SiameseFWI_bad_initial_Overthurst.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1e57b362",
   "metadata": {},
   "source": [
    "## Setting Up the Libs and Overthrust Configurations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c9c04f73",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sadalyom/.conda/envs/FWIGAN/lib/python3.8/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "import time\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import numpy as np\n",
    "import scipy.ndimage\n",
    "import matplotlib\n",
    "#matplotlib.use('Agg')\n",
    "import matplotlib.pyplot as plt\n",
    "import deepwave\n",
    "import os\n",
    "from timeit import default_timer as timer\n",
    "import scipy.io as spio\n",
    "import math\n",
    "import numpy as np\n",
    "from utils.utils import *\n",
    "from torch import optim\n",
    "from IPython.core.debugger import set_trace \n",
    "import random\n",
    "torch.manual_seed(0)\n",
    "random.seed(0)\n",
    "\n",
    "####################################################\n",
    "####   MAIN PARAMETERS FOR FORWARD MODELING         ####\n",
    "####################################################\n",
    "\n",
    "peak_freq = 5.                    # central frequency\n",
    "peak_source_time = 1 / peak_freq  # the time (in secs) of the peak amplitude\n",
    "dx        = 30.0                  # step interval along x/z direction\n",
    "dx1        = 30.0                  # step interval along y/z direction\n",
    "\n",
    "dt        = 0.003                 # time interval (e.g., 3ms)\n",
    "num_dims  = 2                     # dimension of velocity model\n",
    "nz        = 94                   # model shape of z dimension (depth) Overthrust\n",
    "ny        = 400                   # model shape of y dimension Overthrust\n",
    "vmodel_dim = np.array([nz, ny])\n",
    "\n",
    "total_t   = 6.                      # totle sampling time (unit: s)\n",
    "nt        = int(total_t / dt)       # number of time sampling points\n",
    "num_shots = 30                      # nunmber of shots\n",
    "num_sources_per_shot = 1            # number of sources per shot\n",
    "num_receivers_per_shot = 567        # number of receivers per shot\n",
    "source_spacing = np.floor(dx * ny / (num_shots+1))                         # space intervel between neigbouring sources\n",
    "receiver_spacing = np.floor(dx1 * ny / (num_receivers_per_shot +1))         # space intervel between neigbouring receivers\n",
    "source_depth = 0                # the index depth of sources, default is 0\n",
    "receiver_depth = 0              # the index depths of receivers, default is 0\n",
    "order          = 8              # precision order of finite difference in space domain\n",
    "pml_width  = [0,10,10,10,0,0]   # pml padding width for boundary (0 for free surface and 10 for absorbing boundary)\n",
    "\n",
    "fwi_lr            = 80        # learning rate for updating the model.\n",
    "fwi_batch         =   30      # number of batches for FWI.\n",
    "fwi_num_epochs    = 800       # number of FWI iteration.\n",
    "sim_lr            = 1*1e-6    # learning rate for Soamese network.\n",
    "survey_pad = None\n",
    "savepoch = 50                 # Saving models each 50 FWI iterations\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "546faf4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.21, 0.387)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "receiver_spacing/100, source_spacing/1000"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fda360bc",
   "metadata": {},
   "source": [
    "##   Check Cuda Device \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "aaccc70f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cuda')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.backends.cudnn.enabled = True\n",
    "torch.backends.cudnn.benchmark = True\n",
    "\n",
    "cuda_available = torch.cuda.is_available()\n",
    "device = torch.device(\"cuda\" if cuda_available else \"cpu\")\n",
    "device"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6665e0c",
   "metadata": {},
   "source": [
    "## Creating Data Pathes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "744069ea",
   "metadata": {},
   "outputs": [],
   "source": [
    " \n",
    "data_path = 'data/over_94_400.bin'\n",
    "initfile = 'data/over_initmodel_FLAT.mat'\n",
    "initsafile = 'data/over_initsource.mat'\n",
    "fwi_result = 'results/overthrust/'\n",
    "# Loading the true model\n",
    "model_true = loadtruemodel(data_path, num_dims, vmodel_dim).to(device)\n",
    "model, model_init = loadinitmodel(initfile,device)\n",
    "# The model will be updated during FWI\n",
    "model = torch.nn.Parameter(model)\n",
    "# Creating source and reciver locations\n",
    "x_s, x_r = createSR(num_shots, num_sources_per_shot, num_receivers_per_shot, num_dims, source_spacing, receiver_spacing,source_depth,receiver_depth)\n",
    "x_s, x_r = x_s.to(device), x_r.to(device)\n",
    "# Source Amplitude\n",
    "source_amplitudes_init, source_amplitudes_true = loadinitsource(initsafile,device)\n",
    "source = source_amplitudes_init.repeat(1,num_shots, num_sources_per_shot)\n",
    "# receiver Amplitude\n",
    "receiver_amplitudes = createdata(model_true,dx,source, \\\n",
    "                                x_s,x_r,dt,pml_width,order, \\\n",
    "                                 survey_pad,device).to(device)\n",
    "receiver_amplitudes_true = receiver_amplitudes\n",
    "rcv_amps_true = receiver_amplitudes_true.clone()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f7c4c84",
   "metadata": {},
   "source": [
    "## Plotting the inital and true model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "10af97f6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['normal'] not found. Falling back to DejaVu Sans.\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2160x936 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.ticker import MaxNLocator\n",
    "import matplotlib.pyplot as plt\n",
    "font = {'family' : 'normal',\n",
    "        'weight' : 'bold',\n",
    "        'size'   : 18}\n",
    "\n",
    "y = np.array([0, 1, 2, 3])\n",
    "box_min = 2360\n",
    "box_max = 6000\n",
    "\n",
    "f, ax = plt.subplots(1,2,figsize=(30, 13))\n",
    "plt.rc('font', **font)\n",
    "\n",
    "\n",
    "im = ax[0].imshow(model_init, cmap='jet', vmin=box_min, vmax=box_max, \n",
    "                extent=[0, model_init.shape[1] * dx / 1000, model_init.shape[0] * dx1 / 1000, 0])\n",
    "ax[0].set_xlabel('Position (km)',fontsize='large', fontweight='bold')\n",
    "ax[0].set_ylabel('Depth (km)',fontsize='large', fontweight='bold')\n",
    "ax[0].set_title('Initial_Model',fontsize='large', fontweight='bold')\n",
    "ax[0].text(0, -0.15, 'b)', fontsize = 22) \n",
    "ax[0].xaxis.set_major_locator(MaxNLocator(integer=True))\n",
    "ax[0].set_yticks(np.arange(y.min(), y.max(), 1)) \n",
    "\n",
    "model1 = model_true.detach().cpu().numpy()\n",
    "im = ax[1].imshow(model1, cmap='jet', vmin=box_min, vmax=box_max, \n",
    "                extent=[0, model1.shape[1] * dx / 1000, model1.shape[0] * dx1 / 1000, 0])\n",
    "ax[1].set_xlabel('Position (km)',fontsize='large', fontweight='bold')\n",
    "ax[1].set_ylabel('Depth (km)',fontsize='large', fontweight='bold')\n",
    "ax[1].set_title('True_Model',fontsize='large', fontweight='bold')\n",
    "ax[1].text(0, -0.15, 'b)', fontsize = 22) \n",
    "ax[1].xaxis.set_major_locator(MaxNLocator(integer=True))\n",
    "ax[1].set_yticks(np.arange(y.min(), y.max(), 1)) \n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f248883",
   "metadata": {},
   "source": [
    "## Setting Up the Siamese and Optimizers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dfc2a6df",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of netD params: 565\n",
      "----------------------------------------------------------------\n",
      "        Layer (type)               Output Shape         Param #\n",
      "================================================================\n",
      "            Conv2d-1         [-1, 1, 2000, 567]              10\n",
      "         LeakyReLU-2         [-1, 1, 2000, 567]               0\n",
      "            Conv2d-3         [-1, 2, 2000, 567]              20\n",
      "         LeakyReLU-4         [-1, 2, 2000, 567]               0\n",
      "            Conv2d-5         [-1, 2, 2000, 567]              20\n",
      "         LeakyReLU-6         [-1, 2, 2000, 567]               0\n",
      "            Conv2d-7         [-1, 4, 2000, 567]              40\n",
      "         LeakyReLU-8         [-1, 4, 2000, 567]               0\n",
      "            Conv2d-9         [-1, 4, 2000, 567]              40\n",
      "        LeakyReLU-10         [-1, 4, 2000, 567]               0\n",
      "           Conv2d-11         [-1, 2, 2000, 567]              20\n",
      "        LeakyReLU-12         [-1, 2, 2000, 567]               0\n",
      "           Conv2d-13         [-1, 1, 2000, 567]              10\n",
      "        LeakyReLU-14         [-1, 1, 2000, 567]               0\n",
      "           Conv2d-15         [-1, 1, 2000, 567]              10\n",
      "           Conv2d-16         [-1, 1, 2000, 567]              10\n",
      "        LeakyReLU-17         [-1, 1, 2000, 567]               0\n",
      "           Conv2d-18         [-1, 2, 2000, 567]              20\n",
      "        LeakyReLU-19         [-1, 2, 2000, 567]               0\n",
      "           Conv2d-20         [-1, 2, 2000, 567]              38\n",
      "        LeakyReLU-21         [-1, 2, 2000, 567]               0\n",
      "           Conv2d-22         [-1, 4, 2000, 567]              76\n",
      "        LeakyReLU-23         [-1, 4, 2000, 567]               0\n",
      "           Conv2d-24         [-1, 4, 2000, 567]             148\n",
      "        LeakyReLU-25         [-1, 4, 2000, 567]               0\n",
      "           Conv2d-26         [-1, 2, 2000, 567]              74\n",
      "        LeakyReLU-27         [-1, 2, 2000, 567]               0\n",
      "           Conv2d-28         [-1, 1, 2000, 567]              19\n",
      "        LeakyReLU-29         [-1, 1, 2000, 567]               0\n",
      "           Conv2d-30         [-1, 1, 2000, 567]              10\n",
      "           Conv2d-31         [-1, 1, 2000, 567]              10\n",
      "        LeakyReLU-32         [-1, 1, 2000, 567]               0\n",
      "           Conv2d-33         [-1, 2, 2000, 567]              20\n",
      "        LeakyReLU-34         [-1, 2, 2000, 567]               0\n",
      "           Conv2d-35         [-1, 2, 2000, 567]              20\n",
      "        LeakyReLU-36         [-1, 2, 2000, 567]               0\n",
      "           Conv2d-37         [-1, 4, 2000, 567]              40\n",
      "        LeakyReLU-38         [-1, 4, 2000, 567]               0\n",
      "           Conv2d-39         [-1, 4, 2000, 567]              40\n",
      "        LeakyReLU-40         [-1, 4, 2000, 567]               0\n",
      "           Conv2d-41         [-1, 2, 2000, 567]              20\n",
      "        LeakyReLU-42         [-1, 2, 2000, 567]               0\n",
      "           Conv2d-43         [-1, 1, 2000, 567]              10\n",
      "        LeakyReLU-44         [-1, 1, 2000, 567]               0\n",
      "           Conv2d-45         [-1, 1, 2000, 567]              10\n",
      "           Conv2d-46         [-1, 1, 2000, 567]              10\n",
      "        LeakyReLU-47         [-1, 1, 2000, 567]               0\n",
      "           Conv2d-48         [-1, 2, 2000, 567]              20\n",
      "        LeakyReLU-49         [-1, 2, 2000, 567]               0\n",
      "           Conv2d-50         [-1, 2, 2000, 567]              38\n",
      "        LeakyReLU-51         [-1, 2, 2000, 567]               0\n",
      "           Conv2d-52         [-1, 4, 2000, 567]              76\n",
      "        LeakyReLU-53         [-1, 4, 2000, 567]               0\n",
      "           Conv2d-54         [-1, 4, 2000, 567]             148\n",
      "        LeakyReLU-55         [-1, 4, 2000, 567]               0\n",
      "           Conv2d-56         [-1, 2, 2000, 567]              74\n",
      "        LeakyReLU-57         [-1, 2, 2000, 567]               0\n",
      "           Conv2d-58         [-1, 1, 2000, 567]              19\n",
      "        LeakyReLU-59         [-1, 1, 2000, 567]               0\n",
      "           Conv2d-60         [-1, 1, 2000, 567]              10\n",
      "================================================================\n",
      "Total params: 1,130\n",
      "Trainable params: 1,130\n",
      "Non-trainable params: 0\n",
      "----------------------------------------------------------------\n",
      "Input size (MB): 4905532.84\n",
      "Forward/backward pass size (MB): 1142.03\n",
      "Params size (MB): 0.00\n",
      "Estimated Total Size (MB): 4906674.87\n",
      "----------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "from torchsummary import summary\n",
    "from Model.Module22 import *\n",
    "import  torch\n",
    "\n",
    "\n",
    "#Learning Schedule\n",
    "def lr_lambda(epoch):\n",
    "    return 10\n",
    "\n",
    "\n",
    "### optimizer for the velocity model\n",
    "optimizer = optim.Adam([{'params': model, 'lr':fwi_lr, 'betas':(0.5, 0.99), 'eps':1e-8,\n",
    "                          'weight_decay':0}])\n",
    "\n",
    "#Best\n",
    "#leak_value = 0.0001\n",
    "leak_value = 0.00001\n",
    "D1 = 1\n",
    "netD = SiameseNetwork(D1)\n",
    "### init the hyper-parameters of netD ###\n",
    "netD = netD.apply(lambda m: weights_init(m, leak_value))\n",
    "netD = netD.to(device)\n",
    "\n",
    "\n",
    "# Optimizer for Siamese network.\n",
    "optim_s = optim.Adam(netD.parameters(),lr=sim_lr,betas=(0.5, 0.99), \\\n",
    "                    eps=1e-6, weight_decay=0)\n",
    "\n",
    "scheduler = torch.optim.lr_scheduler.MultiplicativeLR(optim_s, lr_lambda=lr_lambda)\n",
    "\n",
    "\"\"\"Compute number of parameters\"\"\"\n",
    "s  = sum(np.prod(list(p.size())) for p in netD.parameters())\n",
    "print ('Number of netD params: %d' % s)\n",
    "\n",
    "summary(netD,[(1,nt,num_receivers_per_shot),(1,nt,num_receivers_per_shot)])\n",
    "\n",
    "\n",
    "############## Init  ################# \n",
    "SNR = 0.0\n",
    "SSIM = 0.0\n",
    "Loss = 0.0\n",
    "ERROR = 0.0\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b616a63",
   "metadata": {},
   "source": [
    "## Main SiameseFWI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "24d27e4c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1 Loss:  0.2549637014667193 lr_DL: 1e-06 lr_vel: 80 SNR: 17.427830912928112 SSIM: 0.046952873\n",
      "Epoch: 2 Loss:  0.2128858615954717 lr_DL: 1e-06 lr_vel: 80 SNR: 17.221712580249605 SSIM: 0.05166131\n",
      "Epoch: 3 Loss:  0.19940580129623414 lr_DL: 1e-06 lr_vel: 80 SNR: 16.98712823052216 SSIM: 0.062039115\n",
      "Epoch: 4 Loss:  0.19270853052536646 lr_DL: 1e-06 lr_vel: 80 SNR: 16.823336756989434 SSIM: 0.06375052\n",
      "Epoch: 5 Loss:  0.19276578774054845 lr_DL: 1e-06 lr_vel: 80 SNR: 16.88743883157848 SSIM: 0.07568432\n",
      "Epoch: 6 Loss:  0.19086138506730396 lr_DL: 1e-06 lr_vel: 80 SNR: 16.71940367610371 SSIM: 0.09394214\n",
      "Epoch: 7 Loss:  0.18560894628365834 lr_DL: 1e-06 lr_vel: 80 SNR: 16.705614270997767 SSIM: 0.11299824\n",
