fix pylint errors

adnc/analysis/analyzer.py

@@ -21,10 +21,20 @@ from adnc.analysis.plot_functionality import PlotFunctionality
 from adnc.analysis.prepare_variables import Bucket
 from adnc.model.utils import softmax
 
+"""
+
+"""
 
 class Analyser():
     def __init__(self, data_set, record_dir, save_variables=False, save_fig=False):
+        """
 
+        Args:
+            data_set:
+            record_dir:
+            save_variables:
+            save_fig:
+        """
         self.data_set = data_set
         self.record_dir = record_dir
         self.save_variables = save_variables
@@ -72,7 +82,8 @@ class Analyser():
 
         return self.estimate_memory_usage(variables)
 
-    def plot_analysis(self, variables, plot_dir, name='variables'):
+    @staticmethod
+    def plot_analysis(variables, plot_dir, name='variables'):
 
         buck = Bucket(variables)
         plotter = PlotFunctionality(bucket=buck)
@@ -80,7 +91,8 @@ class Analyser():
         plotter.plot_basic_functionality(batch=0, plot_dir=plot_dir, name=name, show=True)
         plotter.plot_advanced_functionality(batch=0, plot_dir=plot_dir, name=name, show=True)
 
-    def estimate_memory_usage(self, variables):
+    @staticmethod
+    def estimate_memory_usage(variables):
 
         analyse_values, prediction, decoded_predictions, data_sample, weights_dict = variables
         data, target, mask, x_word = data_sample

adnc/data/tasks/babi.py

@@ -71,7 +71,8 @@ class bAbI():
             self.word_dict = word_dict
             self.re_word_dict = re_word_dict
 
-    def download_data(self, data_dir):
+    @staticmethod
+    def download_data(data_dir):
 
         folder_name = 'tasks_1-20_v1-2'
 

adnc/data/tasks/cnn_rc.py

@@ -106,7 +106,8 @@ class ReadingComprehension():
         self.re_entity_dict = {v: k for k, v in self.entity_dict.items()}
         self.idx_word_dict = {v: k for k, v in self.word_idx_dict.items()}
 
-    def download_data(self, data_dir):
+    @staticmethod
+    def download_data(data_dir):
 
         folder_name = 'cnn'
 

adnc/data/tasks/repeat_copy.py

@@ -123,7 +123,8 @@ class CopyTask():
 
         return batch
 
-    def decode_output(self, sample, prediction):
+    @staticmethod
+    def decode_output(sample, prediction):
         if prediction.shape.__len__() == 3:
             prediction_decode_list = []
             target_decode_list = []

adnc/data/utils/batch_generator.py

@@ -47,7 +47,7 @@ class BatchGenerator():
                     self.order = self.data_set.rng.permutation(self.order)
 
     def __iter__(self):
-        return self
+        return next(self)
 
     def __next__(self):
 

adnc/model/mann.py

@@ -163,14 +163,7 @@ class MANN():
     @property
     def parameter_amount(self):
         var_list = tf.trainable_variables()
-        parameters = 0
-        for variable in var_list:
-            shape = variable.get_shape()
-            variable_parametes = 1
-            for dim in shape:
-                variable_parametes *= dim.value
-            parameters += variable_parametes
-        return parameters
+        return self.count_parameter_amount(var_list)
 
 
     def get_loss(self, prediction):

adnc/model/memory_units/base_cell.py

@@ -39,6 +39,10 @@ class BaseMemoryUnitCell():
         self.reuse = reuse
         self.name = name
 
+        self.const_memory_ones = None # will be defined with use of batch size in call method
+        self.const_batch_memory_range = None # will be defined with use of batch size in call method
+        self.const_link_matrix_inv_eye = None # will be defined with use of batch size in call method
+
     @property
     @abstractmethod
     def state_size(self):

adnc/model/memory_units/multi_write_dnc_cell.py

@@ -233,7 +233,8 @@ class MWDNCMemoryUnitCell(BaseMemoryUnitCell):
 
         return write_weighting
 
-    def _update_memory(self, pre_memory, write_weighting, write_vector, erase_vector):
+    @staticmethod
+    def _update_memory(pre_memory, write_weighting, write_vector, erase_vector):
 
         write_w = tf.expand_dims(write_weighting, 3)
         erase_vector = tf.expand_dims(erase_vector, 2)

adnc/model/supporter.py

@@ -24,14 +24,15 @@ import yaml
 
 
 class color_code:
-    bold = '\033[1m'
-    underline = '\033[4m'
-    blue = '\033[94m'
-    darkcyan = '\033[36m'
-    green = '\033[92m'
-    red = '\033[91m'
-    yellow = '\033[93m'
-    end = '\033[0m'
+    def __init__(self):
+        self.bold = '\033[1m'
+        self.underline = '\033[4m'
+        self.blue = '\033[94m'
+        self.darkcyan = '\033[36m'
+        self.green = '\033[92m'
+        self.red = '\033[91m'
+        self.yellow = '\033[93m'
+        self.end = '\033[0m'
 
 
 class Supporter():

adnc/model/utils/holistic_multi_rnn_cell.py

@@ -41,6 +41,11 @@ class HolisticMultiRNNCell(RNNCell):
                                  "state_is_tuple is not set.  State sizes are: %s"
                                  % str([c.state_size for c in self._cells]))
 
+
+    def compute_output_shape(self, input_shape):
+        sizes = [cell.output_size for cell in self._cells]
+        return sum(sizes)
+
     @property
     def state_size(self):
         if self._state_is_tuple:
@@ -62,7 +67,7 @@ class HolisticMultiRNNCell(RNNCell):
                 # presumably does not contain TensorArrays or anything else fancy
                 return super(HolisticMultiRNNCell, self).zero_state(batch_size, dtype)
 
-    def call(self, inputs, state):
+    def call(self, inputs, state, scope=None):
         """Run this multi-layer cell on inputs, starting from state."""
         cur_state_pos = 0
         cur_inp = inputs

adnc/model/utils/word_embedding.py

@@ -42,7 +42,8 @@ class WordEmbedding():
         embed = tf.nn.embedding_lookup(self.embeddings, word_idx, name='embedding_lookup')
         return embed
 
-    def initialize_random(self, vocabulary_size, embedding_size, dtype):
+    @staticmethod
+    def initialize_random(vocabulary_size, embedding_size, dtype):
         return tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0, dtype=dtype)
 
     def initialize_with_glove(self, word_idx_dict, embedding_size, tmp_dir, dtype):