Fix issues suggested by codacy. (#344)

* remove two dangerous default values

* fix mnist tutorial based on codacy

* address hao's comments.

* remove unused y_op

* hao conv.py

* hao prepro.py

* hao files.py

* remove str statement

* hao example mnist

* yapf

* hao cifar10

* hao inceptionv3

* hao ptb tfrecord image processing

* hao tutorials

* str comment

* str docs

* Update README.md

* remove unused code

* minor fix

* small fix

Author: lgarithm

README.md

@@ -19,7 +19,7 @@
 [![Documentation Status](https://readthedocs.org/projects/tensorlayer/badge/?version=latest)](http://tensorlayer.readthedocs.io/en/latest/?badge=latest)
 [![Docker Pulls](https://img.shields.io/docker/pulls/tensorlayer/tensorlayer.svg?maxAge=604800)](https://hub.docker.com/r/tensorlayer/tensorlayer/)
 
-TensorLayer is a deep learning and reinforcement learning library on top of [TensorFlow](https://www.tensorflow.org). It provides rich neural layers and utility functions to help researchers and engineers build real-world AI applications. TensorLayer is awarded the 2017 Best Open Source Software Award by the prestigious [ACM Multimedia Society](http://www.acmmm.org/2017/mm-2017-awardees/).
+TensorLayer is a deep learning and reinforcement learning library on top of [TensorFlow](https://www.tensorflow.org). It provides rich neural layers and utility functions to help researchers and engineers build real-world AI applications. TensorLayer is awarded the 2017 Best Open Source Software by the prestigious [ACM Multimedia Society](http://www.acmmm.org/2017/mm-2017-awardees/).
 
 - Useful links: [Documentation](http://tensorlayer.readthedocs.io), [Examples](http://tensorlayer.readthedocs.io/en/latest/user/example.html), [中文文档](https://tensorlayercn.readthedocs.io), [中文书](http://www.broadview.com.cn/book/5059)
 
@@ -116,7 +116,7 @@ Examples can be found [in this folder](https://github.com/zsdonghao/tensorlayer/
 - Float 16 half-precision model, see [tutorial\_mnist_float16.py](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_mnist_float16.py)
 
 ## Notes
-TensorLayer provides two set of Convolutional layer APIs, see [(Professional)](http://tensorlayer.readthedocs.io/en/latest/modules/layers.html#convolutional-layer-pro) and [(Simplified)](http://tensorlayer.readthedocs.io/en/latest/modules/layers.html#convolutional-layer-simplified) on readthedocs website.
+TensorLayer provides two set of Convolutional layer APIs, see [(Advanced)](http://tensorlayer.readthedocs.io/en/latest/modules/layers.html#convolutional-layer-pro) and [(Basic)](http://tensorlayer.readthedocs.io/en/latest/modules/layers.html#convolutional-layer-simplified) on readthedocs website.
 <!-- 
 * If you get into trouble, you can start a discussion on [Slack](https://join.slack.com/t/tensorlayer/shared_invite/MjI1NjQ5NTUxOTY5LTE1MDI3MDYwNTItYzYwNmFiZmZkOA), [Gitter](https://gitter.im/tensorlayer/Lobby#?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge>),
 [Help Wanted Issues](https://waffle.io/zsdonghao/tensorlayer),
@@ -128,11 +128,11 @@ TensorLayer provides two set of Convolutional layer APIs, see [(Professional)](h
 
 ## Design Philosophy
 
-As deep learning practitioners, we have been looking for a TensorFlow wrapper library that can serve for various development phases. This library is easy for beginners by offering rich neural network implementations,
-examples and tutorials. Later, its APIs do not prohibit users from manipulating the low-level powerful features of TensorFlow, which is necessary in tackling real-world problems. In the end, the extra wrappers shall not compromise TensorFlow performance, and thus suit for production deployment. TensorLayer is a novel library that aims to satisfy these requirements that can occur in various phases. It has three key features:
+As TensorFlow users, we have been looking for a library that can serve for various development phases. This library is easy for beginners by providing rich neural network implementations,
+examples and tutorials. Later, its APIs shall naturally allow users to leverage the powerful features of TensorFlow, exhibiting best performance in addressing real-world problems. In the end, the extra abstraction shall not compromise TensorFlow performance, and thus suit for production deployment. TensorLayer is a novel library that aims to satisfy these requirements. It has three key features:
 
