API - Visualization¶
TensorFlow provides TensorBoard to visualize the model, activations etc. Here we provide more functions for data visualization.
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. |
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. |
Save and read images¶
Read one image¶
Read multiple images¶
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tensorlayer.visualize.
read_images
(img_list, path='', n_threads=10, printable=True)[source]¶ 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.
Save one image¶
Save multiple images¶
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tensorlayer.visualize.
save_images
(images, size, image_path='')[source]¶ 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')
Visualize model parameters¶
Visualize weight matrix¶
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tensorlayer.visualize.
W
(W=None, second=10, saveable=True, shape=[28, 28], name='mnist', fig_idx=2396512)[source]¶ 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)
Visualize CNN 2d filter¶
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tensorlayer.visualize.
CNN2d
(CNN=None, second=10, saveable=True, name='cnn', fig_idx=3119362)[source]¶ 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)
Visualize images¶
Image by matplotlib¶
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tensorlayer.visualize.
frame
(I=None, second=5, saveable=True, name='frame', cmap=None, fig_idx=12836)[source]¶ 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)
Images by matplotlib¶
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tensorlayer.visualize.
images2d
(images=None, second=10, saveable=True, name='images', dtype=None, fig_idx=3119362)[source]¶ 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)
Visualize embeddings¶
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tensorlayer.visualize.
tsne_embedding
(embeddings, reverse_dictionary, plot_only=500, second=5, saveable=False, name='tsne', fig_idx=9862)[source]¶ 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')