dens169+fine tuning+dropout.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import Dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/storage/omid/venv3/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n",
      "/storage/omid/venv3/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "custom_layers\n",
      "data_gen\n",
      "dens121.ipynb\n",
      "dense169.ipynb\n",
      "densenet121.py\n",
      "densenet161.py\n",
      "densenet169.py\n",
      "denset201.ipynb\n",
      "googlenet.ipynb\n",
      "googlenet.py\n",
      "imagenet_models\n",
      "inception v3.ipynb\n",
      "inception_v3.py\n",
      "INCEPTION_v3_starter.ipynb\n",
      "inception_v4.py\n",
      "inception_version4.ipynb\n",
      "keras_VGG19_without pretrain.ipynb\n",
      "labels.csv\n",
      "LICENSE\n",
      "load_cifar10.py\n",
      "load_cifar10.pyc\n",
      "mixed.ipynb\n",
      "README.md\n",
      "resnet101.ipynb\n",
      "resnet_101.py\n",
      "resnet-151.ipynb\n",
      "resnet_152.py\n",
      "resnet50.ipynb\n",
      "resnet_50.py\n",
      "result-2.csv\n",
      "result-3.csv\n",
      "result-4.csv\n",
      "result-5.csv\n",
      "result-6.csv\n",
      "result-8.csv\n",
      "sample_submission.csv\n",
      "test\n",
      "train\n",
      "untitled\n",
      "Untitled1.ipynb\n",
      "Untitled.ipynb\n",
      "vgg16.py\n",
      "VGG19.ipynb\n",
      "vgg19.py\n",
      "view\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10222/10222 [00:37<00:00, 269.12it/s]\n",
      "100%|██████████| 10357/10357 [00:30<00:00, 338.56it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10222, 224, 224, 3)\n",
      "(10222, 120)\n",
      "(10357, 224, 224, 3)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:84: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (7, 7), use_bias=False, strides=(2, 2), name=\"conv1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv2_1_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv2_1_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:226: UserWarning: The `merge` function is deprecated and will be removed after 08/2017. Use instead layers from `keras.layers.merge`, e.g. `add`, `concatenate`, etc.\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/Keras-2.1.2-py3.5.egg/keras/legacy/layers.py:458: UserWarning: The `Merge` layer is deprecated and will be removed after 08/2017. Use instead layers from `keras.layers.merge`, e.g. `add`, `concatenate`, etc.\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv2_2_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv2_2_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv2_3_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv2_3_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv2_4_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv2_4_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv2_5_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv2_5_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv2_6_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv2_6_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:197: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv2_blk\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv3_1_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv3_1_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv3_2_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv3_2_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv3_3_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv3_3_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv3_4_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv3_4_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv3_5_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv3_5_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv3_6_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv3_6_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv3_7_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv3_7_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv3_8_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv3_8_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv3_9_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv3_9_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv3_10_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv3_10_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv3_11_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv3_11_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv3_12_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv3_12_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:197: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(256, (1, 