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#! /usr/bin/python
# -*- coding: utf8 -*-
import matplotlib.pyplot as plt
import numpy as np
import os
def W(W=None, second=10, saveable=True, shape=[28,28], name='mnist', fig_idx=2396512):
"""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)
"""
if saveable is False:
plt.ion()
fig = plt.figure(fig_idx) # show all feature images
size = W.shape[0]
n_units = W.shape[1]
num_r = int(np.sqrt(n_units)) # 每行显示的个数 若25个hidden unit -> 每行显示5个
num_c = int(np.ceil(n_units/num_r))
count = int(1)
for row in range(1, num_r+1):
for col in range(1, num_c+1):
if count > n_units:
break
a = fig.add_subplot(num_r, num_c, count)
# ------------------------------------------------------------
# plt.imshow(np.reshape(W[:,count-1],(28,28)), cmap='gray')
# ------------------------------------------------------------
feature = W[:,count-1] / np.sqrt( (W[:,count-1]**2).sum())
# feature[feature<0.0001] = 0 # value threshold
# if count == 1 or count == 2:
# print(np.mean(feature))
# if np.std(feature) < 0.03: # condition threshold
# feature = np.zeros_like(feature)
# if np.mean(feature) < -0.015: # condition threshold
# feature = np.zeros_like(feature)
plt.imshow(np.reshape(feature ,(shape[0],shape[1])),
cmap='gray', interpolation="nearest")#, vmin=np.min(feature), vmax=np.max(feature))
# plt.title(name)
# ------------------------------------------------------------
# plt.imshow(np.reshape(W[:,count-1] ,(np.sqrt(size),np.sqrt(size))), cmap='gray', interpolation="nearest")
plt.gca().xaxis.set_major_locator(plt.NullLocator()) # distable tick
plt.gca().yaxis.set_major_locator(plt.NullLocator())
count = count + 1
if saveable:
plt.savefig(name+'.pdf',format='pdf')
else:
plt.draw()
plt.pause(second)
def frame(I=None, second=5, saveable=True, name='frame', cmap=None, fig_idx=12836):
"""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)
"""
if saveable is False:
plt.ion()
fig = plt.figure(fig_idx) # show all feature images
if len(I.shape) and I.shape[-1]==1: # (10,10,1) --> (10,10)
I = I[:,:,0]
plt.imshow(I, cmap)
plt.title(name)
# plt.gca().xaxis.set_major_locator(plt.NullLocator()) # distable tick
# plt.gca().yaxis.set_major_locator(plt.NullLocator())
if saveable:
plt.savefig(name+'.pdf',format='pdf')
else:
plt.draw()
plt.pause(second)
def CNN2d(CNN=None, second=10, saveable=True, name='cnn', fig_idx=3119362):
"""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)
"""
# print(CNN.shape) # (5, 5, 3, 64)
# exit()
n_mask = CNN.shape[3]
n_row = CNN.shape[0]
n_col = CNN.shape[1]
n_color = CNN.shape[2]
row = int(np.sqrt(n_mask))
col = int(np.ceil(n_mask/row))
plt.ion() # active mode
fig = plt.figure(fig_idx)
count = 1
for ir in range(1, row+1):
for ic in range(1, col+1):
if count > n_mask:
break
a = fig.add_subplot(col, row, count)
# print(CNN[:,:,:,count-1].shape, n_row, n_col) # (5, 1, 32) 5 5
# exit()
# plt.imshow(
# np.reshape(CNN[count-1,:,:,:], (n_row, n_col)),
# cmap='gray', interpolation="nearest") # theano
if n_color == 1:
plt.imshow(
np.reshape(CNN[:,:,:,count-1], (n_row, n_col)),
cmap='gray', interpolation="nearest")
elif n_color == 3:
plt.imshow(
np.reshape(CNN[:,:,:,count-1], (n_row, n_col, n_color)),
cmap='gray', interpolation="nearest")
else:
raise Exception("Unknown n_color")
plt.gca().xaxis.set_major_locator(plt.NullLocator()) # distable tick
plt.gca().yaxis.set_major_locator(plt.NullLocator())
count = count + 1
if saveable:
plt.savefig(name+'.pdf',format='pdf')
else:
plt.draw()
plt.pause(second)
def images2d(images=None, second=10, saveable=True, name='images', dtype=None,
fig_idx=3119362):
"""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)
"""
# print(images.shape) # (50000, 32, 32, 3)
# exit()
if dtype:
images = np.asarray(images, dtype=dtype)
n_mask = images.shape[0]
n_row = images.shape[1]
n_col = images.shape[2]
n_color = images.shape[3]
row = int(np.sqrt(n_mask))
col = int(np.ceil(n_mask/row))
plt.ion() # active mode
fig = plt.figure(fig_idx)
count = 1
for ir in range(1, row+1):
for ic in range(1, col+1):
if count > n_mask:
break
a = fig.add_subplot(col, row, count)
# print(images[:,:,:,count-1].shape, n_row, n_col) # (5, 1, 32) 5 5
# plt.imshow(
# np.reshape(images[count-1,:,:,:], (n_row, n_col)),
# cmap='gray', interpolation="nearest") # theano
if n_color == 1:
plt.imshow(
np.reshape(images[count-1,:,:], (n_row, n_col)),
cmap='gray', interpolation="nearest")
# plt.title(name)
elif n_color == 3:
plt.imshow(images[count-1,:,:],
cmap='gray', interpolation="nearest")
# plt.title(name)
else:
raise Exception("Unknown n_color")
plt.gca().xaxis.set_major_locator(plt.NullLocator()) # distable tick
plt.gca().yaxis.set_major_locator(plt.NullLocator())
count = count + 1
if saveable:
plt.savefig(name+'.pdf',format='pdf')
else:
plt.draw()
plt.pause(second)
def tsne_embedding(embeddings, reverse_dictionary, plot_only=500,
second=5, saveable=False, name='tsne', fig_idx=9862):
"""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')
"""
def plot_with_labels(low_dim_embs, labels, figsize=(18, 18), second=5,
saveable=True, name='tsne', fig_idx=9862):
assert low_dim_embs.shape[0] >= len(labels), "More labels than embeddings"
if saveable is False:
plt.ion()
plt.figure(fig_idx)
plt.figure(figsize=figsize) #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')
if saveable:
plt.savefig(name+'.pdf',format='pdf')
else:
plt.draw()
plt.pause(second)
try:
from sklearn.manifold import TSNE
import matplotlib.pyplot as plt
from six.moves import xrange
tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000)
# plot_only = 500
low_dim_embs = tsne.fit_transform(embeddings[:plot_only,:])
labels = [reverse_dictionary[i] for i in xrange(plot_only)]
plot_with_labels(low_dim_embs, labels, second=second, saveable=saveable, \
name=name, fig_idx=fig_idx)
except ImportError:
print("Please install sklearn and matplotlib to visualize embeddings.")
#
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