码]¶-
Draw bboxes and class labels on image. Return or save the image with bboxes, example in the docs of
tl.prepro.Parameters: image : RGB image in numpy.array, [height, width, channel].
classes : a list of class ID (int).
coords : a list of list for coordinates.
- Should be [x, y, x2, y2] (up-left and botton-right format)
- If [x_center, y_center, w, h] (set is_center to True).
scores : a list of score (float). (Optional)
classes_list : list of string, for converting ID to string on image.
is_center : boolean, defalt is True.
If coords is [x_center, y_center, w, h], set it to True for converting [x_center, y_center, w, h] to [x, y, x2, y2] (up-left and botton-right).
If coords is [x1, x2, y1, y2], set it to False.is_rescale : boolean, defalt is True.
If True, the input coordinates are the portion of width and high, this API will scale the coordinates to pixel unit internally.
If False, feed the coordinates with pixel unit format.save_name : None or string
The name of image file (i.e. image.png), if None, not to save image.
References
- OpenCV rectangle and putText.
- scikit-image.
可视化模型参数¶
可视化Weight Matrix¶
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tensorlayer.visualize.W(W=None, second=10, saveable=True, shape=[28, 28], name='mnist', fig_idx=2396512)[源代码]¶ -
Visualize every columns of the weight matrix to a group of Greyscale img.
Parameters: W : numpy.array
The weight matrix
second : int
The display second(s) for the image(s), if saveable is False.
saveable : boolean
Save or plot the figure.
shape : a list with 2 int
The shape of feature image, MNIST is [28, 80].
name : a string
A name to save the image, if saveable is True.
fig_idx : int
matplotlib figure index.
Examples
>>> tl.visualize.W(network.all_params[0].eval(), second=10, saveable=True, name='weight_of_1st_layer', fig_idx=2012)
可视化CNN 2d filter¶
tensorlayer.visualize.CNN2d(CNN=None, second=10, saveable=True, name='cnn', fig_idx=3119362)[源代码]¶-
Display a group of RGB or Greyscale CNN masks.
Parameters: CNN : numpy.array
The image. e.g: 64 5x5 RGB images can be (5, 5, 3, 64).
second : int
The display second(s) for the image(s), if saveable is False.
saveable : boolean
Save or plot the figure.
name : a string
A name to save the image, if saveable is True.
fig_idx : int
matplotlib figure index.
Examples
>>> tl.visualize.CNN2d(network.all_params[0].eval(), second=10, saveable=True, name='cnn1_mnist', fig_idx=2012)
可视化图像¶
matplotlib显示单图¶
tensorlayer.visualize.frame(I=None, second=5, saveable=True, name='frame', cmap=None, fig_idx=12836)[源代码]¶-
Display a frame(image). Make sure OpenAI Gym render() is disable before using it.
Parameters: I : numpy.array
The image
second : int
The display second(s) for the image(s), if saveable is False.
saveable : boolean
Save or plot the figure.
name : a string
A name to save the image, if saveable is True.
cmap : None or string
'gray' for greyscale, None for default, etc.
fig_idx : int
matplotlib figure index.
Examples
>>> env = gym.make("Pong-v0") >>> observation = env.reset() >>> tl.visualize.frame(observation)
matplotlib显示多图¶
tensorlayer.visualize.images2d(images=None, second=10, saveable=True, name='images', dtype=None, fig_idx=3119362)[源代码]¶-
Display a group of RGB or Greyscale images.
Parameters: images : numpy.array
The images.
second : int
The display second(s) for the image(s), if saveable is False.
saveable : boolean
Save or plot the figure.
name : a string
A name to save the image, if saveable is True.
dtype : None or numpy data type
The data type for displaying the images.
fig_idx : int
matplotlib figure index.
Examples
>>> X_train, y_train, X_test, y_test = tl.files.load_cifar10_dataset(shape=(-1, 32, 32, 3), plotable=False) >>> tl.visualize.images2d(X_train[0:100,:,:,:], second=10, saveable=False, name='cifar10', dtype=np.uint8, fig_idx=20212)
可视化词嵌入矩阵¶
tensorlayer.visualize.tsne_embedding(embeddings, reverse_dictionary, plot_only=500, second=5, saveable=False, name='tsne', fig_idx=9862)[源代码]¶-
Visualize the embeddings by using t-SNE.
Parameters: embeddings : a matrix
The images.
reverse_dictionary : a dictionary
id_to_word, mapping id to unique word.
plot_only : int
The number of examples to plot, choice the most common words.
second : int
The display second(s) for the image(s), if saveable is False.
saveable : boolean
Save or plot the figure.
name : a string
A name to save the image, if saveable is True.
fig_idx : int
matplotlib figure index.
Examples
>>> see 'tutorial_word2vec_basic.py' >>> final_embeddings = normalized_embeddings.eval() >>> tl.visualize.tsne_embedding(final_embeddings, labels, reverse_dictionary, ... plot_only=500, second=5, saveable=False, name='tsne')
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