train_test_limited_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-28T00:25:48.077839Z",
     "start_time": "2020-03-28T00:25:48.075097Z"
    }
   },
   "source": [
    "## Study effect of less training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T21:04:24.301768Z",
     "start_time": "2020-06-15T21:04:24.298637Z"
    }
   },
   "outputs": [],
   "source": [
    "nlines = 10 #How many lines of data to use for training?\n",
    "nltest = 60 #How many lines for the test set?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T21:04:26.583788Z",
     "start_time": "2020-06-15T21:04:24.304459Z"
    },
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "apJbCsBHl-2A"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "## Choose GPUs and CPUs\n",
    "\n",
    "import os\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\n",
    "\n",
    "\n",
    "import tensorflow as tf\n",
    "import keras\n",
    "from keras import backend as K\n",
    "from keras.datasets import mnist\n",
    "\n",
    "from keras.layers import Conv2D, MaxPool2D\n",
    "from keras.models import Sequential\n",
    "from keras.utils import to_categorical, multi_gpu_model, plot_model\n",
    "from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D, Dropout\n",
    "from keras.models import Model\n",
    "\n",
    "GPU=True\n",
    "CPU=False\n",
    "num_cores = 32\n",
    "\n",
    "if GPU:\n",
    "    num_GPU = len(os.environ[\"CUDA_VISIBLE_DEVICES\"].split(','))\n",
    "    num_CPU = 4\n",
    "if CPU:\n",
    "    num_CPU = 4\n",
    "    num_GPU = 0\n",
    "\n",
    "config = tf.ConfigProto(\n",
    "                       )\n",
    "config.gpu_options.allow_growth=True \n",
    "session = tf.Session(config=config)\n",
    "K.set_session(session)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T21:04:26.995273Z",
     "start_time": "2020-06-15T21:04:26.588890Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import glob\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "# Config the matplotlib backend as plotting inline in IPython\n",
    "%matplotlib inline\n",
    "\n",
    "import keras_helper\n",
    "from keras_helper import *\n",
    "from skimage.transform import resize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T21:04:27.004313Z",
     "start_time": "2020-06-15T21:04:26.999452Z"
    }
   },
   "outputs": [],
   "source": [
    "h,w=64,64\n",
    "nepochs=75\n",
    "wt_path = 'wts5'\n",
    "batch_size = 32\n",
    "\n",
    "if (not os.path.isdir(wt_path)):\n",
    "    os.mkdir(wt_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T21:04:39.664673Z",
     "start_time": "2020-06-15T21:04:27.006565Z"
    }
   },
   "outputs": [],
   "source": [
    "data_diffr = np.load('data/20191008_39_diff.npz')['arr_0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T21:05:16.514058Z",
     "start_time": "2020-06-15T21:04:39.668764Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(161, 161, 256, 256)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6e9e89a13a504393b6fdf2f62ae89930",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=161), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "print(data_diffr.shape)\n",
    "#plt.matshow(np.log10(data_diffr[0,0]))\n",
    "\n",
    "data_diffr_red = np.zeros((data_diffr.shape[0],data_diffr.shape[1],64,64), float)\n",
    "for i in tqdm(range(data_diffr.shape[0])):\n",
    "    for j in range(data_diffr.shape[1]):\n",
    "        data_diffr_red[i,j] = resize(data_diffr[i,j,32:-32,32:-32],(64,64),preserve_range=True, anti_aliasing=True)\n",
    "        data_diffr_red[i,j] = np.where(data_diffr_red[i,j]<3,0,data_diffr_red[i,j])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T21:05:19.942346Z",
     "start_time": "2020-06-15T21:05:16.516991Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(161, 161, 64, 64)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#amp = np.load('../expt_data/s26_data/20191008_30_10nm_amp.npz')['arr_0']\n",
    "#ph = np.load('../expt_data/s26_data/20191008_39_10nm.npz')['arr_0']\n",
    "real_space = np.load('data/20191008_39_amp_pha_10nm_full.npy')\n",
    "amp = np.abs(real_space)\n",
    "ph = np.angle(real_space)\n",
    "amp.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T21:05:20.669288Z",
     "start_time": "2020-06-15T21:05:19.944779Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f7231818da0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Logging before flag parsing goes to stderr.\n",
      "W0615 21:05:20.024102 140129710683968 font_manager.py:1282] findfont: Font family ['Times New Roman'] not found. Falling back to DejaVu Sans.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(1,2,figsize=(20,10))\n",
    "ax[0].imshow(amp[:,:,32,32])\n",
    "ax[1].imshow(ph[:,:,32,32], vmin=-1.5, vmax=1.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split data and then shuffle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T21:05:20.675497Z",
     "start_time": "2020-06-15T21:05:20.671654Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "101\n"
     ]
    }
   ],
   "source": [
    "tst_strt = amp.shape[0]-nltest #Where to index from\n",
    "print(tst_strt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T21:05:20.930155Z",
     "start_time": "2020-06-15T21:05:20.677800Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1610, 64, 64, 1) (3600, 64, 64, 1)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.utils import shuffle\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "\n",
    "X_train = data_diffr_red[:nlines,:].reshape(-1,h,w)[:,:,:,np.newaxis]\n",
