AE w/ and w/o classifier

LorenzoValente3 · Nov 30, 2021 · 1894d71 · 1894d71
1 parent d9fdee0
commit 1894d71
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Showing 95 changed files with 474 additions and 25,287 deletions.
diff --git a/AE.ipynb b/AE.ipynb
diff --git a/AE_model/KERAS_check_model_w_classifier.h5 b/AE_model/KERAS_check_model_w_classifier.h5
diff --git a/AE_model/KERAS_check_model_wo_classifier.h5 b/AE_model/KERAS_check_model_wo_classifier.h5
diff --git a/MNIST_dataset.py b/MNIST_dataset.py
@@ -11,6 +11,7 @@
 # | MNIST data class |
 # +------------------+
 
+
 class MNISTData:
     """MNIST data class. You can adjust the data_fraction to use when creating
     the data, according to your system capabilities."""
@@ -20,29 +21,23 @@ def __init__(self, data_fraction=1., zoom_factor = None):
         (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()
+
+        if zoom_factor is not None:
+            self.interpolate(zoom_factor)
 
         self.reshape_to_color_channel()
 
-
+        self.flatten_pictures()
 
     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,])
-
-
+        self.x_train =  scipy.ndimage.zoom(self.x_train, 
+                                            (1, zoom_factor, zoom_factor))
         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)
@@ -64,3 +59,7 @@ 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)
diff --git a/__pycache__/MNIST_dataset.cpython-38.pyc b/__pycache__/MNIST_dataset.cpython-38.pyc
diff --git a/images/AE/Accuracy of Autoencoder without classifier.png b/images/AE/Accuracy of Autoencoder without classifier.png
diff --git a/images/AE/Latent dimension of Autoencoder without classifier.png b/images/AE/Latent dimension of Autoencoder without classifier.png
diff --git a/images/AE/reconstructed images.png b/images/AE/reconstructed images.png
diff --git a/model_1/KERAS_check_best_model.h5 b/model_1/KERAS_check_best_model.h5
diff --git a/model_1/hls4ml_prj.tar.gz b/model_1/hls4ml_prj.tar.gz
diff --git a/model_1/hls4ml_prj/build_lib.sh b/model_1/hls4ml_prj/build_lib.sh
diff --git a/model_1/hls4ml_prj/build_prj.tcl b/model_1/hls4ml_prj/build_prj.tcl