Train_DCVAE_MPS45_Keras.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n"
     ]
    }
   ],
   "source": [
    "from Model.DCVAE import DCVAE\n",
    "from Model.Utils import PlotHistory, PlotDataAE, Save_Model,model_plot\n",
    "from Model.Utils import load_from_tfrecords, convert_to_tfrecords, load_numpy\n",
    "from keras.optimizers import RMSprop,Adam\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import os\n",
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load DataSet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "load_tfrecords = True \n",
    "create_tfrecords = False\n",
    "\n",
    "path_tfr = 'DataSet/MPS45'\n",
    "path_npy = '/share/GeoFacies/DataSet/MPS45/MPS45.npy'\n",
    "batch_size = 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "if load_tfrecords:    \n",
    "    if create_tfrecords: \n",
    "        x_train, x_test = load_numpy(path_npy,random_state=0,split_data=0.30)\n",
    "        convert_to_tfrecords(path_tfr,x_train,x_test)\n",
    "        gen_train, gen_test = load_from_tfrecords(path_tfr, batch_size)\n",
    "    else:\n",
    "        try:\n",
    "            gen_train, gen_test = load_from_tfrecords(path_tfr, batch_size)\n",
    "        except:\n",
    "            print(\"Data not found. Change 'create_tfrecords' to True\")\n",
    "else:\n",
    "    x_train, x_test = load_numpy(path_npy,random_state=0,split_data=0.30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create Convolutional Variational Autoencoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_weights = os.path.join(path_tfr, 'w45.hdf5')\n",
    "\n",
    "model = DCVAE(input_shape=(45, 45, 2),filters=[32,32,16],strides=[2,2,1],KernelDim=(3,3,3),hidden_dim=1024,\n",
    "              latent_dim=500,opt=RMSprop(lr=0.001),dropout=0.1, filepath = path_weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting up model...\n",
      "WARNING:tensorflow:From /work/Smith/.local/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 45, 45, 2)    0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 23, 23, 32)   608         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, 12, 12, 32)   9248        conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, 12, 12, 16)   4624        conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "flatten_1 (Flatten)             (None, 2304)         0           conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 1024)         2360320     flatten_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dropout_1 (Dropout)             (None, 1024)         0           dense_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 500)          512500      dropout_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dense_3 (Dense)                 (None, 500)          512500      dropout_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "lambda_1 (Lambda)               (None, 500)          0           dense_2[0][0]                    \n",
      "                                                                 dense_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_4 (Dense)                 (None, 1024)         513024      lambda_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dropout_2 (Dropout)             (None, 1024)         0           dense_4[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_5 (Dense)                 (None, 2304)         2361600     dropout_2[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "reshape_1 (Reshape)             (None, 12, 12, 16)   0           dense_5[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_1 (Conv2DTrans (None, 12, 12, 16)   2320        reshape_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_2 (Conv2DTrans (None, 24, 24, 32)   4640        conv2d_transpose_1[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_transpose_3 (Conv2DTrans (None, 48, 48, 32)   9248        conv2d_transpose_2[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "bilinear_upsampling_1 (Bilinear (None, 45, 45, 32)   0           conv2d_transpose_3[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "generated (Conv2D)              (None, 45, 45, 2)    258         bilinear_upsampling_1[0][0]      \n",
      "==================================================================================================\n",
      "Total params: 6,290,890\n",
      "Trainable params: 6,290,890\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n",
      "Completed model setup.\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n"
     ]
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      "Epoch 1/500\n"
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      "656/656 [==============================] - 18s 27ms/step - loss: 704.2112 - acc_pred: 0.8535 - val_loss: 571.1780 - val_acc_pred: 0.8929\n",
      "Epoch 2/500\n"
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      "656/656 [==============================] - 14s 21ms/step - loss: 515.5032 - acc_pred: 0.9060 - val_loss: 481.1356 - val_acc_pred: 0.9131\n",
      "Epoch 3/500\n"
