model_visualizer.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Visualizer\n",
    "This notebook provides the tools required to view the model both as a summary and as a graph by training the model once then have tensorboard capture the tensor flowing once"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TensorFlow version:  2.1.0\n"
     ]
    }
   ],
   "source": [
    "# Load the TensorBoard notebook extension.\n",
    "%load_ext tensorboard\n",
    "\n",
    "from datetime import datetime\n",
    "from packaging import version\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import numpy as np\n",
    "from tensorflow.keras.models import load_model\n",
    "from tensorflow.keras.callbacks import TensorBoard\n",
    "\n",
    "print(\"TensorFlow version: \", tf.__version__)\n",
    "assert version.parse(tf.__version__).release[0] >= 2, \\\n",
    "    \"This notebook requires TensorFlow 2.0 or above.\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:No training configuration found in save file: the model was *not* compiled. Compile it manually.\n"
     ]
    }
   ],
   "source": [
    "model = load_model('supercombo.keras')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_imgs (InputLayer)         [(None, 12, 128, 256 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "permute (Permute)               (None, 128, 256, 12) 0           input_imgs[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "efficientnet-b2 (Model)         (None, 4, 8, 1408)   6442016     permute[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d (Conv2D)                 (None, 4, 8, 32)     45088       efficientnet-b2[1][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization (BatchNorma (None, 4, 8, 32)     128         conv2d[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "elu (ELU)                       (None, 4, 8, 32)     0           batch_normalization[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "desire (InputLayer)             [(None, 8)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "traffic_convention (InputLayer) [(None, 2)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "vision_features (Flatten)       (None, 1024)         0           elu[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "snpe_desire_pleaser (Dense)     (None, 8)            72          desire[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "snpe_traffic_pleaser (Dense)    (None, 2)            6           traffic_convention[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "proc_features (Concatenate)     (None, 1034)         0           vision_features[0][0]            \n",
      "                                                                 snpe_desire_pleaser[0][0]        \n",
      "                                                                 snpe_traffic_pleaser[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "pre_gru_dense (Dense)           (None, 1024)         1059840     proc_features[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "re_lu (ReLU)                    (None, 1024)         0           pre_gru_dense[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "rnn_state (InputLayer)          [(None, 512)]        0                                            \n",
      "__________________________________________________________________________________________________\n",
      "rnn_r (Dense)                   (None, 512)          524800      re_lu[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "rnn_rr (Dense)                  (None, 512)          262656      rnn_state[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "snpe_pleaser (Dense)            (None, 512)          262656      rnn_state[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_1 (Add)                     (None, 512)          0           rnn_r[0][0]                      \n",
      "                                                                 rnn_rr[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "rnn_z (Dense)                   (None, 512)          524800      re_lu[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "rnn_rz (Dense)                  (None, 512)          262656      rnn_state[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "rnn_rh (Dense)                  (None, 512)          262656      snpe_pleaser[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation (Activation)         (None, 512)          0           add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "add (Add)                       (None, 512)          0           rnn_z[0][0]                      \n",
      "                                                                 rnn_rz[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "rnn_h (Dense)                   (None, 512)          524800      re_lu[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "multiply (Multiply)             (None, 512)          0           rnn_rh[0][0]                     \n",
      "                                                                 activation[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 512)          0           add[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "add_2 (Add)                     (None, 512)          0           rnn_h[0][0]                      \n",
      "                                                                 multiply[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "one_minus (Dense)               (None, 512)          262656      activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 512)          0           add_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "multiply_1 (Multiply)           (None, 512)          0           activation_1[0][0]               \n",
      "                                                                 snpe_pleaser[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "multiply_2 (Multiply)           (None, 512)          0           one_minus[0][0]                  \n",
