upd

pinky/README.md

@@ -18,3 +18,19 @@ You can dump your examples to TFRecordDatasets to accelerate io operations:
 
 and use the newly created config file to run `--train`
 
+
+Tests
+-----
+
+ - basic learning (synthetics, real data)
+ - synthetics, layered cake, train with top and bottom layer containing events.
+        Validate with events within middle layer
+ - test extrapolation with fault plane geometry
+ - synthetic pretraining
+ - hyperparameter optimization using skopt
+
+Outlook
+-------
+
+ - increase z error weight -> improve z estimates
+

pinky/src/model.py

@@ -68,6 +68,8 @@ class Model(Object):
         :param kernel_width: convolution kernel size accross time axis
 
         (Needs some debugging and checking)
+        TODO:
+         - cross_channel_kernel
         '''
         _, n_channels, n_samples, _ = input.shape
 
@@ -113,7 +115,7 @@ class Model(Object):
 
         # tf.summary.image('input', features)
         for ilayer in range(params.get('n_layers', 3)):
-            input = self.time_axis_cnn(input, n_filters, 1,
+            input = self.time_axis_cnn(input, n_filters * (1 + ilayer), 1,
                     kernel_width=int(kernel_width + ilayer*kernel_width_factor),
                     name='conv_%s' % ilayer, training=training)
         # conv = self.time_axis_cnn(conv, n_filters*2, 1, kernel_width=2, name='conv2',

pinky/src/optimize.py

+import matplotlib as mpl
+mpl.use('PDF')
+
 import os
 from .util import delete_if_exists
 from skopt import gp_minimize
@@ -32,6 +35,12 @@ class Optimizer(Object):
     path_out = String.T(default='optimizer-results', help='base path where to store results, plots and logs')
 
     def __init__(self, **kwargs):
+
+        '''
+        TODO:
+         - optimize kernel heigth (cross_channel_kernel)
+
+        '''
         super().__init__(**kwargs)
         self.model = None
         self.result = None