    "X_test = data_diffr_red[tst_strt:,tst_strt:].reshape(-1,h,w)[:,:,:,np.newaxis]\n",
    "Y_I_train = amp[:nlines,:].reshape(-1,h,w)[:,:,:,np.newaxis]\n",
    "Y_I_test = amp[tst_strt:,tst_strt:].reshape(-1,h,w)[:,:,:,np.newaxis]\n",
    "Y_phi_train = ph[:nlines,:].reshape(-1,h,w)[:,:,:,np.newaxis]\n",
    "Y_phi_test = ph[tst_strt:,tst_strt:].reshape(-1,h,w)[:,:,:,np.newaxis]\n",
    "\n",
    "ntrain = X_train.shape[0]*X_train.shape[1]\n",
    "ntest = X_test.shape[0]*X_test.shape[1]\n",
    "\n",
    "print(X_train.shape, X_test.shape)\n",
    "\n",
    "X_train, Y_I_train, Y_phi_train = shuffle(X_train, Y_I_train, Y_phi_train, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Autoencoder-structure predictor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T21:05:21.453392Z",
     "start_time": "2020-06-15T21:05:20.932426Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0615 21:05:20.945630 140129710683968 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:95: The name tf.reset_default_graph is deprecated. Please use tf.compat.v1.reset_default_graph instead.\n",
      "\n",
      "W0615 21:05:20.947160 140129710683968 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:98: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
      "\n",
      "W0615 21:05:20.952667 140129710683968 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:102: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n",
      "W0615 21:05:20.983655 140129710683968 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
      "\n",
      "W0615 21:05:20.986429 140129710683968 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
      "\n",
      "W0615 21:05:21.016185 140129710683968 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3976: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
      "\n",
      "W0615 21:05:21.100060 140129710683968 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:2018: The name tf.image.resize_nearest_neighbor is deprecated. Please use tf.compat.v1.image.resize_nearest_neighbor instead.\n",
      "\n",
      "W0615 21:05:21.402607 140129710683968 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 64, 64, 1)    0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 64, 64, 32)   320         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, 64, 64, 32)   9248        conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)  (None, 32, 32, 32)   0           conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, 32, 32, 64)   18496       max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_4 (Conv2D)               (None, 32, 32, 64)   36928       conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2D)  (None, 16, 16, 64)   0           conv2d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_5 (Conv2D)               (None, 16, 16, 128)  73856       max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_6 (Conv2D)               (None, 16, 16, 128)  147584      conv2d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2D)  (None, 8, 8, 128)    0           conv2d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_7 (Conv2D)               (None, 8, 8, 128)    147584      max_pooling2d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_14 (Conv2D)              (None, 8, 8, 128)    147584      max_pooling2d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_8 (Conv2D)               (None, 8, 8, 128)    147584      conv2d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_15 (Conv2D)              (None, 8, 8, 128)    147584      conv2d_14[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d_1 (UpSampling2D)  (None, 16, 16, 128)  0           conv2d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d_4 (UpSampling2D)  (None, 16, 16, 128)  0           conv2d_15[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_9 (Conv2D)               (None, 16, 16, 64)   73792       up_sampling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_16 (Conv2D)              (None, 16, 16, 64)   73792       up_sampling2d_4[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_10 (Conv2D)              (None, 16, 16, 64)   36928       conv2d_9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_17 (Conv2D)              (None, 16, 16, 64)   36928       conv2d_16[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d_2 (UpSampling2D)  (None, 32, 32, 64)   0           conv2d_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d_5 (UpSampling2D)  (None, 32, 32, 64)   0           conv2d_17[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_11 (Conv2D)              (None, 32, 32, 32)   18464       up_sampling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_18 (Conv2D)              (None, 32, 32, 32)   18464       up_sampling2d_5[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_12 (Conv2D)              (None, 32, 32, 32)   9248        conv2d_11[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_19 (Conv2D)              (None, 32, 32, 32)   9248        conv2d_18[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d_3 (UpSampling2D)  (None, 64, 64, 32)   0           conv2d_12[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d_6 (UpSampling2D)  (None, 64, 64, 32)   0           conv2d_19[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_13 (Conv2D)              (None, 64, 64, 1)    289         up_sampling2d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_20 (Conv2D)              (None, 64, 64, 1)    289         up_sampling2d_6[0][0]            \n",