1), use_bias=False, name=\"conv3_blk\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv4_1_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv4_1_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv4_2_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv4_2_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv4_3_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv4_3_x2\")`\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv4_4_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv4_4_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv4_5_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv4_5_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv4_6_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv4_6_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv4_7_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv4_7_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv4_8_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv4_8_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv4_9_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv4_9_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv4_10_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv4_10_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv4_11_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv4_11_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv4_12_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv4_12_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv4_13_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv4_13_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv4_14_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv4_14_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv4_15_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv4_15_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv4_16_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv4_16_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv4_17_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv4_17_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv4_18_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv4_18_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv4_19_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv4_19_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv4_20_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv4_20_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv4_21_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv4_21_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv4_22_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv4_22_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv4_23_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv4_23_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv4_24_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv4_24_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv4_25_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv4_25_x2\")`\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv4_26_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv4_26_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv4_27_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv4_27_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv4_28_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv4_28_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv4_29_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv4_29_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv4_30_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv4_30_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv4_31_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv4_31_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv4_32_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv4_32_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:197: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(640, (1, 1), use_bias=False, name=\"conv4_blk\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv5_1_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv5_1_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv5_2_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv5_2_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv5_3_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv5_3_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv5_4_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv5_4_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv5_5_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv5_5_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv5_6_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv5_6_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv5_7_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv5_7_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv5_8_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv5_8_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv5_9_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv5_9_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv5_10_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv5_10_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv5_11_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv5_11_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv5_12_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv5_12_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv5_13_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv5_13_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv5_14_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv5_14_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv5_15_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv5_15_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv5_16_x1\")`\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv5_16_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv5_17_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv5_17_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv5_18_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv5_18_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv5_19_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv5_19_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv5_20_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv5_20_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv5_21_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv5_21_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv5_22_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv5_22_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv5_23_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv5_23_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv5_24_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv5_24_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv5_25_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv5_25_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv5_26_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv5_26_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv5_27_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv5_27_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv5_28_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv5_28_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv5_29_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv5_29_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv5_30_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv5_30_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv5_31_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv5_31_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:160: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(128, (1, 1), use_bias=False, name=\"conv5_32_x1\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:170: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), use_bias=False, name=\"conv5_32_x2\")`\n",
      "/storage/omid/venv3/lib/python3.5/site-packages/ipykernel_launcher.py:298: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 7155 samples, validate on 3067 samples\n",
      "Epoch 1/50\n",
      "7155/7155 [==============================] - 272s 38ms/step - loss: 4.2551 - acc: 0.1326 - val_loss: 3.2691 - val_acc: 0.3668\n",
      "Epoch 2/50\n",
      "7155/7155 [==============================] - 277s 39ms/step - loss: 2.9532 - acc: 0.4324 - val_loss: 2.3163 - val_acc: 0.5921\n",
      "Epoch 3/50\n",
      "7155/7155 [==============================] - 277s 39ms/step - loss: 2.3192 - acc: 0.5632 - val_loss: 1.8258 - val_acc: 0.6824\n",
      "Epoch 4/50\n",
      "7155/7155 [==============================] - 278s 39ms/step - loss: 2.0233 - acc: 0.6099 - val_loss: 1.5532 - val_acc: 0.7268\n",
