Train_DCVAE_MPS60.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
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   "outputs": [],
   "source": [
    "from Model.DCVAE import DCVAE\n",
    "from Model.Utils import PlotHistory, PlotDataAE, Save_Model\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",
    "# from sklearn.model_selection import train_test_split\n",
    "import tensorflow as tf\n",
    "import os\n",
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load DataSet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "load_tfrecords = True \n",
    "create_tfrecords = False\n",
    "\n",
    "path_tfr = 'DataSet/MPS60'\n",
    "path_npy = '/share_delta/GeoFacies/ModelosDeFacies/ModelMPS60/DataSet/DataSet.npy'\n",
    "batch_size = 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_weights = os.path.join(path_tfr, 'w60.hdf5')\n",
    "\n",
    "model = DCVAE(input_shape=(60, 60, 2),filters=[128,64,32],strides=[2,2,1],KernelDim=(3,3,3),hidden_dim=2048,\n",
    "              latent_dim=500,opt=Adam(lr=0.0001),dropout=0.1,epochs_drop=200, filepath=path_weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "num_epochs = 500\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": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "PlotHistory(model.history.history,listKeys=['val_loss','loss']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "PlotHistory(model.history.history,listKeys=['val_acc_pred','acc_pred'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "PlotHistory(model.history.history,listKeys=['lr'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluate Model with Test dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if load_tfrecords:\n",
    "    x_test = gen_test.get_numpy_batch()\n",
    "\n",
    "x_rec = model.model.predict(x_test)\n",
    "PlotDataAE(np.argmax(x_test[:,:,:,:],axis=-1),np.argmax(x_rec[:,:,:,:],axis=-1),digit_size=(60,60))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generate random sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_gen=model.generate(binary=True)\n",
    "PlotDataAE([],x_gen[:,:,:],digit_size=(60,60),Only_Result=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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