      "==================================================================================================\n",
      "Total params: 1,154,210\n",
      "Trainable params: 1,154,210\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "\n",
    "K.clear_session()\n",
    "np.random.seed(123)\n",
    "model=Sequential()\n",
    "\n",
    "files=glob.glob('%s/*' %wt_path)\n",
    "for file in files:\n",
    "    os.remove(file)\n",
    "\n",
    "\n",
    "input_img = Input(shape=(h, w, 1)) \n",
    "\n",
    "x = Conv_Pool_block(input_img,32,w1=3,w2=3,p1=2,p2=2, padding='same', data_format='channels_last')\n",
    "x = Conv_Pool_block(x,64,w1=3,w2=3,p1=2,p2=2, padding='same', data_format='channels_last')\n",
    "x = Conv_Pool_block(x,128,w1=3,w2=3,p1=2,p2=2, padding='same', data_format='channels_last')\n",
    "#Activations are all ReLu\n",
    "\n",
    "encoded=x\n",
    "\n",
    "#Decoding arm 1\n",
    "x1=Conv_Up_block(encoded,128,w1=3,w2=3,p1=2,p2=2,padding='same', data_format='channels_last')\n",
    "x1=Conv_Up_block(x1,64,w1=3,w2=3,p1=2,p2=2,padding='same', data_format='channels_last')\n",
    "x1=Conv_Up_block(x1,32,w1=3,w2=3,p1=2,p2=2,padding='same', data_format='channels_last')\n",
    "\n",
    "decoded1 = Conv2D(1, (3, 3), padding='same')(x1)\n",
    "\n",
    "\n",
    "#Decoding arm 2\n",
    "x2=Conv_Up_block(encoded,128,w1=3,w2=3,p1=2,p2=2,padding='same', data_format='channels_last')\n",
    "x2=Conv_Up_block(x2,64,w1=3,w2=3,p1=2,p2=2,padding='same', data_format='channels_last')\n",
    "x2=Conv_Up_block(x2,32,w1=3,w2=3,p1=2,p2=2,padding='same', data_format='channels_last')\n",
    "\n",
    "decoded2 = Conv2D(1, (3, 3), padding='same')(x2)\n",
    "\n",
    "#Put together\n",
    "autoencoder = Model(input_img, [decoded1, decoded2])\n",
    "parallel_model = ModelMGPU(autoencoder, gpus=num_GPU)\n",
    "parallel_model.compile(optimizer='adam', loss='mean_absolute_error')\n",
    "\n",
    "print (autoencoder.summary())\n",
    "#plot_model(autoencoder, to_file='paper_data/str_model.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T21:07:00.282273Z",
     "start_time": "2020-06-15T21:05:21.455570Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1529 samples, validate on 81 samples\n",
      "Epoch 1/75\n",
      "1529/1529 [==============================] - 5s 3ms/step - loss: 1.5876 - conv2d_13_loss: 0.3368 - conv2d_20_loss: 1.2508 - val_loss: 0.5978 - val_conv2d_13_loss: 0.0137 - val_conv2d_20_loss: 0.5841\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 0.59783, saving model to wts5/weights.01.hdf5\n",
      "Epoch 2/75\n",
      "1529/1529 [==============================] - 1s 784us/step - loss: 0.5837 - conv2d_13_loss: 0.0118 - conv2d_20_loss: 0.5719 - val_loss: 0.5643 - val_conv2d_13_loss: 0.0103 - val_conv2d_20_loss: 0.5541\n",
      "\n",
      "Epoch 00002: val_loss improved from 0.59783 to 0.56435, saving model to wts5/weights.02.hdf5\n",
      "Epoch 3/75\n",
      "1529/1529 [==============================] - 1s 806us/step - loss: 0.5566 - conv2d_13_loss: 0.0099 - conv2d_20_loss: 0.5467 - val_loss: 0.5289 - val_conv2d_13_loss: 0.0092 - val_conv2d_20_loss: 0.5197\n",
      "\n",
      "Epoch 00003: val_loss improved from 0.56435 to 0.52891, saving model to wts5/weights.03.hdf5\n",
      "Epoch 4/75\n",
      "1529/1529 [==============================] - 1s 809us/step - loss: 0.5238 - conv2d_13_loss: 0.0091 - conv2d_20_loss: 0.5147 - val_loss: 0.5496 - val_conv2d_13_loss: 0.0093 - val_conv2d_20_loss: 0.5404\n",
      "\n",
      "Epoch 00004: val_loss did not improve from 0.52891\n",
      "Epoch 5/75\n",
      "1529/1529 [==============================] - 1s 820us/step - loss: 0.5012 - conv2d_13_loss: 0.0087 - conv2d_20_loss: 0.4925 - val_loss: 0.4529 - val_conv2d_13_loss: 0.0084 - val_conv2d_20_loss: 0.4446\n",
      "\n",
      "Epoch 00005: val_loss improved from 0.52891 to 0.45292, saving model to wts5/weights.05.hdf5\n",
      "Epoch 6/75\n",
      "1529/1529 [==============================] - 1s 791us/step - loss: 0.4518 - conv2d_13_loss: 0.0084 - conv2d_20_loss: 0.4435 - val_loss: 0.4352 - val_conv2d_13_loss: 0.0083 - val_conv2d_20_loss: 0.4270\n",
      "\n",
      "Epoch 00006: val_loss improved from 0.45292 to 0.43523, saving model to wts5/weights.06.hdf5\n",
      "Epoch 7/75\n",
      "1529/1529 [==============================] - 1s 794us/step - loss: 0.4298 - conv2d_13_loss: 0.0082 - conv2d_20_loss: 0.4216 - val_loss: 0.3981 - val_conv2d_13_loss: 0.0082 - val_conv2d_20_loss: 0.3899\n",
      "\n",
      "Epoch 00007: val_loss improved from 0.43523 to 0.39810, saving model to wts5/weights.07.hdf5\n",
      "Epoch 8/75\n",
      "1529/1529 [==============================] - 1s 819us/step - loss: 0.3971 - conv2d_13_loss: 0.0082 - conv2d_20_loss: 0.3890 - val_loss: 0.4114 - val_conv2d_13_loss: 0.0080 - val_conv2d_20_loss: 0.4034\n",
      "\n",
      "Epoch 00008: val_loss did not improve from 0.39810\n",
      "Epoch 9/75\n",
      "1529/1529 [==============================] - 1s 801us/step - loss: 0.3717 - conv2d_13_loss: 0.0081 - conv2d_20_loss: 0.3636 - val_loss: 0.3508 - val_conv2d_13_loss: 0.0080 - val_conv2d_20_loss: 0.3429\n",
      "\n",
      "Epoch 00009: val_loss improved from 0.39810 to 0.35082, saving model to wts5/weights.09.hdf5\n",
      "Epoch 10/75\n",
      "1529/1529 [==============================] - 1s 823us/step - loss: 0.3439 - conv2d_13_loss: 0.0080 - conv2d_20_loss: 0.3359 - val_loss: 0.3413 - val_conv2d_13_loss: 0.0079 - val_conv2d_20_loss: 0.3334\n",
      "\n",
      "Epoch 00010: val_loss improved from 0.35082 to 0.34134, saving model to wts5/weights.10.hdf5\n",