      "Epoch: 8 Loss:  0.18237162232398987 lr_DL: 1e-06 lr_vel: 80 SNR: 16.617591460331266 SSIM: 0.11450108\n",
      "Epoch: 9 Loss:  0.1834998627503713 lr_DL: 1e-06 lr_vel: 80 SNR: 16.64334147953895 SSIM: 0.1284822\n",
      "Epoch: 10 Loss:  0.18095881789922713 lr_DL: 1e-06 lr_vel: 80 SNR: 16.60364614540871 SSIM: 0.12608643\n",
      "Epoch: 11 Loss:  0.18150978038708368 lr_DL: 1e-06 lr_vel: 80 SNR: 16.56066582449092 SSIM: 0.16192482\n",
      "Epoch: 12 Loss:  0.18124364068110785 lr_DL: 1e-06 lr_vel: 80 SNR: 16.486734307345316 SSIM: 0.1548236\n",
      "Epoch: 13 Loss:  0.17550407499074935 lr_DL: 1e-06 lr_vel: 80 SNR: 16.5042116615935 SSIM: 0.16105996\n",
      "Epoch: 14 Loss:  0.17567952275276183 lr_DL: 1e-06 lr_vel: 80 SNR: 16.58147238343558 SSIM: 0.18195531\n",
      "Epoch: 15 Loss:  0.17527111619710922 lr_DL: 1e-06 lr_vel: 80 SNR: 16.46487411997045 SSIM: 0.19106364\n",
      "Epoch: 16 Loss:  0.17694288939237596 lr_DL: 1e-06 lr_vel: 80 SNR: 16.458440819994408 SSIM: 0.20268495\n",
      "Epoch: 17 Loss:  0.16993035152554511 lr_DL: 1e-06 lr_vel: 80 SNR: 16.510470423281436 SSIM: 0.21589741\n",
      "Epoch: 18 Loss:  0.1680607999364535 lr_DL: 1e-06 lr_vel: 80 SNR: 16.46823121228391 SSIM: 0.2139922\n",
      "Epoch: 19 Loss:  0.1675723815957705 lr_DL: 1e-06 lr_vel: 80 SNR: 16.426478196769374 SSIM: 0.22333543\n",
      "Epoch: 20 Loss:  0.16681975945830346 lr_DL: 1e-06 lr_vel: 80 SNR: 16.39307105569703 SSIM: 0.24061248\n",
      "Epoch: 21 Loss:  0.16954450458288192 lr_DL: 1e-06 lr_vel: 80 SNR: 16.496481884896006 SSIM: 0.24061558\n",
      "Epoch: 22 Loss:  0.16338572998841602 lr_DL: 1e-06 lr_vel: 80 SNR: 16.434945728737432 SSIM: 0.2541355\n",
      "Epoch: 23 Loss:  0.16300587952136994 lr_DL: 1e-06 lr_vel: 80 SNR: 16.402486595973965 SSIM: 0.2612557\n",
      "Epoch: 24 Loss:  0.16324554930130641 lr_DL: 1e-06 lr_vel: 80 SNR: 16.511615744424304 SSIM: 0.2722543\n",
      "Epoch: 25 Loss:  0.1631966307759285 lr_DL: 1e-06 lr_vel: 80 SNR: 16.392409119957655 SSIM: 0.2706911\n",
      "Epoch: 26 Loss:  0.16103353276848792 lr_DL: 1e-06 lr_vel: 80 SNR: 16.413286447412517 SSIM: 0.28917438\n",
      "Epoch: 27 Loss:  0.15846006001035373 lr_DL: 1e-06 lr_vel: 80 SNR: 16.39163757567635 SSIM: 0.28825277\n",
      "Epoch: 28 Loss:  0.1548420528570811 lr_DL: 1e-06 lr_vel: 80 SNR: 16.45721080645987 SSIM: 0.29245794\n",
      "Epoch: 29 Loss:  0.15742726922035216 lr_DL: 1e-06 lr_vel: 80 SNR: 16.45324623386022 SSIM: 0.30317527\n",
      "Epoch: 30 Loss:  0.15738328223427137 lr_DL: 1e-06 lr_vel: 80 SNR: 16.360632350914916 SSIM: 0.3075789\n",
      "Epoch: 31 Loss:  0.15575379778941473 lr_DL: 1e-06 lr_vel: 80 SNR: 16.474286166279065 SSIM: 0.3109971\n",
      "Epoch: 32 Loss:  0.16151244615515073 lr_DL: 1e-06 lr_vel: 80 SNR: 16.37280632657969 SSIM: 0.30591866\n",
      "Epoch: 33 Loss:  0.15993048946062724 lr_DL: 1e-06 lr_vel: 80 SNR: 16.423523796724613 SSIM: 0.3205867\n",
      "Epoch: 34 Loss:  0.16224350531895956 lr_DL: 1e-06 lr_vel: 80 SNR: 16.399751227849716 SSIM: 0.32207197\n",
      "Epoch: 35 Loss:  0.1588894692560037 lr_DL: 1e-06 lr_vel: 80 SNR: 16.42474116618849 SSIM: 0.3232624\n",
      "Epoch: 36 Loss:  0.15236733754475912 lr_DL: 1e-06 lr_vel: 80 SNR: 16.431097358154517 SSIM: 0.3275406\n",
      "Epoch: 37 Loss:  0.15673277204235395 lr_DL: 1e-06 lr_vel: 80 SNR: 16.464152871595804 SSIM: 0.32543895\n",
      "Epoch: 38 Loss:  0.15913333569963772 lr_DL: 1e-06 lr_vel: 80 SNR: 16.319843150482885 SSIM: 0.32094336\n",
      "Epoch: 39 Loss:  0.15781072328488033 lr_DL: 1e-06 lr_vel: 80 SNR: 16.370429894823864 SSIM: 0.3278338\n",
      "Epoch: 40 Loss:  0.15695643946528434 lr_DL: 1e-06 lr_vel: 80 SNR: 16.36128583273893 SSIM: 0.32515964\n",
      "Epoch: 41 Loss:  0.1532544458905856 lr_DL: 1e-06 lr_vel: 80 SNR: 16.37040231309615 SSIM: 0.33450916\n",
      "Epoch: 42 Loss:  0.1558112477262815 lr_DL: 1e-06 lr_vel: 80 SNR: 16.312243059130903 SSIM: 0.33093882\n",
      "Epoch: 43 Loss:  0.15420920625329018 lr_DL: 1e-06 lr_vel: 80 SNR: 16.391545467998718 SSIM: 0.34270906\n",
      "Epoch: 44 Loss:  0.15459014251828193 lr_DL: 1e-06 lr_vel: 80 SNR: 16.402984726770665 SSIM: 0.3379009\n",
      "Epoch: 45 Loss:  0.15813150977094967 lr_DL: 1e-06 lr_vel: 80 SNR: 16.3550023348169 SSIM: 0.33884194\n",
      "Epoch: 46 Loss:  0.15258563235402106 lr_DL: 1e-06 lr_vel: 80 SNR: 16.413254738824428 SSIM: 0.35307628\n",
      "Epoch: 47 Loss:  0.15251946449279785 lr_DL: 1e-06 lr_vel: 80 SNR: 16.41677926290982 SSIM: 0.346922\n",
      "Epoch: 48 Loss:  0.15638429149985314 lr_DL: 1e-06 lr_vel: 80 SNR: 16.41378282826055 SSIM: 0.34854433\n",
      "Epoch: 49 Loss:  0.15261133859554926 lr_DL: 1e-06 lr_vel: 80 SNR: 16.338432599934233 SSIM: 0.3498782\n",
      "Epoch: 50 Loss:  0.15128433654705684 lr_DL: 1e-06 lr_vel: 80 SNR: 16.344723950153863 SSIM: 0.35816783\n",
      "Epoch: 51 Loss:  0.15117575799425442 lr_DL: 1e-06 lr_vel: 80 SNR: 16.311979152322905 SSIM: 0.35730678\n",
      "Epoch: 52 Loss:  0.14974138041337332 lr_DL: 1e-06 lr_vel: 80 SNR: 16.31947478400448 SSIM: 0.35855278\n",
      "Epoch: 53 Loss:  0.1481152874728044 lr_DL: 1e-06 lr_vel: 80 SNR: 16.302516549304702 SSIM: 0.35795575\n",
      "Epoch: 54 Loss:  0.1500425711274147 lr_DL: 1e-06 lr_vel: 80 SNR: 16.25979834226094 SSIM: 0.36128658\n",
      "Epoch: 55 Loss:  0.15014061232407888 lr_DL: 1e-06 lr_vel: 80 SNR: 16.30428427374693 SSIM: 0.36360255\n",
      "Epoch: 56 Loss:  0.14723099221785863 lr_DL: 1e-06 lr_vel: 80 SNR: 16.26014895676882 SSIM: 0.365954\n",
      "Epoch: 57 Loss:  0.14597055464982986 lr_DL: 1e-06 lr_vel: 80 SNR: 16.212901749142794 SSIM: 0.35685027\n",
      "Epoch: 58 Loss:  0.15059427171945572 lr_DL: 1e-06 lr_vel: 80 SNR: 16.225535518426774 SSIM: 0.35988888\n",
      "Epoch: 59 Loss:  0.14904924804965655 lr_DL: 1e-06 lr_vel: 80 SNR: 16.264301854540353 SSIM: 0.35845158\n",
      "Epoch: 60 Loss:  0.1502005472779274 lr_DL: 1e-06 lr_vel: 80 SNR: 16.21034152797976 SSIM: 0.3476404\n",
      "Epoch: 61 Loss:  0.14631951476136842 lr_DL: 1e-06 lr_vel: 80 SNR: 16.199548366908406 SSIM: 0.3526113\n",
      "Epoch: 62 Loss:  0.15193861325581867 lr_DL: 1e-06 lr_vel: 80 SNR: 16.27283782730238 SSIM: 0.34568074\n",
      "Epoch: 63 Loss:  0.1516006790101528 lr_DL: 1e-06 lr_vel: 80 SNR: 16.277488156605045 SSIM: 0.35380858\n",
      "Epoch: 64 Loss:  0.1494817887743314 lr_DL: 1e-06 lr_vel: 80 SNR: 16.246899267666848 SSIM: 0.34865427\n",
      "Epoch: 65 Loss:  0.14788963074485462 lr_DL: 1e-06 lr_vel: 80 SNR: 16.28016884177884 SSIM: 0.3537632\n",
      "Epoch: 66 Loss:  0.14601238444447517 lr_DL: 1e-06 lr_vel: 80 SNR: 16.268305946903688 SSIM: 0.35354927\n",
      "Epoch: 67 Loss:  0.1534853662053744 lr_DL: 1e-06 lr_vel: 80 SNR: 16.2703976066359 SSIM: 0.34977883\n",
      "Epoch: 68 Loss:  0.1498392179608345 lr_DL: 1e-06 lr_vel: 80 SNR: 16.25848113520142 SSIM: 0.3563116\n",
      "Epoch: 69 Loss:  0.14354287162423135 lr_DL: 1e-06 lr_vel: 80 SNR: 16.32856489295992 SSIM: 0.36213723\n",
      "Epoch: 70 Loss:  0.1474274290104707 lr_DL: 1e-06 lr_vel: 80 SNR: 16.23513464572521 SSIM: 0.35665962\n",
      "Epoch: 71 Loss:  0.15032211517294247 lr_DL: 1e-06 lr_vel: 80 SNR: 16.251247469649723 SSIM: 0.35864684\n",
      "Epoch: 72 Loss:  0.15121678014596304 lr_DL: 1e-06 lr_vel: 80 SNR: 16.27076688581791 SSIM: 0.35835758\n",
      "Epoch: 73 Loss:  0.14824874674280483 lr_DL: 1e-06 lr_vel: 80 SNR: 16.278346617396966 SSIM: 0.35439637\n",
      "Epoch: 74 Loss:  0.144960085550944 lr_DL: 1e-06 lr_vel: 80 SNR: 16.291958163691405 SSIM: 0.3584787\n",
      "Epoch: 75 Loss:  0.14544713571667672 lr_DL: 1e-06 lr_vel: 80 SNR: 16.324064599481364 SSIM: 0.36377275\n",
      "Epoch: 76 Loss:  0.14643928954998653 lr_DL: 1e-06 lr_vel: 80 SNR: 16.34567259439001 SSIM: 0.368136\n",
      "Epoch: 77 Loss:  0.1484673298895359 lr_DL: 1e-06 lr_vel: 80 SNR: 16.36047180893752 SSIM: 0.36604223\n",
      "Epoch: 78 Loss:  0.14846514984965326 lr_DL: 1e-06 lr_vel: 80 SNR: 16.329821364852336 SSIM: 0.35898328\n",
      "Epoch: 79 Loss:  0.14672528728842735 lr_DL: 1e-06 lr_vel: 80 SNR: 16.41640964765212 SSIM: 0.3655639\n",
      "Epoch: 80 Loss:  0.14796360383431117 lr_DL: 1e-06 lr_vel: 80 SNR: 16.405484679294403 SSIM: 0.3641277\n",
      "Epoch: 81 Loss:  0.1462346573670705 lr_DL: 1e-06 lr_vel: 80 SNR: 16.444318834514846 SSIM: 0.3706433\n",
      "Epoch: 82 Loss:  0.146950497229894 lr_DL: 1e-06 lr_vel: 80 SNR: 16.449202355540677 SSIM: 0.36697552\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 83 Loss:  0.14092660695314407 lr_DL: 1e-06 lr_vel: 80 SNR: 16.555091015503727 SSIM: 0.37684393\n",
      "Epoch: 84 Loss:  0.1419843167066574 lr_DL: 1e-06 lr_vel: 80 SNR: 16.598169205868707 SSIM: 0.37664402\n",
      "Epoch: 85 Loss:  0.1461867223183314 lr_DL: 1e-06 lr_vel: 80 SNR: 16.64620886148755 SSIM: 0.3798662\n",
      "Epoch: 86 Loss:  0.14715647051731745 lr_DL: 1e-06 lr_vel: 80 SNR: 16.60661388603312 SSIM: 0.37624958\n",
      "Epoch: 87 Loss:  0.14612966602047284 lr_DL: 1e-06 lr_vel: 80 SNR: 16.65405112382937 SSIM: 0.3858451\n",
      "Epoch: 88 Loss:  0.13879917487502097 lr_DL: 1e-06 lr_vel: 80 SNR: 16.698989751964866 SSIM: 0.39196393\n",
      "Epoch: 89 Loss:  0.14085045109192532 lr_DL: 1e-06 lr_vel: 80 SNR: 16.7284771071571 SSIM: 0.39762887\n",
      "Epoch: 90 Loss:  0.1375355377793312 lr_DL: 1e-06 lr_vel: 80 SNR: 16.811079852873203 SSIM: 0.41075766\n",
      "Epoch: 91 Loss:  0.13656918729345005 lr_DL: 1e-06 lr_vel: 80 SNR: 16.806518753420086 SSIM: 0.41028768\n",
      "Epoch: 92 Loss:  0.13972254594167074 lr_DL: 1e-06 lr_vel: 80 SNR: 16.910798459727726 SSIM: 0.42031687\n",
      "Epoch: 93 Loss:  0.13848036552468937 lr_DL: 1e-06 lr_vel: 80 SNR: 16.879809800597563 SSIM: 0.4283268\n",
      "Epoch: 94 Loss:  0.13258106807867687 lr_DL: 1e-06 lr_vel: 80 SNR: 16.993384281258137 SSIM: 0.44104263\n",
      "Epoch: 95 Loss:  0.13193545639514923 lr_DL: 1e-06 lr_vel: 80 SNR: 17.032203432152443 SSIM: 0.4532317\n",
      "Epoch: 96 Loss:  0.13122795795400938 lr_DL: 1e-06 lr_vel: 80 SNR: 17.059123507009076 SSIM: 0.44846675\n",
      "Epoch: 97 Loss:  0.1341851847867171 lr_DL: 1e-06 lr_vel: 80 SNR: 17.11768018343809 SSIM: 0.45549524\n",
      "Epoch: 98 Loss:  0.14094542438785235 lr_DL: 1e-06 lr_vel: 80 SNR: 17.049370890398677 SSIM: 0.45533207\n",
      "Epoch: 99 Loss:  0.1364018293718497 lr_DL: 1e-06 lr_vel: 80 SNR: 17.07882412240243 SSIM: 0.46567902\n",
      "Epoch: 100 Loss:  0.1293016237517198 lr_DL: 1e-06 lr_vel: 80 SNR: 17.06334532673879 SSIM: 0.47194013\n",
      "Epoch: 101 Loss:  0.12786188175280888 lr_DL: 1e-06 lr_vel: 80 SNR: 17.12197984986549 SSIM: 0.4780087\n",
      "Epoch: 102 Loss:  0.12441839054226875 lr_DL: 1e-06 lr_vel: 80 SNR: 17.186365966209863 SSIM: 0.48548284\n",
      "Epoch: 103 Loss:  0.134198776135842 lr_DL: 1e-06 lr_vel: 80 SNR: 17.211648599425253 SSIM: 0.4766452\n",
      "Epoch: 104 Loss:  0.13217768147587777 lr_DL: 1e-06 lr_vel: 80 SNR: 17.163027169325574 SSIM: 0.48021707\n",
      "Epoch: 105 Loss:  0.1274921029806137 lr_DL: 1e-06 lr_vel: 80 SNR: 17.173521948754072 SSIM: 0.49354553\n",
      "Epoch: 106 Loss:  0.13153834491968155 lr_DL: 1e-06 lr_vel: 80 SNR: 17.179302337856253 SSIM: 0.4884577\n",
      "Epoch: 107 Loss:  0.12483291452129681 lr_DL: 1e-06 lr_vel: 80 SNR: 17.188818913492597 SSIM: 0.4976188\n",
      "Epoch: 108 Loss:  0.12970997442801793 lr_DL: 1e-06 lr_vel: 80 SNR: 17.193093749578342 SSIM: 0.4954216\n",
      "Epoch: 109 Loss:  0.12976123516758284 lr_DL: 1e-06 lr_vel: 80 SNR: 17.2282461863909 SSIM: 0.49544454\n",
      "Epoch: 110 Loss:  0.1334383748471737 lr_DL: 1e-06 lr_vel: 80 SNR: 17.211379633828912 SSIM: 0.49168453\n",
      "Epoch: 111 Loss:  0.12205814147988955 lr_DL: 1e-06 lr_vel: 80 SNR: 17.27845390158231 SSIM: 0.51218784\n",
      "Epoch: 112 Loss:  0.12746569414933523 lr_DL: 1e-06 lr_vel: 80 SNR: 17.31298230609402 SSIM: 0.50394326\n",
      "Epoch: 113 Loss:  0.12363305389881134 lr_DL: 1e-06 lr_vel: 80 SNR: 17.40122532695117 SSIM: 0.50571054\n",
      "Epoch: 114 Loss:  0.12810933341582617 lr_DL: 1e-06 lr_vel: 80 SNR: 17.441479963383433 SSIM: 0.5011661\n",
      "Epoch: 115 Loss:  0.1276650014022986 lr_DL: 1e-06 lr_vel: 80 SNR: 17.506611264793456 SSIM: 0.5088457\n",
      "Epoch: 116 Loss:  0.1253462721904119 lr_DL: 1e-06 lr_vel: 80 SNR: 17.505403557730045 SSIM: 0.5110653\n",
      "Epoch: 117 Loss:  0.12383459284901618 lr_DL: 1e-06 lr_vel: 80 SNR: 17.518413533840654 SSIM: 0.51448745\n",
      "Epoch: 118 Loss:  0.1272853322327137 lr_DL: 1e-06 lr_vel: 80 SNR: 17.606197637427698 SSIM: 0.51932245\n",
      "Epoch: 119 Loss:  0.12281722128391266 lr_DL: 1e-06 lr_vel: 80 SNR: 17.572561576888617 SSIM: 0.51197845\n",
      "Epoch: 120 Loss:  0.12550140569607418 lr_DL: 1e-06 lr_vel: 80 SNR: 17.59404566017842 SSIM: 0.51097924\n",
      "Epoch: 121 Loss:  0.12304922739664713 lr_DL: 1e-06 lr_vel: 80 SNR: 17.65118902653076 SSIM: 0.51530814\n",
      "Epoch: 122 Loss:  0.12299197440346082 lr_DL: 1e-06 lr_vel: 80 SNR: 17.744646806031188 SSIM: 0.5175454\n",
      "Epoch: 123 Loss:  0.12129153932134311 lr_DL: 1e-06 lr_vel: 80 SNR: 17.810365755230787 SSIM: 0.5183975\n",
      "Epoch: 124 Loss:  0.11699375013510387 lr_DL: 1e-06 lr_vel: 80 SNR: 17.90032310107074 SSIM: 0.52187634\n",
      "Epoch: 125 Loss:  0.11886084824800491 lr_DL: 1e-06 lr_vel: 80 SNR: 17.87167564082875 SSIM: 0.5216806\n",
      "Epoch: 126 Loss:  0.12688700606425604 lr_DL: 1e-06 lr_vel: 80 SNR: 17.819049166572928 SSIM: 0.51302487\n",
      "Epoch: 127 Loss:  0.1256975345313549 lr_DL: 1e-06 lr_vel: 80 SNR: 17.847652198444944 SSIM: 0.5105935\n",
      "Epoch: 128 Loss:  0.1252833490570386 lr_DL: 1e-06 lr_vel: 80 SNR: 17.914035238512703 SSIM: 0.5186291\n",
      "Epoch: 129 Loss:  0.1231925643980503 lr_DL: 1e-06 lr_vel: 80 SNR: 17.922344413764066 SSIM: 0.50776136\n",
      "Epoch: 130 Loss:  0.11765800143281618 lr_DL: 1e-06 lr_vel: 80 SNR: 18.012563397370315 SSIM: 0.518275\n",
      "Epoch: 131 Loss:  0.1277404730518659 lr_DL: 1e-06 lr_vel: 80 SNR: 18.039879731101692 SSIM: 0.50617605\n",
      "Epoch: 132 Loss:  0.12314097384611765 lr_DL: 1e-06 lr_vel: 80 SNR: 18.131266036075907 SSIM: 0.51631206\n",
      "Epoch: 133 Loss:  0.11537042533357939 lr_DL: 1e-06 lr_vel: 80 SNR: 18.16394658133116 SSIM: 0.52257025\n",
      "Epoch: 134 Loss:  0.11824684912959735 lr_DL: 1e-06 lr_vel: 80 SNR: 18.257281611636756 SSIM: 0.52086306\n",
      "Epoch: 135 Loss:  0.12171310509244601 lr_DL: 1e-06 lr_vel: 80 SNR: 18.278142310895223 SSIM: 0.5184406\n",
      "Epoch: 136 Loss:  0.12113515039285024 lr_DL: 1e-06 lr_vel: 80 SNR: 18.355110439909936 SSIM: 0.51976097\n",
      "Epoch: 137 Loss:  0.1232581617931525 lr_DL: 1e-06 lr_vel: 80 SNR: 18.433598007284175 SSIM: 0.50990576\n",
      "Epoch: 138 Loss:  0.11792612050970395 lr_DL: 1e-06 lr_vel: 80 SNR: 18.503917552692172 SSIM: 0.5248129\n",
      "Epoch: 139 Loss:  0.12033440073331197 lr_DL: 1e-06 lr_vel: 80 SNR: 18.53290520951805 SSIM: 0.5229459\n",
      "Epoch: 140 Loss:  0.12335380886991819 lr_DL: 1e-06 lr_vel: 80 SNR: 18.523913385453653 SSIM: 0.5250978\n",
      "Epoch: 141 Loss:  0.12486341446638108 lr_DL: 1e-06 lr_vel: 80 SNR: 18.527199218581014 SSIM: 0.5175262\n",
      "Epoch: 142 Loss:  0.11824049403270086 lr_DL: 1e-06 lr_vel: 80 SNR: 18.599405140847455 SSIM: 0.5214207\n",
      "Epoch: 143 Loss:  0.11901416579882304 lr_DL: 1e-06 lr_vel: 80 SNR: 18.676630145712746 SSIM: 0.51704574\n",
      "Epoch: 144 Loss:  0.12142160013318062 lr_DL: 1e-06 lr_vel: 80 SNR: 18.72759990210418 SSIM: 0.5215149\n",
      "Epoch: 145 Loss:  0.11073817958434423 lr_DL: 1e-06 lr_vel: 80 SNR: 18.814637379507342 SSIM: 0.53311163\n",
      "Epoch: 146 Loss:  0.1106954795618852 lr_DL: 1e-06 lr_vel: 80 SNR: 18.84048913072422 SSIM: 0.529127\n",