      "                                                                 activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "add_3 (Add)                     (None, 512)          0           multiply_1[0][0]                 \n",
      "                                                                 multiply_2[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "dense_1_path (Dense)            (None, 256)          131328      add_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "dense_1_left_lane (Dense)       (None, 256)          131328      add_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "dense_1_right_lane (Dense)      (None, 256)          131328      add_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "dense_1_lead (Dense)            (None, 256)          131328      add_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "dense_1_long_x (Dense)          (None, 256)          131328      add_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "dense_1_long_v (Dense)          (None, 256)          131328      add_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "dense_1_long_a (Dense)          (None, 256)          131328      add_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "relu_1_path (ReLU)              (None, 256)          0           dense_1_path[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "relu_1_left_lane (ReLU)         (None, 256)          0           dense_1_left_lane[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "relu_1_right_lane (ReLU)        (None, 256)          0           dense_1_right_lane[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "relu_1_lead (ReLU)              (None, 256)          0           dense_1_lead[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "relu_1_long_x (ReLU)            (None, 256)          0           dense_1_long_x[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "relu_1_long_v (ReLU)            (None, 256)          0           dense_1_long_v[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "relu_1_long_a (ReLU)            (None, 256)          0           dense_1_long_a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "dense_2_path (Dense)            (None, 256)          65792       relu_1_path[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "dense_2_left_lane (Dense)       (None, 256)          65792       relu_1_left_lane[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "dense_2_right_lane (Dense)      (None, 256)          65792       relu_1_right_lane[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "dense_2_lead (Dense)            (None, 256)          65792       relu_1_lead[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "dense_2_long_x (Dense)          (None, 256)          65792       relu_1_long_x[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense_2_long_v (Dense)          (None, 256)          65792       relu_1_long_v[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense_2_long_a (Dense)          (None, 256)          65792       relu_1_long_a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "relu_2_path (ReLU)              (None, 256)          0           dense_2_path[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "relu_2_left_lane (ReLU)         (None, 256)          0           dense_2_left_lane[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "relu_2_right_lane (ReLU)        (None, 256)          0           dense_2_right_lane[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "relu_2_lead (ReLU)              (None, 256)          0           dense_2_lead[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "relu_2_long_x (ReLU)            (None, 256)          0           dense_2_long_x[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "relu_2_long_v (ReLU)            (None, 256)          0           dense_2_long_v[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "relu_2_long_a (ReLU)            (None, 256)          0           dense_2_long_a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "meta_dense_1 (Dense)            (None, 256)          262400      vision_features[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "dense (Dense)                   (None, 64)           65600       vision_features[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "dense_3_path (Dense)            (None, 256)          65792       relu_2_path[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "dense_3_left_lane (Dense)       (None, 256)          65792       relu_2_left_lane[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "dense_3_right_lane (Dense)      (None, 256)          65792       relu_2_right_lane[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "dense_3_lead (Dense)            (None, 256)          65792       relu_2_lead[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "dense_3_long_x (Dense)          (None, 256)          65792       relu_2_long_x[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense_3_long_v (Dense)          (None, 256)          65792       relu_2_long_v[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense_3_long_a (Dense)          (None, 256)          65792       relu_2_long_a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "meta_relu_1 (ReLU)              (None, 256)          0           meta_dense_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "elu_1 (ELU)                     (None, 64)           0           dense[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "relu_3_path (ReLU)              (None, 256)          0           dense_3_path[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "relu_3_left_lane (ReLU)         (None, 256)          0           dense_3_left_lane[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "relu_3_right_lane (ReLU)        (None, 256)          0           dense_3_right_lane[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "relu_3_lead (ReLU)              (None, 256)          0           