Attemp 4 initLr 0.01, decayRate 0.95, decayRate 0.95, decayStep 3000

Author: TruongNM

CNN2Head_train_101age.ipynb

@@ -59,7 +59,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -126,71 +126,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Default GPU Device: /device:GPU:0\n",
-      "Create new model\n",
-      "OK\n",
-      "Epoch: 1\n",
-      "Learning rate: 0.010000\n",
-      "Training on batch 1000/1734\n",
-      "Smile task train accuracy: 83.38556948519606\n",
-      "Gender task train accuracy: 81.4643091574023\n",
-      "Age task train error: 23.746819\n",
-      "Smile loss: 0.44061166\n",
-      "Gender loss: 0.44948837\n",
-      "Age loss: 4.0419455\n",
-      "Total loss: 5.683907. L2-loss: 0.75186163\n",
-      "\n",
-      "\n",
-      "Epoch: 2\n",
-      "Learning rate: 0.010000\n",
-      "Training on batch 1000/1734\n",
-      "Smile task train accuracy: 91.29043014338113\n",
-      "Gender task train accuracy: 86.5926211488169\n",
-      "Age task train error: 23.729975\n",
-      "Smile loss: 0.3214132\n",
-      "Gender loss: 0.3728076\n",
-      "Age loss: 3.963524\n",
-      "Total loss: 5.016013. L2-loss: 0.35826778\n",
-      "\n",
-      "\n",
-      "Epoch: 3\n",
-      "Learning rate: 0.009500\n",
-      "Training on batch 1000/1734\n",
-      "Smile task train accuracy: 92.08868144690781\n",
-      "Gender task train accuracy: 87.20801817007356\n",
-      "Age task train error: 23.71479\n",
-      "Smile loss: 0.30412948\n",
-      "Gender loss: 0.36167595\n",
-      "Age loss: 3.9354455\n",
-      "Total loss: 4.95105. L2-loss: 0.34979847\n",
-      "\n",
-      "\n",
-      "Epoch: 4\n",
-      "Learning rate: 0.009500\n",
-      "Training on batch 1000/1734\n",
-      "Smile task train accuracy: 92.6913843691651\n",
-      "Gender task train accuracy: 87.64547149956762\n",
-      "Age task train error: 23.703087\n",
-      "Smile loss: 0.28748268\n",
-      "Gender loss: 0.35309085\n",
-      "Age loss: 3.916551\n",
-      "Total loss: 4.9108706. L2-loss: 0.35374624\n",
-      "\n",
-      "\n",
-      "Epoch: 5\n",
-      "Learning rate: 0.009025\n",
-      "Training on batch 1000/1734\n",
-      "Smile task train accuracy: 92.90834528056547\n",
-      "Gender task train accuracy: 87.93423218788422\n",
-      "Age task train error: 23.670803\n",
-      "Smile loss: 0.28477466\n",
-      "Gender loss: 0.34857315\n",
-      "Age loss: 3.9004726\n",
-      "Total loss: 4.89257. L2-loss: 0.35875005\n",
-      "\n",
-      "\n",
-      "Epoch: 6\n",
-      "Learning rate: 0.009025\n"
+      "Default GPU Device: /device:GPU:0\n"
      ]
     }
    ],
@@ -242,19 +178,19 @@
     "\n",
     "saver = tf.train.Saver()\n",
     "\n",
-    "if not os.path.isfile(SAVE_FOLDER3 + 'model-age101.ckpt.index'):\n",
+    "if not os.path.isfile('./save/current5/model-age101.ckpt.index'):\n",
     "    print('Create new model')\n",
     "    sess.run(tf.global_variables_initializer())\n",
     "    print('OK')\n",
     "else:\n",
     "    print('Restoring existed model')\n",
-    "    saver.restore(sess, SAVE_FOLDER3 + 'model-age101.ckpt')\n",
+    "    saver.restore(sess, './save/current5/model-age101.ckpt.index')\n",
     "    print('OK')\n",
     "\n",
     "loss_summary_placeholder = tf.placeholder(tf.float32)\n",
     "tf.summary.scalar('loss', loss_summary_placeholder)\n",
     "merge_summary = tf.summary.merge_all()\n",
-    "writer = tf.summary.FileWriter(\"./summary3/\", graph=tf.get_default_graph())\n",
+    "writer = tf.summary.FileWriter(\"./summary/summary5/\", graph=tf.get_default_graph())\n",
     "\n",
     "learning_rate = tf.get_collection('learning_rate')[0]\n",
     "current_epoch = (int)(global_step.eval() / (len(train_data) // BATCH_SIZE))\n",
@@ -289,8 +225,8 @@
     "    print(\"Learning rate: %f\" % learning_rate.eval())\n",
     "#     for batch in range(number_batch):\n",
     "    for batch in range(number_batch):\n",
-    "        if (batch+1)%1000==0:\n",
-    "            print('Training on batch {0}/{1}'.format(str(batch + 1), str(number_batch)))\n",
+    "#         if (batch+1)%1000==0:\n",
+    "#             print('Training on batch {0}/{1}'.format(str(batch + 1), str(number_batch)))\n",
     "        top = batch * BATCH_SIZE\n",
     "        bot = min((batch + 1) * BATCH_SIZE, len(train_data))\n",
     "        batch_img = np.asarray(train_img[top:bot])\n",
@@ -384,10 +320,10 @@
     "    summary = sess.run(merge_summary, feed_dict={loss_summary_placeholder: avg_ttl})\n",
     "    writer.add_summary(summary, global_step=epoch)\n",
     "\n",
-    "    with open('log2.csv', 'a') as f:\n",
+    "    with open('log5.csv', 'a') as f:\n",
     "        # epochs, smile_train_accuracy, gender_train_accuracy, age_train_accuracy,\n",
     "        # avg_smile_loss, avg_gender_loss, avg_age_loss, avg_ttl, avg_rgl\n",
-    "        f.write('{0},{1},{2},{3},{4},{5},{6},{7},{8}\\n'.format(current_epoch, smile_train_accuracy, gender_train_accuracy, age_train_accuracy, avg_smile_loss, avg_gender_loss, avg_age_loss, avg_ttl, avg_rgl))\n",
+    "        f.write('{0},{1},{2},{3},{4},{5},{6},{7},{8}\\n'.format(epoch, smile_train_accuracy, gender_train_accuracy, age_train_accuracy, avg_smile_loss, avg_gender_loss, avg_age_loss, avg_ttl, avg_rgl))\n",
     "\n",
     "    print('Smile task train accuracy: ' + str(smile_train_accuracy * 100))\n",
     "    print('Gender task train accuracy: ' + str(gender_train_accuracy * 100))\n",
@@ -401,7 +337,7 @@
     "\n",
     "    print('\\n')\n",
     "    \n",
-    "    saver.save(sess, SAVE_FOLDER3 + 'model-age101.ckpt')"
+    "    saver.save(sess, './save/current5/model-age101.ckpt.index')"
    ]
   },
   {