Reuploading fix

examples/reuploading_classifier/datasets.py

@@ -53,6 +53,9 @@ def create_target(name):
                              * np.sqrt(2 / 3)], dtype=complex),
                    np.array([1 / np.sqrt(3), np.exp(-1j * 2 * np.pi / 3) * np.sqrt(2 / 3)], dtype=complex)]
 
+    else:
+        raise NotImplementedError('This dataset is not implemented')
+
     return targets
 
 

examples/reuploading_classifier/main.py

@@ -1,14 +1,14 @@
 # /usr/bin/env python
-import datasets as ds
-import numpy as np
 from qlassifier import single_qubit_classifier
 import pickle
 import argparse
 
+#TODO: fix issue with .pkl
+
 parser = argparse.ArgumentParser()
-parser.add_argument("--dataset", default='tricrown',
+parser.add_argument("--dataset", default='circle',
                     help="Name of the example", type=str)
-parser.add_argument("--layers", default=10, help="Number of layers.", type=int)
+parser.add_argument("--layers", default=2, help="Number of layers.", type=int)
 
 
 def main(dataset, layers):
@@ -20,9 +20,14 @@ def main(dataset, layers):
         layers (int): Number of layers to use in the classifier
     """
     ql = single_qubit_classifier(dataset, layers)  # Define classifier
-    with open('saved_parameters.pkl', 'rb') as f:
-        # Load previous results. Have we ever run these problem?
-        data = pickle.load(f)
+    try:
+        with open('saved_parameters.pkl', 'rb') as f:
+            # Load previous results. Have we ever run these problem?
+            data = pickle.load(f)
+    except:
+        data = {}
+        data[dataset] = {}
+
     try:
         parameters = data[dataset][layers]
         print('Problem solved before, obtaining parameters from file...')
@@ -31,6 +36,7 @@ def main(dataset, layers):
         print('Problem never solved, finding optimal parameters...')
         result, parameters = ql.minimize(
             method='l-bfgs-b', options={'disp': True})
+
         data[dataset][layers] = parameters
         with open('saved_parameters.pkl', 'wb') as f:
             pickle.dump(data, f, pickle.HIGHEST_PROTOCOL)