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      "656/656 [==============================] - 14s 21ms/step - loss: 472.0114 - acc_pred: 0.9166 - val_loss: 450.4638 - val_acc_pred: 0.9203\n",
      "Epoch 4/500\n"
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      "656/656 [==============================] - 13s 19ms/step - loss: 446.0817 - acc_pred: 0.9223 - val_loss: 422.6634 - val_acc_pred: 0.9267\n",
      "Epoch 5/500\n"
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      "656/656 [==============================] - 13s 19ms/step - loss: 424.3114 - acc_pred: 0.9269 - val_loss: 411.2509 - val_acc_pred: 0.9284\n",
      "Epoch 6/500\n"
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      "656/656 [==============================] - 13s 19ms/step - loss: 404.8586 - acc_pred: 0.9305 - val_loss: 394.4517 - val_acc_pred: 0.9332\n",
      "Epoch 7/500\n"
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      "656/656 [==============================] - 13s 19ms/step - loss: 389.3424 - acc_pred: 0.9334 - val_loss: 380.7179 - val_acc_pred: 0.9342\n",
      "Epoch 8/500\n"
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      "656/656 [==============================] - 12s 18ms/step - loss: 374.5400 - acc_pred: 0.9362 - val_loss: 363.4531 - val_acc_pred: 0.9380\n",
      "Epoch 9/500\n"
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      "656/656 [==============================] - 12s 19ms/step - loss: 361.9570 - acc_pred: 0.9384 - val_loss: 351.7968 - val_acc_pred: 0.9401\n",
      "Epoch 10/500\n"
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      "656/656 [==============================] - 13s 19ms/step - loss: 351.2068 - acc_pred: 0.9403 - val_loss: 339.4610 - val_acc_pred: 0.9436\n",
      "Epoch 11/500\n"
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      "656/656 [==============================] - 12s 19ms/step - loss: 342.0328 - acc_pred: 0.9420 - val_loss: 328.8776 - val_acc_pred: 0.9440\n",
      "Epoch 12/500\n"
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      "656/656 [==============================] - 13s 19ms/step - loss: 333.2961 - acc_pred: 0.9436 - val_loss: 323.9238 - val_acc_pred: 0.9449\n",
      "Epoch 13/500\n"
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      "656/656 [==============================] - 13s 19ms/step - loss: 326.3838 - acc_pred: 0.9449 - val_loss: 319.3108 - val_acc_pred: 0.9468\n",
      "Epoch 14/500\n"
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      "656/656 [==============================] - 12s 19ms/step - loss: 319.9169 - acc_pred: 0.9460 - val_loss: 314.0272 - val_acc_pred: 0.9477\n",
      "Epoch 15/500\n"
     ]
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      "656/656 [==============================] - 13s 19ms/step - loss: 315.2023 - acc_pred: 0.9470 - val_loss: 305.2313 - val_acc_pred: 0.9493\n",
      "Epoch 16/500\n"
     ]
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      "656/656 [==============================] - 12s 19ms/step - loss: 310.1240 - acc_pred: 0.9479 - val_loss: 303.5104 - val_acc_pred: 0.9486\n",
      "Epoch 17/500\n"
     ]
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      "656/656 [==============================] - 13s 19ms/step - loss: 305.9076 - acc_pred: 0.9487 - val_loss: 296.8426 - val_acc_pred: 0.9502\n",
      "Epoch 18/500\n"
     ]
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      "656/656 [==============================] - 13s 19ms/step - loss: 302.6169 - acc_pred: 0.9494 - val_loss: 297.3170 - val_acc_pred: 0.9497\n",
      "Epoch 19/500\n"
     ]
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      "656/656 [==============================] - 13s 19ms/step - loss: 299.1546 - acc_pred: 0.9500 - val_loss: 290.9168 - val_acc_pred: 0.9522\n",
      "Epoch 20/500\n"
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      "656/656 [==============================] - 13s 19ms/step - loss: 291.9862 - acc_pred: 0.9514 - val_loss: 358.1726 - val_acc_pred: 0.9418\n",
      "Epoch 21/500\n"
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      "656/656 [==============================] - 12s 19ms/step - loss: 289.3701 - acc_pred: 0.9519 - val_loss: 284.3564 - val_acc_pred: 0.9522\n",
      "Epoch 22/500\n"
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     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "656/656 [==============================] - 13s 19ms/step - loss: 230.5216 - acc_pred: 0.9641 - val_loss: 246.0901 - val_acc_pred: 0.9611\n",
      "Epoch 121/500\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch 120', max=656, style=ProgressStyle(description_width='i…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "656/656 [==============================] - 12s 19ms/step - loss: 230.5702 - acc_pred: 0.9641 - val_loss: 246.0859 - val_acc_pred: 0.9611\n",
      "Epoch 122/500\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch 121', max=656, style=ProgressStyle(description_width='i…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "656/656 [==============================] - 12s 19ms/step - loss: 230.2450 - acc_pred: 0.9641 - val_loss: 245.7139 - val_acc_pred: 0.9611\n",
