Save plot of training accuracy

notebooks/wut-train.ipynb

@@ -3,7 +3,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "# wut-train --- What U Think? SatNOGS Observation AI, training application.\n",
@@ -90,11 +92,10 @@
    "outputs": [],
    "source": [
     "ENCODING='FSK9k6'\n",
-    "batch_size = 64\n",
-    "epochs = 4\n",
-    "# Failing with this now:\n",
-    "#batch_size = 128\n",
+    "#batch_size = 64\n",
     "#epochs = 4\n",
+    "batch_size = 128\n",
+    "epochs = 4\n",
     "IMG_WIDTH = 416\n",
     "IMG_HEIGHT = 803"
    ]
@@ -133,8 +134,8 @@
     "print('Validation images:      ', total_val)\n",
     "print('')\n",
     "print('Reduce training and validation set')\n",
-    "total_train = 1000\n",
-    "total_val = 1000\n",
+    "total_train = 5000\n",
+    "total_val = 5000\n",
     "print('Training reduced to:    ', total_train)\n",
     "print('Validation reduced to:  ', total_val)"
    ]
@@ -206,7 +207,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# This function will plot images in the form of a grid with 1 row and 3 columns where images are placed in each column.\n",
     "def plotImages(images_arr):\n",
     "    fig, axes = plt.subplots(1, 3, figsize=(20,20))\n",
     "    axes = axes.flatten()\n",
@@ -265,8 +265,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)\n",
-    "#tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir)\n",
     "tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1, write_graph=True, write_images=True, embeddings_freq=1, update_freq='batch')"
    ]
   },
@@ -295,11 +293,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#wutoptimizer = 'adam'\n",
     "wutoptimizer = tf.keras.optimizers.Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, amsgrad=True)\n",
-    "\n",
     "wutloss = 'binary_crossentropy'\n",
-    "#wutmetrics = 'accuracy'\n",
     "wutmetrics = ['accuracy']"
    ]
   },
@@ -361,7 +356,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Need ~64 gigs RAM+, 20 gig disk\n",
     "history = model.fit(\n",
     "    train_data_gen,\n",
     "    steps_per_epoch=total_train // batch_size,\n",
@@ -404,6 +398,10 @@
     "\n",
     "epochs_range = range(epochs)\n",
     "\n",
+    "plot_file=(\"wut-plot-\" + ENCODING + \".png\")\n",
+    "save_path_plot = os.path.join('/srv/satnogs/data/models/', ENCODING, plot_file)\n",
+    "print(save_path_plot)\n",
+    "\n",
     "plt.figure(figsize=(8, 8))\n",
     "plt.subplot(1, 2, 1)\n",
     "plt.plot(epochs_range, acc, label='Training Accuracy')\n",
@@ -416,6 +414,7 @@
     "plt.plot(epochs_range, val_loss, label='Validation Loss')\n",
     "plt.legend(loc='upper right')\n",
     "plt.title('Training and Validation Loss')\n",
+    "plt.savefig(save_path_plot)\n",
     "plt.show()"
    ]
   },