build classes for saver

action/action.py

@@ -5,6 +5,7 @@ class Action(object):
 
     def __init__(self):
         super(Action, self).__init__()
+        self.logger = None
 
     def load_config(self, args):
         pass
@@ -13,4 +14,15 @@ class Action(object):
         pass
 
     def log(self, args):
+        self.logger.log(args)
+
+    """
+        Basic operations shared between Trainer and Tester.
+    """
+
+    def batchify(self, X, Y=None):
+        # a generator
         pass
+
+    def make_log(self, *args):
+        pass

action/tester.py

@@ -1,9 +1,45 @@
+import numpy as np
+
 from action.action import Action
 
 
 class Tester(Action):
     """docstring for Tester"""
 
-    def __init__(self, arg):
+    def __init__(self, test_args):
+        """
+        :param test_args: named tuple
+        """
         super(Tester, self).__init__()
-        self.arg = arg
+        self.test_args = test_args
+        self.args_dict = {name: value for name, value in self.test_args.__dict__.iteritems()}
+        self.mean_loss = None
+
+    def test(self, network, data):
+        # transform into network input and label
+        X, Y = network.prepare_input(data)
+
+        # split into batches by self.batch_size
+        iterations, test_batch_generator = self.batchify(X, Y)
+
+        loss_history = list()
+        # turn on the testing mode of the network
+        network.mode(test=True)
+
+        for step in range(iterations):
+            batch_x, batch_y = test_batch_generator.__next__()
+
+            # forward pass from test input to predicted output
+            prediction = network.data_forward(batch_x)
+
+            # get the loss
+            loss = network.loss(batch_y, prediction)
+
+            loss_history.append(loss)
+            self.log(self.make_log(step, loss))
+
+        self.mean_loss = np.mean(np.array(loss_history))
+
+    @property
+    def loss(self):
+        return self.mean_loss

model/empty.txt

@@ -0,0 +1,9 @@
+Some useful reference:
+SpaCy "Doc"
+https://github.com/explosion/spaCy/blob/75d2a05c2938f412f0fae44748374e4de19cc2be/spacy/tokens/doc.pyx#L80
+
+SpaCy "Vocab"
+https://github.com/explosion/spaCy/blob/75d2a05c2938f412f0fae44748374e4de19cc2be/spacy/vocab.pyx#L25
+
+SpaCy "Token"
+https://github.com/explosion/spaCy/blob/75d2a05c2938f412f0fae44748374e4de19cc2be/spacy/tokens/token.pyx#L27

reproduction/Char-aware_NLM/train.py

@@ -1,15 +1,15 @@
+import os
+from collections import namedtuple
 
+import numpy as np
 import torch
-from torch.autograd import Variable
 import torch.nn as nn
-import torch.nn.functional as F
 import torch.optim as optim
-import numpy as np
-import os
-from model import charLM
-from utilities import *
-from collections import namedtuple
-from test import test
+from torch.autograd import Variable
+
+from .model import charLM
+from .test import test
+from .utilities import *
 
 
 def preprocess():
@@ -43,7 +43,18 @@ def to_var(x):
 
 
 def train(net, data, opt):
-    
+    """
+    :param net: the pytorch model
+    :param data: numpy array
+    :param opt: named tuple
+    1. random seed
+    2. define local input
+    3. training settting: learning rate, loss, etc
+    4. main loop epoch
+    5. batchify
+    6. validation
+    7. save model
+    """
     torch.manual_seed(1024)
 
     train_input = torch.from_numpy(data.train_input)

saver/base_saver.py

@@ -0,0 +1,14 @@
+class BaseSaver(object):
+    """base class for all savers"""
+
+    def __init__(self, save_path):
+        self.save_path = save_path
+
+    def save_bytes(self):
+        pass
+
+    def save_str(self):
+        pass
+
+    def compress(self):
+        pass

saver/logger.py

@@ -0,0 +1,11 @@
+from saver.base_saver import BaseSaver
+
+
+class Logger(BaseSaver):
+    """Logging"""
+
+    def __init__(self, save_path):
+        super(Logger, self).__init__(save_path)
+
+    def log(self, string):
+        pass

saver/model_saver.py

@@ -0,0 +1,8 @@
+from saver.base_saver import BaseSaver
+
+
+class ModelSaver(BaseSaver):
+    """Save a model"""
+
+    def __init__(self, save_path):
+        super(ModelSaver, self).__init__(save_path)