[WIP] - Documentation Cleaning and Coding Style (#516)

* Activation Cleaning Docstring Test

* Requirements Pinned with range to insure tested versions are used. Range are used to prevent updating requirements all the time.

* setup.cfg file added with PEP8 configuration

* activation.py refactored

* docstring fixed - ready for documentation unittest

* Yapf correction for max_line_length: 120

* test yapf refactored

* Requirements conflict solved

* Yapf Style modified and merged in file "setup.cfg"

* Yapf Confiuguration Updated

* Code Refactored with new YAPF formating style

* Code Refactored with new YAPF formating style

* Code Refactored with new YAPF formating style

* tl.layers.pooling YAPF reformat

* yapf updated

* gitignore updated

* YAPF Style Fixing Attempt

* Space Error Fix

* Style Correction

* Assertion Codacy Errors Corrected

* Error Fix

* Assertion Refactored

* YAPF Style Applied to Master

Author: DEKHTIARJonathan

.gitignore

@@ -12,3 +12,5 @@ tensorlayer.egg-info
 tensorlayer/__pacache__
 venv/
 .pytest_cache/
+update_tl.bat
+update_tl.py

.style.yapf

@@ -1,4 +1,60 @@
 [style]
-based_on_style = pep8
+based_on_style=google
+
+# The number of columns to use for indentation.
 indent_width = 4
-column_limit = 160
+
+# The column limit.
+column_limit=120
+
+# Place each dictionary entry onto its own line.
+each_dict_entry_on_separate_line = True
+
+# Put closing brackets on a separate line, dedented, if the bracketed
+# expression can't fit in a single line. Applies to all kinds of brackets,
+# including function definitions and calls. For example:
+#
+#   config = {
+#       'key1': 'value1',
+#       'key2': 'value2',
+#   }        # <--- this bracket is dedented and on a separate line
+#
+#   time_series = self.remote_client.query_entity_counters(
+#       entity='dev3246.region1',
+#       key='dns.query_latency_tcp',
+#       transform=Transformation.AVERAGE(window=timedelta(seconds=60)),
+#       start_ts=now()-timedelta(days=3),
+#       end_ts=now(),
+#   )        # <--- this bracket is dedented and on a separate line
+dedent_closing_brackets=True
+
+# Do not split consecutive brackets. Only relevant when DEDENT_CLOSING_BRACKETS is set
+coalesce_brackets = False
+
+# Align closing bracket with visual indentation.
+align_closing_bracket_with_visual_indent = False
+
+# Split named assignments onto individual lines.
+split_before_named_assigns = False
+
+# If an argument / parameter list is going to be split, then split before the first argument.
+split_before_first_argument = True
+
+# Split before arguments if the argument list is terminated by a comma.
+split_arguments_when_comma_terminated = False
+
+# Insert a space between the ending comma and closing bracket of a list, etc.
+space_between_ending_comma_and_closing_bracket = True
+
+# Join short lines into one line. E.g., single line if statements.
+join_multiple_lines = True
+
+# Do not include spaces around selected binary operators.
+# Example: 1 + 2 * 3 - 4 / 5     =>     1 + 2*3 - 4/5
+no_spaces_around_selected_binary_operators = True
+
+# Allow lambdas to be formatted on more than one line.
+allow_multiline_lambdas = True
+
+SPLIT_PENALTY_FOR_ADDED_LINE_SPLIT = 10
+SPLIT_PENALTY_AFTER_OPENING_BRACKET = 500

docs/index.rst

@@ -54,6 +54,7 @@ method, this part of the documentation is for you.
   modules/activation
   modules/models
   modules/distributed
+  modules/db
 