      "Epoch 5/50\n",
      "7155/7155 [==============================] - 278s 39ms/step - loss: 1.8156 - acc: 0.6474 - val_loss: 1.3766 - val_acc: 0.7414\n",
      "Epoch 6/50\n",
      "7155/7155 [==============================] - 273s 38ms/step - loss: 1.7297 - acc: 0.6553 - val_loss: 1.2640 - val_acc: 0.7535\n",
      "Epoch 7/50\n",
      "7155/7155 [==============================] - 246s 34ms/step - loss: 1.6491 - acc: 0.6686 - val_loss: 1.1769 - val_acc: 0.7636\n",
      "Epoch 8/50\n",
      "7155/7155 [==============================] - 203s 28ms/step - loss: 1.5519 - acc: 0.6893 - val_loss: 1.1051 - val_acc: 0.7757\n",
      "Epoch 9/50\n",
      "7155/7155 [==============================] - 203s 28ms/step - loss: 1.5206 - acc: 0.6887 - val_loss: 1.0601 - val_acc: 0.7793\n",
      "Epoch 10/50\n",
      "7155/7155 [==============================] - 203s 28ms/step - loss: 1.4662 - acc: 0.6999 - val_loss: 1.0169 - val_acc: 0.7799\n",
      "Epoch 11/50\n",
      "7155/7155 [==============================] - 203s 28ms/step - loss: 1.4567 - acc: 0.7001 - val_loss: 0.9818 - val_acc: 0.7871\n",
      "Epoch 12/50\n",
      "7155/7155 [==============================] - 203s 28ms/step - loss: 1.3782 - acc: 0.7188 - val_loss: 0.9586 - val_acc: 0.7848\n",
      "Epoch 13/50\n",
      "7155/7155 [==============================] - 203s 28ms/step - loss: 1.3393 - acc: 0.7270 - val_loss: 0.9335 - val_acc: 0.7923\n",
      "Epoch 14/50\n",
      "7155/7155 [==============================] - 203s 28ms/step - loss: 1.3535 - acc: 0.7201 - val_loss: 0.9151 - val_acc: 0.7946\n",
      "Epoch 15/50\n",
      "7155/7155 [==============================] - 203s 28ms/step - loss: 1.2900 - acc: 0.7314 - val_loss: 0.8961 - val_acc: 0.8008\n",
      "Epoch 16/50\n",
      "7155/7155 [==============================] - 203s 28ms/step - loss: 1.2737 - acc: 0.7332 - val_loss: 0.8821 - val_acc: 0.7952\n",
      "Epoch 17/50\n",
      "7155/7155 [==============================] - 203s 28ms/step - loss: 1.2517 - acc: 0.7417 - val_loss: 0.8657 - val_acc: 0.7975\n",
      "Epoch 18/50\n",
      "7155/7155 [==============================] - 203s 28ms/step - loss: 1.2667 - acc: 0.7361 - val_loss: 0.8531 - val_acc: 0.7995\n",
      "Epoch 19/50\n",
      "7155/7155 [==============================] - 203s 28ms/step - loss: 1.2355 - acc: 0.7435 - val_loss: 0.8466 - val_acc: 0.7995\n",
      "Epoch 20/50\n",
      "7155/7155 [==============================] - 203s 28ms/step - loss: 1.2539 - acc: 0.7360 - val_loss: 0.8366 - val_acc: 0.7982\n",
      "Epoch 21/50\n",
      "7155/7155 [==============================] - 203s 28ms/step - loss: 1.1814 - acc: 0.7494 - val_loss: 0.8247 - val_acc: 0.8031\n",
      "Epoch 22/50\n",
      "7155/7155 [==============================] - 203s 28ms/step - loss: 1.2116 - acc: 0.7449 - val_loss: 0.8194 - val_acc: 0.8037\n",
      "Epoch 23/50\n",
      "7155/7155 [==============================] - 202s 28ms/step - loss: 1.2064 - acc: 0.7488 - val_loss: 0.8151 - val_acc: 0.8031\n",
      "Epoch 24/50\n",
      "7155/7155 [==============================] - 201s 28ms/step - loss: 1.1640 - acc: 0.7551 - val_loss: 0.8083 - val_acc: 0.8057\n",
      "Epoch 25/50\n",
      "7155/7155 [==============================] - 200s 28ms/step - loss: 1.1761 - acc: 0.7514 - val_loss: 0.7995 - val_acc: 0.8037\n",
      "Epoch 26/50\n",
      "7155/7155 [==============================] - 200s 28ms/step - loss: 1.1987 - acc: 0.7431 - val_loss: 0.7943 - val_acc: 0.8014\n",
      "Epoch 27/50\n",
      "7155/7155 [==============================] - 200s 28ms/step - loss: 1.1728 - acc: 0.7509 - val_loss: 0.7914 - val_acc: 0.8057\n",
      "Epoch 28/50\n",
      "7155/7155 [==============================] - 200s 28ms/step - loss: 1.1586 - acc: 0.7546 - val_loss: 0.7894 - val_acc: 0.8109\n",
      "Epoch 29/50\n",
      "7155/7155 [==============================] - 200s 28ms/step - loss: 1.1161 - acc: 0.7651 - val_loss: 0.7800 - val_acc: 0.7998\n",
      "Epoch 30/50\n",
      "7155/7155 [==============================] - 200s 28ms/step - loss: 1.1173 - acc: 0.7609 - val_loss: 0.7785 - val_acc: 0.8037\n",
      "Epoch 31/50\n",
      "7155/7155 [==============================] - 200s 28ms/step - loss: 1.1078 - acc: 0.7628 - val_loss: 0.7737 - val_acc: 0.8011\n",
      "Epoch 32/50\n",
