mnist

MNIST_dataset.py

@@ -4,6 +4,7 @@
 import matplotlib.pyplot as plt
 import sys
 import tensorflow as tf
+import scipy
 
 
 # +------------------+
@@ -14,42 +15,46 @@ class MNISTData:
     """MNIST data class. You can adjust the data_fraction to use when creating
     the data, according to your system capabilities."""
 
-    def __init__(self, gan=False, data_fraction=1.):
+    def __init__(self, data_fraction=1., zoom_factor = None):
         data = mnist
         (self.x_train, self.y_train), (self.x_test, self.y_test) = data.load_data()
 
         self.get_subset_of_data(data_fraction)
+        if zoom_factor is not None:
+            self.interpolate(zoom_factor)
 
         self.convert_label_to_categorical()
 
-        self.normalize_mnist_images(gan)
+        self.normalize_mnist_images()
 
-        self.reshape_to_color_channel(gan)
+        self.reshape_to_color_channel()
+
+
+
+    def interpolate(self, zoom_factor):
+        #self.x_train =  scipy.ndimage.zoom(self.x_train, 
+        #                                    (1, zoom_factor, zoom_factor, 1))
+        #shape_train = self.x_train.shape[1]*self.x_train.shape[2]
+        #self.x_train.reshape([196,])
+
 
-        if gan is False:
-            self.flatten_pictures()
+        self.x_test =  scipy.ndimage.zoom(self.x_test,
+                                        (1, zoom_factor, zoom_factor))
+
+        #shape_test = self.x_test.shape[1]*self.x_test.shape[2]
+        #self.x_test.reshape([196,])
 
     def convert_label_to_categorical(self):
         self.y_train = to_categorical(self.y_train)
         self.y_test = to_categorical(self.y_test)
 
-    def normalize_mnist_images(self, gan):
-        if gan:
-            """normalize the images to [-1, 1]"""
-            self.x_train = (self.x_train - 127.5) / 127.5
-            self.x_test = (self.x_test - 127.5) / 127.5
-        else:
-            self.x_train = self.x_train / 255.0
-            self.x_test = self.x_test / 255.0
-
+    def normalize_mnist_images(self):
+        self.x_train = self.x_train / 255.0
+        self.x_test = self.x_test / 255.0
 
-    def reshape_to_color_channel(self, gan):
-        if gan:
-            self.x_train = self.x_train.reshape(self.x_train.shape[0], 28, 28, 1).astype('float32')
-            self.x_test = self.x_test.reshape(self.x_test.shape[0], 28, 28, 1).astype('float32')
-        else:
-            self.x_train = self.x_train[:, :, :, np.newaxis]
-            self.x_test = self.x_test[:, :, :, np.newaxis]
+    def reshape_to_color_channel(self):
+        self.x_train = self.x_train[:, :, :, np.newaxis]
+        self.x_test = self.x_test[:, :, :, np.newaxis]
 
     def get_subset_of_data(self, data_fraction):
         """Choosing a fraction of data according to the machine capabilities"""
@@ -59,7 +64,3 @@ def get_subset_of_data(self, data_fraction):
         index = int(len(self.x_test) * data_fraction)
         self.x_test = self.x_test[:index]
         self.y_test = self.y_test[:index]
-
-    def flatten_pictures(self):
-        self.x_train = self.x_train.reshape(self.x_train.shape[0], -1)
-        self.x_test = self.x_test.reshape(self.x_test.shape[0], -1)