Update Application_and_analyses_of_Trained_model.ipynb

Code/Application_and_analyses_of_Trained_model.ipynb

@@ -42,8 +42,10 @@
     "# Initialize TensorFlow session.\n",
     "tf.InteractiveSession()\n",
     "\n",
-    "# final trained generator directory path.\n",
+    "# pre-trained generator directory path.\n",
     "network_dir = '/scratch/users/suihong/ProGAN_MultiChannel_Reusults_ConditionedtoMultiConditions_TF/Unconditional_prog/'\n",
+    "\n",   
+    "# replace with pre-trained generator name.\n",
     "with open(network_dir + 'network-snapshot-011520.pkl', 'rb') as file:\n",
     "    G, D, Gs = pickle.load(file)\n"
    ]
@@ -76,11 +78,11 @@
     "fig, ax = plt.subplots(6, 6, sharex='col', sharey='row')\n",
     "fig.set_size_inches(10.5, 10.5, forward=True)\n",
     "\n",
-    "# Generate latent vectors.\n",
+    "# Generate random latent vectors.\n",
     "latents_plt = np.random.RandomState(63).randn(6 * 6, Gs.input_shapes[0][1]) # 1000 random latents *Gs.input_shapes = [None, 128] [None, 4] [None, 1, 64, 64]\n",
     "labels_plt = np.zeros([6 * 6] + Gs.input_shapes[1][1:]) #np.repeat(labels_plt, 5 * 6, axis=0)\n",
     "\n",
-    "# Run the generator to produce a set of images.\n",
+    "# Run the generator to produce a set of facies models.\n",
     "images_plt = Gs.run(latents_plt, labels_plt)\n",
     "images_plt = np.where(images_plt< -0.3, -1, images_plt)\n",
     "images_plt = np.where(images_plt> 0.3, 1, images_plt)\n",
@@ -97,7 +99,7 @@
     "collapsed": true
    },
    "source": [
-    "#### Randomly sample and plot facies models from trained generator"
+    "#### Randomly sample facies models from training data"
    ]
   },
   {