      "Epoch 11/75\n",
      "1529/1529 [==============================] - 1s 814us/step - loss: 0.3170 - conv2d_13_loss: 0.0079 - conv2d_20_loss: 0.3091 - val_loss: 0.3258 - val_conv2d_13_loss: 0.0078 - val_conv2d_20_loss: 0.3180\n",
      "\n",
      "Epoch 00011: val_loss improved from 0.34134 to 0.32581, saving model to wts5/weights.11.hdf5\n",
      "Epoch 12/75\n",
      "1529/1529 [==============================] - 1s 845us/step - loss: 0.2967 - conv2d_13_loss: 0.0078 - conv2d_20_loss: 0.2889 - val_loss: 0.2947 - val_conv2d_13_loss: 0.0077 - val_conv2d_20_loss: 0.2871\n",
      "\n",
      "Epoch 00012: val_loss improved from 0.32581 to 0.29475, saving model to wts5/weights.12.hdf5\n",
      "Epoch 13/75\n",
      "1529/1529 [==============================] - 1s 816us/step - loss: 0.2787 - conv2d_13_loss: 0.0077 - conv2d_20_loss: 0.2710 - val_loss: 0.2876 - val_conv2d_13_loss: 0.0076 - val_conv2d_20_loss: 0.2801\n",
      "\n",
      "Epoch 00013: val_loss improved from 0.29475 to 0.28762, saving model to wts5/weights.13.hdf5\n",
      "Epoch 14/75\n",
      "1529/1529 [==============================] - 1s 823us/step - loss: 0.2654 - conv2d_13_loss: 0.0076 - conv2d_20_loss: 0.2578 - val_loss: 0.2693 - val_conv2d_13_loss: 0.0074 - val_conv2d_20_loss: 0.2619\n",
      "\n",
      "Epoch 00014: val_loss improved from 0.28762 to 0.26927, saving model to wts5/weights.14.hdf5\n",
      "Epoch 15/75\n",
      "1529/1529 [==============================] - 1s 792us/step - loss: 0.2524 - conv2d_13_loss: 0.0074 - conv2d_20_loss: 0.2450 - val_loss: 0.2538 - val_conv2d_13_loss: 0.0073 - val_conv2d_20_loss: 0.2465\n",
      "\n",
      "Epoch 00015: val_loss improved from 0.26927 to 0.25376, saving model to wts5/weights.15.hdf5\n",
      "Epoch 16/75\n",
      "1529/1529 [==============================] - 1s 805us/step - loss: 0.2437 - conv2d_13_loss: 0.0072 - conv2d_20_loss: 0.2364 - val_loss: 0.2492 - val_conv2d_13_loss: 0.0071 - val_conv2d_20_loss: 0.2420\n",
      "\n",
      "Epoch 00016: val_loss improved from 0.25376 to 0.24917, saving model to wts5/weights.16.hdf5\n",
      "Epoch 17/75\n",
      "1529/1529 [==============================] - 1s 848us/step - loss: 0.2373 - conv2d_13_loss: 0.0071 - conv2d_20_loss: 0.2302 - val_loss: 0.2478 - val_conv2d_13_loss: 0.0071 - val_conv2d_20_loss: 0.2407\n",
      "\n",
      "Epoch 00017: val_loss improved from 0.24917 to 0.24776, saving model to wts5/weights.17.hdf5\n",
      "Epoch 18/75\n",
      "1529/1529 [==============================] - 1s 886us/step - loss: 0.2196 - conv2d_13_loss: 0.0070 - conv2d_20_loss: 0.2127 - val_loss: 0.2409 - val_conv2d_13_loss: 0.0069 - val_conv2d_20_loss: 0.2340\n",
      "\n",
      "Epoch 00018: val_loss improved from 0.24776 to 0.24087, saving model to wts5/weights.18.hdf5\n",
      "Epoch 19/75\n",
      "1529/1529 [==============================] - 1s 873us/step - loss: 0.2122 - conv2d_13_loss: 0.0069 - conv2d_20_loss: 0.2053 - val_loss: 0.2300 - val_conv2d_13_loss: 0.0068 - val_conv2d_20_loss: 0.2231\n",
      "\n",
      "Epoch 00019: val_loss improved from 0.24087 to 0.22997, saving model to wts5/weights.19.hdf5\n",
      "Epoch 20/75\n",
      "1529/1529 [==============================] - 1s 869us/step - loss: 0.2002 - conv2d_13_loss: 0.0067 - conv2d_20_loss: 0.1934 - val_loss: 0.2120 - val_conv2d_13_loss: 0.0067 - val_conv2d_20_loss: 0.2052\n",
      "\n",
      "Epoch 00020: val_loss improved from 0.22997 to 0.21196, saving model to wts5/weights.20.hdf5\n",
      "Epoch 21/75\n",
      "1529/1529 [==============================] - 1s 874us/step - loss: 0.1955 - conv2d_13_loss: 0.0066 - conv2d_20_loss: 0.1888 - val_loss: 0.2241 - val_conv2d_13_loss: 0.0066 - val_conv2d_20_loss: 0.2175\n",
      "\n",
      "Epoch 00021: val_loss did not improve from 0.21196\n",
      "Epoch 22/75\n",
      "1529/1529 [==============================] - 1s 859us/step - loss: 0.1918 - conv2d_13_loss: 0.0066 - conv2d_20_loss: 0.1852 - val_loss: 0.2069 - val_conv2d_13_loss: 0.0066 - val_conv2d_20_loss: 0.2003\n",
      "\n",
      "Epoch 00022: val_loss improved from 0.21196 to 0.20686, saving model to wts5/weights.22.hdf5\n",
      "Epoch 23/75\n",
      "1529/1529 [==============================] - 1s 856us/step - loss: 0.1800 - conv2d_13_loss: 0.0065 - conv2d_20_loss: 0.1735 - val_loss: 0.1965 - val_conv2d_13_loss: 0.0065 - val_conv2d_20_loss: 0.1900\n",
      "\n",
      "Epoch 00023: val_loss improved from 0.20686 to 0.19647, saving model to wts5/weights.23.hdf5\n",
      "Epoch 24/75\n",
      "1529/1529 [==============================] - 1s 894us/step - loss: 0.1761 - conv2d_13_loss: 0.0064 - conv2d_20_loss: 0.1696 - val_loss: 0.2003 - val_conv2d_13_loss: 0.0065 - val_conv2d_20_loss: 0.1939\n",
      "\n",
      "Epoch 00024: val_loss did not improve from 0.19647\n",
      "Epoch 25/75\n",
      "1529/1529 [==============================] - 1s 826us/step - loss: 0.1757 - conv2d_13_loss: 0.0064 - conv2d_20_loss: 0.1693 - val_loss: 0.1966 - val_conv2d_13_loss: 0.0064 - val_conv2d_20_loss: 0.1902\n",
      "\n",
      "Epoch 00025: val_loss did not improve from 0.19647\n",
      "\n",
      "Epoch 00025: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n",
      "Epoch 26/75\n",
      "1529/1529 [==============================] - 1s 833us/step - loss: 0.1518 - conv2d_13_loss: 0.0063 - conv2d_20_loss: 0.1455 - val_loss: 0.1725 - val_conv2d_13_loss: 0.0063 - val_conv2d_20_loss: 0.1663\n",
      "\n",