dense_3_lead[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "relu_3_long_x (ReLU)            (None, 256)          0           dense_3_long_x[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "relu_3_long_v (ReLU)            (None, 256)          0           dense_3_long_v[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "relu_3_long_a (ReLU)            (None, 256)          0           dense_3_long_a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "dense_1_desire_state (Dense)    (None, 128)          65664       add_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "desire_final_dense (Dense)      (None, 32)           8224        meta_relu_1[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 32)           2080        elu_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "dense_final_path (Dense)        (None, 128)          32896       relu_3_path[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "dense_final_left_lane (Dense)   (None, 128)          32896       relu_3_left_lane[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "dense_final_right_lane (Dense)  (None, 128)          32896       relu_3_right_lane[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "dense_final_lead (Dense)        (None, 128)          32896       relu_3_lead[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "dense_final_long_x (Dense)      (None, 128)          32896       relu_3_long_x[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense_final_long_v (Dense)      (None, 128)          32896       relu_3_long_v[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense_final_long_a (Dense)      (None, 128)          32896       relu_3_long_a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "relu_1_desire_state (ReLU)      (None, 128)          0           dense_1_desire_state[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "desire_reshape (Reshape)        (None, 4, 8)         0           desire_final_dense[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "elu_2 (ELU)                     (None, 32)           0           dense_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "relu_final_path (ReLU)          (None, 128)          0           dense_final_path[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "relu_final_left_lane (ReLU)     (None, 128)          0           dense_final_left_lane[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "relu_final_right_lane (ReLU)    (None, 128)          0           dense_final_right_lane[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "relu_final_lead (ReLU)          (None, 128)          0           dense_final_lead[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "relu_final_long_x (ReLU)        (None, 128)          0           dense_final_long_x[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "relu_final_long_v (ReLU)        (None, 128)          0           dense_final_long_v[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "relu_final_long_a (ReLU)        (None, 128)          0           dense_final_long_a[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "final_desire_state (Dense)      (None, 8)            1032        relu_1_desire_state[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "desire_pred (Softmax)           (None, 4, 8)         0           desire_reshape[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 12)           396         elu_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "path (Dense)                    (None, 385)          49665       relu_final_path[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "left_lane (Dense)               (None, 386)          49794       relu_final_left_lane[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "right_lane (Dense)              (None, 386)          49794       relu_final_right_lane[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "lead (Dense)                    (None, 58)           7482        relu_final_lead[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "long_x (Dense)                  (None, 200)          25800       relu_final_long_x[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "long_v (Dense)                  (None, 200)          25800       relu_final_long_v[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "long_a (Dense)                  (None, 200)          25800       relu_final_long_a[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "desire_state (Softmax)          (None, 8)            0           final_desire_state[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "meta (Dense)                    (None, 4)            1028        meta_relu_1[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "flatten (Flatten)               (None, 32)           0           desire_pred[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "pose (Activation)               (None, 12)           0           dense_2[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 13,146,045\n",
      "Trainable params: 13,078,413\n",
      "Non-trainable params: 67,632\n",
      "__________________________________________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1 samples\n",
      "1/1 [==============================] - 2s 2s/sample\n"
     ]
    },
    {
     "ename": "NotFoundError",
