Test documentation (#511)

* 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 * test documentation added * Missing requirement added: sphinx * Allow test on documentation to pass on warning * Fix travis dependencies install * Travis install script fixed * Travis install command fixed * 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 * shorten codes * Various Cleaning * Trailing Slashes removed * Test Recurrent Fixed * Line Width Fix * docs requirements updated * fix example docs style * Codacy Issue Fixed * Merge Errors fixed * YAPF Style Applied
tensorlayer · Apr 21, 2018 · f4a0572 · f4a0572
1 parent e557e62
commit f4a0572
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diff --git a/.style.yapf b/.style.yapf
diff --git a/.travis.yml b/.travis.yml
@@ -23,7 +23,7 @@ env:
 install:
   - pip install tensorflow
   - pip install -r requirements.txt
-  - pip install .[test]
+  - pip install -e .[dev,doc,test]
 
 
 script:

diff --git a/docs/requirements.txt b/docs/requirements.txt
@@ -5,4 +5,4 @@ progressbar2>=3.37,<3.38
 scikit-image>=0.13,<0.14
 scipy>=1.0,<1.1
 sphinx>=1.7,<1.8
-tensorflow==1.5.0
+tensorflow>=1.7,<1.8
diff --git a/example/tutorial_binarynet_cifar10_tfrecord.py b/example/tutorial_binarynet_cifar10_tfrecord.py
@@ -149,11 +149,13 @@ def read_and_decode(filename, is_train=None):
     x_train_, y_train_ = read_and_decode("train.cifar10", True)
     x_test_, y_test_ = read_and_decode("test.cifar10", False)
     # set the number of threads here
-    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)
+    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
+    )
     # 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 """
@@ -239,8 +241,10 @@ def model(x_crop, y_, reuse):
             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))
 

diff --git a/example/tutorial_binarynet_mnist_cnn.py b/example/tutorial_binarynet_mnist_cnn.py
@@ -5,8 +5,7 @@
 import tensorflow as tf
 import tensorlayer as tl
 
-X_train, y_train, X_val, y_val, X_test, y_test = \
-                tl.files.load_mnist_dataset(shape=(-1, 28, 28, 1))
+X_train, y_train, X_val, y_val, X_test, y_test = tl.files.load_mnist_dataset(shape=(-1, 28, 28, 1))
 # X_train, y_train, X_test, y_test = tl.files.load_cropped_svhn(include_extra=False)
 
 sess = tf.InteractiveSession()

diff --git a/example/tutorial_bipedalwalker_a3c_continuous_action.py b/example/tutorial_bipedalwalker_a3c_continuous_action.py
@@ -198,11 +198,14 @@ def work(self):
                 buffer_r.append(r)
 
                 if total_step % UPDATE_GLOBAL_ITER == 0 or done:  # update global and assign to local net
+
                     if done:
                         v_s_ = 0  # terminal
                     else:
                         v_s_ = sess.run(self.AC.v, {self.AC.s: s_[np.newaxis, :]})[0, 0]
+
                     buffer_v_target = []
+
                     for r in buffer_r[::-1]:  # reverse buffer r
                         v_s_ = r + GAMMA * v_s_
                         buffer_v_target.append(v_s_)
@@ -211,12 +214,7 @@ def work(self):
                     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,
-                        self.AC.v_target: buffer_v_target,
-                    }
+                    feed_dict = {self.AC.s: buffer_s, self.AC.a_his: buffer_a, self.AC.v_target: buffer_v_target}
                     # update gradients on global network
                     self.AC.update_global(feed_dict)
                     buffer_s, buffer_a, buffer_r = [], [], []

diff --git a/example/tutorial_dorefanet_cifar10_tfrecord.py b/example/tutorial_dorefanet_cifar10_tfrecord.py
@@ -149,11 +149,13 @@ def read_and_decode(filename, is_train=None):
     x_train_, y_train_ = read_and_decode("train.cifar10", True)
     x_test_, y_test_ = read_and_decode("test.cifar10", False)
     # set the number of threads here
-    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)
+    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
+    )
     # 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 """
@@ -235,8 +237,10 @@ def model(x_crop, y_, reuse):
             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))
 

diff --git a/example/tutorial_dorefanet_mnist_cnn.py b/example/tutorial_dorefanet_mnist_cnn.py
@@ -5,8 +5,7 @@
 import tensorflow as tf
 import tensorlayer as tl
 