      "Epoch: 147 Loss:  0.11863665555914243 lr_DL: 1e-06 lr_vel: 80 SNR: 18.887173688708213 SSIM: 0.5291221\n",
      "Epoch: 148 Loss:  0.11830364763736725 lr_DL: 1e-06 lr_vel: 80 SNR: 18.930796224111095 SSIM: 0.5300086\n",
      "Epoch: 149 Loss:  0.11773719638586044 lr_DL: 1e-06 lr_vel: 80 SNR: 18.871809338433273 SSIM: 0.5198956\n",
      "Epoch: 150 Loss:  0.12029398580392202 lr_DL: 1e-06 lr_vel: 80 SNR: 18.907177453313487 SSIM: 0.52005285\n",
      "Epoch: 151 Loss:  0.11511293724179268 lr_DL: 1e-06 lr_vel: 80 SNR: 18.97795188754406 SSIM: 0.5297931\n",
      "Epoch: 152 Loss:  0.11336366087198257 lr_DL: 1e-06 lr_vel: 80 SNR: 19.003446327670364 SSIM: 0.53157616\n",
      "Epoch: 153 Loss:  0.11577822094162306 lr_DL: 1e-06 lr_vel: 80 SNR: 18.975239939472452 SSIM: 0.5267676\n",
      "Epoch: 154 Loss:  0.11432882050673167 lr_DL: 1e-06 lr_vel: 80 SNR: 19.031084046136712 SSIM: 0.52757764\n",
      "Epoch: 155 Loss:  0.11301359782616298 lr_DL: 1e-06 lr_vel: 80 SNR: 19.045021264515583 SSIM: 0.52652687\n",
      "Epoch: 156 Loss:  0.11486216560006142 lr_DL: 1e-06 lr_vel: 80 SNR: 19.067742174828837 SSIM: 0.52930546\n",
      "Epoch: 157 Loss:  0.11712270130713781 lr_DL: 1e-06 lr_vel: 80 SNR: 19.059619089344654 SSIM: 0.524847\n",
      "Epoch: 158 Loss:  0.11447382618983587 lr_DL: 1e-06 lr_vel: 80 SNR: 19.12772812049046 SSIM: 0.52600795\n",
      "Epoch: 159 Loss:  0.11755296935637792 lr_DL: 1e-06 lr_vel: 80 SNR: 19.1865645317353 SSIM: 0.5246874\n",
      "Epoch: 160 Loss:  0.1210687980055809 lr_DL: 1e-06 lr_vel: 80 SNR: 19.164252977468102 SSIM: 0.5253084\n",
      "Epoch: 161 Loss:  0.11040823186437289 lr_DL: 1e-06 lr_vel: 80 SNR: 19.226009516208308 SSIM: 0.5368101\n",
      "Epoch: 162 Loss:  0.11411146471897761 lr_DL: 1e-06 lr_vel: 80 SNR: 19.233696496666436 SSIM: 0.53244424\n",
      "Epoch: 163 Loss:  0.12445424199104309 lr_DL: 1e-06 lr_vel: 80 SNR: 19.275432259973154 SSIM: 0.5193661\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 164 Loss:  0.12165951505303382 lr_DL: 1e-06 lr_vel: 80 SNR: 19.332260898158914 SSIM: 0.526308\n",
      "Epoch: 165 Loss:  0.11989271591107051 lr_DL: 1e-06 lr_vel: 80 SNR: 19.41988757061996 SSIM: 0.5303401\n",
      "Epoch: 166 Loss:  0.11181562369068464 lr_DL: 1e-06 lr_vel: 80 SNR: 19.476013740971965 SSIM: 0.5327947\n",
      "Epoch: 167 Loss:  0.1139680340886116 lr_DL: 1e-06 lr_vel: 80 SNR: 19.577949360155532 SSIM: 0.5324648\n",
      "Epoch: 168 Loss:  0.11452133705218633 lr_DL: 1e-06 lr_vel: 80 SNR: 19.684288464065776 SSIM: 0.5362278\n",
      "Epoch: 169 Loss:  0.11199159572521845 lr_DL: 1e-06 lr_vel: 80 SNR: 19.802078355196397 SSIM: 0.5361929\n",
      "Epoch: 170 Loss:  0.11731658230225245 lr_DL: 1e-06 lr_vel: 80 SNR: 19.8215651697182 SSIM: 0.52954775\n",
      "Epoch: 171 Loss:  0.115090927730004 lr_DL: 1e-06 lr_vel: 80 SNR: 19.922217398613668 SSIM: 0.5354497\n",
      "Epoch: 172 Loss:  0.10997568567593892 lr_DL: 1e-06 lr_vel: 80 SNR: 20.02550801591303 SSIM: 0.537757\n",
      "Epoch: 173 Loss:  0.11731334601839384 lr_DL: 1e-06 lr_vel: 80 SNR: 20.1082310008073 SSIM: 0.5278021\n",
      "Epoch: 174 Loss:  0.11071906586488088 lr_DL: 1e-06 lr_vel: 80 SNR: 20.26564147146521 SSIM: 0.53743917\n",
      "Epoch: 175 Loss:  0.10564922715226809 lr_DL: 1e-06 lr_vel: 80 SNR: 20.4093521098225 SSIM: 0.5395913\n",
      "Epoch: 176 Loss:  0.1144627089301745 lr_DL: 1e-06 lr_vel: 80 SNR: 20.483717811077447 SSIM: 0.53371376\n",
      "Epoch: 177 Loss:  0.11705471600095431 lr_DL: 1e-06 lr_vel: 80 SNR: 20.540080743946763 SSIM: 0.5297197\n",
      "Epoch: 178 Loss:  0.11361397132277488 lr_DL: 1e-06 lr_vel: 80 SNR: 20.661044605940543 SSIM: 0.53610086\n",
      "Epoch: 179 Loss:  0.1161882979174455 lr_DL: 1e-06 lr_vel: 80 SNR: 20.720639397583906 SSIM: 0.5399532\n",
      "Epoch: 180 Loss:  0.11065597931543986 lr_DL: 1e-06 lr_vel: 80 SNR: 20.787504450996433 SSIM: 0.53913575\n",
      "Epoch: 181 Loss:  0.11208938285708428 lr_DL: 1e-06 lr_vel: 80 SNR: 20.820145716728785 SSIM: 0.53843135\n",
      "Epoch: 182 Loss:  0.11610658590992291 lr_DL: 1e-06 lr_vel: 80 SNR: 20.82853848332992 SSIM: 0.5358836\n",
      "Epoch: 183 Loss:  0.11429461762309075 lr_DL: 1e-06 lr_vel: 80 SNR: 20.892715479213532 SSIM: 0.53359157\n",
      "Epoch: 184 Loss:  0.11438375115394592 lr_DL: 1e-06 lr_vel: 80 SNR: 20.924261659815755 SSIM: 0.53716254\n",
      "Epoch: 185 Loss:  0.11248512168725332 lr_DL: 1e-06 lr_vel: 80 SNR: 20.94073215952841 SSIM: 0.5348836\n",
      "Epoch: 186 Loss:  0.11410728171467781 lr_DL: 1e-06 lr_vel: 80 SNR: 21.001852893918127 SSIM: 0.5356812\n",
      "Epoch: 187 Loss:  0.1150260848303636 lr_DL: 1e-06 lr_vel: 80 SNR: 21.00842020217018 SSIM: 0.5322009\n",
      "Epoch: 188 Loss:  0.11540464907884598 lr_DL: 1e-06 lr_vel: 80 SNR: 21.021217882780064 SSIM: 0.52917355\n",
      "Epoch: 189 Loss:  0.10990288654963175 lr_DL: 1e-06 lr_vel: 80 SNR: 21.077345064838337 SSIM: 0.53149164\n",
      "Epoch: 190 Loss:  0.11721276318033537 lr_DL: 1e-06 lr_vel: 80 SNR: 21.053475518644355 SSIM: 0.5249254\n",
      "Epoch: 191 Loss:  0.11344026128451029 lr_DL: 1e-06 lr_vel: 80 SNR: 21.17590872254074 SSIM: 0.53237903\n",
      "Epoch: 192 Loss:  0.11796340073148409 lr_DL: 1e-06 lr_vel: 80 SNR: 21.1569376751061 SSIM: 0.5256667\n",
      "Epoch: 193 Loss:  0.11017527853449186 lr_DL: 1e-06 lr_vel: 80 SNR: 21.250339937923826 SSIM: 0.53163844\n",
      "Epoch: 194 Loss:  0.1154402715464433 lr_DL: 1e-06 lr_vel: 80 SNR: 21.266345646545023 SSIM: 0.53024215\n",
      "Epoch: 195 Loss:  0.11131603519121806 lr_DL: 1e-06 lr_vel: 80 SNR: 21.34122410073362 SSIM: 0.5342769\n",
      "Epoch: 196 Loss:  0.11090652520457904 lr_DL: 1e-06 lr_vel: 80 SNR: 21.42493550620393 SSIM: 0.53755146\n",
      "Epoch: 197 Loss:  0.11521368945638338 lr_DL: 1e-06 lr_vel: 80 SNR: 21.477556788255704 SSIM: 0.53817374\n",
      "Epoch: 198 Loss:  0.11380980263153712 lr_DL: 1e-06 lr_vel: 80 SNR: 21.48518080676354 SSIM: 0.5349677\n",
      "Epoch: 199 Loss:  0.11137087494134904 lr_DL: 1e-06 lr_vel: 80 SNR: 21.54226464317143 SSIM: 0.5413423\n",
      "Epoch: 200 Loss:  0.11400302263597648 lr_DL: 1e-06 lr_vel: 80 SNR: 21.557464498805693 SSIM: 0.53810716\n",
      "Epoch: 201 Loss:  0.11920968666672707 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 21.556918367727494 SSIM: 0.5296898\n",
      "Epoch: 202 Loss:  0.10959712242086729 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 21.58630249933427 SSIM: 0.5348239\n",
      "Epoch: 203 Loss:  0.1113192804157734 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 21.60587460123051 SSIM: 0.5358384\n",
      "Epoch: 204 Loss:  0.11106241717934609 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 21.63159029191224 SSIM: 0.53215843\n",
      "Epoch: 205 Loss:  0.11183191984891891 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 21.634619761161158 SSIM: 0.534021\n",
      "Epoch: 206 Loss:  0.10592060188452403 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 21.671495774836146 SSIM: 0.5364768\n",
      "Epoch: 207 Loss:  0.10922389229138692 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 21.57828935949049 SSIM: 0.53245014\n",
      "Epoch: 208 Loss:  0.10790352622667948 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 21.63021008746602 SSIM: 0.53633237\n",
      "Epoch: 209 Loss:  0.10583253763616085 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 21.691116002881916 SSIM: 0.537325\n",
      "Epoch: 210 Loss:  0.09928184524178504 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 21.775185980987665 SSIM: 0.54296637\n",
      "Epoch: 211 Loss:  0.10662856710453829 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 21.762822410081984 SSIM: 0.5373693\n",
      "Epoch: 212 Loss:  0.10073706408341726 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 21.88450859646661 SSIM: 0.549505\n",
      "Epoch: 213 Loss:  0.1047514104594787 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 21.77830609317484 SSIM: 0.53819245\n",
      "Epoch: 214 Loss:  0.10156922539075215 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 21.81670805220612 SSIM: 0.5453896\n",
      "Epoch: 215 Loss:  0.10470395895342031 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 21.741999924342164 SSIM: 0.539615\n",
      "Epoch: 216 Loss:  0.10710711777210236 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 21.774585578078124 SSIM: 0.542853\n",
      "Epoch: 217 Loss:  0.10529467066129049 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 21.683778273977047 SSIM: 0.53751063\n",
      "Epoch: 218 Loss:  0.10811312111715475 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 21.76347263189472 SSIM: 0.5413607\n",
      "Epoch: 219 Loss:  0.0966605193912983 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 21.796192164061093 SSIM: 0.5455501\n",
      "Epoch: 220 Loss:  0.1061214787264665 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 21.803348778992927 SSIM: 0.5394618\n",
      "Epoch: 221 Loss:  0.10796096275250117 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 21.747865157464823 SSIM: 0.5334435\n",
      "Epoch: 222 Loss:  0.10712076475222905 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 21.733418839895595 SSIM: 0.53607816\n",
      "Epoch: 223 Loss:  0.10349979574481646 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 21.742059055100693 SSIM: 0.5343579\n",
      "Epoch: 224 Loss:  0.10566037744283677 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 21.706634006425347 SSIM: 0.53403705\n",
      "Epoch: 225 Loss:  0.0991750929504633 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 21.769774789709217 SSIM: 0.53710526\n",
      "Epoch: 226 Loss:  0.0988503293444713 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 21.834322152020764 SSIM: 0.5408373\n",
      "Epoch: 227 Loss:  0.09906497560441493 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 21.88712040013717 SSIM: 0.5409501\n",
      "Epoch: 228 Loss:  0.10118379642566046 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 21.87332172302961 SSIM: 0.5413161\n",
      "Epoch: 229 Loss:  0.09947291575372219 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 21.803745749935356 SSIM: 0.5380323\n",
      "Epoch: 230 Loss:  0.10251652027169864 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 21.8753299633044 SSIM: 0.53339803\n",
      "Epoch: 231 Loss:  0.09403115610281626 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 21.968005161131806 SSIM: 0.5420828\n",
      "Epoch: 232 Loss:  0.10112444323798021 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 21.936490607747334 SSIM: 0.53156126\n",
      "Epoch: 233 Loss:  0.09895006517569224 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.079423442325016 SSIM: 0.5440559\n",
      "Epoch: 234 Loss:  0.09977925966183344 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.10480501819335 SSIM: 0.5383707\n",
      "Epoch: 235 Loss:  0.0980421976496776 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.185843254982608 SSIM: 0.54230726\n",
      "Epoch: 236 Loss:  0.09701052370170753 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.13593851088403 SSIM: 0.53975916\n",
      "Epoch: 237 Loss:  0.09563610975941023 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.158095703373476 SSIM: 0.538631\n",
      "Epoch: 238 Loss:  0.0934347715228796 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.20984900654749 SSIM: 0.5396888\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 239 Loss:  0.09982723407447339 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.218524744419366 SSIM: 0.5383606\n",
      "Epoch: 240 Loss:  0.10035331199566523 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.198570074542737 SSIM: 0.53587675\n",
      "Epoch: 241 Loss:  0.09881762477258842 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.2344883688263 SSIM: 0.5398569\n",
      "Epoch: 242 Loss:  0.09121196245153745 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.29218022504646 SSIM: 0.54165363\n",
      "Epoch: 243 Loss:  0.09545883846779664 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.263841147701918 SSIM: 0.5377562\n",
      "Epoch: 244 Loss:  0.09407198218007883 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.256730984884555 SSIM: 0.53812486\n",
      "Epoch: 245 Loss:  0.09733206033706665 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.21550568707618 SSIM: 0.5370802\n",
      "Epoch: 246 Loss:  0.0936929759879907 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.245761315650647 SSIM: 0.5402264\n",
      "Epoch: 247 Loss:  0.093179377540946 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.24156471525134 SSIM: 0.5399227\n",
      "Epoch: 248 Loss:  0.09151632326344648 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.282850191334195 SSIM: 0.5421734\n",
      "Epoch: 249 Loss:  0.08960749891897042 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.331653820407688 SSIM: 0.54337704\n",
      "Epoch: 250 Loss:  0.09039714609583219 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.345816121164916 SSIM: 0.53870136\n",
      "Epoch: 251 Loss:  0.08997230579455694 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.350636142555686 SSIM: 0.5395715\n",
      "Epoch: 252 Loss:  0.09093055004874866 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.386903823739335 SSIM: 0.54071176\n",
      "Epoch: 253 Loss:  0.08528404707709948 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.370215763645824 SSIM: 0.54438424\n",
      "Epoch: 254 Loss:  0.08696802444756031 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.33810071726923 SSIM: 0.540716\n",
      "Epoch: 255 Loss:  0.08699252593020598 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.32991389043341 SSIM: 0.54260033\n",
      "Epoch: 256 Loss:  0.0871786152323087 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.38862378016378 SSIM: 0.543781\n",
      "Epoch: 257 Loss:  0.08655826362470785 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.37991331557882 SSIM: 0.542694\n",
      "Epoch: 258 Loss:  0.08461301003893217 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.379077378796374 SSIM: 0.54225934\n",
      "Epoch: 259 Loss:  0.08282817465563615 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.404655950302846 SSIM: 0.5437878\n",
      "Epoch: 260 Loss:  0.08461933583021164 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.437265476198913 SSIM: 0.5454394\n",
      "Epoch: 261 Loss:  0.08381080540517966 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.418954910280533 SSIM: 0.5432047\n",
      "Epoch: 262 Loss:  0.08392043349643548 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.416315393132393 SSIM: 0.5441544\n",
      "Epoch: 263 Loss:  0.08286505776147048 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.472801360641647 SSIM: 0.54568845\n",
      "Epoch: 264 Loss:  0.08461816385388374 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.388358685155175 SSIM: 0.5403495\n",
      "Epoch: 265 Loss:  0.08274579072992007 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.420234231754783 SSIM: 0.54188776\n",
      "Epoch: 266 Loss:  0.07878556437790393 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.40954157481154 SSIM: 0.5451315\n",
      "Epoch: 267 Loss:  0.07947154231369495 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.41938616936432 SSIM: 0.5446049\n",
      "Epoch: 268 Loss:  0.08071813620626926 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.395200800482606 SSIM: 0.5425526\n",
      "Epoch: 269 Loss:  0.07616238445043563 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.484768812356847 SSIM: 0.5493292\n",
      "Epoch: 270 Loss:  0.07921422322591146 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.411007291607117 SSIM: 0.54237115\n",