      "Epoch 00026: val_loss improved from 0.19647 to 0.17253, saving model to wts5/weights.26.hdf5\n",
      "Epoch 27/75\n",
      "1529/1529 [==============================] - 1s 775us/step - loss: 0.1391 - conv2d_13_loss: 0.0062 - conv2d_20_loss: 0.1329 - val_loss: 0.1708 - val_conv2d_13_loss: 0.0062 - val_conv2d_20_loss: 0.1646\n",
      "\n",
      "Epoch 00027: val_loss improved from 0.17253 to 0.17085, saving model to wts5/weights.27.hdf5\n",
      "Epoch 28/75\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1529/1529 [==============================] - 1s 778us/step - loss: 0.1352 - conv2d_13_loss: 0.0062 - conv2d_20_loss: 0.1290 - val_loss: 0.1660 - val_conv2d_13_loss: 0.0062 - val_conv2d_20_loss: 0.1597\n",
      "\n",
      "Epoch 00028: val_loss improved from 0.17085 to 0.16595, saving model to wts5/weights.28.hdf5\n",
      "Epoch 29/75\n",
      "1529/1529 [==============================] - 1s 797us/step - loss: 0.1285 - conv2d_13_loss: 0.0062 - conv2d_20_loss: 0.1223 - val_loss: 0.1609 - val_conv2d_13_loss: 0.0062 - val_conv2d_20_loss: 0.1547\n",
      "\n",
      "Epoch 00029: val_loss improved from 0.16595 to 0.16095, saving model to wts5/weights.29.hdf5\n",
      "Epoch 30/75\n",
      "1529/1529 [==============================] - 1s 760us/step - loss: 0.1251 - conv2d_13_loss: 0.0061 - conv2d_20_loss: 0.1190 - val_loss: 0.1674 - val_conv2d_13_loss: 0.0062 - val_conv2d_20_loss: 0.1612\n",
      "\n",
      "Epoch 00030: val_loss did not improve from 0.16095\n",
      "Epoch 31/75\n",
      "1529/1529 [==============================] - 1s 753us/step - loss: 0.1257 - conv2d_13_loss: 0.0061 - conv2d_20_loss: 0.1196 - val_loss: 0.1588 - val_conv2d_13_loss: 0.0062 - val_conv2d_20_loss: 0.1527\n",
      "\n",
      "Epoch 00031: val_loss improved from 0.16095 to 0.15882, saving model to wts5/weights.31.hdf5\n",
      "Epoch 32/75\n",
      "1529/1529 [==============================] - 1s 762us/step - loss: 0.1218 - conv2d_13_loss: 0.0061 - conv2d_20_loss: 0.1157 - val_loss: 0.1607 - val_conv2d_13_loss: 0.0061 - val_conv2d_20_loss: 0.1546\n",
      "\n",
      "Epoch 00032: val_loss did not improve from 0.15882\n",
      "Epoch 33/75\n",
      "1529/1529 [==============================] - 1s 778us/step - loss: 0.1191 - conv2d_13_loss: 0.0061 - conv2d_20_loss: 0.1130 - val_loss: 0.1560 - val_conv2d_13_loss: 0.0061 - val_conv2d_20_loss: 0.1499\n",
      "\n",
      "Epoch 00033: val_loss improved from 0.15882 to 0.15601, saving model to wts5/weights.33.hdf5\n",
      "Epoch 34/75\n",
      "1529/1529 [==============================] - 1s 763us/step - loss: 0.1169 - conv2d_13_loss: 0.0060 - conv2d_20_loss: 0.1109 - val_loss: 0.1567 - val_conv2d_13_loss: 0.0061 - val_conv2d_20_loss: 0.1505\n",
      "\n",
      "Epoch 00034: val_loss did not improve from 0.15601\n",
      "Epoch 35/75\n",
      "1529/1529 [==============================] - 1s 723us/step - loss: 0.1160 - conv2d_13_loss: 0.0060 - conv2d_20_loss: 0.1100 - val_loss: 0.1556 - val_conv2d_13_loss: 0.0061 - val_conv2d_20_loss: 0.1495\n",
      "\n",
      "Epoch 00035: val_loss improved from 0.15601 to 0.15562, saving model to wts5/weights.35.hdf5\n",
      "Epoch 36/75\n",
      "1529/1529 [==============================] - 1s 769us/step - loss: 0.1144 - conv2d_13_loss: 0.0060 - conv2d_20_loss: 0.1084 - val_loss: 0.1510 - val_conv2d_13_loss: 0.0061 - val_conv2d_20_loss: 0.1449\n",
      "\n",
      "Epoch 00036: val_loss improved from 0.15562 to 0.15095, saving model to wts5/weights.36.hdf5\n",
      "Epoch 37/75\n",
      "1529/1529 [==============================] - 1s 746us/step - loss: 0.1139 - conv2d_13_loss: 0.0060 - conv2d_20_loss: 0.1079 - val_loss: 0.1534 - val_conv2d_13_loss: 0.0060 - val_conv2d_20_loss: 0.1474\n",
      "\n",
      "Epoch 00037: val_loss did not improve from 0.15095\n",
      "Epoch 38/75\n",
      "1529/1529 [==============================] - 1s 755us/step - loss: 0.1129 - conv2d_13_loss: 0.0060 - conv2d_20_loss: 0.1069 - val_loss: 0.1507 - val_conv2d_13_loss: 0.0060 - val_conv2d_20_loss: 0.1446\n",
      "\n",
      "Epoch 00038: val_loss improved from 0.15095 to 0.15066, saving model to wts5/weights.38.hdf5\n",
      "Epoch 39/75\n",
      "1529/1529 [==============================] - 1s 780us/step - loss: 0.1128 - conv2d_13_loss: 0.0059 - conv2d_20_loss: 0.1068 - val_loss: 0.1507 - val_conv2d_13_loss: 0.0060 - val_conv2d_20_loss: 0.1447\n",
      "\n",
      "Epoch 00039: val_loss did not improve from 0.15066\n",
      "Epoch 40/75\n",
      "1529/1529 [==============================] - 1s 750us/step - loss: 0.1120 - conv2d_13_loss: 0.0059 - conv2d_20_loss: 0.1060 - val_loss: 0.1498 - val_conv2d_13_loss: 0.0060 - val_conv2d_20_loss: 0.1438\n",
      "\n",
      "Epoch 00040: val_loss improved from 0.15066 to 0.14980, saving model to wts5/weights.40.hdf5\n",
      "Epoch 41/75\n",
      "1529/1529 [==============================] - 1s 773us/step - loss: 0.1096 - conv2d_13_loss: 0.0059 - conv2d_20_loss: 0.1037 - val_loss: 0.1467 - val_conv2d_13_loss: 0.0060 - val_conv2d_20_loss: 0.1407\n",
      "\n",
      "Epoch 00041: val_loss improved from 0.14980 to 0.14668, saving model to wts5/weights.41.hdf5\n",
      "Epoch 42/75\n",
      "1529/1529 [==============================] - 1s 764us/step - loss: 0.1084 - conv2d_13_loss: 0.0059 - conv2d_20_loss: 0.1025 - val_loss: 0.1483 - val_conv2d_13_loss: 0.0060 - val_conv2d_20_loss: 0.1423\n",
      "\n",
      "Epoch 00042: val_loss did not improve from 0.14668\n",
      "Epoch 43/75\n",
