Misc updates

Update r2 models to optimze on that instead of MSE. All models now will print out the summary if print is called on them.

Removed print from base generator

Tinkered with example files

examples/building_hdf5.py

@@ -1,6 +1,5 @@
 from factnn import GammaPreprocessor, GammaDiffusePreprocessor, ProtonPreprocessor
 import os.path
-
 """
 This is just to show how to make all the HDF5 files, its simply the same as in the energy, separation, and source_detection
 files but without the need to import tensorflow or anything
@@ -10,7 +9,7 @@
 obs_dir = [base_dir + "public/"]
 gamma_dir = [base_dir + "sim/gamma/"]
 proton_dir = [base_dir + "sim/proton/"]
-gamma_dl2 = "gamma_simulations_diffuse_facttools_dl2.hdf5"
+gamma_dl2 = "../gamma_simulations_diffuse_facttools_dl2.hdf5"
 
 shape = [35,60]
 rebin_size = 10

examples/energy.py

@@ -23,7 +23,7 @@
 
 energy_generator_configuration = {
     'seed': 1337,
-    'batch_size': 64,
+    'batch_size': 32,
     'input': '../gamma.hdf5',
     'start_slice': 0,
     'number_slices': 25,
@@ -54,7 +54,7 @@
     'strides_lstm': 1,
     'num_fc': 3,
     'pooling': True,
-    'neurons': [32, 16, 8, 16, 32, 48, 64],
+    'neurons': [32, 32, 16, 16, 16, 32, 48, 64],
     'shape': [25, 38, 38, 1],
     'start_slice': 0,
     'number_slices': 25,
@@ -63,6 +63,7 @@
 }
 
 energy_model = EnergyModel(config=energy_model_configuration)
+print(energy_model)
 
 """
 

examples/source_detection.py

@@ -56,8 +56,8 @@
 
 
 source_model_configuration = {
-    'conv_dropout': 0.3,
-    'lstm_dropout': 0.4,
+    'conv_dropout': 0.2,
+    'lstm_dropout': 0.3,
     'fc_dropout': 0.5,
     'num_conv3d': 1,
     'kernel_conv3d': 2,
@@ -76,8 +76,8 @@
 }
 
 sign_model_configuration = {
-    'conv_dropout': 0.3,
-    'lstm_dropout': 0.4,
+    'conv_dropout': 0.2,
+    'lstm_dropout': 0.3,
     'fc_dropout': 0.5,
     'num_conv3d': 2,
     'kernel_conv3d': 2,

factnn/data/base_generator.py

@@ -71,7 +71,6 @@ def __next__(self):
         :return:
         '''
         if not self.from_directory:
-            print("Shape: " + str(self.input_shape))
             if self.chunked:
                 if self.mode == "train":
                     while True:

factnn/models/base_model.py

@@ -222,7 +222,8 @@ def save(self):
         return NotImplemented
 
     def __str__(self):
-        return NotImplemented
+        self.model.summary()
+        return ""
 
     def __repr__(self):
         return NotImplemented

factnn/models/energy_models.py

@@ -8,7 +8,7 @@ def r2(y_true, y_pred):
     from keras import backend as K
     SS_res = K.sum(K.square(y_true - y_pred))
     SS_tot = K.sum(K.square(y_true - K.mean(y_true)))
-    return 1 - SS_res / (SS_tot + K.epsilon())
+    return -1.*(1 - SS_res / (SS_tot + K.epsilon()))
 
 
 class EnergyModel(BaseModel):
@@ -67,8 +67,8 @@ def create(self):
 
         # Final Dense layer
         model.add(Dense(1, activation='linear'))
-        model.compile(optimizer='adam', loss='mse',
-                      metrics=['mae', r2])
+        model.compile(optimizer='adam', loss=r2,
+                      metrics=['mae', 'mse'])
 
         self.model = model
 

factnn/models/source_models.py

@@ -8,7 +8,7 @@ def r2(y_true, y_pred):
     from keras import backend as K
     SS_res = K.sum(K.square(y_true - y_pred))
     SS_tot = K.sum(K.square(y_true - K.mean(y_true)))
-    return (1 - SS_res / (SS_tot + K.epsilon()))
+    return -1.*(1 - SS_res / (SS_tot + K.epsilon()))
 
 
 class DispModel(BaseModel):
@@ -67,8 +67,8 @@ def create(self):
 
         # Final Dense layer
         model.add(Dense(1, activation='linear'))
-        model.compile(optimizer='adam', loss='mse',
-                      metrics=['mae', r2])
+        model.compile(optimizer='adam', loss=r2,
+                      metrics=['mae', 'mse'])
 
         self.model = model
 

requirements.txt

@@ -1,6 +1,5 @@
 # Requirements automatically generated by pigar.
 # https://github.com/damnever/pigar
-Keras==2.2.0
 PyYAML == 3.12
 astropy==3.0.3
 h5py==2.8.0