     "evalue": "Failed to create a directory: logs/train\\plugins\\profile\\2020-09-16_12-12-33; No such file or directory",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\miniconda3\\envs\\carnd\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training_v2.py\u001b[0m in \u001b[0;36mon_start\u001b[1;34m(self, model, callbacks, use_samples, verbose, mode)\u001b[0m\n\u001b[0;32m    752\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 753\u001b[1;33m       \u001b[1;32myield\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    754\u001b[0m       \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_successful_loop_finish\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\envs\\carnd\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training_v2.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[0;32m    341\u001b[0m                 \u001b[0mtraining_context\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtraining_context\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 342\u001b[1;33m                 total_epochs=epochs)\n\u001b[0m\u001b[0;32m    343\u001b[0m             \u001b[0mcbks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmake_logs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepoch_logs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtraining_result\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mModeKeys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTRAIN\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\envs\\carnd\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training_v2.py\u001b[0m in \u001b[0;36mrun_one_epoch\u001b[1;34m(model, iterator, execution_function, dataset_size, batch_size, strategy, steps_per_epoch, num_samples, mode, training_context, total_epochs)\u001b[0m\n\u001b[0;32m    127\u001b[0m       \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 128\u001b[1;33m         \u001b[0mbatch_outs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mexecution_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    129\u001b[0m       \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mStopIteration\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mOutOfRangeError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\envs\\carnd\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training_v2_utils.py\u001b[0m in \u001b[0;36mexecution_function\u001b[1;34m(input_fn)\u001b[0m\n\u001b[0;32m     97\u001b[0m     return nest.map_structure(_non_none_constant_value,\n\u001b[1;32m---> 98\u001b[1;33m                               distributed_function(input_fn))\n\u001b[0m\u001b[0;32m     99\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\envs\\carnd\\lib\\site-packages\\tensorflow_core\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    567\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 568\u001b[1;33m       \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    569\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\envs\\carnd\\lib\\site-packages\\tensorflow_core\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36m_call\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    614\u001b[0m       \u001b[0minitializers\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 615\u001b[1;33m       \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_initialize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0madd_initializers_to\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minitializers\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    616\u001b[0m     \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\envs\\carnd\\lib\\site-packages\\tensorflow_core\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36m_initialize\u001b[1;34m(self, args, kwds, add_initializers_to)\u001b[0m\n\u001b[0;32m    496\u001b[0m         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access\n\u001b[1;32m--> 497\u001b[1;33m             *args, **kwds))\n\u001b[0m\u001b[0;32m    498\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\envs\\carnd\\lib\\site-packages\\tensorflow_core\\python\\eager\\function.py\u001b[0m in \u001b[0;36m_get_concrete_function_internal_garbage_collected\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   2388\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2389\u001b[1;33m       \u001b[0mgraph_function\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_define_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2390\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\envs\\carnd\\lib\\site-packages\\tensorflow_core\\python\\eager\\function.py\u001b[0m in \u001b[0;36m_maybe_define_function\u001b[1;34m(self, args, kwargs)\u001b[0m\n\u001b[0;32m   2702\u001b[0m       \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_function_cache\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmissed\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcall_context_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2703\u001b[1;33m       \u001b[0mgraph_function\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_create_graph_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2704\u001b[0m       \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_function_cache\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprimary\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcache_key\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\envs\\carnd\\lib\\site-packages\\tensorflow_core\\python\\eager\\function.py\u001b[0m in \u001b[0;36m_create_graph_function\u001b[1;34m(self, args, kwargs, override_flat_arg_shapes)\u001b[0m\n\u001b[0;32m   2592\u001b[0m             \u001b[0moverride_flat_arg_shapes\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0moverride_flat_arg_shapes\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2593\u001b[1;33m             capture_by_value=self._capture_by_value),\n\u001b[0m\u001b[0;32m   2594\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_function_attributes\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\envs\\carnd\\lib\\site-packages\\tensorflow_core\\python\\framework\\func_graph.py\u001b[0m in \u001b[0;36mfunc_graph_from_py_func\u001b[1;34m(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)\u001b[0m\n\u001b[0;32m    977\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 978\u001b[1;33m       \u001b[0mfunc_outputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpython_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mfunc_args\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfunc_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    979\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\envs\\carnd\\lib\\site-packages\\tensorflow_core\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36mwrapped_fn\u001b[1;34m(*args, **kwds)\u001b[0m\n\u001b[0;32m    438\u001b[0m         \u001b[1;31m# the function a weak reference to itself to avoid a reference cycle.