Merge pull request #16 from Ahmkel/master

Fixed typos and spacing according to PEP 8

Author: MrGemy95

README.md

@@ -73,7 +73,7 @@ In a nutshell here's how to use this template, so **for example** assume you wan
         pass
 
 ```
-- In main file you create the session and create instance of the following objects "Model", "Logger", "Data_Generator", "Trainer", and config
+- In main file, you create the session and instances of the following objects "Model", "Logger", "Data_Generator", "Trainer", and config
 ```python
     sess = tf.Session()
     # create instance of the model you want
@@ -91,7 +91,7 @@ In a nutshell here's how to use this template, so **for example** assume you wan
     trainer.train()
 
 ```
-**You will a template file and a simple example in the model and trainer folder that shows you how to try your first model simply.** 
+**You will find a template file and a simple example in the model and trainer folder that shows you how to try your first model simply.**
 
 
 # In Details
@@ -186,7 +186,7 @@ Here's where you combine all previous part.
 
 
 # Contributing
-Any kind of enhancement, or contribution is welcomed.
+Any kind of enhancement or contribution is welcomed.
 
 
 # Acknowledgments

base/base_model.py

@@ -1,5 +1,4 @@
 import tensorflow as tf
-import os
 
 
 class BaseModel:
@@ -10,27 +9,27 @@ def __init__(self, config):
         # init the epoch counter
         self.init_cur_epoch()
 
-    # save function thet save the checkpoint in the path defined in configfile
+    # save function that saves the checkpoint in the path defined in the config file
     def save(self, sess):
         print("Saving model...")
         self.saver.save(sess, self.config.checkpoint_dir, self.global_step_tensor)
         print("Model saved")
 
-    # load lateset checkpoint from the experiment path defined in config_file
+    # load latest checkpoint from the experiment path defined in the config file
     def load(self, sess):
         latest_checkpoint = tf.train.latest_checkpoint(self.config.checkpoint_dir)
         if latest_checkpoint:
             print("Loading model checkpoint {} ...\n".format(latest_checkpoint))
             self.saver.restore(sess, latest_checkpoint)
             print("Model loaded")
 
-    # just inialize a tensorflow variable to use it as epoch counter
+    # just initialize a tensorflow variable to use it as epoch counter
     def init_cur_epoch(self):
         with tf.variable_scope('cur_epoch'):
             self.cur_epoch_tensor = tf.Variable(0, trainable=False, name='cur_epoch')
             self.increment_cur_epoch_tensor = tf.assign(self.cur_epoch_tensor, self.cur_epoch_tensor + 1)
 
-    # just inialize a tensorflow variable to use it as global step counter
+    # just initialize a tensorflow variable to use it as global step counter
     def init_global_step(self):
         # DON'T forget to add the global step tensor to the tensorflow trainer
         with tf.variable_scope('global_step'):

base/base_train.py

@@ -1,7 +1,4 @@
 import tensorflow as tf
-import os
-from tqdm import tqdm
-import numpy as np
 
 
 class BaseTrain:
@@ -22,7 +19,7 @@ def train(self):
     def train_epoch(self):
         """
         implement the logic of epoch:
-        -loop ever the number of iteration in the config and call teh train step
+        -loop over the number of iterations in the config and call the train step
         -add any summaries you want using the summary
         """
         raise NotImplementedError

data_loader/data_generator.py

@@ -1,12 +1,13 @@
 import numpy as np
 
 
-class DataGenerator():
+class DataGenerator:
     def __init__(self, config):
         self.config = config
         # load data here
         self.input = np.ones((500, 784))
         self.y = np.ones((500, 10))
+
     def next_batch(self, batch_size):
         idx = np.random.choice(500, batch_size)
         yield self.input[idx], self.y[idx]

mains/example.py

@@ -11,7 +11,7 @@
 
 def main():
     # capture the config path from the run arguments
-    # then process the json configration file
+    # then process the json configuration file
