Merge branch 'master' into make

Author: lgarithm

example/tutorial_bipedalwalker_a3c_continuous_action.py

@@ -32,8 +32,6 @@
 """
 
 import multiprocessing
-import os
-import shutil
 import threading
 
 import gym
@@ -257,8 +255,7 @@ def work(self):
     # start TF threading
     worker_threads = []
     for worker in workers:
-        job = lambda: worker.work()
-        t = threading.Thread(target=job)
+        t = threading.Thread(target=worker.work)
         t.start()
         worker_threads.append(t)
     COORD.join(worker_threads)

example/tutorial_cifar10_tfrecord.py

@@ -42,7 +42,7 @@
 import io
 import os
 import time
-import numpy as np
+# import numpy as np
 import tensorflow as tf
 import tensorlayer as tl
 from PIL import Image

example/tutorial_imdb_fasttext.py

@@ -1,10 +1,9 @@
 #!/usr/bin/env python
+"""
+This demo implements FastText[1] for sentence classification.
 
-__doc__ = """
-
-This demo implements FastText[1] for sentence classification. FastText is a
-simple model for text classification with performance often close to
-state-of-the-art, and is useful as a solid baseline.
+FastText is a simple model for text classification with performance often close
+to state-of-the-art, and is useful as a solid baseline.
 
 There are some important differences between this implementation and what
 is described in the paper. Instead of Hogwild! SGD[2], we use Adam optimizer

example/tutorial_inceptionV3_tfslim.py

@@ -46,7 +46,8 @@ def load_image(path):
     # load image
     img = skimage.io.imread(path)
     img = img / 255.0
-    assert (0 <= img).all() and (img <= 1.0).all()
+    if ((0 <= img).all() and (img <= 1.0).all()) is False:
+        raise Exception("image value should be [0, 1]")
     # print "Original Image Shape: ", img.shape
     # we crop image from center
     short_edge = min(img.shape[:2])

example/tutorial_matrix.py

@@ -1,5 +1,4 @@
 import tensorflow as tf
-import tensorlayer as tl
 
 sess = tf.InteractiveSession()
 

example/tutorial_ptb_lstm.py

@@ -104,7 +104,6 @@
 import numpy as np
 import tensorflow as tf
 import tensorlayer as tl
-from tensorlayer.layers import set_keep
 
 flags = tf.flags
 flags.DEFINE_string("model", "small", "A type of model. Possible options are: small, medium, large.")

example/tutorial_vgg16.py

@@ -166,7 +166,7 @@ def conv_layers(net_in):
 
 
 def conv_layers_simple_api(net_in):
-    with tf.name_scope('preprocess') as scope:
+    with tf.name_scope('preprocess'):
         """
         Notice that we include a preprocessing layer that takes the RGB image
         with pixels values in the range of 0-255 and subtracts the mean image

tensorlayer/db.py

@@ -85,13 +85,16 @@ def __init__(self, ip='localhost', port=27017, db_name='db_name', user_name=None
         self.db_name = db_name
         self.user_name = user_name
 
+    @classmethod
     def __autofill(self, args):
         return args.update({'studyID': self.studyID})
 
-    def __serialization(self, ps):
+    @staticmethod
+    def __serialization(ps):
         return pickle.dumps(ps, protocol=2)
 
-    def __deserialization(self, ps):
+    @staticmethod
+    def __deserialization(ps):
         return pickle.loads(ps)
 
     def save_params(self, params=None, args=None):  #, file_name='parameters'):
@@ -298,20 +301,25 @@ def test_log(self, args=None):
         return _result
 
     @AutoFill
-    def del_test_log(self, args={}):
+    def del_test_log(self, args=None):
         """ Delete test log.
 
         Parameters
         -----------
         args : dictionary, find items to delete, leave it empty to delete all log.
         """
+        if args is None:
+            args = {}
 
         self.db.TestLog.delete_many(args)
         print("[TensorDB] Delete TestLog SUCCESS")
 
-    ## =========================== Network Architecture ================== ##
+    # =========================== Network Architecture ================== ##
     @AutoFill
-    def save_model_architecture(self, s, args={}):
+    def save_model_architecture(self, s, args=None):
+        if args is None:
+            args = {}
+
         self.__autofill(args)
         fid = self.archfs.put(s, filename="modelarchitecture")
         args.update({"fid": fid})

tensorlayer/files.py

@@ -594,7 +594,7 @@ def load_flickr25k_dataset(tag='sky', path="data", n_threads=50, printable=False
     else:
         logging.info("[Flickr25k] reading images with tag: {}".format(tag))
     images_list = []
-    for idx in range(0, len(path_tags)):
+    for idx, _v in enumerate(path_tags):
         tags = read_file(folder_tags + '/' + path_tags[idx]).split('\n')
         # logging.info(idx+1, tags)
         if tag is None or tag in tags:
@@ -1459,6 +1459,13 @@ def load_ckpt(sess=None, mode_name='model.ckpt', save_dir='checkpoint', var_list
 def save_any_to_npy(save_dict={}, name='file.npy'):
     """Save variables to `.npy` file.
 
+    Parameters
+    ------------
+    save_dict : directory
+        The variables to be saved.
+    name : str
+        File name.
+
     Examples
     ---------
     >>> tl.files.save_any_to_npy(save_dict={'data': ['a','b']}, name='test.npy')
@@ -1467,12 +1474,21 @@ def save_any_to_npy(save_dict={}, name='file.npy'):
     ... {'data': ['a','b']}
 
     """
+    if save_dict is None:
+        save_dict = {}
     np.save(name, save_dict)
 
 
 def load_npy_to_any(path='', name='file.npy'):
     """Load `.npy` file.
 
+    Parameters
+    ------------
+    path : str
+        Path to the file (optional).
+    name : str
+        File name.
+
     Examples
     ---------
     - see tl.files.save_any_to_npy()

tensorlayer/layers/convolution.py

@@ -302,7 +302,7 @@ def __init__(
         logging.info("DeConv2dLayer %s: shape:%s out_shape:%s strides:%s pad:%s act:%s" % (self.name, str(shape), str(output_shape), str(strides), padding,
                                                                                            act.__name__))
         # logging.info("  DeConv2dLayer: Untested")
-        with tf.variable_scope(name) as vs:
+        with tf.variable_scope(name):
             W = tf.get_variable(name='W_deconv2d', shape=shape, initializer=W_init, dtype=D_TYPE, **W_init_args)
             if b_init:
                 b = tf.get_variable(name='b_deconv2d', shape=(shape[-2]), initializer=b_init, dtype=D_TYPE, **b_init_args)