Merge pull request #21 from pronobis/fix/link-einsum-benchmark-and-re…

…adme

Fix/link einsum benchmark and readme

README.md

@@ -58,7 +58,7 @@ Currently, the repo is in an alpha state. Hence, one can expect some sporadic br
 univariate as well as *multivariate* inputs.
 
 ## Examples / Tutorials
-1. **Benchmark**: `libspn-keras` andd Einsum Networks. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pronobis/libspn-keras/blob/master/examples/notebooks/Benchmark%20With%20Einsum%20Networks.ipynb)
+1. **Benchmark**: `libspn-keras` and Einsum Networks. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pronobis/libspn-keras/blob/master/examples/notebooks/Benchmark%20With%20Einsum%20Networks.ipynb)
 2. **Image Classification**: A Deep Generalized Convolutional Sum-Product Network (DGC-SPN). [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pronobis/libspn-keras/blob/master/examples/notebooks/DGC-SPN%20Image%20Classification.ipynb)
 3. **Image Completion**: A Deep Generalized Convolutional Sum-Product Network (DGC-SPN). [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pronobis/libspn-keras/blob/master/examples/notebooks/DGC-SPN%20Image%20Completion.ipynb)
 4. **Understanding region SPNs** [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pronobis/libspn-keras/blob/master/examples/notebooks/Understanding%20Region%20SPNs.ipynb)

examples/notebooks/DGC-SPN Image Completion.ipynb

@@ -141,6 +141,7 @@
    "source": [
     "def define_spn(sum_op, infer_no_evidence=False):\n",
     "    spnk.set_default_sum_op(sum_op)\n",
+    "    spnk.set_default_linear_accumulator_constraint(spnk.constraints.GreaterEqualEpsilon())\n",
     "    stack = [\n",
     "        spnk.layers.NormalizeStandardScore(input_shape=(64, 64, 1)),\n",
     "        # Non-overlapping products\n",
@@ -270,7 +271,7 @@
     ")\n",
     "\n",
     "sum_product_network.compile(\n",
-    "    optimizer=spnk.optimizers.OnlineExpectationMaximization(), \n",
+    "    optimizer=spnk.optimizers.OnlineExpectationMaximization(learning_rate=1.0, gliding_average=False), \n",
     "    loss=spnk.losses.NegativeLogLikelihood(),\n",
     "    metrics=[spnk.metrics.LogLikelihood()]\n",
     ")\n",