 
 Command-line Reference

example/tutorial_atari_pong.py

@@ -133,7 +133,11 @@ def prepro(I):
             prev_x = None
 
         if reward != 0:
-            print(('episode %d: game %d took %.5fs, reward: %f' % (episode_number, game_number, time.time() - start_time, reward)),
-                  ('' if reward == -1 else ' !!!!!!!!'))
+            print(
+                (
+                    'episode %d: game %d took %.5fs, reward: %f' %
+                    (episode_number, game_number, time.time() - start_time, reward)
+                ), ('' if reward == -1 else ' !!!!!!!!')
+            )
             start_time = time.time()
             game_number += 1

example/tutorial_binarynet_cifar10_tfrecord.py

@@ -83,7 +83,9 @@ def data_to_tfrecord(images, labels, filename):
                 feature={
                     "label": tf.train.Feature(int64_list=tf.train.Int64List(value=[label])),
                     'img_raw': tf.train.Feature(bytes_list=tf.train.BytesList(value=[img_raw])),
-                }))
+                }
+            )
+        )
         writer.write(example.SerializeToString())  # Serialize To String
     writer.close()
 
@@ -97,12 +99,13 @@ def read_and_decode(filename, is_train=None):
         serialized_example, features={
             'label': tf.FixedLenFeature([], tf.int64),
             'img_raw': tf.FixedLenFeature([], tf.string),
-        })
+        }
+    )
     # You can do more image distortion here for training data
     img = tf.decode_raw(features['img_raw'], tf.float32)
     img = tf.reshape(img, [32, 32, 3])
     # img = tf.cast(img, tf.float32) #* (1. / 255) - 0.5
-    if is_train == True:
+    if is_train ==True:
         # 1. Randomly crop a [height, width] section of the image.
         img = tf.random_crop(img, [24, 24, 3])
         # 2. Randomly flip the image horizontally.
@@ -147,9 +150,12 @@ def read_and_decode(filename, is_train=None):
     x_test_, y_test_ = read_and_decode("test.cifar10", False)
 
     x_train_batch, y_train_batch = tf.train.shuffle_batch(
-        [x_train_, y_train_], batch_size=batch_size, capacity=2000, min_after_dequeue=1000, num_threads=32)  # set the number of threads here
+        [x_train_, y_train_], batch_size=batch_size, capacity=2000, min_after_dequeue=1000, num_threads=32
+    )  # set the number of threads here
     # for testing, uses batch instead of shuffle_batch
-    x_test_batch, y_test_batch = tf.train.batch([x_test_, y_test_], batch_size=batch_size, capacity=50000, num_threads=32)
+    x_test_batch, y_test_batch = tf.train.batch(
+        [x_test_, y_test_], batch_size=batch_size, capacity=50000, num_threads=32
+    )
 
     def model(x_crop, y_, reuse):
         """ For more simplified CNN APIs, check tensorlayer.org """
@@ -161,16 +167,28 @@ def model(x_crop, y_, reuse):
             net = tl.layers.Conv2d(net, 64, (5, 5), (1, 1), act=tf.nn.relu, padding='SAME', W_init=W_init, name='cnn1')
             net = tl.layers.SignLayer(net)
             net = tl.layers.MaxPool2d(net, (3, 3), (2, 2), padding='SAME', name='pool1')
-            net = tl.layers.LocalResponseNormLayer(net, depth_radius=4, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name='norm1')
-            net = tl.layers.BinaryConv2d(net, 64, (5, 5), (1, 1), act=tf.nn.relu, padding='SAME', W_init=W_init, name='cnn2')
-            net = tl.layers.LocalResponseNormLayer(net, depth_radius=4, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name='norm2')
+            net = tl.layers.LocalResponseNormLayer(
+                net, depth_radius=4, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name='norm1'
+            )
+            net = tl.layers.BinaryConv2d(
+                net, 64, (5, 5), (1, 1), act=tf.nn.relu, padding='SAME', W_init=W_init, name='cnn2'
+            )
+            net = tl.layers.LocalResponseNormLayer(
+                net, depth_radius=4, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name='norm2'
+            )
             net = tl.layers.MaxPool2d(net, (3, 3), (2, 2), padding='SAME', name='pool2')
             net = tl.layers.FlattenLayer(net, name='flatten')  # output: (batch_size, 2304)
             net = tl.layers.SignLayer(net)
-            net = tl.layers.BinaryDenseLayer(net, n_units=384, act=tf.nn.relu, W_init=W_init2, b_init=b_init2, name='d1relu')  # output: (batch_size, 384)
+            net = tl.layers.BinaryDenseLayer(
+                net, n_units=384, act=tf.nn.relu, W_init=W_init2, b_init=b_init2, name='d1relu'
+            )  # output: (batch_size, 384)
             net = tl.layers.SignLayer(net)
-            net = tl.layers.BinaryDenseLayer(net, n_units=192, act=tf.nn.relu, W_init=W_init2, b_init=b_init2, name='d2relu')  # output: (batch_size, 192)
-            net = tl.layers.DenseLayer(net, n_units=10, act=tf.identity, W_init=W_init2, name='output')  # output: (batch_size, 10)
+            net = tl.layers.BinaryDenseLayer(
+                net, n_units=192, act=tf.nn.relu, W_init=W_init2, b_init=b_init2, name='d2relu'
+            )  # output: (batch_size, 192)
+            net = tl.layers.DenseLayer(
+                net, n_units=10, act=tf.identity, W_init=W_init2, name='output'
+            )  # output: (batch_size, 10)
             y = net.outputs
 