      "1529/1529 [==============================] - 1s 772us/step - loss: 0.1062 - conv2d_13_loss: 0.0059 - conv2d_20_loss: 0.1004 - val_loss: 0.1457 - val_conv2d_13_loss: 0.0060 - val_conv2d_20_loss: 0.1398\n",
      "\n",
      "Epoch 00043: val_loss improved from 0.14668 to 0.14573, saving model to wts5/weights.43.hdf5\n",
      "Epoch 44/75\n",
      "1529/1529 [==============================] - 1s 746us/step - loss: 0.1078 - conv2d_13_loss: 0.0058 - conv2d_20_loss: 0.1019 - val_loss: 0.1469 - val_conv2d_13_loss: 0.0060 - val_conv2d_20_loss: 0.1410\n",
      "\n",
      "Epoch 00044: val_loss did not improve from 0.14573\n",
      "Epoch 45/75\n",
      "1529/1529 [==============================] - 1s 767us/step - loss: 0.1057 - conv2d_13_loss: 0.0058 - conv2d_20_loss: 0.0998 - val_loss: 0.1455 - val_conv2d_13_loss: 0.0059 - val_conv2d_20_loss: 0.1396\n",
      "\n",
      "Epoch 00045: val_loss improved from 0.14573 to 0.14547, saving model to wts5/weights.45.hdf5\n",
      "Epoch 46/75\n",
      "1529/1529 [==============================] - 1s 751us/step - loss: 0.1043 - conv2d_13_loss: 0.0058 - conv2d_20_loss: 0.0985 - val_loss: 0.1439 - val_conv2d_13_loss: 0.0059 - val_conv2d_20_loss: 0.1379\n",
      "\n",
      "Epoch 00046: val_loss improved from 0.14547 to 0.14386, saving model to wts5/weights.46.hdf5\n",
      "Epoch 47/75\n",
      "1529/1529 [==============================] - 1s 735us/step - loss: 0.1043 - conv2d_13_loss: 0.0058 - conv2d_20_loss: 0.0985 - val_loss: 0.1410 - val_conv2d_13_loss: 0.0059 - val_conv2d_20_loss: 0.1351\n",
      "\n",
      "Epoch 00047: val_loss improved from 0.14386 to 0.14099, saving model to wts5/weights.47.hdf5\n",
      "Epoch 48/75\n",
      "1529/1529 [==============================] - 1s 753us/step - loss: 0.1007 - conv2d_13_loss: 0.0058 - conv2d_20_loss: 0.0949 - val_loss: 0.1396 - val_conv2d_13_loss: 0.0059 - val_conv2d_20_loss: 0.1337\n",
      "\n",
      "Epoch 00048: val_loss improved from 0.14099 to 0.13957, saving model to wts5/weights.48.hdf5\n",
      "Epoch 49/75\n",
      "1529/1529 [==============================] - 1s 767us/step - loss: 0.1012 - conv2d_13_loss: 0.0058 - conv2d_20_loss: 0.0955 - val_loss: 0.1395 - val_conv2d_13_loss: 0.0059 - val_conv2d_20_loss: 0.1337\n",
      "\n",
      "Epoch 00049: val_loss improved from 0.13957 to 0.13954, saving model to wts5/weights.49.hdf5\n",
      "Epoch 50/75\n",
      "1529/1529 [==============================] - 1s 747us/step - loss: 0.1003 - conv2d_13_loss: 0.0057 - conv2d_20_loss: 0.0945 - val_loss: 0.1383 - val_conv2d_13_loss: 0.0059 - val_conv2d_20_loss: 0.1325\n",
      "\n",
      "Epoch 00050: val_loss improved from 0.13954 to 0.13833, saving model to wts5/weights.50.hdf5\n",
      "Epoch 51/75\n",
      "1529/1529 [==============================] - 1s 777us/step - loss: 0.1007 - conv2d_13_loss: 0.0057 - conv2d_20_loss: 0.0950 - val_loss: 0.1377 - val_conv2d_13_loss: 0.0058 - val_conv2d_20_loss: 0.1319\n",
      "\n",
      "Epoch 00051: val_loss improved from 0.13833 to 0.13770, saving model to wts5/weights.51.hdf5\n",
      "Epoch 52/75\n",
      "1529/1529 [==============================] - 1s 748us/step - loss: 0.0996 - conv2d_13_loss: 0.0057 - conv2d_20_loss: 0.0939 - val_loss: 0.1377 - val_conv2d_13_loss: 0.0058 - val_conv2d_20_loss: 0.1319\n",
      "\n",
      "Epoch 00052: val_loss did not improve from 0.13770\n",
      "Epoch 53/75\n",
      "1529/1529 [==============================] - 1s 758us/step - loss: 0.0994 - conv2d_13_loss: 0.0057 - conv2d_20_loss: 0.0937 - val_loss: 0.1384 - val_conv2d_13_loss: 0.0058 - val_conv2d_20_loss: 0.1326\n",
      "\n",
      "Epoch 00053: val_loss did not improve from 0.13770\n",
      "\n",
      "Epoch 00053: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
      "Epoch 54/75\n",
      "1529/1529 [==============================] - 1s 755us/step - loss: 0.0909 - conv2d_13_loss: 0.0056 - conv2d_20_loss: 0.0852 - val_loss: 0.1297 - val_conv2d_13_loss: 0.0058 - val_conv2d_20_loss: 0.1239\n",
      "\n",
      "Epoch 00054: val_loss improved from 0.13770 to 0.12969, saving model to wts5/weights.54.hdf5\n",
      "Epoch 55/75\n",
      "1529/1529 [==============================] - 1s 732us/step - loss: 0.0844 - conv2d_13_loss: 0.0056 - conv2d_20_loss: 0.0788 - val_loss: 0.1288 - val_conv2d_13_loss: 0.0058 - val_conv2d_20_loss: 0.1231\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 00055: val_loss improved from 0.12969 to 0.12883, saving model to wts5/weights.55.hdf5\n",
      "Epoch 56/75\n",
      "1529/1529 [==============================] - 1s 744us/step - loss: 0.0816 - conv2d_13_loss: 0.0056 - conv2d_20_loss: 0.0760 - val_loss: 0.1278 - val_conv2d_13_loss: 0.0058 - val_conv2d_20_loss: 0.1221\n",
      "\n",
      "Epoch 00056: val_loss improved from 0.12883 to 0.12784, saving model to wts5/weights.56.hdf5\n",
      "Epoch 57/75\n",
      "1529/1529 [==============================] - 1s 736us/step - loss: 0.0806 - conv2d_13_loss: 0.0056 - conv2d_20_loss: 0.0750 - val_loss: 0.1275 - val_conv2d_13_loss: 0.0058 - val_conv2d_20_loss: 0.1218\n",
      "\n",
      "Epoch 00057: val_loss improved from 0.12784 to 0.12752, saving model to wts5/weights.57.hdf5\n",
      "Epoch 58/75\n",
      "1529/1529 [==============================] - 1s 765us/step - loss: 0.0796 - conv2d_13_loss: 0.0056 - conv2d_20_loss: 0.0741 - val_loss: 0.1274 - val_conv2d_13_loss: 0.0057 - val_conv2d_20_loss: 0.1217\n",
      "\n",
      "Epoch 00058: val_loss improved from 0.12752 to 0.12743, saving model to wts5/weights.58.hdf5\n",
      "Epoch 59/75\n",