      "Epoch: 271 Loss:  0.08033347552021344 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.3490249479355 SSIM: 0.5426456\n",
      "Epoch: 272 Loss:  0.07824089266359806 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.474505992813327 SSIM: 0.5468881\n",
      "Epoch: 273 Loss:  0.07806234260400137 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.472554341044603 SSIM: 0.5454619\n",
      "Epoch: 274 Loss:  0.07882080028454462 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.517653298574416 SSIM: 0.55063796\n",
      "Epoch: 275 Loss:  0.07265877313911914 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.55791452463786 SSIM: 0.55043596\n",
      "Epoch: 276 Loss:  0.07311918872098128 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.57047895895079 SSIM: 0.553866\n",
      "Epoch: 277 Loss:  0.07808717290560405 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.49997451018032 SSIM: 0.5477294\n",
      "Epoch: 278 Loss:  0.07337865581115087 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.575438677090744 SSIM: 0.54966456\n",
      "Epoch: 279 Loss:  0.07392271161079407 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.561516971612065 SSIM: 0.54958415\n",
      "Epoch: 280 Loss:  0.07246849313378334 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.572074025841967 SSIM: 0.5486461\n",
      "Epoch: 281 Loss:  0.0723766757796208 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.536046253432104 SSIM: 0.5452981\n",
      "Epoch: 282 Loss:  0.07894818807641665 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.524296162964916 SSIM: 0.5479093\n",
      "Epoch: 283 Loss:  0.06964182940622171 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.56489469511476 SSIM: 0.5504585\n",
      "Epoch: 284 Loss:  0.07212939982612927 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.524133858875913 SSIM: 0.54596615\n",
      "Epoch: 285 Loss:  0.07106960713863372 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.555855563848674 SSIM: 0.5509503\n",
      "Epoch: 286 Loss:  0.07171571838359038 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.540136584606675 SSIM: 0.5470688\n",
      "Epoch: 287 Loss:  0.06607955371340116 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.553847338322388 SSIM: 0.5486336\n",
      "Epoch: 288 Loss:  0.06955764566858609 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.534334398409115 SSIM: 0.548087\n",
      "Epoch: 289 Loss:  0.06564400543769201 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.57342876432485 SSIM: 0.5479071\n",
      "Epoch: 290 Loss:  0.06212699711322785 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.568101246884247 SSIM: 0.54973334\n",
      "Epoch: 291 Loss:  0.06565778081615765 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.497034245334405 SSIM: 0.5479164\n",
      "Epoch: 292 Loss:  0.06269327513873577 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.532765974334353 SSIM: 0.5519737\n",
      "Epoch: 293 Loss:  0.061692613363265994 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.58111159746571 SSIM: 0.5499646\n",
      "Epoch: 294 Loss:  0.06298367430766423 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.575688034931662 SSIM: 0.54655474\n",
      "Epoch: 295 Loss:  0.061858907962838806 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.5846295194677 SSIM: 0.5531492\n",
      "Epoch: 296 Loss:  0.05692715545495351 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.651641884247073 SSIM: 0.5563392\n",
      "Epoch: 297 Loss:  0.061578191444277765 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.60229316337592 SSIM: 0.55263776\n",
      "Epoch: 298 Loss:  0.06051698798934619 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.559581865659087 SSIM: 0.5517186\n",
      "Epoch: 299 Loss:  0.06126532865067323 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.538272730494377 SSIM: 0.5490889\n",
      "Epoch: 300 Loss:  0.05726094245910644 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.578763735743614 SSIM: 0.55212003\n",
      "Epoch: 301 Loss:  0.05587863934536775 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.55454543611326 SSIM: 0.55505323\n",
      "Epoch: 302 Loss:  0.0540313105409344 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.56196331913568 SSIM: 0.5554374\n",
      "Epoch: 303 Loss:  0.05120802981158098 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.592421218374792 SSIM: 0.55664176\n",
      "Epoch: 304 Loss:  0.05354211926460266 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.590775156876873 SSIM: 0.55064887\n",
      "Epoch: 305 Loss:  0.055176969369252524 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.528131034558267 SSIM: 0.5499511\n",
      "Epoch: 306 Loss:  0.05142915143320958 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.56823887048348 SSIM: 0.5543894\n",
      "Epoch: 307 Loss:  0.052976795968910056 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.53263363123287 SSIM: 0.54861754\n",
      "Epoch: 308 Loss:  0.0527513425797224 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.51748223782126 SSIM: 0.54824144\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 309 Loss:  0.046057627474268274 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.583062729802048 SSIM: 0.5574607\n",
      "Epoch: 310 Loss:  0.04535859984656175 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.514054616696917 SSIM: 0.55094635\n",
      "Epoch: 311 Loss:  0.04695051622887452 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.574192223636963 SSIM: 0.55175024\n",
      "Epoch: 312 Loss:  0.04486961116393407 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.593781538751387 SSIM: 0.5538106\n",
      "Epoch: 313 Loss:  0.04596644223978122 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.504005020216738 SSIM: 0.5502553\n",
      "Epoch: 314 Loss:  0.04613760765641928 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.55598305355406 SSIM: 0.5541193\n",
      "Epoch: 315 Loss:  0.0424306221306324 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.58446515125032 SSIM: 0.5534627\n",
      "Epoch: 316 Loss:  0.04076420838634173 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.578341859249086 SSIM: 0.55447483\n",
      "Epoch: 317 Loss:  0.039532046454648175 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.582078028135662 SSIM: 0.55497485\n",
      "Epoch: 318 Loss:  0.0412436260531346 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.569571183844953 SSIM: 0.55459046\n",
      "Epoch: 319 Loss:  0.03833627483497063 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.54313163404412 SSIM: 0.5533036\n",
      "Epoch: 320 Loss:  0.036771138571202754 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.60691367651454 SSIM: 0.56202096\n",
      "Epoch: 321 Loss:  0.03551501464098692 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.618153016488694 SSIM: 0.5566532\n",
      "Epoch: 322 Loss:  0.03371752972404162 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.623285542311585 SSIM: 0.5597282\n",
      "Epoch: 323 Loss:  0.03250610617299875 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.652992287978797 SSIM: 0.5618103\n",
      "Epoch: 324 Loss:  0.03125436566770077 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.624629168507575 SSIM: 0.5592492\n",
      "Epoch: 325 Loss:  0.03133746975411971 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.657556997452815 SSIM: 0.5637161\n",
      "Epoch: 326 Loss:  0.033020588320990404 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.606119249923783 SSIM: 0.55739963\n",
      "Epoch: 327 Loss:  0.029220739534745615 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.64695145834283 SSIM: 0.56343067\n",
      "Epoch: 328 Loss:  0.025386578403413296 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.659959775525103 SSIM: 0.561925\n",
      "Epoch: 329 Loss:  0.02435288093984127 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.701666282078513 SSIM: 0.56619585\n",
      "Epoch: 330 Loss:  0.024585187590370577 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.66709641044296 SSIM: 0.5628826\n",
      "Epoch: 331 Loss:  0.023063643494000037 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.66917470889496 SSIM: 0.5644517\n",
      "Epoch: 332 Loss:  0.022089622262865306 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.679263791182077 SSIM: 0.5633385\n",
      "Epoch: 333 Loss:  0.022981474331269663 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.694878016779224 SSIM: 0.56588304\n",
      "Epoch: 334 Loss:  0.020681932227065165 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.679722128748008 SSIM: 0.5662908\n",
      "Epoch: 335 Loss:  0.018300719497104486 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.68807612294723 SSIM: 0.56767404\n",
      "Epoch: 336 Loss:  0.018561066780239343 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.7357470005742 SSIM: 0.5661805\n",
      "Epoch: 337 Loss:  0.01893694739167889 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.7084202235232 SSIM: 0.56697404\n",
      "Epoch: 338 Loss:  0.017421113171925146 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.757592970198992 SSIM: 0.5710048\n",
      "Epoch: 339 Loss:  0.018411067438622315 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.77147761177882 SSIM: 0.5699885\n",
      "Epoch: 340 Loss:  0.01843056973690788 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.783118880912284 SSIM: 0.5704865\n",
      "Epoch: 341 Loss:  0.016838136843095222 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.756749690882963 SSIM: 0.57001966\n",
      "Epoch: 342 Loss:  0.016832345941414435 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.78709738428278 SSIM: 0.57160985\n",
      "Epoch: 343 Loss:  0.016521816700696947 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.75307896013515 SSIM: 0.56828386\n",
      "Epoch: 344 Loss:  0.01731543205678463 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.83512813309345 SSIM: 0.5742028\n",
      "Epoch: 345 Loss:  0.01670508589595556 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.7965831944285 SSIM: 0.5673241\n",
      "Epoch: 346 Loss:  0.01785612686847647 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.812430560206955 SSIM: 0.57038313\n",
      "Epoch: 347 Loss:  0.01672161885847648 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.794739988958543 SSIM: 0.5707972\n",
      "Epoch: 348 Loss:  0.016330462756256263 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.815812874166866 SSIM: 0.56901485\n",
      "Epoch: 349 Loss:  0.017205152908960977 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.802554216154608 SSIM: 0.5691809\n",
      "Epoch: 350 Loss:  0.01709389469275872 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.79679640623185 SSIM: 0.56889576\n",
      "Epoch: 351 Loss:  0.01720600351691246 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.79448070705714 SSIM: 0.56768733\n",
      "Epoch: 352 Loss:  0.01598590612411499 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.81376399366203 SSIM: 0.5683874\n",
      "Epoch: 353 Loss:  0.015442571261276801 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.79463491114661 SSIM: 0.57228565\n",
      "Epoch: 354 Loss:  0.01667100895817081 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.810723772520376 SSIM: 0.5688586\n",
      "Epoch: 355 Loss:  0.01819536170611779 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.781591706263512 SSIM: 0.56758595\n",
      "Epoch: 356 Loss:  0.017495600103090207 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.8064955461942 SSIM: 0.5717438\n",
      "Epoch: 357 Loss:  0.01706965354581674 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.79626412926453 SSIM: 0.5707179\n",
      "Epoch: 358 Loss:  0.01590469085300962 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.81226312386756 SSIM: 0.5743326\n",
      "Epoch: 359 Loss:  0.015467627470691999 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.825522392527123 SSIM: 0.57090944\n",
      "Epoch: 360 Loss:  0.015285345911979675 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.836680497850054 SSIM: 0.57277274\n",
      "Epoch: 361 Loss:  0.016475163990010817 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.81131289151253 SSIM: 0.56862926\n",
      "Epoch: 362 Loss:  0.016950911718110244 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.8057167105069 SSIM: 0.5692599\n",
      "Epoch: 363 Loss:  0.016452679100135963 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.7921299952575 SSIM: 0.5681991\n",
      "Epoch: 364 Loss:  0.01595961026226481 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.804989878416553 SSIM: 0.5693105\n",
      "Epoch: 365 Loss:  0.015274018452813228 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.818716138561207 SSIM: 0.57113224\n",
      "Epoch: 366 Loss:  0.015233109053224326 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.806120010543587 SSIM: 0.5702892\n",
      "Epoch: 367 Loss:  0.014587433015306791 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.82838232609322 SSIM: 0.57015926\n",
      "Epoch: 368 Loss:  0.014952076971530915 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.8378379238063 SSIM: 0.5702015\n",
      "Epoch: 369 Loss:  0.01519276931261023 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.839847062923884 SSIM: 0.56991035\n",
      "Epoch: 370 Loss:  0.015000844777872165 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.841202003627362 SSIM: 0.57054955\n",
      "Epoch: 371 Loss:  0.014992376634230217 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.8326979426389 SSIM: 0.56943065\n",
      "Epoch: 372 Loss:  0.014354661169151465 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.82072535214734 SSIM: 0.5695679\n",
      "Epoch: 373 Loss:  0.014400959604730208 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.832406438955488 SSIM: 0.56986564\n",
      "Epoch: 374 Loss:  0.01500932096193234 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.827450397143085 SSIM: 0.5687968\n",
      "Epoch: 375 Loss:  0.01508317639430364 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.81879756749116 SSIM: 0.5693755\n",
      "Epoch: 376 Loss:  0.014427024529625972 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.825407238741327 SSIM: 0.56986815\n",
      "Epoch: 377 Loss:  0.0142727167190363 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.828364154600997 SSIM: 0.5690847\n",
      "Epoch: 378 Loss:  0.013085954015453656 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.860917839029376 SSIM: 0.57235473\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 379 Loss:  0.013988608463356892 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.848425319486662 SSIM: 0.56997174\n",
      "Epoch: 380 Loss:  0.014814417498807112 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.808095869876993 SSIM: 0.56781936\n",
      "Epoch: 381 Loss:  0.014099529789139828 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.846692575292376 SSIM: 0.57329816\n",
      "Epoch: 382 Loss:  0.014357004516447584 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.849549464520443 SSIM: 0.5711762\n",
      "Epoch: 383 Loss:  0.014160143025219441 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.866654681658545 SSIM: 0.5732657\n",
      "Epoch: 384 Loss:  0.013414552171404163 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.846306559340334 SSIM: 0.57367736\n",
      "Epoch: 385 Loss:  0.013909658199797075 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.87855246949936 SSIM: 0.57295626\n",
