Changes by Utente

main.py

@@ -16,6 +16,8 @@
 from dataloader import FMA2D_spec
 from architectures import SimpleCNN, ResNet
 from simplebinmi import bin_calc_information2
+import kde
+import keras.backend as K
 
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
@@ -43,6 +45,8 @@
 for i in range(NUM_LABELS):
     labelixs[i] = y == i
 
+labelprobs = np.mean(y, axis=0)
+
 
 BATCH = 256
 EPOCHS = 100
@@ -80,6 +84,19 @@
 
 #summary(model, (1, 128, 1290))
 
+#------------KDE functions
+# nats to bits conversion factor
+nats2bits = 1.0/np.log(2)
+#upper/lower entropy estimates
+noise_variance = 1e-3  # Added Gaussian noise variance
+binsize = 0.07  # size of bins for binning method
+
+# Functions to return upper and lower bounds on entropy of layer activity
+Klayer_activity = K.placeholder(ndim=2)  # Keras placeholder
+entropy_func_upper = torch.function([Klayer_activity,], [kde.entropy_estimator_kl(Klayer_activity, noise_variance),])
+entropy_func_lower = torch.function([Klayer_activity,], [kde.entropy_estimator_bd(Klayer_activity, noise_variance),])
+#------------------------
+
 # Adam optimizer01
 lr = 0.01
 optimizer = torch.optim.Adam(model.parameters())
@@ -118,7 +135,7 @@
         activity[ixs] = a1.cpu().detach().numpy()
         activity2[ixs] = a2.cpu().detach().numpy()
         loss = loss_fn(output, label)
-
+        cepochdata = defaultdict(list)
 
         # backward pass
         optimizer.zero_grad()

simplebinmi.py

@@ -3,7 +3,6 @@
 
 
 def get_unique_probs(x):
-    print(x.shape)# 
     uniqueids = np.ascontiguousarray(x).view(np.dtype((np.void, x.dtype.itemsize * x.shape[1])))
     _, unique_inverse, unique_counts = np.unique(uniqueids, return_index=False, return_inverse=True, return_counts=True)
     return np.asarray(unique_counts / float(sum(unique_counts))), unique_inverse

utils.py

@@ -228,7 +228,7 @@ def get_audio_path(audio_dir, track_id):
     --------
     >>> import utils
     >>> AUDIO_DIR = os.environ.get('AUDIO_DIR')
-    >>> utils.get_audio_path(AUDIO_DIR, 2)
+    >>> utils.get_audio_path(AUDIO_DIR, 2)jupyte
     '../data/fma_small/000/000002.mp3'
     """
     tid_str = '{:06d}'.format(track_id)