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 439\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mweak_wrapped_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__wrapped__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    440\u001b[0m     \u001b[0mweak_wrapped_fn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mweakref\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mref\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwrapped_fn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\envs\\carnd\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training_v2_utils.py\u001b[0m in \u001b[0;36mdistributed_function\u001b[1;34m(input_iterator)\u001b[0m\n\u001b[0;32m     84\u001b[0m     outputs = strategy.experimental_run_v2(\n\u001b[1;32m---> 85\u001b[1;33m         per_replica_function, args=args)\n\u001b[0m\u001b[0;32m     86\u001b[0m     \u001b[1;31m# Out of PerReplica outputs reduce or pick values to return.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\envs\\carnd\\lib\\site-packages\\tensorflow_core\\python\\distribute\\distribute_lib.py\u001b[0m in \u001b[0;36mexperimental_run_v2\u001b[1;34m(self, fn, args, kwargs)\u001b[0m\n\u001b[0;32m    762\u001b[0m                                 convert_by_default=False)\n\u001b[1;32m--> 763\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_extended\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcall_for_each_replica\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    764\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\envs\\carnd\\lib\\site-packages\\tensorflow_core\\python\\distribute\\distribute_lib.py\u001b[0m in \u001b[0;36mcall_for_each_replica\u001b[1;34m(self, fn, args, kwargs)\u001b[0m\n\u001b[0;32m   1818\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_container_strategy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscope\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1819\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call_for_each_replica\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1820\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\envs\\carnd\\lib\\site-packages\\tensorflow_core\\python\\distribute\\distribute_lib.py\u001b[0m in \u001b[0;36m_call_for_each_replica\u001b[1;34m(self, fn, args, kwargs)\u001b[0m\n\u001b[0;32m   2163\u001b[0m         replica_id_in_sync_group=constant_op.constant(0, dtypes.int32)):\n\u001b[1;32m-> 2164\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2165\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\envs\\carnd\\lib\\site-packages\\tensorflow_core\\python\\autograph\\impl\\api.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    291\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0mag_ctx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mControlStatusCtx\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstatus\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mag_ctx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mStatus\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDISABLED\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 292\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    293\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\envs\\carnd\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training_v2_utils.py\u001b[0m in \u001b[0;36mtrain_on_batch\u001b[1;34m(model, x, y, sample_weight, class_weight, reset_metrics, standalone)\u001b[0m\n\u001b[0;32m    432\u001b[0m       \u001b[0msample_weights\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msample_weights\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 433\u001b[1;33m       output_loss_metrics=model._output_loss_metrics)\n\u001b[0m\u001b[0;32m    434\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\envs\\carnd\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training_eager.py\u001b[0m in \u001b[0;36mtrain_on_batch\u001b[1;34m(model, inputs, targets, sample_weights, output_loss_metrics)\u001b[0m\n\u001b[0;32m    311\u001b[0m           \u001b[0mtraining\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 312\u001b[1;33m           output_loss_metrics=output_loss_metrics))\n\u001b[0m\u001b[0;32m    313\u001b[0m   \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\envs\\carnd\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training_eager.py\u001b[0m in \u001b[0;36m_process_single_batch\u001b[1;34m(model, inputs, targets, output_loss_metrics, sample_weights, training)\u001b[0m\n\u001b[0;32m    252\u001b[0m               \u001b[0msample_weights\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msample_weights\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 253\u001b[1;33m               training=training))\n\u001b[0m\u001b[0;32m    254\u001b[0m       \u001b[1;32mif\u001b[0m \u001b[0mtotal_loss\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\envs\\carnd\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training_eager.py\u001b[0m in \u001b[0;36m_model_loss\u001b[1;34m(model, inputs, targets, output_loss_metrics, sample_weights, training)\u001b[0m\n\u001b[0;32m    166\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mloss_fn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'reduction'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 167\u001b[1;33m           \u001b[0mper_sample_losses\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mloss_fn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtargets\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mouts\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    168\u001b[0m           weighted_losses = losses_utils.compute_weighted_loss(\n",