      "Epoch: 386 Loss:  0.014376269994924466 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.846184500935816 SSIM: 0.5718131\n",
      "Epoch: 387 Loss:  0.01343424200701217 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.84706304386941 SSIM: 0.57242584\n",
      "Epoch: 388 Loss:  0.012948314286768436 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.883076196756363 SSIM: 0.57393867\n",
      "Epoch: 389 Loss:  0.012850113135452071 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.881953424084664 SSIM: 0.5742895\n",
      "Epoch: 390 Loss:  0.012571738086019952 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.88211892713676 SSIM: 0.5761701\n",
      "Epoch: 391 Loss:  0.013360349554568529 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.904714704205936 SSIM: 0.5736976\n",
      "Epoch: 392 Loss:  0.013930415331075589 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.864196944524323 SSIM: 0.5707377\n",
      "Epoch: 393 Loss:  0.013101003831252456 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.86908326864253 SSIM: 0.5697555\n",
      "Epoch: 394 Loss:  0.012539125295976799 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.90713032069208 SSIM: 0.57753426\n",
      "Epoch: 395 Loss:  0.012982107155645886 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.872368517644773 SSIM: 0.5725082\n",
      "Epoch: 396 Loss:  0.012134554563090205 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.896294360656682 SSIM: 0.5760285\n",
      "Epoch: 397 Loss:  0.013095299558093151 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.88409113011854 SSIM: 0.5742032\n",
      "Epoch: 398 Loss:  0.013399038805315892 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.882967340293185 SSIM: 0.57293105\n",
      "Epoch: 399 Loss:  0.011589989547307293 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.922213974418245 SSIM: 0.5759957\n",
      "Epoch: 400 Loss:  0.012164981539050738 lr_DL: 9.999999999999999e-06 lr_vel: 80 SNR: 22.906097505463215 SSIM: 0.5741138\n",
      "Epoch: 401 Loss:  0.011594500470285615 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 22.92767849670223 SSIM: 0.576697\n",
      "Epoch: 402 Loss:  0.012170921933526794 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 22.937771552110885 SSIM: 0.5756918\n",
      "Epoch: 403 Loss:  0.011226061622922619 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 22.93799989621856 SSIM: 0.5764679\n",
      "Epoch: 404 Loss:  0.009973405705144009 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 22.981184685506115 SSIM: 0.5799466\n",
      "Epoch: 405 Loss:  0.009233228567366799 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 22.986786879663427 SSIM: 0.5806356\n",
      "Epoch: 406 Loss:  0.009162615813935796 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 22.979583159672327 SSIM: 0.57915443\n",
      "Epoch: 407 Loss:  0.008765757068370779 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 22.99105998072628 SSIM: 0.5803057\n",
      "Epoch: 408 Loss:  0.008613373603050907 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 22.987109177100407 SSIM: 0.5797408\n",
      "Epoch: 409 Loss:  0.00791113981977105 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.000677195179613 SSIM: 0.5815983\n",
      "Epoch: 410 Loss:  0.007622156121457616 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.011155490665807 SSIM: 0.57982755\n",
      "Epoch: 411 Loss:  0.007416327111423015 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.011699885941542 SSIM: 0.5791061\n",
      "Epoch: 412 Loss:  0.007059116366629799 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 22.997578664781425 SSIM: 0.57903093\n",
      "Epoch: 413 Loss:  0.007318804025029143 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.014258164291416 SSIM: 0.5781946\n",
      "Epoch: 414 Loss:  0.007325198734179139 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 22.99572188881923 SSIM: 0.5775856\n",
      "Epoch: 415 Loss:  0.007120323258762559 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 22.99970576942521 SSIM: 0.5769191\n",
      "Epoch: 416 Loss:  0.006341996183618903 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.000616400863983 SSIM: 0.57752985\n",
      "Epoch: 417 Loss:  0.007011131119603912 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.00613438924499 SSIM: 0.5777486\n",
      "Epoch: 418 Loss:  0.0063566915302847825 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.052767742421224 SSIM: 0.58082044\n",
      "Epoch: 419 Loss:  0.006881056213751435 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.027021843321762 SSIM: 0.5791613\n",
      "Epoch: 420 Loss:  0.006087797259291013 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.044208635944546 SSIM: 0.5793567\n",
      "Epoch: 421 Loss:  0.005959939328022301 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.047328703696728 SSIM: 0.58187705\n",
      "Epoch: 422 Loss:  0.006557991627293328 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.024620055220574 SSIM: 0.5781078\n",
      "Epoch: 423 Loss:  0.006252500135451556 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.050301339816926 SSIM: 0.5816642\n",
      "Epoch: 424 Loss:  0.005861013475805521 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.04913826291638 SSIM: 0.58021414\n",
      "Epoch: 425 Loss:  0.005650462900909285 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.06253020306579 SSIM: 0.5816318\n",
      "Epoch: 426 Loss:  0.005965240447161098 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.06541791690609 SSIM: 0.5825812\n",
      "Epoch: 427 Loss:  0.005601502466015517 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.08274258530217 SSIM: 0.58516324\n",
      "Epoch: 428 Loss:  0.0054719252123807864 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.066798635375072 SSIM: 0.5829005\n",
      "Epoch: 429 Loss:  0.0057486500823870305 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.06226959946633 SSIM: 0.5823762\n",
      "Epoch: 430 Loss:  0.005773351939084629 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.07146180054084 SSIM: 0.5819713\n",
      "Epoch: 431 Loss:  0.005438130154895286 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.073648884612616 SSIM: 0.583322\n",
      "Epoch: 432 Loss:  0.004874734596038858 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.08616204378085 SSIM: 0.58394295\n",
      "Epoch: 433 Loss:  0.004901712721524139 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.09125881029521 SSIM: 0.5835825\n",
      "Epoch: 434 Loss:  0.004728401475585997 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.130227157559148 SSIM: 0.58755934\n",
      "Epoch: 435 Loss:  0.004473078739829361 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.13172980971604 SSIM: 0.58796793\n",
      "Epoch: 436 Loss:  0.004436416396250327 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.13272100050789 SSIM: 0.58702666\n",
      "Epoch: 437 Loss:  0.004674916078026096 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.13344525915005 SSIM: 0.58801\n",
      "Epoch: 438 Loss:  0.0047113400651142 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.120372261829367 SSIM: 0.5872425\n",
      "Epoch: 439 Loss:  0.004417235380969942 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.145696332444295 SSIM: 0.58878285\n",
      "Epoch: 440 Loss:  0.003961048616717259 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.165986952611007 SSIM: 0.59014845\n",
      "Epoch: 441 Loss:  0.004159410763531923 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.161111233418353 SSIM: 0.5889734\n",
      "Epoch: 442 Loss:  0.0043966334002713365 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.169997087549817 SSIM: 0.5899955\n",
      "Epoch: 443 Loss:  0.0042054658522829415 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.168180198528944 SSIM: 0.5897987\n",
      "Epoch: 444 Loss:  0.004193334436664978 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.163698479807017 SSIM: 0.5882444\n",
      "Epoch: 445 Loss:  0.0037953540217131377 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.183938282709107 SSIM: 0.59129554\n",
      "Epoch: 446 Loss:  0.003696353166985015 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.195244782592823 SSIM: 0.5926719\n",
      "Epoch: 447 Loss:  0.003917309052000443 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.18186082618186 SSIM: 0.5903533\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 448 Loss:  0.003946816618554294 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.18581650738198 SSIM: 0.5903586\n",
      "Epoch: 449 Loss:  0.003488663281314075 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.19278332507878 SSIM: 0.5916226\n",
      "Epoch: 450 Loss:  0.0035856475199883184 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.193170880687283 SSIM: 0.59099436\n",
      "Epoch: 451 Loss:  0.0033864611526951195 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.207615527024778 SSIM: 0.5928748\n",
      "Epoch: 452 Loss:  0.0031977511631945768 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.219920692008124 SSIM: 0.59393007\n",
      "Epoch: 453 Loss:  0.0032864301620672146 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.201195864043243 SSIM: 0.59284264\n",
      "Epoch: 454 Loss:  0.0034992752984787027 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.210891261473453 SSIM: 0.59282875\n",
      "Epoch: 455 Loss:  0.0032855442414681116 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.226064259775193 SSIM: 0.59352666\n",
      "Epoch: 456 Loss:  0.0032158650069807966 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.22640117527459 SSIM: 0.59316456\n",
      "Epoch: 457 Loss:  0.002940188926489403 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.242765860450476 SSIM: 0.5954322\n",
      "Epoch: 458 Loss:  0.003027499719367673 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.23963512054643 SSIM: 0.5946614\n",
      "Epoch: 459 Loss:  0.0029969456877248983 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.239962641835454 SSIM: 0.5940122\n",
      "Epoch: 460 Loss:  0.0032482461732191346 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.22481241717211 SSIM: 0.59329724\n",
      "Epoch: 461 Loss:  0.0029912992341754336 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.24423321585552 SSIM: 0.5953896\n",
      "Epoch: 462 Loss:  0.002988821151666343 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.224327374747258 SSIM: 0.5935193\n",
      "Epoch: 463 Loss:  0.0029283262556418777 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.24563517172286 SSIM: 0.593917\n",
      "Epoch: 464 Loss:  0.002888405912866195 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.240243471928714 SSIM: 0.59518856\n",
      "Epoch: 465 Loss:  0.002944123725562046 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.256209404522295 SSIM: 0.59654033\n",
      "Epoch: 466 Loss:  0.002705376292578876 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.262113158550907 SSIM: 0.59594226\n",
      "Epoch: 467 Loss:  0.0027446633942114812 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.254262603081344 SSIM: 0.59542\n",
      "Epoch: 468 Loss:  0.0030364513979293404 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.253820484999363 SSIM: 0.5962443\n",
      "Epoch: 469 Loss:  0.0028468463957930603 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.280696545319582 SSIM: 0.597972\n",
      "Epoch: 470 Loss:  0.0026189380597012738 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.278270475143827 SSIM: 0.5975195\n",
      "Epoch: 471 Loss:  0.0023962252967370053 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.285640574773353 SSIM: 0.5987773\n",
      "Epoch: 472 Loss:  0.0026683857742076118 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.285805652374275 SSIM: 0.59938407\n",
      "Epoch: 473 Loss:  0.002647978513656805 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.290786149439956 SSIM: 0.59866846\n",
      "Epoch: 474 Loss:  0.0025854926245907944 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.29098020329068 SSIM: 0.59899974\n",
      "Epoch: 475 Loss:  0.002288433909416199 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.307841632987557 SSIM: 0.60028815\n",
      "Epoch: 476 Loss:  0.002501714590471238 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.304294216970742 SSIM: 0.6010503\n",
      "Epoch: 477 Loss:  0.0023176661188093325 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.304507608053584 SSIM: 0.6001163\n",
      "Epoch: 478 Loss:  0.0023315462400205433 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.318154501670758 SSIM: 0.60179955\n",
      "Epoch: 479 Loss:  0.0021902875353892646 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.32966851573872 SSIM: 0.60245025\n",
      "Epoch: 480 Loss:  0.002260275049290309 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.32537082599365 SSIM: 0.6021842\n",
      "Epoch: 481 Loss:  0.0020646929275244474 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.353191723325978 SSIM: 0.6047813\n",
      "Epoch: 482 Loss:  0.0020055548870004714 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.330309346115087 SSIM: 0.6036588\n",
      "Epoch: 483 Loss:  0.002050911646801978 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.354873906304682 SSIM: 0.60471004\n",
      "Epoch: 484 Loss:  0.002162947078856329 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.346803602298504 SSIM: 0.60397357\n",
      "Epoch: 485 Loss:  0.0020975856731335323 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.361697575431563 SSIM: 0.6062329\n",
      "Epoch: 486 Loss:  0.001773716730531305 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.36223026202105 SSIM: 0.60560673\n",
      "Epoch: 487 Loss:  0.0019395374227315187 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.37311104424039 SSIM: 0.6064579\n",
      "Epoch: 488 Loss:  0.00192737061297521 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.377533641197665 SSIM: 0.60750914\n",
      "Epoch: 489 Loss:  0.0018495080216477315 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.373945659898986 SSIM: 0.6058778\n",
      "Epoch: 490 Loss:  0.00209290722074608 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.378425105431354 SSIM: 0.60631615\n",
      "Epoch: 491 Loss:  0.0019146376949114104 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.395815803155532 SSIM: 0.608604\n",
      "Epoch: 492 Loss:  0.001627145994765063 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.39425721407928 SSIM: 0.6075498\n",
      "Epoch: 493 Loss:  0.0017493192882587513 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.39567754165762 SSIM: 0.60773927\n",
      "Epoch: 494 Loss:  0.0017310786060988903 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.41818819622429 SSIM: 0.61057055\n",
      "Epoch: 495 Loss:  0.0016184864313496898 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.414269177616585 SSIM: 0.6100077\n",
      "Epoch: 496 Loss:  0.0016263115995874009 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.4209111053385 SSIM: 0.6107277\n",
      "Epoch: 497 Loss:  0.0016752452783597013 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.42513223555509 SSIM: 0.6099094\n",
      "Epoch: 498 Loss:  0.0016702835409281155 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.426696335432375 SSIM: 0.6109537\n",
      "Epoch: 499 Loss:  0.0016477708239108324 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.42967059798028 SSIM: 0.6112126\n",