      "7155/7155 [==============================] - 200s 28ms/step - loss: 1.1068 - acc: 0.7638 - val_loss: 0.7756 - val_acc: 0.8031\n",
      "Epoch 33/50\n",
      "7155/7155 [==============================] - 200s 28ms/step - loss: 1.0995 - acc: 0.7644 - val_loss: 0.7665 - val_acc: 0.8047\n",
      "Epoch 34/50\n",
      "7155/7155 [==============================] - 200s 28ms/step - loss: 1.0931 - acc: 0.7653 - val_loss: 0.7637 - val_acc: 0.8014\n",
      "Epoch 35/50\n",
      "7155/7155 [==============================] - 200s 28ms/step - loss: 1.1047 - acc: 0.7607 - val_loss: 0.7598 - val_acc: 0.8047\n",
      "Epoch 36/50\n",
      "7155/7155 [==============================] - 200s 28ms/step - loss: 1.0989 - acc: 0.7637 - val_loss: 0.7587 - val_acc: 0.8018\n",
      "Epoch 37/50\n",
      "7155/7155 [==============================] - 200s 28ms/step - loss: 1.0618 - acc: 0.7706 - val_loss: 0.7593 - val_acc: 0.8040\n",
      "Epoch 38/50\n",
      "7155/7155 [==============================] - 200s 28ms/step - loss: 1.0667 - acc: 0.7723 - val_loss: 0.7562 - val_acc: 0.8018\n",
      "Epoch 39/50\n",
      "7155/7155 [==============================] - 199s 28ms/step - loss: 1.0505 - acc: 0.7734 - val_loss: 0.7535 - val_acc: 0.8076\n",
      "Epoch 40/50\n",
      "7155/7155 [==============================] - 200s 28ms/step - loss: 1.0579 - acc: 0.7718 - val_loss: 0.7506 - val_acc: 0.8047\n",
      "Epoch 41/50\n",
      "7155/7155 [==============================] - 199s 28ms/step - loss: 1.0244 - acc: 0.7793 - val_loss: 0.7485 - val_acc: 0.8037\n",
      "Epoch 42/50\n",
      "7155/7155 [==============================] - 200s 28ms/step - loss: 1.0396 - acc: 0.7741 - val_loss: 0.7502 - val_acc: 0.8034\n",
      "Epoch 43/50\n",
      "7155/7155 [==============================] - 200s 28ms/step - loss: 1.0630 - acc: 0.7683 - val_loss: 0.7442 - val_acc: 0.8047\n",
      "Epoch 44/50\n",
      "7155/7155 [==============================] - 199s 28ms/step - loss: 1.0028 - acc: 0.7838 - val_loss: 0.7441 - val_acc: 0.8018\n",
      "Epoch 45/50\n",
      "7155/7155 [==============================] - 199s 28ms/step - loss: 1.0394 - acc: 0.7715 - val_loss: 0.7415 - val_acc: 0.8037\n",
      "Epoch 46/50\n",
      "7155/7155 [==============================] - 199s 28ms/step - loss: 1.0371 - acc: 0.7722 - val_loss: 0.7401 - val_acc: 0.8060\n",
      "Epoch 47/50\n",
      "7155/7155 [==============================] - 199s 28ms/step - loss: 1.0171 - acc: 0.7769 - val_loss: 0.7419 - val_acc: 0.8014\n",
      "Epoch 48/50\n",
      "7155/7155 [==============================] - 199s 28ms/step - loss: 0.9878 - acc: 0.7860 - val_loss: 0.7391 - val_acc: 0.8037\n",
      "Epoch 49/50\n",
      "7155/7155 [==============================] - 200s 28ms/step - loss: 1.0164 - acc: 0.7776 - val_loss: 0.7364 - val_acc: 0.8044\n",
      "Epoch 50/50\n",
      "7155/7155 [==============================] - 140s 20ms/step - loss: 0.9889 - acc: 0.7825 - val_loss: 0.7407 - val_acc: 0.8034\n",
      "3067/3067 [==============================] - 67s 22ms/step\n"
     ]
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "# -*- coding: utf-8 -*-\n",
    "\n",
    "import tensorflow as tf\n",
    "from keras.backend.tensorflow_backend import set_session\n",
    "config = tf.ConfigProto()\n",
    "config.gpu_options.per_process_gpu_memory_fraction = 0.3\n",
    "#config.gpu_options.visible_device_list = \"0\"\n",
    "set_session(tf.Session(config=config))\n",
    "from keras.optimizers import SGD\n",
    "from keras.layers import Input, merge, ZeroPadding2D\n",
    "from keras.layers.core import Dense, Dropout, Activation\n",
    "from keras.layers.convolutional import Convolution2D\n",
    "from keras.layers.pooling import AveragePooling2D, GlobalAveragePooling2D, MaxPooling2D\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras.models import Model\n",
    "import keras.backend as K\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import log_loss\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import keras\n",
    "from keras.applications.vgg19 import VGG19\n",
    "from keras.models import Model\n",
    "from keras.layers import Dense, Dropout, Flatten\n",
    "\n",
    "import os\n",
    "from tqdm import tqdm\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split\n",
    "import cv2\n",
    "\n",
    "from custom_layers.scale_layer import Scale\n",
    "\n",
    "from subprocess import check_output\n",
    "\n",
    "\n",