      "\u001b[1;32m~\\miniconda3\\envs\\carnd\\lib\\site-packages\\tensorflow_core\\python\\keras\\losses.py\u001b[0m in \u001b[0;36mcall\u001b[1;34m(self, y_true, y_pred)\u001b[0m\n\u001b[0;32m    220\u001b[0m           y_pred, y_true)\n\u001b[1;32m--> 221\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_fn_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    222\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\envs\\carnd\\lib\\site-packages\\tensorflow_core\\python\\keras\\losses.py\u001b[0m in \u001b[0;36msparse_categorical_crossentropy\u001b[1;34m(y_true, y_pred, from_logits, axis)\u001b[0m\n\u001b[0;32m    977\u001b[0m   return K.sparse_categorical_crossentropy(\n\u001b[1;32m--> 978\u001b[1;33m       y_true, y_pred, from_logits=from_logits, axis=axis)\n\u001b[0m\u001b[0;32m    979\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\envs\\carnd\\lib\\site-packages\\tensorflow_core\\python\\keras\\backend.py\u001b[0m in \u001b[0;36msparse_categorical_crossentropy\u001b[1;34m(target, output, from_logits, axis)\u001b[0m\n\u001b[0;32m   4572\u001b[0m       res = nn.sparse_softmax_cross_entropy_with_logits_v2(\n\u001b[1;32m-> 4573\u001b[1;33m           labels=target, logits=output)\n\u001b[0m\u001b[0;32m   4574\u001b[0m   \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\envs\\carnd\\lib\\site-packages\\tensorflow_core\\python\\ops\\nn_ops.py\u001b[0m in \u001b[0;36msparse_softmax_cross_entropy_with_logits_v2\u001b[1;34m(labels, logits, name)\u001b[0m\n\u001b[0;32m   3536\u001b[0m   return sparse_softmax_cross_entropy_with_logits(\n\u001b[1;32m-> 3537\u001b[1;33m       labels=labels, logits=logits, name=name)\n\u001b[0m\u001b[0;32m   3538\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\envs\\carnd\\lib\\site-packages\\tensorflow_core\\python\\ops\\nn_ops.py\u001b[0m in \u001b[0;36msparse_softmax_cross_entropy_with_logits\u001b[1;34m(_sentinel, labels, logits, name)\u001b[0m\n\u001b[0;32m   3452\u001b[0m                        \"dimension (received %s).\" % (labels_static_shape,\n\u001b[1;32m-> 3453\u001b[1;33m                                                      logits.get_shape()))\n\u001b[0m\u001b[0;32m   3454\u001b[0m     \u001b[1;31m# Check if no reshapes are required.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Shape mismatch: The shape of labels (received (385,)) should equal the shape of logits except for the last dimension (received (1, 385)).",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mNotFoundError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-6-4ac53fcf3523>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     16\u001b[0m           \u001b[0mbatch_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     17\u001b[0m           \u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 18\u001b[1;33m           callbacks=[tensorboard_callback])\n\u001b[0m\u001b[0;32m     19\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     20\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'tensorboard'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'--logdir logs/'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\envs\\carnd\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[0;32m    817\u001b[0m         \u001b[0mmax_queue_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmax_queue_size\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    818\u001b[0m         \u001b[0mworkers\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mworkers\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 819\u001b[1;33m         use_multiprocessing=use_multiprocessing)\n\u001b[0m\u001b[0;32m    820\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    821\u001b[0m   def evaluate(self,\n",
      "\u001b[1;32m~\\miniconda3\\envs\\carnd\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training_v2.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[0;32m    395\u001b[0m                       total_epochs=1)\n\u001b[0;32m    396\u001b[0m                   cbks.make_logs(model, epoch_logs, eval_result, ModeKeys.TEST,\n\u001b[1;32m--> 397\u001b[1;33m                                  prefix='val_')\n\u001b[0m\u001b[0;32m    398\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    399\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\envs\\carnd\\lib\\contextlib.py\u001b[0m in \u001b[0;36m__exit__\u001b[1;34m(self, type, value, traceback)\u001b[0m\n\u001b[0;32m     97\u001b[0m                 \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     98\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 99\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgen\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mthrow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtraceback\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    100\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    101\u001b[0m                 \u001b[1;31m# Suppress StopIteration *unless* it's the same exception that\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\envs\\carnd\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training_v2.py\u001b[0m in \u001b[0;36mon_start\u001b[1;34m(self, model, callbacks, use_samples, verbose, mode)\u001b[0m\n\u001b[0;32m    755\u001b[0m     \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    756\u001b[0m       \u001b[1;31m# End of all epochs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 757\u001b[1;33m       \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call_end_hook\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    758\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    759\u001b[0m   \u001b[1;33m@\u001b[0m\u001b[0mtf_contextlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcontextmanager\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\envs\\carnd\\lib\\site-packages\\tensorflow_core\\python\\keras\\callbacks.py\u001b[0m in \u001b[0;36m_call_end_hook\u001b[1;34m(self, mode)\u001b[0m\n\u001b[0;32m    260\u001b[0m     \u001b[1;34m\"\"\"Helper function for on_{train|test|predict}_end methods.