-- *Simplicity* : TensorLayer lifts the low-level dataflow abstraction of TensorFlow to **high-level** layers. It also provides users with massive examples and tutorials to help bootstrap.
-- *Flexibility* : TensorLayer APIs are transparent: it does not mask TensorFlow from users but leaving massive hooks that allow **low-level tuning**.
+- *Simplicity* : TensorLayer lifts the low-level dataflow abstraction of TensorFlow to **high-level** layers. It also provides users with massive examples and tutorials to minimize learning barrier.
+- *Flexibility* : TensorLayer APIs are transparent: it does not mask TensorFlow from users; but leaving massive hooks that support diverse **low-level tuning**.
 - *Zero-cost Abstraction* : TensorLayer is able to achieve the **full performance** of TensorFlow.
 
 ## Negligible Overhead
@@ -150,12 +150,12 @@ on a Titan X Pascal GPU. Here are the training speeds of respective tasks:
 
 Similar to TensorLayer, Keras and TFLearn are also popular TensorFlow wrapper libraries.
 These libraries are comfortable to start with. They provide high-level abstractions; 
-but in turn mask the underlying engine features from users. Though good for bootstrap, 
-it becomes hard to manipulate the low-level powerful features of TensorFlow. 
+but mask the underlying engine from users. It is thus hard to 
+customize model behaviors and touch the essential features of TensorFlow. 
 Without compromise in simplicity, TensorLayer APIs are generally more flexible and transparent.
 Users often find it easy to start with the examples and tutorials of TensorLayer, and 
 then dive into the TensorFlow low-level APIs only if need.
-TensorLayer does not intend to create library lock-in. Users can easily import models from Keras, TFSlim and TFLearn into
+TensorLayer does not create library lock-in. Users can easily import models from Keras, TFSlim and TFLearn into
 a TensorLayer environment.
 
 

example/tutorial_atari_pong.py

@@ -1,6 +1,6 @@
 #! /usr/bin/python
 # -*- coding: utf-8 -*-
-""" Monte-Carlo Policy Network π(a|s)  (REINFORCE)
+""" Monte-Carlo Policy Network π(a|s)  (REINFORCE).
 
 To understand Reinforcement Learning, we let computer to learn how to play
 Pong game from the original screen inputs. Before we start, we highly recommend

example/tutorial_bipedalwalker_a3c_continuous_action.py

@@ -28,6 +28,7 @@
 speed, angular velocity, horizontal speed, vertical speed, position of joints
 and joints angular speed, legs contact with ground, and 10 lidar rangefinder
 measurements. There's no coordinates in the state vector.
+
 """
 
 import multiprocessing
@@ -181,7 +182,7 @@ def work(self):
                 if self.name == 'Worker_0' and total_step % 30 == 0:
                     self.env.render()
                 a = self.AC.choose_action(s)
-                s_, r, done, info = self.env.step(a)
+                s_, r, done, _info = self.env.step(a)
 
                 # set robot falls reward to -2 instead of -100
                 if r == -100: r = -2

example/tutorial_cartpole_ac.py

@@ -1,5 +1,4 @@
-"""
-Actor-Critic using TD-error as the Advantage, Reinforcement Learning.
+"""Actor-Critic using TD-error as the Advantage, Reinforcement Learning.
 