      "Epoch: 500 Loss:  0.0015275291128394504 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.44393621721305 SSIM: 0.6128988\n",
      "Epoch: 501 Loss:  0.0013399239726519833 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.45203438287795 SSIM: 0.612698\n",
      "Epoch: 502 Loss:  0.0015103073208592832 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.458384229900084 SSIM: 0.6141838\n",
      "Epoch: 503 Loss:  0.0014800932821041593 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.453528361191474 SSIM: 0.6127544\n",
      "Epoch: 504 Loss:  0.001380900691340988 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.471984553401846 SSIM: 0.61518407\n",
      "Epoch: 505 Loss:  0.0013329887199991692 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.46752888220481 SSIM: 0.61462504\n",
      "Epoch: 506 Loss:  0.0013138230210946252 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.482255690810142 SSIM: 0.6157246\n",
      "Epoch: 507 Loss:  0.0013049162322810541 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.47929063615449 SSIM: 0.61596215\n",
      "Epoch: 508 Loss:  0.0012449827375045667 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.48942016675573 SSIM: 0.617066\n",
      "Epoch: 509 Loss:  0.001274851271106551 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.48803308043465 SSIM: 0.61716986\n",
      "Epoch: 510 Loss:  0.0010681646371570727 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.49733020369934 SSIM: 0.6181918\n",
      "Epoch: 511 Loss:  0.0012347263478053113 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.507766709449438 SSIM: 0.61868614\n",
      "Epoch: 512 Loss:  0.0011533106327988208 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.516377770385887 SSIM: 0.6195049\n",
      "Epoch: 513 Loss:  0.0011545309447683395 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.519218153143335 SSIM: 0.6197517\n",
      "Epoch: 514 Loss:  0.0010812191428461423 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.527199348989623 SSIM: 0.62055963\n",
      "Epoch: 515 Loss:  0.0011690886516589672 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.521841862023145 SSIM: 0.6201279\n",
      "Epoch: 516 Loss:  0.0010657845142607889 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.532900893740475 SSIM: 0.6207695\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 517 Loss:  0.0010229851565478991 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.53722318078836 SSIM: 0.62150586\n",
      "Epoch: 518 Loss:  0.0009462700283620507 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.54555071077459 SSIM: 0.6223665\n",
      "Epoch: 519 Loss:  0.0010250372831554463 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.55022863992625 SSIM: 0.62298095\n",
      "Epoch: 520 Loss:  0.0009731827061235284 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.549929546159657 SSIM: 0.623003\n",
      "Epoch: 521 Loss:  0.0009200465574394912 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.56173478388679 SSIM: 0.6239953\n",
      "Epoch: 522 Loss:  0.0008917624334571883 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.564574417683794 SSIM: 0.6245416\n",
      "Epoch: 523 Loss:  0.0008501620031893253 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.575305233855314 SSIM: 0.62559193\n",
      "Epoch: 524 Loss:  0.0007953222850725675 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.572849554477664 SSIM: 0.62569875\n",
      "Epoch: 525 Loss:  0.0008500227937474847 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.581084193775247 SSIM: 0.62600625\n",
      "Epoch: 526 Loss:  0.000857250658251966 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.589437349971117 SSIM: 0.62714046\n",
      "Epoch: 527 Loss:  0.0007704404367056364 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.590906091019114 SSIM: 0.62738097\n",
      "Epoch: 528 Loss:  0.0007124138054981206 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.602415914887857 SSIM: 0.62809354\n",
      "Epoch: 529 Loss:  0.000779775096452795 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.598822814217822 SSIM: 0.62835056\n",
      "Epoch: 530 Loss:  0.0007543583487858996 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.60819607019273 SSIM: 0.62880486\n",
      "Epoch: 531 Loss:  0.000698012220285212 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.614706501633815 SSIM: 0.6301475\n",
      "Epoch: 532 Loss:  0.0006333671015454456 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.620159877539706 SSIM: 0.6302435\n",
      "Epoch: 533 Loss:  0.000616242988811185 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.62093501334335 SSIM: 0.63104266\n",
      "Epoch: 534 Loss:  0.000674176622608987 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.628616425013625 SSIM: 0.6318152\n",
      "Epoch: 535 Loss:  0.0006678858791322758 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.63553354805856 SSIM: 0.6322034\n",
      "Epoch: 536 Loss:  0.0006130022249029328 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.63615427476903 SSIM: 0.6329449\n",
      "Epoch: 537 Loss:  0.0005881801422219724 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.644398785119073 SSIM: 0.6334031\n",
      "Epoch: 538 Loss:  0.0005636625641879315 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.653018894343397 SSIM: 0.6346183\n",
      "Epoch: 539 Loss:  0.0005062300634259979 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.656465713424268 SSIM: 0.6346327\n",
      "Epoch: 540 Loss:  0.0005233305312382678 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.660318697562104 SSIM: 0.634938\n",
      "Epoch: 541 Loss:  0.0005096841467699657 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.668361374979348 SSIM: 0.6356418\n",
      "Epoch: 542 Loss:  0.0004946207297810664 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.67035295062812 SSIM: 0.63644993\n",
      "Epoch: 543 Loss:  0.0004664736693181718 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.677772721729454 SSIM: 0.6369647\n",
      "Epoch: 544 Loss:  0.00047821639745961877 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.68001242507383 SSIM: 0.63743883\n",
      "Epoch: 545 Loss:  0.0004083150502992794 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.689891539358676 SSIM: 0.63803846\n",
      "Epoch: 546 Loss:  0.0004329818912083283 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.691154794188847 SSIM: 0.63865787\n",
      "Epoch: 547 Loss:  0.00042447980619423714 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.698845223453226 SSIM: 0.639041\n",
      "Epoch: 548 Loss:  0.0004053012138077368 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.700656007450164 SSIM: 0.63970643\n",
      "Epoch: 549 Loss:  0.000371277732483577 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.709988520836482 SSIM: 0.6403092\n",
      "Epoch: 550 Loss:  0.0003824510669801384 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.71415271332166 SSIM: 0.6412072\n",
      "Epoch: 551 Loss:  0.0003572815456815685 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.720949223659943 SSIM: 0.64157474\n",
      "Epoch: 552 Loss:  0.0003650290872125576 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.722599808835483 SSIM: 0.6422355\n",
      "Epoch: 553 Loss:  0.00033892893261509014 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.731544659828497 SSIM: 0.6427283\n",
      "Epoch: 554 Loss:  0.0003336971921574635 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.733813795720113 SSIM: 0.643372\n",
      "Epoch: 555 Loss:  0.00031531620382641755 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.7414447415007 SSIM: 0.643773\n",
      "Epoch: 556 Loss:  0.00031717652066921197 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.745066365257863 SSIM: 0.6445212\n",
      "Epoch: 557 Loss:  0.00029618364011791223 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.75261604378833 SSIM: 0.6449126\n",
      "Epoch: 558 Loss:  0.00030183069126602885 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.755291416133325 SSIM: 0.64556473\n",
      "Epoch: 559 Loss:  0.0002770350140053779 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.763576602778535 SSIM: 0.64600396\n",
      "Epoch: 560 Loss:  0.0002805895016839107 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.76604201834837 SSIM: 0.64664197\n",
      "Epoch: 561 Loss:  0.0002612561089335941 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.77364524968626 SSIM: 0.64708006\n",
      "Epoch: 562 Loss:  0.0002825563181734954 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.776240382164254 SSIM: 0.647751\n",
      "Epoch: 563 Loss:  0.00022954871383262798 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.785233020125048 SSIM: 0.6483055\n",
      "Epoch: 564 Loss:  0.0002337758206219102 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.789240466152442 SSIM: 0.64881945\n",
      "Epoch: 565 Loss:  0.000225827772616564 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.796704638053676 SSIM: 0.649402\n",
      "Epoch: 566 Loss:  0.00023011429730104282 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.801473965884547 SSIM: 0.6499997\n",
      "Epoch: 567 Loss:  0.00022658685581215346 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.80901738744487 SSIM: 0.6503199\n",
      "Epoch: 568 Loss:  0.0002084627194562927 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.813021637636723 SSIM: 0.6510491\n",
      "Epoch: 569 Loss:  0.00020906769253391152 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.82128194258115 SSIM: 0.6513626\n",
      "Epoch: 570 Loss:  0.00021512835616401086 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.823206121118176 SSIM: 0.651945\n",
      "Epoch: 571 Loss:  0.00019085892466440176 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.832139690441004 SSIM: 0.6525837\n",
      "Epoch: 572 Loss:  0.00018219572327022132 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.83687112210894 SSIM: 0.65303504\n",
      "Epoch: 573 Loss:  0.0001993269501933052 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.839514281212608 SSIM: 0.6536141\n",
      "Epoch: 574 Loss:  0.0002632306204759516 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.846720405413503 SSIM: 0.65363884\n",
      "Epoch: 575 Loss:  0.0002053465994928653 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.852137718297406 SSIM: 0.65442914\n",
      "Epoch: 576 Loss:  0.00017550975705186525 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.859418634194455 SSIM: 0.6549807\n",
      "Epoch: 577 Loss:  0.00017389594334720943 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.862837882695676 SSIM: 0.65545106\n",
      "Epoch: 578 Loss:  0.0001686663951356119 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.868672440615107 SSIM: 0.65580285\n",
      "Epoch: 579 Loss:  0.00017230224305725034 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.873111912152126 SSIM: 0.6563092\n",
      "Epoch: 580 Loss:  0.0001706376674216396 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.87501171358851 SSIM: 0.65656227\n",
      "Epoch: 581 Loss:  0.0001918435431434773 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.88422386084099 SSIM: 0.6570993\n",
      "Epoch: 582 Loss:  0.00018041017125748718 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.888719798460222 SSIM: 0.65729874\n",
      "Epoch: 583 Loss:  0.00017343857277107115 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.891870058337894 SSIM: 0.6575117\n",
      "Epoch: 584 Loss:  0.00017161749565275386 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.89885479429202 SSIM: 0.6580072\n",
      "Epoch: 585 Loss:  0.00015970292709729014 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.902260808320133 SSIM: 0.6584449\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 586 Loss:  0.00015002960960070293 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.908560086345794 SSIM: 0.65895075\n",
      "Epoch: 587 Loss:  0.00016351581531732034 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.911877494143937 SSIM: 0.6590503\n",
      "Epoch: 588 Loss:  0.00017891341558424757 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.915096366164917 SSIM: 0.65944916\n",
      "Epoch: 589 Loss:  0.0001600028966398289 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.92163100407938 SSIM: 0.6597514\n",
      "Epoch: 590 Loss:  0.0001594755985327841 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.92719014170785 SSIM: 0.6601901\n",
      "Epoch: 591 Loss:  0.0002757470288391536 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.925419679198434 SSIM: 0.65962964\n",
      "Epoch: 592 Loss:  0.00016998795108520427 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.936496987828004 SSIM: 0.6606004\n",
      "Epoch: 593 Loss:  0.00015214442052335168 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.940278817532963 SSIM: 0.6610123\n",
      "Epoch: 594 Loss:  0.00014906108626746572 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.946893436502826 SSIM: 0.66153663\n",
      "Epoch: 595 Loss:  0.0001485753547361431 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.950395135915798 SSIM: 0.6617253\n",
      "Epoch: 596 Loss:  0.00015282445529010146 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.95639766078113 SSIM: 0.66206604\n",
      "Epoch: 597 Loss:  0.0001569916710044102 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.958941133287567 SSIM: 0.66237897\n",
      "Epoch: 598 Loss:  0.00015562658348547604 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.962305190105162 SSIM: 0.66265166\n",
      "Epoch: 599 Loss:  0.00016021912985403713 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.96809428705609 SSIM: 0.6630255\n",
      "Epoch: 600 Loss:  0.00014435917522253778 lr_DL: 9.999999999999999e-05 lr_vel: 80 SNR: 23.973850210926052 SSIM: 0.6633853\n",
      "Epoch: 601 Loss:  0.0001603141545880741 lr_DL: 0.001 lr_vel: 80 SNR: 23.97437389568129 SSIM: 0.6634169\n",
      "Epoch: 602 Loss:  0.002751333065680228 lr_DL: 0.001 lr_vel: 80 SNR: 23.818369190971993 SSIM: 0.6433495\n",
      "Epoch: 603 Loss:  0.0011896958070186277 lr_DL: 0.001 lr_vel: 80 SNR: 23.897914456704452 SSIM: 0.65183866\n",
      "Epoch: 604 Loss:  0.0003318393636921731 lr_DL: 0.001 lr_vel: 80 SNR: 23.932754898804227 SSIM: 0.6556827\n",
      "Epoch: 605 Loss:  0.00027372213323057317 lr_DL: 0.001 lr_vel: 80 SNR: 23.95676484545877 SSIM: 0.6587574\n",
      "Epoch: 606 Loss:  0.00024618000024929644 lr_DL: 0.001 lr_vel: 80 SNR: 23.972145177372347 SSIM: 0.66087633\n",
      "Epoch: 607 Loss:  0.0002075825359497685 lr_DL: 0.001 lr_vel: 80 SNR: 23.986657532777723 SSIM: 0.66277164\n",
      "Epoch: 608 Loss:  0.00017732158764071452 lr_DL: 0.001 lr_vel: 80 SNR: 23.995303174133088 SSIM: 0.6636154\n",
      "Epoch: 609 Loss:  0.00022264278765457373 lr_DL: 0.001 lr_vel: 80 SNR: 23.985525125579024 SSIM: 0.6633633\n",
      "Epoch: 610 Loss:  0.0013738997382461092 lr_DL: 0.001 lr_vel: 80 SNR: 23.930087369466612 SSIM: 0.6568622\n",
      "Epoch: 611 Loss:  0.0010525374993449077 lr_DL: 0.001 lr_vel: 80 SNR: 23.952531849100303 SSIM: 0.6580315\n",
      "Epoch: 612 Loss:  0.00046949692526444174 lr_DL: 0.001 lr_vel: 80 SNR: 23.988493742200507 SSIM: 0.66182005\n",
      "Epoch: 613 Loss:  0.00018570770698715932 lr_DL: 0.001 lr_vel: 80 SNR: 24.003154620626542 SSIM: 0.6629971\n",
      "Epoch: 614 Loss:  0.0001707279217953328 lr_DL: 0.001 lr_vel: 80 SNR: 24.013950737693122 SSIM: 0.6641472\n",