-X_train, y_train, X_val, y_val, X_test, y_test = \
-                tl.files.load_mnist_dataset(shape=(-1, 28, 28, 1))
+X_train, y_train, X_val, y_val, X_test, y_test = tl.files.load_mnist_dataset(shape=(-1, 28, 28, 1))
 # X_train, y_train, X_test, y_test = tl.files.load_cropped_svhn(include_extra=False)
 
 sess = tf.InteractiveSession()

diff --git a/example/tutorial_generate_text.py b/example/tutorial_generate_text.py
@@ -230,10 +230,13 @@ def inference(x, is_train, sequence_length, reuse=None):
         rnn_init = tf.random_uniform_initializer(-init_scale, init_scale)
         with tf.variable_scope("model", reuse=reuse):
             network = EmbeddingInputlayer(x, vocab_size, hidden_size, rnn_init, name='embedding')
-            network = RNNLayer(network, cell_fn=tf.contrib.rnn.BasicLSTMCell, \
-                cell_init_args={'forget_bias': 0.0, 'state_is_tuple': True}, \
-                n_hidden=hidden_size, initializer=rnn_init, n_steps=sequence_length, return_last=False,
-                return_seq_2d=True, name='lstm1')
+            network = RNNLayer(
+                network, cell_fn=tf.contrib.rnn.BasicLSTMCell, cell_init_args={
+                    'forget_bias': 0.0,
+                    'state_is_tuple': True
+                }, n_hidden=hidden_size, initializer=rnn_init, n_steps=sequence_length, return_last=False,
+                return_seq_2d=True, name='lstm1'
+            )
             lstm1 = network
             network = DenseLayer(network, vocab_size, W_init=rnn_init, b_init=rnn_init, act=tf.identity, name='output')
         return network, lstm1
@@ -297,14 +300,21 @@ def loss_fn(outputs, targets, batch_size, sequence_length):
         ## reset all states at the begining of every epoch
         state1 = tl.layers.initialize_rnn_state(lstm1.initial_state)
         for step, (x, y) in enumerate(tl.iterate.ptb_iterator(train_data, batch_size, sequence_length)):
-            _cost, state1, _ = sess.run([cost, lstm1.final_state, train_op], \
-                feed_dict={input_data: x, targets: y, lstm1.initial_state: state1})
+            _cost, state1, _ = sess.run(
+                [cost, lstm1.final_state, train_op], feed_dict={
+                    input_data: x,
+                    targets: y,
+                    lstm1.initial_state: state1
+                }
+            )
             costs += _cost
             iters += sequence_length
 
             if step % (epoch_size // 10) == 1:
-                print("%.3f perplexity: %.3f speed: %.0f wps" % \
-                    (step * 1.0 / epoch_size, np.exp(costs / iters), iters * batch_size / (time.time() - start_time)))
+                print(
+                    "%.3f perplexity: %.3f speed: %.0f wps" %
+                    (step * 1.0 / epoch_size, np.exp(costs / iters), iters * batch_size / (time.time() - start_time))
+                )
         train_perplexity = np.exp(costs / iters)
         # print("Epoch: %d Train Perplexity: %.3f" % (i + 1, train_perplexity))
         print("Epoch: %d/%d Train Perplexity: %.3f" % (i + 1, max_max_epoch, train_perplexity))
@@ -319,14 +329,22 @@ def loss_fn(outputs, targets, batch_size, sequence_length):
             # feed the seed to initialize the state for generation.
             for ids in outs_id[:-1]:
                 a_id = np.asarray(ids).reshape(1, 1)
-                state1 = sess.run([lstm1_test.final_state], \
-                    feed_dict={input_data_test: a_id, lstm1_test.initial_state: state1})
+                state1 = sess.run(
+                    [lstm1_test.final_state], feed_dict={
+                        input_data_test: a_id,
+                        lstm1_test.initial_state: state1
+                    }
+                )
             # feed the last word in seed, and start to generate sentence.
             a_id = outs_id[-1]
             for _ in range(print_length):
                 a_id = np.asarray(a_id).reshape(1, 1)
-                out, state1 = sess.run([y_soft, lstm1_test.final_state], \
-                    feed_dict={input_data_test: a_id, lstm1_test.initial_state: state1})
+                out, state1 = sess.run(
+                    [y_soft, lstm1_test.final_state], feed_dict={
+                        input_data_test: a_id,
+                        lstm1_test.initial_state: state1
+                    }
+                )
                 ## Without sampling
                 # a_id = np.argmax(out[0])
                 ## Sample from all words, if vocab_size is large,

diff --git a/example/tutorial_imagenet_inceptionV3_distributed.py b/example/tutorial_imagenet_inceptionV3_distributed.py
@@ -25,8 +25,7 @@
 from tensorflow.python.framework.errors_impl import OutOfRangeError
 from tensorflow.python.training import session_run_hook
 from tensorflow.python.training.basic_session_run_hooks import StopAtStepHook
-from tensorflow.python.training.monitored_session import \
-    SingularMonitoredSession
+from tensorflow.python.training.monitored_session import SingularMonitoredSession
 