             ce = tl.cost.cross_entropy(y, y_, name='cost')
@@ -201,9 +219,15 @@ def model_batch_norm(x_crop, y_, reuse, is_train):
             net = tl.layers.BatchNormLayer(net, is_train, act=tf.nn.relu, name='batch2')
             net = tl.layers.MaxPool2d(net, (3, 3), (2, 2), padding='SAME', name='pool2')
             net = tl.layers.FlattenLayer(net, name='flatten')  # output: (batch_size, 2304)
-            net = tl.layers.DenseLayer(net, n_units=384, act=tf.nn.relu, W_init=W_init2, b_init=b_init2, name='d1relu')  # output: (batch_size, 384)
-            net = tl.layers.DenseLayer(net, n_units=192, act=tf.nn.relu, W_init=W_init2, b_init=b_init2, name='d2relu')  # output: (batch_size, 192)
-            net = tl.layers.DenseLayer(net, n_units=10, act=tf.identity, W_init=W_init2, name='output')  # output: (batch_size, 10)
+            net = tl.layers.DenseLayer(
+                net, n_units=384, act=tf.nn.relu, W_init=W_init2, b_init=b_init2, name='d1relu'
+            )  # output: (batch_size, 384)
+            net = tl.layers.DenseLayer(
+                net, n_units=192, act=tf.nn.relu, W_init=W_init2, b_init=b_init2, name='d2relu'
+            )  # output: (batch_size, 192)
+            net = tl.layers.DenseLayer(
+                net, n_units=10, act=tf.identity, W_init=W_init2, name='output'
+            )  # output: (batch_size, 10)
             y = net.outputs
 
             ce = tl.cost.cross_entropy(y, y_, name='cost')
@@ -273,7 +297,10 @@ def model_batch_norm(x_crop, y_, reuse, is_train):
             n_batch += 1
 
         if epoch + 1 == 1 or (epoch + 1) % print_freq == 0:
-            print("Epoch %d : Step %d-%d of %d took %fs" % (epoch, step, step + n_step_epoch, n_step, time.time() - start_time))
+            print(
+                "Epoch %d : Step %d-%d of %d took %fs" %
+                (epoch, step, step + n_step_epoch, n_step, time.time() - start_time)
+            )
             print("   train loss: %f" % (train_loss / n_batch))
             print("   train acc: %f" % (train_acc / n_batch))
 

example/tutorial_bipedalwalker_a3c_continuous_action.py

@@ -67,6 +67,7 @@
 
 
 class ACNet(object):
+
     def __init__(self, scope, globalAC=None):
         self.scope = scope
         if scope == GLOBAL_NET_SCOPE:
@@ -144,7 +145,8 @@ def _build_net(self):
             self.v = v.outputs
 
     def update_global(self, feed_dict):  # run by a local
-        _, _, t = sess.run([self.update_a_op, self.update_c_op, self.test], feed_dict)  # local grads applies to global net
+        _, _, t = sess.run([self.update_a_op, self.update_c_op, self.test],
+                           feed_dict)  # local grads applies to global net
         return t
 
     def pull_global(self):  # run by a local
@@ -156,14 +158,18 @@ def choose_action(self, s):  # run by a local
 