    "from sklearn.metrics import log_loss\n",
    "\n",
    "from custom_layers.scale_layer import Scale\n",
    "\n",
    "#from load_cifar10 import load_cifar10_data\n",
    "\n",
    "def densenet169_model(img_rows, img_cols, color_type=1, nb_dense_block=4, growth_rate=32, nb_filter=64, reduction=0.5, dropout_rate=0.0, weight_decay=1e-4, num_classes=None):\n",
    "    '''\n",
    "    DenseNet 169 Model for Keras\n",
    "    Model Schema is based on \n",
    "    https://github.com/flyyufelix/DenseNet-Keras\n",
    "    ImageNet Pretrained Weights \n",
    "    Theano: https://drive.google.com/open?id=0Byy2AcGyEVxfN0d3T1F1MXg0NlU\n",
    "    TensorFlow: https://drive.google.com/open?id=0Byy2AcGyEVxfSEc5UC1ROUFJdmM\n",
    "    # Arguments\n",
    "        nb_dense_block: number of dense blocks to add to end\n",
    "        growth_rate: number of filters to add per dense block\n",
    "        nb_filter: initial number of filters\n",
    "        reduction: reduction factor of transition blocks.\n",
    "        dropout_rate: dropout rate\n",
    "        weight_decay: weight decay factor\n",
    "        classes: optional number of classes to classify images\n",
    "        weights_path: path to pre-trained weights\n",
    "    # Returns\n",
    "        A Keras model instance.\n",
    "    '''\n",
    "    eps = 1.1e-5\n",
    "\n",
    "    # compute compression factor\n",
    "    compression = 1.0 - reduction\n",
    "\n",
    "    # Handle Dimension Ordering for different backends\n",
    "    global concat_axis\n",
    "    if K.image_dim_ordering() == 'tf':\n",
    "      concat_axis = 3\n",
    "      img_input = Input(shape=(224, 224, 3), name='data')\n",
    "    else:\n",
    "      concat_axis = 1\n",
    "      img_input = Input(shape=(3, 224, 224), name='data')\n",
    "\n",
    "    # From architecture for ImageNet (Table 1 in the paper)\n",
    "    nb_filter = 64\n",
    "    nb_layers = [6,12,32,32] # For DenseNet-169\n",
    "\n",
    "    # Initial convolution\n",
    "    x = ZeroPadding2D((3, 3), name='conv1_zeropadding')(img_input)\n",
    "    x = Convolution2D(nb_filter, 7, 7, subsample=(2, 2), name='conv1', bias=False)(x)\n",
    "    x = BatchNormalization(epsilon=eps, axis=concat_axis, name='conv1_bn')(x)\n",
    "    x = Scale(axis=concat_axis, name='conv1_scale')(x)\n",
    "    x = Activation('relu', name='relu1')(x)\n",
    "    x = ZeroPadding2D((1, 1), name='pool1_zeropadding')(x)\n",
    "    x = MaxPooling2D((3, 3), strides=(2, 2), name='pool1')(x)\n",
    "\n",
    "    # Add dense blocks\n",
    "    for block_idx in range(nb_dense_block - 1):\n",
    "        stage = block_idx+2\n",
    "        x, nb_filter = dense_block(x, stage, nb_layers[block_idx], nb_filter, growth_rate, dropout_rate=dropout_rate, weight_decay=weight_decay)\n",
    "\n",
    "        # Add transition_block\n",
    "        x = transition_block(x, stage, nb_filter, compression=compression, dropout_rate=dropout_rate, weight_decay=weight_decay)\n",
    "        nb_filter = int(nb_filter * compression)\n",
    "\n",
    "    final_stage = stage + 1\n",
    "    x, nb_filter = dense_block(x, final_stage, nb_layers[-1], nb_filter, growth_rate, dropout_rate=dropout_rate, weight_decay=weight_decay)\n",
    "\n",
    "    x = BatchNormalization(epsilon=eps, axis=concat_axis, name='conv'+str(final_stage)+'_blk_bn')(x)\n",
    "    x = Scale(axis=concat_axis, name='conv'+str(final_stage)+'_blk_scale')(x)\n",
    "    x = Activation('relu', name='relu'+str(final_stage)+'_blk')(x)\n",
    "\n",
    "    x_fc = GlobalAveragePooling2D(name='pool'+str(final_stage))(x)\n",
    "    x_fc = Dense(1000, name='fc6')(x_fc)\n",
    "    x_fc = Activation('softmax', name='prob')(x_fc)\n",
    "\n",
    "    model = Model(img_input, x_fc, name='densenet')\n",
    "\n",
    "    if K.image_dim_ordering() == 'th':\n",
    "      # Use pre-trained weights for Theano backend\n",
    "      weights_path = 'imagenet_models/densenet169_weights_th.h5'\n",
    "    else:\n",
    "      # Use pre-trained weights for Tensorflow backend\n",
    "      weights_path = 'imagenet_models/densenet169_weights_tf.h5'\n",
    "\n",
    "    model.load_weights(weights_path, by_name=True)\n",
    "    \n",
    "    for layer in model.layers[:-4]:\n",
    "        layer.trainable = False\n",
    "    # Truncate and replace softmax layer for transfer learning\n",
    "    # Cannot use model.layers.pop() since model is not of Sequential() type\n",