 Actor Critic History
 ----------------------
@@ -30,6 +29,7 @@
 A reward of +1 is provided for every timestep that the pole remains upright.
 The episode ends when the pole is more than 15 degrees from vertical, or the
 cart moves more than 2.4 units from the center.
+
 """
 
 import time

example/tutorial_cifar10.py

@@ -1,15 +1,13 @@
 #! /usr/bin/python
 # -*- coding: utf-8 -*-
-""" tl.prepro for data augmentation """
 
-import io
-import os
+# tl.prepro for data augmentation
+
 import time
 
 import numpy as np
 import tensorflow as tf
 import tensorlayer as tl
-from PIL import Image
 from tensorlayer.layers import *
 
 sess = tf.InteractiveSession()

example/tutorial_cifar10_tfrecord.py

@@ -1,16 +1,6 @@
 #! /usr/bin/python
 # -*- coding: utf-8 -*-
-
-import io
-import os
-import time
-
-import numpy as np
-import tensorflow as tf
-import tensorlayer as tl
-from PIL import Image
-from tensorlayer.layers import *
-"""Reimplementation of the TensorFlow official CIFAR-10 CNN tutorials:
+"""Reimplementation of the TensorFlow official CIFAR-10 CNN tutorials.
 
 - 1. This model has 1,068,298 paramters, after few hours of training with GPU,
 accurcy of 86% was found.
@@ -46,7 +36,18 @@
 Reading images from disk and distorting them can use a non-trivial amount
 of processing time. To prevent these operations from slowing down training,
 we run them inside 16 separate threads which continuously fill a TensorFlow queue.
+
 """
+
+import io
+import os
+import time
+import numpy as np
+import tensorflow as tf
+import tensorlayer as tl
+from PIL import Image
+from tensorlayer.layers import *
+
 model_file_name = "model_cifar10_tfrecord.ckpt"
 resume = False  # load model, resume from previous checkpoint?
 
@@ -71,7 +72,7 @@ def data_to_tfrecord(images, labels, filename):
         print("%s exists" % filename)
         return
     print("Converting data into %s ..." % filename)
-    cwd = os.getcwd()
+    # cwd = os.getcwd()
     writer = tf.python_io.TFRecordWriter(filename)
     for index, img in enumerate(images):
         img_raw = img.tobytes()

example/tutorial_frozenlake_dqn.py

@@ -1,13 +1,4 @@
-import random
-import time
-
-import gym
-import matplotlib.pyplot as plt
-import numpy as np
-import tensorflow as tf
-import tensorlayer as tl
-from tensorlayer.layers import *
-""" Q-Network Q(a, s) - TD Learning, Off-Policy, e-Greedy Exploration (GLIE)
+"""Q-Network Q(a, s) - TD Learning, Off-Policy, e-Greedy Exploration (GLIE).
 
 Q(S, A) <- Q(S, A) + alpha * (R + lambda * Q(newS, newA) - Q(S, A))
 delta_w = R + lambda * Q(newS, newA)
@@ -18,20 +9,31 @@
 CN: https://zhuanlan.zhihu.com/p/25710327
 
 Note: Policy Network has been proved to be better than Q-Learning, see tutorial_atari_pong.py
+
+# The FrozenLake v0 environment
+https://gym.openai.com/envs/FrozenLake-v0
+The agent controls the movement of a character in a grid world. Some tiles of
+the grid are walkable, and others lead to the agent falling into the water.
+Additionally, the movement direction of the agent is uncertain and only partially
+depends on the chosen direction. The agent is rewarded for finding a walkable
+path to a goal tile.
+SFFF       (S: starting point, safe)
+FHFH       (F: frozen surface, safe)
+FFFH       (H: hole, fall to your doom)
+HFFG       (G: goal, where the frisbee is located)
+The episode ends when you reach the goal or fall in a hole. You receive a reward
+of 1 if you reach the goal, and zero otherwise.
+
 """
-## The FrozenLake v0 environment
-# https://gym.openai.com/envs/FrozenLake-v0
-# The agent controls the movement of a character in a grid world. Some tiles of
-# the grid are walkable, and others lead to the agent falling into the water.
-# Additionally, the movement direction of the agent is uncertain and only partially
-# depends on the chosen direction. The agent is rewarded for finding a walkable
-# path to a goal tile.
-# SFFF       (S: starting point, safe)
-# FHFH       (F: frozen surface, safe)
-# FFFH       (H: hole, fall to your doom)
-# HFFG       (G: goal, where the frisbee is located)
-# The episode ends when you reach the goal or fall in a hole. You receive a reward
-# of 1 if you reach the goal, and zero otherwise.
+
+import time
+import gym
+import matplotlib.pyplot as plt
+import numpy as np
+import tensorflow as tf
+import tensorlayer as tl
+from tensorlayer.layers import *
+
 env = gym.make('FrozenLake-v0')
 