      "Epoch: 615 Loss:  0.00014886079225107096 lr_DL: 0.001 lr_vel: 80 SNR: 24.021110572046798 SSIM: 0.66500574\n",
      "Epoch: 616 Loss:  0.00020151693825027906 lr_DL: 0.001 lr_vel: 80 SNR: 24.020148113042108 SSIM: 0.66506815\n",
      "Epoch: 617 Loss:  0.0010835353459697217 lr_DL: 0.001 lr_vel: 80 SNR: 23.977458065681432 SSIM: 0.6589049\n",
      "Epoch: 618 Loss:  0.0005130577585077845 lr_DL: 0.001 lr_vel: 80 SNR: 24.006521075078986 SSIM: 0.661693\n",
      "Epoch: 619 Loss:  0.00021256895512730505 lr_DL: 0.001 lr_vel: 80 SNR: 24.02321096919821 SSIM: 0.6641189\n",
      "Epoch: 620 Loss:  0.00014645599731011316 lr_DL: 0.001 lr_vel: 80 SNR: 24.031104104421406 SSIM: 0.66505444\n",
      "Epoch: 621 Loss:  0.00015280114481962907 lr_DL: 0.001 lr_vel: 80 SNR: 24.03787334251461 SSIM: 0.6660858\n",
      "Epoch: 622 Loss:  0.00013516875502925056 lr_DL: 0.001 lr_vel: 80 SNR: 24.043671242507408 SSIM: 0.66635025\n",
      "Epoch: 623 Loss:  0.00046526923858133766 lr_DL: 0.001 lr_vel: 80 SNR: 24.005090134152397 SSIM: 0.66374254\n",
      "Epoch: 624 Loss:  0.0012125442367202292 lr_DL: 0.001 lr_vel: 80 SNR: 23.979014143336105 SSIM: 0.65697163\n",
      "Epoch: 625 Loss:  0.0008276848719106055 lr_DL: 0.001 lr_vel: 80 SNR: 23.998805550214808 SSIM: 0.65886354\n",
      "Epoch: 626 Loss:  0.00036295565660111607 lr_DL: 0.001 lr_vel: 80 SNR: 24.023270660457573 SSIM: 0.6619258\n",
      "Epoch: 627 Loss:  0.00020621878356905655 lr_DL: 0.001 lr_vel: 80 SNR: 24.04278189446236 SSIM: 0.6641594\n",
      "Epoch: 628 Loss:  0.0001582430935134956 lr_DL: 0.001 lr_vel: 80 SNR: 24.055117508110474 SSIM: 0.665434\n",
      "Epoch: 629 Loss:  0.00015358337186626158 lr_DL: 0.001 lr_vel: 80 SNR: 24.064409346172834 SSIM: 0.6667732\n",
      "Epoch: 630 Loss:  0.00012547840621361198 lr_DL: 0.001 lr_vel: 80 SNR: 24.06908788630105 SSIM: 0.66740286\n",
      "Epoch: 631 Loss:  0.0001248650759710775 lr_DL: 0.001 lr_vel: 80 SNR: 24.0729017394443 SSIM: 0.6677514\n",
      "Epoch: 632 Loss:  0.00021330083909560927 lr_DL: 0.001 lr_vel: 80 SNR: 24.05979539896385 SSIM: 0.66714585\n",
      "Epoch: 633 Loss:  0.0006536513222575498 lr_DL: 0.001 lr_vel: 80 SNR: 24.04642558844534 SSIM: 0.6637648\n",
      "Epoch: 634 Loss:  0.0007777826336678118 lr_DL: 0.001 lr_vel: 80 SNR: 24.04863904452331 SSIM: 0.66425276\n",
      "Epoch: 635 Loss:  0.00025703692250923874 lr_DL: 0.001 lr_vel: 80 SNR: 24.07061192119945 SSIM: 0.6661258\n",
      "Epoch: 636 Loss:  0.00014264217776750835 lr_DL: 0.001 lr_vel: 80 SNR: 24.081515902128636 SSIM: 0.6671593\n",
      "Epoch: 637 Loss:  0.00012399854628408017 lr_DL: 0.001 lr_vel: 80 SNR: 24.088220864138297 SSIM: 0.6680847\n",
      "Epoch: 638 Loss:  0.0001141163023324528 lr_DL: 0.001 lr_vel: 80 SNR: 24.092456758762232 SSIM: 0.6684848\n",
      "Epoch: 639 Loss:  0.0001393271057168022 lr_DL: 0.001 lr_vel: 80 SNR: 24.09280624131879 SSIM: 0.6686247\n",
      "Epoch: 640 Loss:  0.0006240778209757991 lr_DL: 0.001 lr_vel: 80 SNR: 24.058965721915364 SSIM: 0.66278887\n",
      "Epoch: 641 Loss:  0.00033235419420331406 lr_DL: 0.001 lr_vel: 80 SNR: 24.08349554176211 SSIM: 0.6657674\n",
      "Epoch: 642 Loss:  0.00021436159246756385 lr_DL: 0.001 lr_vel: 80 SNR: 24.097912640423385 SSIM: 0.6670824\n",
      "Epoch: 643 Loss:  0.00015062826132634656 lr_DL: 0.001 lr_vel: 80 SNR: 24.107289363202757 SSIM: 0.66811895\n",
      "Epoch: 644 Loss:  0.00012134131732940052 lr_DL: 0.001 lr_vel: 80 SNR: 24.110893619894522 SSIM: 0.66873306\n",
      "Epoch: 645 Loss:  0.00018839616386685521 lr_DL: 0.001 lr_vel: 80 SNR: 24.106773873174774 SSIM: 0.669039\n",
      "Epoch: 646 Loss:  0.0009438667480329362 lr_DL: 0.001 lr_vel: 80 SNR: 24.058085124853687 SSIM: 0.66207075\n",
      "Epoch: 647 Loss:  0.0004156401480334656 lr_DL: 0.001 lr_vel: 80 SNR: 24.098234707726288 SSIM: 0.6650999\n",
      "Epoch: 648 Loss:  0.00014779430396932486 lr_DL: 0.001 lr_vel: 80 SNR: 24.11351890613643 SSIM: 0.6666928\n",
      "Epoch: 649 Loss:  0.00012278250311889376 lr_DL: 0.001 lr_vel: 80 SNR: 24.124268090918456 SSIM: 0.6681258\n",
      "Epoch: 650 Loss:  0.00010400152944688065 lr_DL: 0.001 lr_vel: 80 SNR: 24.131010746526464 SSIM: 0.6691584\n",
      "Epoch: 651 Loss:  0.0001073179269345322 lr_DL: 0.001 lr_vel: 80 SNR: 24.136912493897352 SSIM: 0.6697974\n",
      "Epoch: 652 Loss:  0.0001062289229594171 lr_DL: 0.001 lr_vel: 80 SNR: 24.141145572431437 SSIM: 0.6702317\n",
      "Epoch: 653 Loss:  0.00012731263729316803 lr_DL: 0.001 lr_vel: 80 SNR: 24.14115352434888 SSIM: 0.6704808\n",
      "Epoch: 654 Loss:  0.00034790965170638323 lr_DL: 0.001 lr_vel: 80 SNR: 24.125427985600783 SSIM: 0.6689206\n",
      "Epoch: 655 Loss:  0.0004240927368906947 lr_DL: 0.001 lr_vel: 80 SNR: 24.107220719530726 SSIM: 0.6682901\n",
      "Epoch: 656 Loss:  0.0005140281908097676 lr_DL: 0.001 lr_vel: 80 SNR: 24.103680630230325 SSIM: 0.668032\n",
      "Epoch: 657 Loss:  0.0004558129963697866 lr_DL: 0.001 lr_vel: 80 SNR: 24.13211897040764 SSIM: 0.66830164\n",
      "Epoch: 658 Loss:  0.0002282124762132298 lr_DL: 0.001 lr_vel: 80 SNR: 24.13976716416215 SSIM: 0.6699086\n",
      "Epoch: 659 Loss:  0.00011433936015237123 lr_DL: 0.001 lr_vel: 80 SNR: 24.151150798372043 SSIM: 0.67067426\n",
      "Epoch: 660 Loss:  9.637338759299988e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.156513728579185 SSIM: 0.67133534\n",
      "Epoch: 661 Loss:  9.874080302931058e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.16005407109633 SSIM: 0.6716044\n",
      "Epoch: 662 Loss:  9.528861604242896e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.164380404110332 SSIM: 0.6719291\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 663 Loss:  0.00010348522943483355 lr_DL: 0.001 lr_vel: 80 SNR: 24.168554379321698 SSIM: 0.6720734\n",
      "Epoch: 664 Loss:  0.0003087579270262116 lr_DL: 0.001 lr_vel: 80 SNR: 24.15706661100053 SSIM: 0.6709998\n",
      "Epoch: 665 Loss:  0.00017228163026932936 lr_DL: 0.001 lr_vel: 80 SNR: 24.1692486629036 SSIM: 0.67181206\n",
      "Epoch: 666 Loss:  9.78576438986541e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.174710544522068 SSIM: 0.67228925\n",
      "Epoch: 667 Loss:  0.00010556967972661369 lr_DL: 0.001 lr_vel: 80 SNR: 24.177920659285412 SSIM: 0.67244035\n",
      "Epoch: 668 Loss:  0.00031668280813998233 lr_DL: 0.001 lr_vel: 80 SNR: 24.165424616831984 SSIM: 0.67115724\n",
      "Epoch: 669 Loss:  0.00019489391294579643 lr_DL: 0.001 lr_vel: 80 SNR: 24.179257995426383 SSIM: 0.6720324\n",
      "Epoch: 670 Loss:  0.0001043459555755059 lr_DL: 0.001 lr_vel: 80 SNR: 24.18636125781916 SSIM: 0.6726926\n",
      "Epoch: 671 Loss:  0.00010843526336733097 lr_DL: 0.001 lr_vel: 80 SNR: 24.184310235107695 SSIM: 0.6727449\n",
      "Epoch: 672 Loss:  0.00021827690773837578 lr_DL: 0.001 lr_vel: 80 SNR: 24.184971872591507 SSIM: 0.6722734\n",
      "Epoch: 673 Loss:  0.0005125652455414335 lr_DL: 0.001 lr_vel: 80 SNR: 24.170445193762053 SSIM: 0.6703802\n",
      "Epoch: 674 Loss:  0.0003041373119534304 lr_DL: 0.001 lr_vel: 80 SNR: 24.188961945787696 SSIM: 0.672237\n",
      "Epoch: 675 Loss:  0.00010217407640690604 lr_DL: 0.001 lr_vel: 80 SNR: 24.19665853513702 SSIM: 0.6728284\n",
      "Epoch: 676 Loss:  9.237447641983939e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.200637675309377 SSIM: 0.67341673\n",
      "Epoch: 677 Loss:  9.2487937581609e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.203161147731475 SSIM: 0.6736645\n",
      "Epoch: 678 Loss:  9.537794176139869e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.209010225102933 SSIM: 0.67397666\n",
      "Epoch: 679 Loss:  0.00014397698735895876 lr_DL: 0.001 lr_vel: 80 SNR: 24.20910440795776 SSIM: 0.6739042\n",
      "Epoch: 680 Loss:  0.00010516791880945675 lr_DL: 0.001 lr_vel: 80 SNR: 24.21284225664487 SSIM: 0.67416626\n",
      "Epoch: 681 Loss:  0.00013444061720898997 lr_DL: 0.001 lr_vel: 80 SNR: 24.208344917847697 SSIM: 0.6739822\n",
      "Epoch: 682 Loss:  0.0004154793908431505 lr_DL: 0.001 lr_vel: 80 SNR: 24.19228511088269 SSIM: 0.6710418\n",
      "Epoch: 683 Loss:  0.0002914010879370229 lr_DL: 0.001 lr_vel: 80 SNR: 24.208009217874814 SSIM: 0.6724333\n",
      "Epoch: 684 Loss:  0.0002733742051835482 lr_DL: 0.001 lr_vel: 80 SNR: 24.210041264473126 SSIM: 0.67275155\n",
      "Epoch: 685 Loss:  0.00011887024302268401 lr_DL: 0.001 lr_vel: 80 SNR: 24.22081562697043 SSIM: 0.6735033\n",
      "Epoch: 686 Loss:  9.442219234188088e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.227179085502783 SSIM: 0.67401904\n",
      "Epoch: 687 Loss:  8.741981813121432e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.229686735842893 SSIM: 0.67432886\n",
      "Epoch: 688 Loss:  8.481721342832316e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.23454545677002 SSIM: 0.6746133\n",
      "Epoch: 689 Loss:  8.25015477554795e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.23764046196275 SSIM: 0.6749811\n",
      "Epoch: 690 Loss:  0.00011457377161908275 lr_DL: 0.001 lr_vel: 80 SNR: 24.227489931721564 SSIM: 0.67470586\n",
      "Epoch: 691 Loss:  0.00040856228491369014 lr_DL: 0.001 lr_vel: 80 SNR: 24.217324502980663 SSIM: 0.6728524\n",
      "Epoch: 692 Loss:  0.0003742301239981316 lr_DL: 0.001 lr_vel: 80 SNR: 24.218692121417995 SSIM: 0.67312217\n",
      "Epoch: 693 Loss:  0.00018637124691546584 lr_DL: 0.001 lr_vel: 80 SNR: 24.237621165452726 SSIM: 0.674507\n",
      "Epoch: 694 Loss:  9.86049861239735e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.24478132627405 SSIM: 0.6748999\n",
      "Epoch: 695 Loss:  8.131246177072171e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.25015163195427 SSIM: 0.67529166\n",
      "Epoch: 696 Loss:  7.697471543603267e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.25344977126739 SSIM: 0.6755614\n",
      "Epoch: 697 Loss:  7.962224760073392e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.256102065462862 SSIM: 0.67581624\n",
      "Epoch: 698 Loss:  7.739196128871602e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.26082841709946 SSIM: 0.67606527\n",
      "Epoch: 699 Loss:  7.877268326410558e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.262693812221087 SSIM: 0.6761719\n",
      "Epoch: 700 Loss:  9.41407014276289e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.263450632393088 SSIM: 0.6763143\n",
      "Epoch: 701 Loss:  8.771401529277985e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.267791583897885 SSIM: 0.67657965\n",
      "Epoch: 702 Loss:  0.0001599744980921969 lr_DL: 0.001 lr_vel: 80 SNR: 24.263729270531428 SSIM: 0.6761248\n",
      "Epoch: 703 Loss:  0.0001658879892299107 lr_DL: 0.001 lr_vel: 80 SNR: 24.2680054934766 SSIM: 0.67596054\n",
      "Epoch: 704 Loss:  0.0001088257362425793 lr_DL: 0.001 lr_vel: 80 SNR: 24.2722526474293 SSIM: 0.67646927\n",
      "Epoch: 705 Loss:  9.020433766030086e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.272689340329134 SSIM: 0.6766496\n",
      "Epoch: 706 Loss:  0.00011136915563838556 lr_DL: 0.001 lr_vel: 80 SNR: 24.276539625403345 SSIM: 0.6767447\n",
      "Epoch: 707 Loss:  0.00012475506931271715 lr_DL: 0.001 lr_vel: 80 SNR: 24.27825574239311 SSIM: 0.6767139\n",
      "Epoch: 708 Loss:  9.507392145072421e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.28295187784801 SSIM: 0.6773861\n",
      "Epoch: 709 Loss:  9.812585255228138e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.283981911217374 SSIM: 0.6773026\n",
      "Epoch: 710 Loss:  9.940779830988808e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.286729438781226 SSIM: 0.6774714\n",
      "Epoch: 711 Loss:  0.00016335850159521215 lr_DL: 0.001 lr_vel: 80 SNR: 24.27055429253958 SSIM: 0.6765795\n",
      "Epoch: 712 Loss:  0.0003008336321120927 lr_DL: 0.001 lr_vel: 80 SNR: 24.28203139711945 SSIM: 0.67643523\n",
      "Epoch: 713 Loss:  0.0003205943564050055 lr_DL: 0.001 lr_vel: 80 SNR: 24.27595360791695 SSIM: 0.67557573\n",
      "Epoch: 714 Loss:  0.00012793968295833716 lr_DL: 0.001 lr_vel: 80 SNR: 24.289445135650993 SSIM: 0.6766533\n",
      "Epoch: 715 Loss:  8.76319155698487e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.296741741875127 SSIM: 0.6770249\n",
      "Epoch: 716 Loss:  7.919406574122453e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.30041131396395 SSIM: 0.6773195\n",
      "Epoch: 717 Loss:  7.596283382251082e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.303535535098376 SSIM: 0.6776311\n",
      "Epoch: 718 Loss:  7.261608940704416e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.307802101453746 SSIM: 0.6779591\n",
      "Epoch: 719 Loss:  7.657691482260513e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.309307445109596 SSIM: 0.6781011\n",
      "Epoch: 720 Loss:  7.837144330551382e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.313326017982476 SSIM: 0.6783607\n",
      "Epoch: 721 Loss:  0.00010533491828634093 lr_DL: 0.001 lr_vel: 80 SNR: 24.312198655818655 SSIM: 0.6782102\n",
      "Epoch: 722 Loss:  9.25008758107045e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.31581193033187 SSIM: 0.6786343\n",
      "Epoch: 723 Loss:  8.73797534344097e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.317320553761917 SSIM: 0.67851794\n",
      "Epoch: 724 Loss:  8.991621022384304e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.319645249138404 SSIM: 0.6788403\n",
      "Epoch: 725 Loss:  0.00011622143841426199 lr_DL: 0.001 lr_vel: 80 SNR: 24.319352991768536 SSIM: 0.6785343\n",
      "Epoch: 726 Loss:  0.00018092461444515115 lr_DL: 0.001 lr_vel: 80 SNR: 24.313403658239956 SSIM: 0.6777342\n",
      "Epoch: 727 Loss:  0.0001536723362126698 lr_DL: 0.001 lr_vel: 80 SNR: 24.325076056598313 SSIM: 0.67839587\n",
      "Epoch: 728 Loss:  7.953391990061694e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.32824520676946 SSIM: 0.67880476\n",
      "Epoch: 729 Loss:  7.581993692535131e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.332204168605625 SSIM: 0.67919195\n",
      "Epoch: 730 Loss:  7.779792443519303e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.335101417735657 SSIM: 0.67945963\n",
      "Epoch: 731 Loss:  8.218655687718032e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.336742810250396 SSIM: 0.6794352\n",
      "Epoch: 732 Loss:  0.00013398894298006782 lr_DL: 0.001 lr_vel: 80 SNR: 24.328978959934275 SSIM: 0.67914706\n",
      "Epoch: 733 Loss:  0.0002339481773863857 lr_DL: 0.001 lr_vel: 80 SNR: 24.3333313776934 SSIM: 0.6784885\n",
      "Epoch: 734 Loss:  0.0003406087974629675 lr_DL: 0.001 lr_vel: 80 SNR: 24.331725674280378 SSIM: 0.6779483\n",
      "Epoch: 735 Loss:  9.694191394373774e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.343099209662363 SSIM: 0.6786213\n",
      "Epoch: 736 Loss:  8.046305192692671e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.34723209072792 SSIM: 0.6791047\n",
      "Epoch: 737 Loss:  7.531918078408731e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.34976244819749 SSIM: 0.67948705\n",
      "Epoch: 738 Loss:  7.360936421415923e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.353550232054765 SSIM: 0.6798188\n",
      "Epoch: 739 Loss:  7.21420964206724e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.356817848249793 SSIM: 0.6800636\n",
      "Epoch: 740 Loss:  7.868268439779059e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.357603565046254 SSIM: 0.68033046\n",
      "Epoch: 741 Loss:  8.293937923250874e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.357252943263525 SSIM: 0.6803609\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 742 Loss:  0.0001342652981596378 lr_DL: 0.001 lr_vel: 80 SNR: 24.35810839738825 SSIM: 0.6799277\n",
