small SoL cleanup

diffphys-code-sol.ipynb

@@ -170,7 +170,7 @@
    },
    "outputs": [],
    "source": [
-    "# ??? !pip install --upgrade --quiet phiflow\n",
+    "!pip install --upgrade --quiet phiflow\n",
     "\n",
     "from phi.tf.flow import *\n",
     "import tensorflow as tf\n",
@@ -369,13 +369,9 @@
    "source": [
     "\n",
     "def to_keras(dens_vel_grid_array, ext_const_channel):\n",
-    "    # drop the unused edges of the staggered velocity grid making its dim same to the centered grid's\n",
+    "    # align the sides the staggered velocity grid making its size the same as the centered grid\n",
     "    return math.stack(\n",
     "        [\n",
-    "            #dens_vel_grid_array[1].vector['x'].x[:-1].values,         # u\n",
-    "            # ? tf.pad( dens_vel_grid_array[1].vector['x'].values, [(0,0), (0,0), (0,1)] ),  \n",
-    "            # ? tf.pad( dens_vel_grid_array[1].native(['batch', 'y', 'x']), [(0,0), (0,0), (0,1)] ),  \n",
-    "            # works! tf.pad( dens_vel_grid_array[1].vector['x'].values.native(['batch', 'y', 'x']), [(0,0), (0,0), (0,1)] ),  \n",
     "            math.pad( dens_vel_grid_array[1].vector['x'].values, {'x':(0,1)} , math.extrapolation.ZERO),\n",
     "            dens_vel_grid_array[1].vector['y'].y[:-1].values,         # v\n",
     "            math.ones(dens_vel_grid_array[0].shape)*ext_const_channel # Re\n",
@@ -388,8 +384,6 @@
     "        math.stack(\n",
     "            [\n",
     "                math.tensor(tf.pad(tf_tensor[..., 1], [(0,0), (0,1), (0,0)]), math.batch('batch'), math.spatial('y, x')), # v\n",
-    "                #math.tensor(tf.pad(tf_tensor[..., 0], [(0,0), (0,0), (0,1)]), math.batch('batch'), math.spatial('y, x')), # u\n",
-    "                # NT_DEBUG check\n",
     "                math.tensor( tf_tensor[...,:-1, 0], math.batch('batch'), math.spatial('y, x')), # u \n",
     "            ], math.channel('vector')\n",
     "        )\n",
@@ -447,9 +441,7 @@
     "            self.extConstChannelPerSim = { self.dataSims[i]:[ReNrs[i]] for i in range(num_sims) }\n",
     "        else:\n",
     "            self.dataSims = ['karman-fdt-hires-testset/sim_%06d'%i for i in range(num_sims) ]\n",
-    "            #ReNrs = [240000.0, 480000.0, 960000.0, 1920000.0, 3840000.0] \n",
+    "            ReNrs = [120000.0, 480000.0, 1920000.0, 7680000.0] \n",
-    "            #ReNrs = [120000.0, 240000.0, 480000.0, 960000.0, 1920000.0, 3840000.0, 7680000.0] # new extende\n",
-    "            ReNrs = [120000.0, 480000.0, 1920000.0, 7680000.0] # new reduced to 4\n",
     "            self.extConstChannelPerSim = { self.dataSims[i]:[ReNrs[i]] for i in range(num_sims) }\n",
     "\n",
     "        self.dataFrames = [ np.arange(num_frames) for _ in self.dataSims ]  \n",
@@ -593,7 +585,7 @@
     "batch_size = 3\n",
     "simsteps = 500\n",
     "\n",
-    "dataset = Dataset( data_preloaded=data_preloaded, num_frames=simsteps, num_sims=nsims, batch_size=batch_size )\n"
+    "dataset = Dataset( data_preloaded=data_preloaded, num_frames=simsteps, num_sims=nsims, batch_size=batch_size )"
    ]
   },
   {
@@ -694,7 +686,6 @@
    "source": [
     "def training_step(dens_gt, vel_gt, Re, i_step):\n",
     "    with tf.GradientTape() as tape:\n",
-    "        #prediction, correction = [], [] # predicted states with correction, inferred velocity corrections\n",
     "        prediction, correction = [ [dens_gt[0],vel_gt[0]] ], [0] # predicted states with correction, inferred velocity corrections\n",
     "\n",
     "        for i in range(msteps):\n",
@@ -1325,8 +1316,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "---\n",
-    "\n",
     "## Next steps\n",
     "\n",
     "* Modify the training to further reduce the training error. With the _medium_ network you should be able to get the loss down to around 1.\n",