 

example/tutorial_frozenlake_q_table.py

@@ -1,8 +1,6 @@
-import time
+"""Q-Table learning algorithm.
 
-import gym
-import numpy as np
-"""Q-Table learning algorithm, non deep learning - TD Learning, Off-Policy, e-Greedy Exploration
+Non deep learning - TD Learning, Off-Policy, e-Greedy Exploration
 
 Q(S, A) <- Q(S, A) + alpha * (R + lambda * Q(newS, newA) - Q(S, A))
 
@@ -12,8 +10,13 @@
 
 EN: https://medium.com/emergent-future/simple-reinforcement-learning-with-tensorflow-part-0-q-learning-with-tables-and-neural-networks-d195264329d0#.5m3361vlw
 CN: https://zhuanlan.zhihu.com/p/25710327
+
 """
 
+import time
+import gym
+import numpy as np
+
 ## Load the environment
 env = gym.make('FrozenLake-v0')
 render = False  # display the game environment

example/tutorial_generate_text.py

@@ -1,7 +1,6 @@
 #! /usr/bin/python
 # -*- coding: utf-8 -*-
-
-# Copyright 2016 TensorLayer. All Rights Reserved.
+# Copyright 2018 TensorLayer. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -14,15 +13,15 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-# ==============================================================================
-"""Example of Synced sequence input and output.
+"""
+Example of Synced sequence input and output.
+
 Generate text using LSTM.
 
 Data: https://github.com/zsdonghao/tensorlayer/tree/master/example/data/
 
 """
 
-import os
 import re
 import time
 
@@ -154,7 +153,6 @@ def main_restore_embedding_layer():
     load_params = tl.files.load_npz(name=model_file_name + '.npz')
 
     x = tf.placeholder(tf.int32, shape=[batch_size])
-    y_ = tf.placeholder(tf.int32, shape=[batch_size, 1])
 
     emb_net = tl.layers.EmbeddingInputlayer(inputs=x, vocabulary_size=vocabulary_size, embedding_size=embedding_size, name='embedding_layer')
 
@@ -369,9 +367,10 @@ def loss_fn(outputs, targets, batch_size, sequence_length):
 
 if __name__ == '__main__':
     sess = tf.InteractiveSession()
-    """Restore a pretrained embedding matrix."""
+    # Restore a pretrained embedding matrix
     # main_restore_embedding_layer()
-    """How to generate text from a given context."""
+
+    # How to generate text from a given context
     main_lstm_generate_text()
 
 #

example/tutorial_image_preprocess.py

@@ -1,16 +1,13 @@
-import time
-
-import numpy as np
-import tensorflow as tf
-import tensorlayer as tl
-from tensorlayer.prepro import *
-"""
-Data Augmentation by numpy, scipy, threading and queue.
+"""Data Augmentation by numpy, scipy, threading and queue.
 
 Alternatively, we can use TFRecord to preprocess data,
 see `tutorial_cifar10_tfrecord.py` for more details.
+
 """
 
+import time
+import tensorlayer as tl
+
 X_train, y_train, X_test, y_test = tl.files.load_cifar10_dataset(shape=(-1, 32, 32, 3), plotable=False)