    "    # The method below works since pre-trained weights are stored in layers but not in the model\n",
    "    x_newfc = GlobalAveragePooling2D(name='pool'+str(final_stage))(x)\n",
    "    x_newfc = Dense(num_classes, name='fc6')(x_newfc)\n",
    "    x_newfc = Dropout(0.2)(x_newfc)\n",
    "    x_newfc = Activation('softmax', name='prob')(x_newfc)\n",
    "\n",
    "    model = Model(img_input, x_newfc)\n",
    "\n",
    "    # Learning rate is changed to 0.001\n",
    "    sgd = SGD(lr=1e-3, decay=1e-6, momentum=0.9, nesterov=True)\n",
    "    model.compile(optimizer=sgd, loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "    return model\n",
    "\n",
    "\n",
    "def conv_block(x, stage, branch, nb_filter, dropout_rate=None, weight_decay=1e-4):\n",
    "    '''Apply BatchNorm, Relu, bottleneck 1x1 Conv2D, 3x3 Conv2D, and option dropout\n",
    "        # Arguments\n",
    "            x: input tensor \n",
    "            stage: index for dense block\n",
    "            branch: layer index within each dense block\n",
    "            nb_filter: number of filters\n",
    "            dropout_rate: dropout rate\n",
    "            weight_decay: weight decay factor\n",
    "    '''\n",
    "    eps = 1.1e-5\n",
    "    conv_name_base = 'conv' + str(stage) + '_' + str(branch)\n",
    "    relu_name_base = 'relu' + str(stage) + '_' + str(branch)\n",
    "\n",
    "    # 1x1 Convolution (Bottleneck layer)\n",
    "    inter_channel = nb_filter * 4  \n",
    "    x = BatchNormalization(epsilon=eps, axis=concat_axis, name=conv_name_base+'_x1_bn')(x)\n",
    "    x = Scale(axis=concat_axis, name=conv_name_base+'_x1_scale')(x)\n",
    "    x = Activation('relu', name=relu_name_base+'_x1')(x)\n",
    "    x = Convolution2D(inter_channel, 1, 1, name=conv_name_base+'_x1', bias=False)(x)\n",
    "\n",
    "    if dropout_rate:\n",
    "        x = Dropout(dropout_rate)(x)\n",
    "\n",
    "    # 3x3 Convolution\n",
    "    x = BatchNormalization(epsilon=eps, axis=concat_axis, name=conv_name_base+'_x2_bn')(x)\n",
    "    x = Scale(axis=concat_axis, name=conv_name_base+'_x2_scale')(x)\n",
    "    x = Activation('relu', name=relu_name_base+'_x2')(x)\n",
    "    x = ZeroPadding2D((1, 1), name=conv_name_base+'_x2_zeropadding')(x)\n",
    "    x = Convolution2D(nb_filter, 3, 3, name=conv_name_base+'_x2', bias=False)(x)\n",
    "\n",
    "    if dropout_rate:\n",
    "        x = Dropout(dropout_rate)(x)\n",
    "\n",
    "    return x\n",
    "\n",
    "\n",
    "def transition_block(x, stage, nb_filter, compression=1.0, dropout_rate=None, weight_decay=1E-4):\n",
    "    ''' Apply BatchNorm, 1x1 Convolution, averagePooling, optional compression, dropout \n",
    "        # Arguments\n",
    "            x: input tensor\n",
    "            stage: index for dense block\n",
    "            nb_filter: number of filters\n",
    "            compression: calculated as 1 - reduction. Reduces the number of feature maps in the transition block.\n",
    "            dropout_rate: dropout rate\n",
    "            weight_decay: weight decay factor\n",
    "    '''\n",
    "\n",
    "    eps = 1.1e-5\n",
    "    conv_name_base = 'conv' + str(stage) + '_blk'\n",
    "    relu_name_base = 'relu' + str(stage) + '_blk'\n",
    "    pool_name_base = 'pool' + str(stage) \n",
    "\n",
    "    x = BatchNormalization(epsilon=eps, axis=concat_axis, name=conv_name_base+'_bn')(x)\n",
    "    x = Scale(axis=concat_axis, name=conv_name_base+'_scale')(x)\n",
    "    x = Activation('relu', name=relu_name_base)(x)\n",
    "    x = Convolution2D(int(nb_filter * compression), 1, 1, name=conv_name_base, bias=False)(x)\n",
    "\n",
    "    if dropout_rate:\n",
    "        x = Dropout(dropout_rate)(x)\n",
    "\n",
    "    x = AveragePooling2D((2, 2), strides=(2, 2), name=pool_name_base)(x)\n",
    "\n",
    "    return x\n",
    "\n",
    "\n",
    "def dense_block(x, stage, nb_layers, nb_filter, growth_rate, dropout_rate=None, weight_decay=1e-4, grow_nb_filters=True):\n",
    "    ''' Build a dense_block where the output of each conv_block is fed to subsequent ones\n",
    "        # Arguments\n",
    "            x: input tensor\n",
    "            stage: index for dense block\n",
    "            nb_layers: the number of layers of conv_block to append to the model.\n",
    "            nb_filter: number of filters\n",
    "            growth_rate: growth rate\n",