      "Epoch: 743 Loss:  0.00011179626380908303 lr_DL: 0.001 lr_vel: 80 SNR: 24.364411195451055 SSIM: 0.68039185\n",
      "Epoch: 744 Loss:  8.0234673320471e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.36750253713096 SSIM: 0.6807541\n",
      "Epoch: 745 Loss:  7.205271273657369e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.369224273719283 SSIM: 0.6808417\n",
      "Epoch: 746 Loss:  0.00010597422624414321 lr_DL: 0.001 lr_vel: 80 SNR: 24.360395124794515 SSIM: 0.68044436\n",
      "Epoch: 747 Loss:  0.0003319777587118248 lr_DL: 0.001 lr_vel: 80 SNR: 24.352430234153392 SSIM: 0.6791788\n",
      "Epoch: 748 Loss:  0.00015654864085566563 lr_DL: 0.001 lr_vel: 80 SNR: 24.36661457909212 SSIM: 0.6796683\n",
      "Epoch: 749 Loss:  7.878875088257094e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.374801851578436 SSIM: 0.6805218\n",
      "Epoch: 750 Loss:  7.2514111889177e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.377860780110055 SSIM: 0.6809142\n",
      "Epoch: 751 Loss:  7.041388635116163e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.381816159636614 SSIM: 0.68119216\n",
      "Epoch: 752 Loss:  6.966972250666004e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.384151845702462 SSIM: 0.68139267\n",
      "Epoch: 753 Loss:  7.161284374888056e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.385671717034985 SSIM: 0.6815445\n",
      "Epoch: 754 Loss:  8.575454739911947e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.384056643221804 SSIM: 0.6813728\n",
      "Epoch: 755 Loss:  0.00013756175030721352 lr_DL: 0.001 lr_vel: 80 SNR: 24.38556381591182 SSIM: 0.6811697\n",
      "Epoch: 756 Loss:  0.00012910974207140194 lr_DL: 0.001 lr_vel: 80 SNR: 24.387614251295467 SSIM: 0.6814498\n",
      "Epoch: 757 Loss:  0.00019189859352385005 lr_DL: 0.001 lr_vel: 80 SNR: 24.386364927637256 SSIM: 0.679992\n",
      "Epoch: 758 Loss:  9.044382022693753e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.39634402403225 SSIM: 0.6812384\n",
      "Epoch: 759 Loss:  6.857067201053724e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.399533670537835 SSIM: 0.6816833\n",
      "Epoch: 760 Loss:  6.661671856515265e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.40158984642577 SSIM: 0.6819068\n",
      "Epoch: 761 Loss:  6.59221196353125e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.404154564781138 SSIM: 0.6821342\n",
      "Epoch: 762 Loss:  0.0001012389639072353 lr_DL: 0.001 lr_vel: 80 SNR: 24.396957218084392 SSIM: 0.6816586\n",
      "Epoch: 763 Loss:  0.0002276072887373933 lr_DL: 0.001 lr_vel: 80 SNR: 24.39094859075503 SSIM: 0.6809931\n",
      "Epoch: 764 Loss:  0.00021337620225191737 lr_DL: 0.001 lr_vel: 80 SNR: 24.391781119099782 SSIM: 0.6805309\n",
      "Epoch: 765 Loss:  0.0001360339288415465 lr_DL: 0.001 lr_vel: 80 SNR: 24.40610519751717 SSIM: 0.6815414\n",
      "Epoch: 766 Loss:  6.607022927103874e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.410246360653286 SSIM: 0.68210775\n",
      "Epoch: 767 Loss:  5.6783066853919686e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.412595713019527 SSIM: 0.68238825\n",
      "Epoch: 768 Loss:  5.799884177880206e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.413881917586874 SSIM: 0.6826122\n",
      "Epoch: 769 Loss:  0.00011448306104284711 lr_DL: 0.001 lr_vel: 80 SNR: 24.410742712744558 SSIM: 0.68227744\n",
      "Epoch: 770 Loss:  7.213506335877659e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.41559504198888 SSIM: 0.6826705\n",
      "Epoch: 771 Loss:  6.167215021074905e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.415823406081213 SSIM: 0.682772\n",
      "Epoch: 772 Loss:  0.00012623221797791 lr_DL: 0.001 lr_vel: 80 SNR: 24.40860921565138 SSIM: 0.6819846\n",
      "Epoch: 773 Loss:  0.00019523132117077086 lr_DL: 0.001 lr_vel: 80 SNR: 24.405374801546582 SSIM: 0.6821032\n",
      "Epoch: 774 Loss:  0.00023072595989409212 lr_DL: 0.001 lr_vel: 80 SNR: 24.399496324244836 SSIM: 0.6816909\n",
      "Epoch: 775 Loss:  0.00025029738535522485 lr_DL: 0.001 lr_vel: 80 SNR: 24.383397523428698 SSIM: 0.680591\n",
      "Epoch: 776 Loss:  0.00018599513932713308 lr_DL: 0.001 lr_vel: 80 SNR: 24.39895601855642 SSIM: 0.6808825\n",
      "Epoch: 777 Loss:  0.0001474541678665749 lr_DL: 0.001 lr_vel: 80 SNR: 24.406035691835587 SSIM: 0.68161994\n",
      "Epoch: 778 Loss:  8.422981591138523e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.41247414124085 SSIM: 0.6821743\n",
      "Epoch: 779 Loss:  5.08130164234899e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.414473045155848 SSIM: 0.68235177\n",
      "Epoch: 780 Loss:  5.0622038664490295e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.415091938621103 SSIM: 0.68255633\n",
      "Epoch: 781 Loss:  4.652819843613543e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.418420341939225 SSIM: 0.6829322\n",
      "Epoch: 782 Loss:  4.8743692847589655e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.418215834162304 SSIM: 0.6830343\n",
      "Epoch: 783 Loss:  4.6887550585476374e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.420731384135195 SSIM: 0.68332636\n",
      "Epoch: 784 Loss:  5.0852068064462704e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.420154929891776 SSIM: 0.6832904\n",
      "Epoch: 785 Loss:  7.839040796776923e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.416944600488293 SSIM: 0.68317646\n",
      "Epoch: 786 Loss:  0.00013204032111389097 lr_DL: 0.001 lr_vel: 80 SNR: 24.417021227087353 SSIM: 0.68261063\n",
      "Epoch: 787 Loss:  8.821488554531243e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.420089016330344 SSIM: 0.6830484\n",
      "Epoch: 788 Loss:  7.563853553923157e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.418025681201286 SSIM: 0.6834189\n",
      "Epoch: 789 Loss:  0.0001586767425275563 lr_DL: 0.001 lr_vel: 80 SNR: 24.416385194441567 SSIM: 0.6829324\n",
      "Epoch: 790 Loss:  0.00016003592051371622 lr_DL: 0.001 lr_vel: 80 SNR: 24.41102110147996 SSIM: 0.68178034\n",
      "Epoch: 791 Loss:  0.00012557368451477185 lr_DL: 0.001 lr_vel: 80 SNR: 24.419762574943892 SSIM: 0.68244284\n",
      "Epoch: 792 Loss:  6.308874617388938e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.423780753857258 SSIM: 0.68289405\n",
      "Epoch: 793 Loss:  4.979179805862562e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.425382992818598 SSIM: 0.68309164\n",
      "Epoch: 794 Loss:  4.325185461008611e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.427120808615914 SSIM: 0.68331677\n",
      "Epoch: 795 Loss:  4.39885461673839e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.427607752473076 SSIM: 0.68337667\n",
      "Epoch: 796 Loss:  4.4226246124405104e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.42855568117967 SSIM: 0.6834739\n",
      "Epoch: 797 Loss:  7.264228412774779e-05 lr_DL: 0.001 lr_vel: 80 SNR: 24.422792107895923 SSIM: 0.6831014\n",
      "Epoch: 798 Loss:  0.0006245439341000747 lr_DL: 0.001 lr_vel: 80 SNR: 24.380078516681273 SSIM: 0.67864496\n",
      "Epoch: 799 Loss:  0.00015386703829184018 lr_DL: 0.001 lr_vel: 80 SNR: 24.409678704545307 SSIM: 0.6801855\n",
      "Epoch: 800 Loss:  0.00010644619408897901 lr_DL: 0.001 lr_vel: 80 SNR: 24.420666429053522 SSIM: 0.6813324\n",
      "Running complete in 56m  11s\n"
     ]
    }
   ],
   "source": [
    " \n",
    "import torch.nn.functional as F\n",
    "# Starting the FWI loop.\n",
    "global model_true, source_amplitudes_init, SNR, SSIM, Loss, ERROR\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "t_start = time.time()\n",
    "model_true = model_true.view(nz,ny)\n",
    "# number of shots per batch\n",
    "num_shots_per_batch = int(num_shots / fwi_batch) \n",
    "criterion = nn.MSELoss()\n",
    "for i in range(fwi_num_epochs):\n",
    "    # initialization of the loss\n",
    "    epoch_loss = 0.0\n",
    "\n",
    "    for it in range(fwi_batch):\n",
    "        iteration = i*fwi_batch+it+1\n",
    "        # Set both optimizers to zero grad.\n",
    "        optimizer.zero_grad()\n",
    "        netD.train()                \n",
    "        netD.zero_grad() \n",
    "        \n",
    "        # Forward modeling based on AWE (DeepWave).\n",
    "        prop = deepwave.scalar.Propagator({'vp': model},dx,pml_width, \\\n",
    "                                          order,survey_pad)\n",
    "        batch_src_amps = source_amplitudes_init.repeat(1, num_shots_per_batch, 1)\n",
    "        batch_rcv_amps_true = rcv_amps_true[:,it::fwi_batch].to(device)\n",
    "        batch_x_s = x_s[it::fwi_batch].to(device)\n",
    "        batch_x_r = x_r[it::fwi_batch].to(device)\n",
    "        batch_rcv_amps_pred = prop(batch_src_amps, batch_x_s, batch_x_r, dt)\n",
    "\n",
    "        # Prepare the data for Siamese network.\n",
    "        d_real = batch_rcv_amps_true.permute(1,0,2)\n",
    "        d_real = d_real.unsqueeze(1)\n",
    "        # train with fake data\n",
    "        d_fake = batch_rcv_amps_pred.permute(1,0,2)\n",
    "        d_fake = d_fake.unsqueeze(1)\n",
    "        disc_fake, disc_real, o1, o2 = netD(d_fake,d_real)\n",
    "        # Measure the Eculedian Loss.\n",
    "        loss =   F.pairwise_distance(o1, o2, keepdim = True).mean()\n",
    "\n",
    "        epoch_loss += loss.item()\n",
    "        loss.backward()\n",
    "        # Updating the Siamese network.\n",
    "        optim_s.step()\n",
    "        \n",
    "        # Clips gradient value of model\n",
    "        torch.nn.utils.clip_grad_value_(model,1e3) \n",
    "        # Update the velocity model\n",
    "        optimizer.step()\n",
    "        # clip the model value that keep the minimum value is larger than 0\n",
    "        model.data=torch.clamp(model.data,min=1e-12)\n",
    "\n",
    "\n",
    "    # Update the learning rate each 100 FWI iterations .\n",
    "    if (i>0) and (i%200==0):\n",
    "        scheduler.step()\n",
    "    after_lr = optim_s.param_groups[0][\"lr\"]\n",
    "    after_lr1 = optimizer.param_groups[0][\"lr\"]\n",
    "    \n",
    "    Loss = np.append(Loss, epoch_loss / fwi_batch)\n",
    "\n",
    "    # compute the SNR and SSIM\n",
    "    snr = ComputeSNR(model.detach().cpu().numpy(),  \n",
    "              model_true.detach().cpu().numpy())\n",
    "    SNR = np.append(SNR, snr)\n",
    "\n",
    "    ssim = ComputeSSIM(model.detach().cpu().numpy(), \\\n",
    "              model_true.detach().cpu().numpy())\n",
    "    SSIM = np.append(SSIM, ssim)\n",
    "\n",
    "    rerror = ComputeRE(model.detach().cpu().numpy(), \\\n",
    "              model_true.detach().cpu().numpy())\n",
    "    ERROR = np.append(ERROR, rerror)\n",
    "\n",
    "    print('Epoch:', i+1, 'Loss: ', epoch_loss / fwi_batch, 'lr_DL:' , str(after_lr),  'lr_vel:' , str(after_lr1), 'SNR:', snr, 'SSIM:', ssim.cpu().detach().cpu().numpy())\n",
    "    if (i+1) % savepoch == 0 or (i+1) == fwi_num_epochs:\n",
    "    # Save the model and the metrics.\n",
    "        spio.savemat(fwi_result+'SIAMESERec_.mat', \\\n",
    "                         {'rec':model.cpu().data.numpy()}) \n",
    "        spio.savemat(fwi_result+'SIAMESEMetric_.mat', \\\n",
    "                         {'SNR':SNR,'SSIM':SSIM, \\\n",
    "                          'Loss':Loss,'ERROR':ERROR}) \n",
    "\n",
    "# Estimating the FWI time\n",
    "t_end = time.time()\n",
    "elapsed_time = t_end - t_start\n",
    "print('Running complete in {:.0f}m  {:.0f}s' .format(elapsed_time //60 , elapsed_time % 60))\n",
    "# Save the final metrics.\n",
    "np.savetxt(fwi_result+'run_result.txt', np.hstack((fwi_num_epochs,elapsed_time//60,elapsed_time % 60,snr,ssim,rerror)), fmt='%5.4f') \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a432cad8",
   "metadata": {},
   "source": [
    "## Plotting the Reconstructed Velocity Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "173492a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['normal'] not found. Falling back to DejaVu Sans.\n",
      "findfont: Font family ['normal'] not found. Falling back to DejaVu Sans.\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2880x1440 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The reconstructed velocity model has SNR: 24.420666429053522 dB and SSIM: 0.6813324\n"
     ]
    }
   ],
   "source": [
    "\n",
    "f, ax = plt.subplots(3,1,figsize=(40, 20))\n",
    "plt.rc('font', **font)\n",
    "\n",
    "# True Model\n",
    "modeltrue = model1\n",
    "im = ax[0].imshow(modeltrue, cmap='jet', vmin=box_min, vmax=box_max, \n",
    "                extent=[0, modeltrue.shape[1] * dx / 1000, modeltrue.shape[0] * dx1 / 1000, 0])\n",
    "\n",
    "#ax[0].set_xlabel('Position (km)',fontsize='large', fontweight='bold')\n",
    "ax[0].set_ylabel('Depth (km)',fontsize='large', fontweight='bold')\n",
    "ax[0].set_title('True_Model',fontsize='large', fontweight='bold')\n",
    "ax[0].text(0, -0.15, 'a)', fontsize = 22) \n",
    "ax[0].xaxis.set_major_locator(MaxNLocator(integer=True))\n",
    "ax[0].set_yticks(np.arange(y.min(), y.max(), 1)) \n",
    "\n",
    "# Initial Model\n",
    "init_V = model_init\n",
    "im = ax[1].imshow(init_V, cmap='jet', vmin=box_min, vmax=box_max, \n",
    "                extent=[0, init_V.shape[1] * dx / 1000, init_V.shape[0] * dx1 / 1000, 0])\n",
    "#ax[1].set_xlabel('Position (km)',fontsize='large', fontweight='bold')\n",
    "ax[1].set_ylabel('Depth (km)',fontsize='large', fontweight='bold')\n",
    "ax[1].set_title('Initial_Model',fontsize='large', fontweight='bold')\n",
    "ax[1].text(0, -0.15, 'b)', fontsize = 22) \n",
    "ax[1].xaxis.set_major_locator(MaxNLocator(integer=True))\n",
    "ax[1].set_yticks(np.arange(y.min(), y.max(), 1)) \n",
    "\n",
    "# Siamese Model\n",
    "Siamese_Vel = scipy.io.loadmat(fwi_result + 'SIAMESERec_.mat')\n",
    "Siamese_Vel = Siamese_Vel['rec']\n",
    "im = ax[2].imshow(Siamese_Vel, cmap='jet', vmin=box_min, vmax=box_max, \n",
    "                extent=[0, Siamese_Vel.shape[1] * dx / 1000, Siamese_Vel.shape[0] * dx1 / 1000, 0])\n",
    "ax[2].set_xlabel('Position (km)',fontsize='large', fontweight='bold')\n",
    "ax[2].set_ylabel('Depth (km)',fontsize='large', fontweight='bold')\n",
    "ax[2].set_title('SiameseFWI',fontsize='large', fontweight='bold')\n",
    "ax[2].text(0, -0.15, 'f)', fontsize = 22) \n",
    "ax[2].xaxis.set_major_locator(MaxNLocator(integer=True))\n",
    "ax[2].set_yticks(np.arange(y.min(), y.max(), 1)) \n",
    "\n",
    "\n",
    "cbar_ax = f.add_axes([0.8, 0.1, 0.01, 0.8])\n",
    "f.colorbar(im, fraction=0.005, pad=0.04,cax=cbar_ax)\n",
    "cbar_ax.set_ylabel('Velocity (m/s)',fontsize='large', fontweight='bold')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "snr = ComputeSNR(Siamese_Vel,  \n",
    "          model_true.detach().cpu().numpy())\n",
    "\n",
    "ssim = ComputeSSIM(Siamese_Vel, \\\n",
    "          model_true.detach().cpu().numpy())\n",
    "\n",
    "print('The reconstructed velocity model has SNR:', str(snr), 'dB', 'and SSIM:',str(ssim.cpu().detach().cpu().numpy()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3e22606",
   "metadata": {},
   "source": [
    "## Plotting Velocities Profiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31c3ecbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#torch.save(netD.state_dict(), fwi_result + 'netD')\n",
    "#netD = torch.load(fwi_result + 'netD')\n",
    "#netD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "91dbee1c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "FWIGAN",
   "language": "python",
   "name": "fwigan"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}