more tensorboard logging + reset default graph

main/experiments/parameter_files/november_2018/lazyfile_classif_end_to_end_with_augment.yml

+all:
+  dense:
+#  deepfried:
+  deepstrom:
+
+base:
+  epoch_numbers: {"-e": [200]}
+  batch_sizes: {"-s": [64]}
+  val_size: {"-v": [10000]}
+  seed: {"-a": "range(1)"}
+  quiet: ["-q"]
+  dataset: ["--mnist", "--cifar10", "--cifar100", "--svhn"]
+
+dense:
+  network: ["dense"]
+  base:
+  repr_dim: {"-D": [16, 64, 128, 1024]}
+
+deepfried:
+  network: ["deepfriedconvnet"]
+  base:
+  nbstacks: {"-N": [1, 3, 5, 7]}
+
+deepstrom:
+  network: ["deepstrom"]
+  base:
+  nys_size: {"-m": [4, 8, 16, 64, 128, 256, 512]}
+  kernel: ["-C", "-L"]
+

main/experiments/scripts/november_2018/end_to_end_subsample_conv_hand/deepstrom_classif_end_to_end.py

@@ -162,11 +162,13 @@ def main(paraman, resman, printman):
     output_dim = y_train.shape[1]
 
     x = tf.placeholder(tf.float32, shape=[None, *input_dim], name="x")
+    tf.summary.image("input_images", x)
     y = tf.placeholder(tf.float32, shape=[None, output_dim], name="label")
 
     convnet_model = convmodel_func(x.shape[1:])
 
     repr_x = convnet_model(x)
+    tf.summary.histogram("convolved_examples", repr_x)
 
     repr_sub = tf.placeholder(tf.float32, shape=[paraman["--nys-size"], *repr_x.shape[1:]], name="subsample_conv_input")
 
@@ -213,7 +215,7 @@ def main(paraman, resman, printman):
 
     summary_writer = None
     if paraman["--tensorboard"]:
-        summary_writer = tf.summary.FileWriter(f"log/{paraman['dataset']}/nys_size_{paraman['--nys-size']}/")
+        summary_writer = tf.summary.FileWriter(f"log/{int(t.time())}/{paraman['dataset']}/nys_size_{paraman['--nys-size']}/")
 
     # In[7]:
 
@@ -289,6 +291,7 @@ def main(paraman, resman, printman):
 
 
 if __name__ == "__main__":
+    tf.reset_default_graph()
     paraman_obj = ParameterManagerMain(docopt.docopt(__doc__))
     resman_obj = ResultManagerMain()
     printman_obj = ResultPrinter(paraman_obj, resman_obj)