enh(trainers)

trainer implementation

closes #43

trainers/trainer.py

@@ -0,0 +1,41 @@
+import tensorflow as tf
+
+
+class Trainer:
+    def __init__(self, model, filepath, saved_model_path):
+      self.model = model
+      self.filepath = filepath
+      self.saved_model_path = saved_model_path
+      self.batchsize =  # TODO
+      self.epochs =  # TODO
+      self.validation_data =  # TODO
+      self.verbose =  # TODO
+
+    def loadData(filepath):
+        feature = {'train/image': tf.FixedLenFeature([], tf.string),
+               'train/label': tf.FixedLenFeature([], tf.int64)}
+        # Create a list of filenames and pass it to a queue
+        filename_queue = tf.train.string_input_producer(
+            [data_path], num_epochs=1)
+        # Define a reader and read the next record
+        reader = tf.TFRecordReader()
+        _, serialized_example = reader.read(filename_queue)
+        # Decode the record read by the reader
+        features = tf.parse_single_example(
+            serialized_example, features=feature)
+        # Convert the image data from string back to the numbers
+        image = tf.decode_raw(features['train/image'], tf.float32)
+
+        # Cast label data into int32
+        label = tf.cast(features['train/label'], tf.int32)
+        # Reshape image data into the original shape
+        image = tf.reshape(image, [224, 224, 3])
+        return images, labels
+
+    def training():
+        images, labels = loadData(self.filepath)
+        self.model.fit(images, labels, batchsize=self.batchsize, epochs=self.epochs,  # TODO)
+        # validation_data=testing_set.make_one_shot_iterator(),validation_steps=len(x_test) // _BATCH_SIZE,verbose = 1)
+
+        tf.contrib.saved_model.save_keras_model(
+            model, saved_model_path, custom_objects=None, as_text=None)