 import tensorlayer as tl
 
@@ -294,18 +293,15 @@ def calculate_metrics(predicted_batch, real_batch, threshold=0.5, is_training=Fa
 def run_evaluator(task_spec, checkpoints_path, batch_size=32):
     with tf.Graph().as_default():
         # load dataset
-        images_input, one_hot_classes, num_classes, _dataset_size = \
-            load_data(file=VAL_FILE,
-                      task_spec=task_spec,
-                      batch_size=batch_size,
-                      epochs=1)
+        images_input, one_hot_classes, num_classes, _dataset_size = load_data(
+            file=VAL_FILE, task_spec=task_spec, batch_size=batch_size, epochs=1
+        )
         _network, predictions = build_network(images_input, num_classes=num_classes, is_training=False)
         saver = tf.train.Saver()
         # metrics
-        metrics_init_ops, _, metrics_ops = \
-            calculate_metrics(predicted_batch=predictions,
-                              real_batch=one_hot_classes,
-                              is_training=False)
+        metrics_init_ops, _, metrics_ops = calculate_metrics(
+            predicted_batch=predictions, real_batch=one_hot_classes, is_training=False
+        )
         # tensorboard summary
         summary_op = tf.summary.merge_all()
         # session hook
@@ -338,12 +334,9 @@ def run_worker(task_spec, checkpoints_path, batch_size=32, epochs=10):
         global_step = tf.train.get_or_create_global_step()
         with tf.device(device_fn):
             # load dataset
-            images_input, one_hot_classes, num_classes, dataset_size = \
-                load_data(file=TRAIN_FILE,
-                          task_spec=task_spec,
-                          batch_size=batch_size,
-                          epochs=epochs,
-                          shuffle_size=10000)
+            images_input, one_hot_classes, num_classes, dataset_size = load_data(
+                file=TRAIN_FILE, task_spec=task_spec, batch_size=batch_size, epochs=epochs, shuffle_size=10000
+            )
             # network
             network, predictions = build_network(images_input, num_classes=num_classes, is_training=True)
             # training operations
@@ -390,8 +383,9 @@ def run_worker(task_spec, checkpoints_path, batch_size=32, epochs=10):
                 last_log_time = time.time()
                 next_log_time = last_log_time + 60
                 while not sess.should_stop():
-                    step, loss_val, learning_rate_val, _, metrics = \
-                        sess.run([global_step, loss, learning_rate, train_op, metrics_ops])
+                    step, loss_val, learning_rate_val, _, metrics = sess.run(
+                        [global_step, loss, learning_rate, train_op, metrics_ops]
+                    )
                     if task_spec is None or task_spec.is_master():
                         now = time.time()
                         if now > next_log_time:

diff --git a/example/tutorial_imdb_fasttext.py b/example/tutorial_imdb_fasttext.py
@@ -110,8 +110,7 @@ def hash_ngram(ngram):
 
 def load_and_preprocess_imdb_data(n_gram=None):
     """Load IMDb data and augment with hashed n-gram features."""
-    X_train, y_train, X_test, y_test = \
-        tl.files.load_imdb_dataset(nb_words=VOCAB_SIZE)
+    X_train, y_train, X_test, y_test = tl.files.load_imdb_dataset(nb_words=VOCAB_SIZE)
 
     if n_gram is not None:
         X_train = np.array([augment_with_ngrams(x, VOCAB_SIZE, N_BUCKETS, n=n_gram) for x in X_train])

diff --git a/example/tutorial_keras.py b/example/tutorial_keras.py
@@ -8,8 +8,7 @@
 from keras.layers import *
 from tensorlayer.layers import *
 
-X_train, y_train, X_val, y_val, X_test, y_test = \
-                tl.files.load_mnist_dataset(shape=(-1, 784))
+X_train, y_train, X_val, y_val, X_test, y_test = tl.files.load_mnist_dataset(shape=(-1, 784))
 
 sess = tf.InteractiveSession()
 
@@ -43,8 +42,7 @@ def keras_block(x):
 learning_rate = 0.0001
 
 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).minimize(cost, var_list=train_params)
 
 tl.layers.initialize_global_variables(sess)
 

diff --git a/example/tutorial_mlp_dropout1.py b/example/tutorial_mlp_dropout1.py
@@ -5,8 +5,7 @@
 sess = tf.InteractiveSession()
 
 # prepare data
-X_train, y_train, X_val, y_val, X_test, y_test = \
-                                tl.files.load_mnist_dataset(shape=(-1,784))
+X_train, y_train, X_val, y_val, X_test, y_test = tl.files.load_mnist_dataset(shape=(-1, 784))
 # define placeholder
 x = tf.placeholder(tf.float32, shape=[None, 784], name='x')
 y_ = tf.placeholder(tf.int64, shape=[None], name='y_')

diff --git a/example/tutorial_mlp_dropout2.py b/example/tutorial_mlp_dropout2.py
@@ -5,8 +5,7 @@
 sess = tf.InteractiveSession()
 
 # prepare data
-X_train, y_train, X_val, y_val, X_test, y_test = \
-                                tl.files.load_mnist_dataset(shape=(-1,784))
+X_train, y_train, X_val, y_val, X_test, y_test = tl.files.load_mnist_dataset(shape=(-1, 784))
 # define placeholder
 x = tf.placeholder(tf.float32, shape=[None, 784], name='x')
 y_ = tf.placeholder(tf.int64, shape=[None], name='y_')
@@ -41,8 +40,7 @@ def mlp(x, is_train=True, reuse=False):
 
 # define the optimizer
 train_params = tl.layers.get_variables_with_name('MLP', train_only=True, printable=False)
-train_op = tf.train.AdamOptimizer(learning_rate=0.0001, 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=0.0001).minimize(cost, var_list=train_params)
 
 # initialize all variables in the session
 tl.layers.initialize_global_variables(sess)