move pyplot into functions

tensorlayer/visualize.py

@@ -9,7 +9,6 @@
 
 # matplotlib.use('Agg')
 
-import matplotlib.pyplot as plt
 import numpy as np
 import os
 from . import prepro
@@ -114,6 +113,7 @@ def W(W=None, second=10, saveable=True, shape=[28,28], name='mnist', fig_idx=239
     --------
     >>> tl.visualize.W(network.all_params[0].eval(), second=10, saveable=True, name='weight_of_1st_layer', fig_idx=2012)
     """
+    import matplotlib.pyplot as plt
     if saveable is False:
         plt.ion()
     fig = plt.figure(fig_idx)      # show all feature images
@@ -177,6 +177,7 @@ def frame(I=None, second=5, saveable=True, name='frame', cmap=None, fig_idx=1283
     >>> observation = env.reset()
     >>> tl.visualize.frame(observation)
     """
+    import matplotlib.pyplot as plt
     if saveable is False:
         plt.ion()
     fig = plt.figure(fig_idx)      # show all feature images
@@ -215,6 +216,7 @@ def CNN2d(CNN=None, second=10, saveable=True, name='cnn', fig_idx=3119362):
     --------
     >>> tl.visualize.CNN2d(network.all_params[0].eval(), second=10, saveable=True, name='cnn1_mnist', fig_idx=2012)
     """
+    import matplotlib.pyplot as plt
     # print(CNN.shape)    # (5, 5, 3, 64)
     # exit()
     n_mask = CNN.shape[3]
@@ -280,6 +282,7 @@ def images2d(images=None, second=10, saveable=True, name='images', dtype=None,
     >>> 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)
     """
+    import matplotlib.pyplot as plt
     # print(images.shape)    # (50000, 32, 32, 3)
     # exit()
     if dtype:
@@ -350,6 +353,7 @@ def tsne_embedding(embeddings, reverse_dictionary, plot_only=500,
     >>> tl.visualize.tsne_embedding(final_embeddings, labels, reverse_dictionary,
     ...                   plot_only=500, second=5, saveable=False, name='tsne')
     """
+    import matplotlib.pyplot as plt
     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"