    "            dropout_rate: dropout rate\n",
    "            weight_decay: weight decay factor\n",
    "            grow_nb_filters: flag to decide to allow number of filters to grow\n",
    "    '''\n",
    "\n",
    "    eps = 1.1e-5\n",
    "    concat_feat = x\n",
    "\n",
    "    for i in range(nb_layers):\n",
    "        branch = i+1\n",
    "        x = conv_block(concat_feat, stage, branch, growth_rate, dropout_rate, weight_decay)\n",
    "        concat_feat = merge([concat_feat, x], mode='concat', concat_axis=concat_axis, name='concat_'+str(stage)+'_'+str(branch))\n",
    "\n",
    "        if grow_nb_filters:\n",
    "            nb_filter += growth_rate\n",
    "\n",
    "    return concat_feat, nb_filter\n",
    "\n",
    "if __name__ == '__main__':\n",
    "\n",
    "    # Example to fine-tune on 3000 samples from Cifar10\n",
    "\n",
    "    img_rows, img_cols = 224, 224 # Resolution of inputs\n",
    "    channel = 3\n",
    "    num_classes = 120\n",
    "    batch_size = 32\n",
    "    nb_epoch = 50\n",
    "    \n",
    "    directory_path = \"/storage/aydin/kaggle-dog/Kaggle-dog-breeds-classification/cnn_finetune-master/\"\n",
    "    \n",
    "    print(check_output([\"ls\", directory_path]).decode(\"utf8\"))\n",
    "\n",
    "    df_train = pd.read_csv(directory_path+'labels.csv')\n",
    "    df_test = pd.read_csv(directory_path+'sample_submission.csv')\n",
    "\n",
    "    targets_series = pd.Series(df_train['breed'])\n",
    "    one_hot = pd.get_dummies(targets_series, sparse = True)\n",
    "\n",
    "    one_hot_labels = np.asarray(one_hot)\n",
    "\n",
    "    im_size = 224\n",
    "    x_train = []\n",
    "    y_train = []\n",
    "    x_test = []\n",
    "\n",
    "    i = 0 \n",
    "    for f, breed in tqdm(df_train.values):\n",
    "        img = cv2.imread(directory_path+'train/{}.jpg'.format(f))\n",
    "        label = one_hot_labels[i]\n",
    "        x_train.append(cv2.resize(img, (im_size, im_size)))\n",
    "        y_train.append(label)\n",
    "        i += 1\n",
    "\n",
    "\n",
    "    for f in tqdm(df_test['id'].values):\n",
    "        img = cv2.imread(directory_path+'test/{}.jpg'.format(f))\n",
    "        x_test.append(cv2.resize(img, (im_size, im_size)))\n",
    "\n",
    "    y_train_raw = np.array(y_train, np.uint8)\n",
    "    x_train_raw = np.array(x_train, np.float32) / 255.\n",
    "    x_test  = np.array(x_test, np.float32) / 255.\n",
    "\n",
    "    print(x_train_raw.shape)\n",
    "    print(y_train_raw.shape)\n",
    "    print(x_test.shape)\n",
    "\n",
    "    num_class = y_train_raw.shape[1]\n",
    "\n",
    "    # Load Cifar10 data. Please implement your own load_data() module for your own dataset\n",
    "    #X_train, Y_train, X_valid, Y_valid = load_cifar10_data(img_rows, img_cols)\n",
    "    \n",
    "    X_train, X_valid, Y_train, Y_valid = train_test_split(x_train_raw, y_train_raw, test_size=0.3, random_state=1)\n",
    "    \n",
    "\n",
    "    # Load our model\n",
    "    model = densenet169_model(img_rows=img_rows, img_cols=img_cols, color_type=channel, num_classes=num_classes)\n",
    "\n",
    "    # Start Fine-tuning\n",
    "    model.fit(X_train, Y_train,\n",
    "              batch_size=batch_size,\n",
    "              nb_epoch=nb_epoch,\n",
    "              shuffle=True,\n",
    "              verbose=1,\n",
    "              validation_data=(X_valid, Y_valid),\n",
    "              )\n",
    "\n",
    "    # Make predictions\n",
    "    predictions_valid = model.predict(X_valid, batch_size=batch_size, verbose=1)\n",
    "\n",
    "    # Cross-entropy loss score\n",
    "    score = log_loss(Y_valid, predictions_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10357/10357 [==============================] - 345s 33ms/step\n"
     ]
    }
   ],
   "source": [
    "preds = model.predict(x_test, verbose=1)\n",
    "\n",
    "sub = pd.DataFrame(preds)\n",
    "# Set column names to those generated by the one-hot encoding earlier\n",
    "col_names = one_hot.columns.values\n",
    "sub.columns = col_names\n",
    "# Insert the column id from the sample_submission at the start of the data frame\n",
    "sub.insert(0, 'id', df_test['id'])\n",
    "sub.head(5)\n",
    "\n",
    "sub.to_csv(directory_path+\"result-9.csv\", encoding='utf-8', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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