      "Epoch 123/500\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch 122', max=656, style=ProgressStyle(description_width='i…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "656/656 [==============================] - 13s 19ms/step - loss: 230.2447 - acc_pred: 0.9641 - val_loss: 246.0986 - val_acc_pred: 0.9611\n",
      "Epoch 124/500\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch 123', max=656, style=ProgressStyle(description_width='i…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "656/656 [==============================] - 12s 19ms/step - loss: 230.3891 - acc_pred: 0.9641 - val_loss: 245.8842 - val_acc_pred: 0.9611\n",
      "\n",
      "Epoch 00124: ReduceLROnPlateau reducing learning rate to 8.19200067780912e-06.\n",
      "Epoch 125/500\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch 124', max=656, style=ProgressStyle(description_width='i…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "656/656 [==============================] - 13s 19ms/step - loss: 230.1735 - acc_pred: 0.9641 - val_loss: 245.7681 - val_acc_pred: 0.9612\n",
      "Epoch 126/500\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch 125', max=656, style=ProgressStyle(description_width='i…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "656/656 [==============================] - 13s 19ms/step - loss: 230.3014 - acc_pred: 0.9641 - val_loss: 245.9502 - val_acc_pred: 0.9612\n",
      "Epoch 127/500\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch 126', max=656, style=ProgressStyle(description_width='i…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "656/656 [==============================] - 13s 19ms/step - loss: 230.1215 - acc_pred: 0.9642 - val_loss: 245.9590 - val_acc_pred: 0.9611\n",
      "Epoch 128/500\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch 127', max=656, style=ProgressStyle(description_width='i…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "656/656 [==============================] - 13s 19ms/step - loss: 229.8781 - acc_pred: 0.9642 - val_loss: 245.8982 - val_acc_pred: 0.9610\n",
      "Epoch 129/500\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch 128', max=656, style=ProgressStyle(description_width='i…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "656/656 [==============================] - 13s 19ms/step - loss: 230.3263 - acc_pred: 0.9641 - val_loss: 245.7954 - val_acc_pred: 0.9611\n",
      "\n",
      "Epoch 00129: ReduceLROnPlateau reducing learning rate to 4.09600033890456e-06.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "num_epochs = 500\n",
    "\n",
    "if load_tfrecords:\n",
    "    x_train = gen_train.mps_generator()\n",
    "    x_val   = gen_test.mps_generator()    \n",
    "    model.fit_generator(x_train,\n",
    "                        num_epochs=num_epochs, verbose=1, \n",
    "                        steps_per_epoch = len(gen_train),\n",
    "                        val_set = x_val,\n",
    "                        validation_steps = len(gen_test))\n",
    "else:    \n",
    "    model.fit(x_train, num_epochs=num_epochs, batch_size = batch_size, x_v=x_test,verbose=1) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ploting Train History"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val_loss   :  [245.76810539489964, 245.95015941904958, 245.95897427487628, 245.89820459983528]\n",
      "loss   :  [230.17350229402868, 230.30142335193912, 230.12149092046226, 229.87814763697182]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "PlotHistory(model.history.history,listKeys=['val_loss','loss']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val_acc_pred   :  [0.9611695990019422, 0.9611598768692424, 0.9611154506214997, 0.961029227518102]\n",
      "acc_pred   :  [0.9641407203020119, 0.9641392133766558, 0.9641518230300125, 0.9642198566438221]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "PlotHistory(model.history.history,listKeys=['val_acc_pred','acc_pred'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lr   :  [8.192001e-06, 8.192001e-06, 8.192001e-06, 8.192001e-06]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "PlotHistory(model.history.history,listKeys=['lr'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluate Model with Test dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x288 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "if load_tfrecords:\n",
    "    x_test = gen_test.get_numpy_batch()\n",
    "\n",
    "model_plot(x_test, model, num_plots = 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generate random sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x144 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_random = np.random.normal(0, 1, size=(20, 500))\n",
    "model_plot(x_random, model, num_plots = 10, mode = \"gen\", figsize = (45,45) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Save Encoder and Decoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "Save_Model(model.encoder,'Model/TrainModel/CVAE45_encoder')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "Save_Model(model.generator,'Model/TrainModel/CVAE45_decoder')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}