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    261\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mmode\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mModeKeys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTRAIN\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 262\u001b[1;33m       \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mon_train_end\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    263\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mmode\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mModeKeys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTEST\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    264\u001b[0m       \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mon_test_end\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\envs\\carnd\\lib\\site-packages\\tensorflow_core\\python\\keras\\callbacks.py\u001b[0m in \u001b[0;36mon_train_end\u001b[1;34m(self, logs)\u001b[0m\n\u001b[0;32m    377\u001b[0m     \"\"\"\n\u001b[0;32m    378\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mcallback\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 379\u001b[1;33m       \u001b[0mcallback\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mon_train_end\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlogs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    380\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    381\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0mon_test_begin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\envs\\carnd\\lib\\site-packages\\tensorflow_core\\python\\keras\\callbacks.py\u001b[0m in \u001b[0;36mon_train_end\u001b[1;34m(self, logs)\u001b[0m\n\u001b[0;32m   1718\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0mon_train_end\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1719\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_is_tracing\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1720\u001b[1;33m       \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_log_trace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1721\u001b[0m     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_close_writers\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1722\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\envs\\carnd\\lib\\site-packages\\tensorflow_core\\python\\keras\\callbacks.py\u001b[0m in \u001b[0;36m_log_trace\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1745\u001b[0m             \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'batch_%d'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mstep\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1746\u001b[0m             \u001b[0mstep\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1747\u001b[1;33m             profiler_outdir=os.path.join(self._log_write_dir, 'train'))\n\u001b[0m\u001b[0;32m   1748\u001b[0m       \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_is_tracing\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1749\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\envs\\carnd\\lib\\site-packages\\tensorflow_core\\python\\ops\\summary_ops_v2.py\u001b[0m in \u001b[0;36mtrace_export\u001b[1;34m(name, step, profiler_outdir)\u001b[0m\n\u001b[0;32m   1238\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1239\u001b[0m   \u001b[1;32mif\u001b[0m \u001b[0mprofiler\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1240\u001b[1;33m     \u001b[0m_profiler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprofiler_outdir\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_profiler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1241\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1242\u001b[0m   \u001b[0mtrace_off\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\envs\\carnd\\lib\\site-packages\\tensorflow_core\\python\\eager\\profiler.py\u001b[0m in \u001b[0;36msave\u001b[1;34m(logdir, result)\u001b[0m\n\u001b[0;32m    139\u001b[0m       \u001b[0mlogdir\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'plugins'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'profile'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    140\u001b[0m       datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S'))\n\u001b[1;32m--> 141\u001b[1;33m   \u001b[0mgfile\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mMakeDirs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mplugin_dir\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    142\u001b[0m   \u001b[0mmaybe_create_event_file\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlogdir\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    143\u001b[0m   \u001b[1;32mwith\u001b[0m \u001b[0mgfile\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mOpen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mplugin_dir\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'local.trace'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'wb'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\envs\\carnd\\lib\\site-packages\\tensorflow_core\\python\\lib\\io\\file_io.py\u001b[0m in \u001b[0;36mrecursive_create_dir\u001b[1;34m(dirname)\u001b[0m\n\u001b[0;32m    438\u001b[0m     \u001b[0merrors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mOpError\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mIf\u001b[0m \u001b[0mthe\u001b[0m \u001b[0moperation\u001b[0m \u001b[0mfails\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    439\u001b[0m   \"\"\"\n\u001b[1;32m--> 440\u001b[1;33m   \u001b[0mrecursive_create_dir_v2\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdirname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    441\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    442\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\envs\\carnd\\lib\\site-packages\\tensorflow_core\\python\\lib\\io\\file_io.py\u001b[0m in \u001b[0;36mrecursive_create_dir_v2\u001b[1;34m(path)\u001b[0m\n\u001b[0;32m    453\u001b[0m     \u001b[0merrors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mOpError\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mIf\u001b[0m \u001b[0mthe\u001b[0m \u001b[0moperation\u001b[0m \u001b[0mfails\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    454\u001b[0m   \"\"\"\n\u001b[1;32m--> 455\u001b[1;33m   \u001b[0mpywrap_tensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mRecursivelyCreateDir\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcompat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mas_bytes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    456\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    457\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNotFoundError\u001b[0m: Failed to create a directory: logs/train\\plugins\\profile\\2020-09-16_12-12-33; No such file or directory"