     def save_ckpt(self):
         tl.files.exists_or_mkdir(self.scope)
-        tl.files.save_ckpt(sess=sess, mode_name='model.ckpt', var_list=self.a_params + self.c_params, save_dir=self.scope, printable=True)
+        tl.files.save_ckpt(
+            sess=sess, mode_name='model.ckpt', var_list=self.a_params + self.c_params, save_dir=self.scope,
+            printable=True
+        )
 
     def load_ckpt(self):
         tl.files.load_ckpt(sess=sess, var_list=self.a_params + self.c_params, save_dir=self.scope, printable=True)
         # tl.files.load_ckpt(sess=sess, mode_name='model.ckpt', var_list=self.a_params+self.c_params, save_dir=self.scope, is_latest=False, printable=True)
 
 
 class Worker(object):
+
     def __init__(self, name, globalAC):
         self.env = gym.make(GAME)
         self.name = name
@@ -202,7 +208,10 @@ def work(self):
                         buffer_v_target.append(v_s_)
                     buffer_v_target.reverse()
 
-                    buffer_s, buffer_a, buffer_v_target = np.vstack(buffer_s), np.vstack(buffer_a), np.vstack(buffer_v_target)
+                    buffer_s, buffer_a, buffer_v_target = (
+                        np.vstack(buffer_s), np.vstack(buffer_a), np.vstack(buffer_v_target)
+                    )
+
                     feed_dict = {
                         self.AC.s: buffer_s,
                         self.AC.a_his: buffer_a,

example/tutorial_cartpole_ac.py

@@ -69,6 +69,7 @@
 
 
 class Actor(object):
+
     def __init__(self, sess, n_features, n_actions, lr=0.001):
         self.sess = sess
         self.s = tf.placeholder(tf.float32, [1, n_features], "state")
@@ -85,7 +86,9 @@ def __init__(self, sess, n_features, n_actions, lr=0.001):
 
         # Hao Dong
         with tf.variable_scope('loss'):
-            self.exp_v = tl.rein.cross_entropy_reward_loss(logits=self.acts_logits, actions=self.a, rewards=self.td_error, name='actor_weighted_loss')
+            self.exp_v = tl.rein.cross_entropy_reward_loss(
+                logits=self.acts_logits, actions=self.a, rewards=self.td_error, name='actor_weighted_loss'
+            )
 
         with tf.variable_scope('train'):
             self.train_op = tf.train.AdamOptimizer(lr).minimize(self.exp_v)
@@ -113,6 +116,7 @@ def choose_action_greedy(self, s):
 
 
 class Critic(object):
+
     def __init__(self, sess, n_features, lr=0.01):
         self.sess = sess
         self.s = tf.placeholder(tf.float32, [1, n_features], "state")
@@ -143,7 +147,9 @@ def learn(self, s, r, s_):
 sess = tf.Session()
 
 actor = Actor(sess, n_features=N_F, n_actions=N_A, lr=LR_A)
-critic = Critic(sess, n_features=N_F, lr=LR_C)  # we need a good teacher, so the teacher should learn faster than the actor
+critic = Critic(
+    sess, n_features=N_F, lr=LR_C
+)  # we need a good teacher, so the teacher should learn faster than the actor
 
 tl.layers.initialize_global_variables(sess)
 
@@ -187,7 +193,10 @@ def learn(self, s, r, s_):
                 running_reward = running_reward * 0.95 + ep_rs_sum * 0.05
             # start rending if running_reward greater than a threshold
             # if running_reward > DISPLAY_REWARD_THRESHOLD: RENDER = True
-            print("Episode: %d reward: %f running_reward %f took: %.5f" % (i_episode, ep_rs_sum, running_reward, time.time() - episode_time))
+            print(
+                "Episode: %d reward: %f running_reward %f took: %.5f" %
+                (i_episode, ep_rs_sum, running_reward, time.time() - episode_time)
+            )
 