     try:
         args = get_args()
         config = process_config(args.config)
@@ -24,15 +24,15 @@ def main():
     create_dirs([config.summary_dir, config.checkpoint_dir])
     # create tensorflow session
     sess = tf.Session()
-    # create instance of the model you want
+    # create an instance of the model you want
     model = ExampleModel(config)
-    #load model if exist
+    #load model if exists
     model.load(sess)
     # create your data generator
     data = DataGenerator(config)
     # create tensorboard logger
     logger = Logger(sess, config)
-    # create trainer and path all previous components to it
+    # create trainer and pass all the previous components to it
     trainer = ExampleTrainer(sess, model, data, config, logger)
 
     # here you train your model

models/example_model.py

@@ -14,9 +14,9 @@ def build_model(self):
         self.x = tf.placeholder(tf.float32, shape=[None] + self.config.state_size)
         self.y = tf.placeholder(tf.float32, shape=[None, 10])
 
-        # network_architecture
-        d1 = tf.layers.dense(self.x, 512, activation=tf.nn.relu, name="densee2")
-        d2 = tf.layers.dense(d1, 10)
+        # network architecture
+        d1 = tf.layers.dense(self.x, 512, activation=tf.nn.relu, name="dense1")
+        d2 = tf.layers.dense(d1, 10, name="dense2")
 
         with tf.name_scope("loss"):
             self.cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=self.y, logits=d2))
@@ -27,6 +27,6 @@ def build_model(self):
 
 
     def init_saver(self):
-        # here you initalize the tensorflow saver that will be used in saving the checkpoints.
+        # here you initialize the tensorflow saver that will be used in saving the checkpoints.
         self.saver = tf.train.Saver(max_to_keep=self.config.max_to_keep)
 

models/template_model.py

@@ -14,7 +14,7 @@ def build_model(self):
         pass
 
     def init_saver(self):
-        #here you initalize the tensorflow saver that will be used in saving the checkpoints.
+        # here you initialize the tensorflow saver that will be used in saving the checkpoints.
         # self.saver = tf.train.Saver(max_to_keep=self.config.max_to_keep)
 
         pass

trainers/example_trainer.py

@@ -2,27 +2,30 @@
 from tqdm import tqdm
 import numpy as np
 
+
 class ExampleTrainer(BaseTrain):
     def __init__(self, sess, model, data, config,logger):
         super(ExampleTrainer, self).__init__(sess, model, data, config,logger)
 
     def train_epoch(self):
         loop = tqdm(range(self.config.num_iter_per_epoch))
-        losses=[]
-        accs=[]
-        for it in loop:
+        losses = []
+        accs = []
+        for _ in loop:
             loss, acc = self.train_step()
             losses.append(loss)
             accs.append(acc)
-        loss=np.mean(losses)
-        acc=np.mean(accs)
+        loss = np.mean(losses)
+        acc = np.mean(accs)
 
         cur_it = self.model.global_step_tensor.eval(self.sess)
-        summaries_dict = {}
-        summaries_dict['loss'] = loss
-        summaries_dict['acc'] = acc
+        summaries_dict = {
+            'loss': loss,
+            'acc': acc,
+        }
         self.logger.summarize(cur_it, summaries_dict=summaries_dict)
         self.model.save(self.sess)
+
     def train_step(self):
         batch_x, batch_y = next(self.data.next_batch(self.config.batch_size))
         feed_dict = {self.model.x: batch_x, self.model.y: batch_y, self.model.is_training: True}

utils/config.py

@@ -19,8 +19,8 @@ def get_config_from_json(json_file):
     return config, config_dict
 
 
-def process_config(jsonfile):
-    config, _ = get_config_from_json(jsonfile)
+def process_config(json_file):
+    config, _ = get_config_from_json(json_file)
     config.summary_dir = os.path.join("../experiments", config.exp_name, "summary/")
     config.checkpoint_dir = os.path.join("../experiments", config.exp_name, "checkpoint/")
-    return config
+    return config

utils/dirs.py

@@ -1,4 +1,6 @@
 import os
+
+
 def create_dirs(dirs):
     """
     dirs - a list of directories to create if these directories are not found