     ]
    }
   ],
   "source": [
    "x_train = [tf.zeros((1, 12, 128, 256)),np.zeros((1,8)),np.zeros((1,2)),np.zeros((1,512))]\n",
    "# ['path', 'left_lane', 'right_lane', 'lead', 'long_x', 'long_v', 'long_a', 'meta', 'snpe_pleaser2', 'pose', 'add_3']\n",
    "y_train = [np.zeros((1,385)), np.zeros((1,386)), np.zeros((1,386)), np.zeros((1,58)), np.zeros((1,200)), np.zeros((1,200)), np.zeros((1,200)), np.zeros((1,4)), np.zeros((1,32)),np.zeros((1,32)), np.zeros((1,12)), np.zeros((1,512))]\n",
    "\n",
    "model.compile(\n",
    "    optimizer='sgd',\n",
    "    loss='sparse_categorical_crossentropy',\n",
    "    metrics=['accuracy'])\n",
    "\n",
    "logdir = \"logs/\"\n",
    "\n",
    "tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=logdir)\n",
    "\n",
    "model.fit(x_train,\n",
    "          y_train,\n",
    "          batch_size=1,\n",
    "          epochs=1,\n",
    "          callbacks=[tensorboard_callback])\n",
    "\n",
    "%tensorboard --logdir logs/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Done\n",
    "NotFoundError can be ignore. Now launch tensorboard by $ tensorboard --log-dir=\"< path_to_log >\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "InvocationException",
     "evalue": "GraphViz's executables not found",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mInvocationException\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-10-526994049cf2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mkeras\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"multi_input_and_output_model.png\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshow_shapes\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\miniconda3\\envs\\carnd\\lib\\site-packages\\tensorflow_core\\python\\keras\\utils\\vis_utils.py\u001b[0m in \u001b[0;36mplot_model\u001b[1;34m(model, to_file, show_shapes, show_layer_names, rankdir, expand_nested, dpi)\u001b[0m\n\u001b[0;32m    281\u001b[0m                      \u001b[0mrankdir\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrankdir\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    282\u001b[0m                      \u001b[0mexpand_nested\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mexpand_nested\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 283\u001b[1;33m                      dpi=dpi)\n\u001b[0m\u001b[0;32m    284\u001b[0m   \u001b[1;32mif\u001b[0m \u001b[0mdot\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    285\u001b[0m     \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\envs\\carnd\\lib\\site-packages\\tensorflow_core\\python\\keras\\utils\\vis_utils.py\u001b[0m in \u001b[0;36mmodel_to_dot\u001b[1;34m(model, show_shapes, show_layer_names, rankdir, expand_nested, dpi, subgraph)\u001b[0m\n\u001b[0;32m    100\u001b[0m   \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mnetwork\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    101\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 102\u001b[1;33m   \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mcheck_pydot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    103\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'IPython.core.magics.namespace'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodules\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    104\u001b[0m       \u001b[1;31m# We don't raise an exception here in order to avoid crashing notebook\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\envs\\carnd\\lib\\site-packages\\tensorflow_core\\python\\keras\\utils\\vis_utils.py\u001b[0m in \u001b[0;36mcheck_pydot\u001b[1;34m()\u001b[0m\n\u001b[0;32m     48\u001b[0m     \u001b[1;31m# Attempt to create an image of a blank graph\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     49\u001b[0m     \u001b[1;31m# to check the pydot/graphviz installation.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 50\u001b[1;33m     \u001b[0mpydot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpydot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     51\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     52\u001b[0m   \u001b[1;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\miniconda3\\envs\\carnd\\lib\\site-packages\\pydotplus\\graphviz.py\u001b[0m in \u001b[0;36mcreate\u001b[1;34m(self, prog, format)\u001b[0m\n\u001b[0;32m   1958\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprogs\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1959\u001b[0m                 raise InvocationException(\n\u001b[1;32m-> 1960\u001b[1;33m                     'GraphViz\\'s executables not found')\n\u001b[0m\u001b[0;32m   1961\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1962\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mprog\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprogs\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mInvocationException\u001b[0m: GraphViz's executables not found"
     ]
    }
   ],
   "source": [
    "keras.utils.plot_model(model, \"multi_input_and_output_model.png\", show_shapes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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