             # Early Stopping for quick check
             if t >= MAX_EP_STEPS:

example/tutorial_cifar10.py

@@ -44,9 +44,15 @@ def model(x, y_, reuse):
         # net = PoolLayer(net, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1],
         #             padding='SAME', pool = tf.nn.max_pool, name ='pool2') # output: (batch_size, 6, 6, 64)
         net = FlattenLayer(net, name='flatten')  # output: (batch_size, 2304)
-        net = DenseLayer(net, n_units=384, act=tf.nn.relu, W_init=W_init2, b_init=b_init2, name='d1relu')  # output: (batch_size, 384)
-        net = DenseLayer(net, n_units=192, act=tf.nn.relu, W_init=W_init2, b_init=b_init2, name='d2relu')  # output: (batch_size, 192)
-        net = DenseLayer(net, n_units=10, act=tf.identity, W_init=tf.truncated_normal_initializer(stddev=1 / 192.0), name='output')  # output: (batch_size, 10)
+        net = DenseLayer(
+            net, n_units=384, act=tf.nn.relu, W_init=W_init2, b_init=b_init2, name='d1relu'
+        )  # output: (batch_size, 384)
+        net = DenseLayer(
+            net, n_units=192, act=tf.nn.relu, W_init=W_init2, b_init=b_init2, name='d2relu'
+        )  # output: (batch_size, 192)
+        net = DenseLayer(
+            net, n_units=10, act=tf.identity, W_init=tf.truncated_normal_initializer(stddev=1 / 192.0), name='output'
+        )  # output: (batch_size, 10)
         y = net.outputs
 
         ce = tl.cost.cross_entropy(y, y_, name='cost')
@@ -89,9 +95,15 @@ def model_batch_norm(x, y_, reuse, is_train):
         #            padding='SAME', pool = tf.nn.max_pool, name ='pool2')  # output: (batch_size, 6, 6, 64)
 
         net = FlattenLayer(net, name='flatten')  # output: (batch_size, 2304)
-        net = DenseLayer(net, n_units=384, act=tf.nn.relu, W_init=W_init2, b_init=b_init2, name='d1relu')  # output: (batch_size, 384)
-        net = DenseLayer(net, n_units=192, act=tf.nn.relu, W_init=W_init2, b_init=b_init2, name='d2relu')  # output: (batch_size, 192)
-        net = DenseLayer(net, n_units=10, act=tf.identity, W_init=tf.truncated_normal_initializer(stddev=1 / 192.0), name='output')  # output: (batch_size, 10)
+        net = DenseLayer(
+            net, n_units=384, act=tf.nn.relu, W_init=W_init2, b_init=b_init2, name='d1relu'
+        )  # output: (batch_size, 384)
+        net = DenseLayer(
+            net, n_units=192, act=tf.nn.relu, W_init=W_init2, b_init=b_init2, name='d2relu'
+        )  # output: (batch_size, 192)
+        net = DenseLayer(
+            net, n_units=10, act=tf.identity, W_init=tf.truncated_normal_initializer(stddev=1 / 192.0), name='output'
+        )  # output: (batch_size, 10)
         y = net.outputs
 
         ce = tl.cost.cross_entropy(y, y_, name='cost')
@@ -136,11 +148,8 @@ def distort_fn(x, is_train=False):
     return x
 
 
-x = tf.placeholder(tf.float32, shape=[None, 24, 24, 3], name='x')
-y_ = tf.placeholder(
-    tf.int64, shape=[
-        None,
-    ], name='y_')
+x = tf.placeholder(dtype=tf.float32, shape=[None, 24, 24, 3], name='x')
+y_ = tf.placeholder(dtype=tf.int64, shape=[None], name='y_')
 
 ## using local response normalization
 # network, cost, _ = model(x, y_, False)
@@ -156,7 +165,8 @@ def distort_fn(x, is_train=False):
 batch_size = 128
 
 train_params = network.all_params
-train_op = tf.train.AdamOptimizer(learning_rate, beta1=0.9, beta2=0.999, epsilon=1e-08, use_locking=False).minimize(cost, var_list=train_params)
+train_op = tf.train.AdamOptimizer(learning_rate, beta1=0.9, beta2=0.999, epsilon=1e-08,
+                                  use_locking=False).minimize(cost, var_list=train_params)
 
 tl.layers.initialize_global_variables(sess)