      "1529/1529 [==============================] - 1s 761us/step - loss: 0.0788 - conv2d_13_loss: 0.0055 - conv2d_20_loss: 0.0733 - val_loss: 0.1254 - val_conv2d_13_loss: 0.0057 - val_conv2d_20_loss: 0.1197\n",
      "\n",
      "Epoch 00059: val_loss improved from 0.12743 to 0.12545, saving model to wts5/weights.59.hdf5\n",
      "Epoch 60/75\n",
      "1529/1529 [==============================] - 1s 765us/step - loss: 0.0782 - conv2d_13_loss: 0.0055 - conv2d_20_loss: 0.0727 - val_loss: 0.1265 - val_conv2d_13_loss: 0.0057 - val_conv2d_20_loss: 0.1208\n",
      "\n",
      "Epoch 00060: val_loss did not improve from 0.12545\n",
      "Epoch 61/75\n",
      "1529/1529 [==============================] - 1s 765us/step - loss: 0.0780 - conv2d_13_loss: 0.0055 - conv2d_20_loss: 0.0725 - val_loss: 0.1263 - val_conv2d_13_loss: 0.0057 - val_conv2d_20_loss: 0.1206\n",
      "\n",
      "Epoch 00061: val_loss did not improve from 0.12545\n",
      "\n",
      "Epoch 00061: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814.\n",
      "Epoch 62/75\n",
      "1529/1529 [==============================] - 1s 766us/step - loss: 0.0755 - conv2d_13_loss: 0.0055 - conv2d_20_loss: 0.0700 - val_loss: 0.1248 - val_conv2d_13_loss: 0.0057 - val_conv2d_20_loss: 0.1191\n",
      "\n",
      "Epoch 00062: val_loss improved from 0.12545 to 0.12481, saving model to wts5/weights.62.hdf5\n",
      "Epoch 63/75\n",
      "1529/1529 [==============================] - 1s 767us/step - loss: 0.0738 - conv2d_13_loss: 0.0055 - conv2d_20_loss: 0.0684 - val_loss: 0.1252 - val_conv2d_13_loss: 0.0057 - val_conv2d_20_loss: 0.1195\n",
      "\n",
      "Epoch 00063: val_loss did not improve from 0.12481\n",
      "Epoch 64/75\n",
      "1529/1529 [==============================] - 1s 790us/step - loss: 0.0732 - conv2d_13_loss: 0.0055 - conv2d_20_loss: 0.0677 - val_loss: 0.1252 - val_conv2d_13_loss: 0.0057 - val_conv2d_20_loss: 0.1195\n",
      "\n",
      "Epoch 00064: val_loss did not improve from 0.12481\n",
      "\n",
      "Epoch 00064: ReduceLROnPlateau reducing learning rate to 0.0001.\n",
      "Epoch 65/75\n",
      "1529/1529 [==============================] - 1s 761us/step - loss: 0.0726 - conv2d_13_loss: 0.0055 - conv2d_20_loss: 0.0671 - val_loss: 0.1247 - val_conv2d_13_loss: 0.0057 - val_conv2d_20_loss: 0.1190\n",
      "\n",
      "Epoch 00065: val_loss improved from 0.12481 to 0.12472, saving model to wts5/weights.65.hdf5\n",
      "Epoch 66/75\n",
      "1529/1529 [==============================] - 1s 753us/step - loss: 0.0722 - conv2d_13_loss: 0.0055 - conv2d_20_loss: 0.0667 - val_loss: 0.1246 - val_conv2d_13_loss: 0.0057 - val_conv2d_20_loss: 0.1189\n",
      "\n",
      "Epoch 00066: val_loss improved from 0.12472 to 0.12456, saving model to wts5/weights.66.hdf5\n",
      "Epoch 67/75\n",
      "1529/1529 [==============================] - 1s 731us/step - loss: 0.0718 - conv2d_13_loss: 0.0055 - conv2d_20_loss: 0.0664 - val_loss: 0.1249 - val_conv2d_13_loss: 0.0057 - val_conv2d_20_loss: 0.1191\n",
      "\n",
      "Epoch 00067: val_loss did not improve from 0.12456\n",
      "Epoch 68/75\n",
      "1529/1529 [==============================] - 1s 755us/step - loss: 0.0716 - conv2d_13_loss: 0.0055 - conv2d_20_loss: 0.0661 - val_loss: 0.1244 - val_conv2d_13_loss: 0.0057 - val_conv2d_20_loss: 0.1187\n",
      "\n",
      "Epoch 00068: val_loss improved from 0.12456 to 0.12441, saving model to wts5/weights.68.hdf5\n",
      "Epoch 69/75\n",
      "1529/1529 [==============================] - 1s 744us/step - loss: 0.0713 - conv2d_13_loss: 0.0055 - conv2d_20_loss: 0.0659 - val_loss: 0.1244 - val_conv2d_13_loss: 0.0057 - val_conv2d_20_loss: 0.1187\n",
      "\n",
      "Epoch 00069: val_loss did not improve from 0.12441\n",
      "Epoch 70/75\n",
      "1529/1529 [==============================] - 1s 748us/step - loss: 0.0711 - conv2d_13_loss: 0.0054 - conv2d_20_loss: 0.0656 - val_loss: 0.1245 - val_conv2d_13_loss: 0.0057 - val_conv2d_20_loss: 0.1188\n",
      "\n",
      "Epoch 00070: val_loss did not improve from 0.12441\n",
      "Epoch 71/75\n",
      "1529/1529 [==============================] - 1s 765us/step - loss: 0.0709 - conv2d_13_loss: 0.0054 - conv2d_20_loss: 0.0654 - val_loss: 0.1245 - val_conv2d_13_loss: 0.0057 - val_conv2d_20_loss: 0.1188\n",
      "\n",
      "Epoch 00071: val_loss did not improve from 0.12441\n",
      "Epoch 72/75\n",
      "1529/1529 [==============================] - 1s 731us/step - loss: 0.0707 - conv2d_13_loss: 0.0054 - conv2d_20_loss: 0.0652 - val_loss: 0.1242 - val_conv2d_13_loss: 0.0057 - val_conv2d_20_loss: 0.1185\n",
      "\n",
      "Epoch 00072: val_loss improved from 0.12441 to 0.12424, saving model to wts5/weights.72.hdf5\n",
      "Epoch 73/75\n",
      "1529/1529 [==============================] - 1s 743us/step - loss: 0.0704 - conv2d_13_loss: 0.0054 - conv2d_20_loss: 0.0650 - val_loss: 0.1243 - val_conv2d_13_loss: 0.0057 - val_conv2d_20_loss: 0.1186\n",
      "\n",
      "Epoch 00073: val_loss did not improve from 0.12424\n",
      "Epoch 74/75\n",
      "1529/1529 [==============================] - 1s 735us/step - loss: 0.0702 - conv2d_13_loss: 0.0054 - conv2d_20_loss: 0.0647 - val_loss: 0.1243 - val_conv2d_13_loss: 0.0057 - val_conv2d_20_loss: 0.1186\n",
      "\n",
      "Epoch 00074: val_loss did not improve from 0.12424\n",
      "Epoch 75/75\n",
      "1529/1529 [==============================] - 1s 745us/step - loss: 0.0699 - conv2d_13_loss: 0.0054 - conv2d_20_loss: 0.0645 - val_loss: 0.1243 - val_conv2d_13_loss: 0.0057 - val_conv2d_20_loss: 0.1186\n",
      "\n",
      "Epoch 00075: val_loss did not improve from 0.12424\n"
     ]
    }
   ],
   "source": [
    "reduce_lr = keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.5,\n",
    "                              patience=2, min_lr=0.0001, verbose=1)\n",
    "\n",
    "checkpoints=keras.callbacks.ModelCheckpoint('%s/weights.{epoch:02d}.hdf5' %wt_path, \n",
    "                                            monitor='val_loss', verbose=1, save_best_only=True, \n",
    "                                            save_weights_only=False, mode='auto', period=1)\n",
    "\n",
    "history=parallel_model.fit(X_train, [Y_I_train, Y_phi_train], shuffle=True, batch_size=batch_size, verbose=1, \n",
    "                           epochs=nepochs, validation_split = 0.05, callbacks=[checkpoints, reduce_lr])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T21:07:01.540714Z",
     "start_time": "2020-06-15T21:07:00.286396Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0615 21:07:00.422184 140129710683968 font_manager.py:1282] findfont: Font family ['Times New Roman'] not found. Falling back to DejaVu Sans.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x576 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist=history\n",
    "epochs=np.asarray(history.epoch)+1\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "plt.style.use('seaborn-white')\n",
    "matplotlib.rc('font',family='Times New Roman')\n",
    "matplotlib.rcParams['font.size'] = 20\n",
    "\n",
    "f, axarr = plt.subplots(3, sharex=True, figsize=(12, 8))\n",
    "\n",
    "axarr[0].set(ylabel='Loss')\n",
    "axarr[0].plot(epochs,hist.history['loss'], 'C3o', label='Total Training')\n",
    "axarr[0].plot(epochs,hist.history['val_loss'], 'C3-', label='Total Validation')\n",
    "axarr[0].grid()\n",
    "axarr[0].legend(loc='center right', bbox_to_anchor=(1.5, 0.5))\n",
    "\n",
    "axarr[1].set(ylabel='Loss')\n",
    "axarr[1].plot(epochs,hist.history['conv2d_13_loss'], 'C0o', label='Structure Training')\n",
    "axarr[1].plot(epochs,hist.history['val_conv2d_13_loss'], 'C0-', label='Structure Validation')\n",
    "axarr[1].legend(loc='center right', bbox_to_anchor=(1.5, 0.5))\n",
    "plt.xlabel('Epochs')\n",
    "plt.tight_layout()\n",
    "axarr[1].grid()\n",
    "\n",
    "\n",
    "axarr[2].set(ylabel='Loss')\n",
    "axarr[2].plot(epochs,hist.history['conv2d_20_loss'], 'C0o', label='Phase Training')\n",
    "axarr[2].plot(epochs,hist.history['val_conv2d_20_loss'], 'C0-', label='Phase Validation')\n",
    "axarr[2].legend(loc='center right', bbox_to_anchor=(1.5, 0.5))\n",
    "plt.tight_layout()\n",
    "axarr[2].grid()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T21:07:01.545684Z",
     "start_time": "2020-06-15T21:07:01.542830Z"
    }
   },
   "outputs": [],
   "source": [
    "#np.save('paper_data/str_history', history.history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save the model with the lowest validation error for future reference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T21:07:01.557299Z",
     "start_time": "2020-06-15T21:07:01.547732Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "72\n"
     ]
    }
   ],
   "source": [
    "val_losses=hist.history['val_loss']\n",
    "min_epoch=np.argmin(val_losses)+1\n",
    "print (min_epoch)\n",
    "np.save('%s/min_epoch' %(wt_path), min_epoch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T21:07:02.458036Z",
     "start_time": "2020-06-15T21:07:01.559699Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/keras/engine/saving.py:292: UserWarning: No training configuration found in save file: the model was *not* compiled. Compile it manually.\n",
      "  warnings.warn('No training configuration found in save file: '\n"
     ]
    }
   ],
   "source": [
    "min_epoch = np.load('%s/min_epoch.npy' %wt_path)\n",
    "from keras.models import Model, load_model\n",
    "model=load_model('%s/weights.%02d.hdf5' %(wt_path,min_epoch))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make predictions using the sparse-sampled test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T21:07:03.404934Z",
     "start_time": "2020-06-15T21:07:02.460699Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3600, 64, 64, 1)\n"
     ]
    }
   ],
   "source": [
    "preds_intens=(model.predict(X_test))\n",
    "print (preds_intens[0].shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stitch NN predictions "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T21:07:03.422338Z",
     "start_time": "2020-06-15T21:07:03.418555Z"
    }
   },
   "outputs": [],
   "source": [
    "tst_side = nltest\n",
    "tst_side\n",
    "point_size = 3\n",
    "overlap = 4*point_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T21:07:03.497849Z",
     "start_time": "2020-06-15T21:07:03.431812Z"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "composite_amp = np.zeros((tst_side*point_size+overlap,tst_side*point_size+overlap),float)\n",
    "ctr = np.zeros_like(composite_amp)\n",
    "data_reshaped = preds_intens[0].reshape(tst_side,tst_side,64,64)[:,:,32-int(overlap/2):32+int(overlap/2),\n",
    "                                                     32-int(overlap/2):32+int(overlap/2)]\n",
    "\n",
    "for i in range(tst_side):\n",
    "    for j in range(tst_side):\n",
    "        composite_amp[point_size*i:point_size*i+overlap, point_size*j:point_size*j+overlap] += data_reshaped[i,j]\n",
    "        ctr[point_size*i:point_size*i+overlap, point_size*j:point_size*j+overlap] +=1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T21:07:03.564509Z",
     "start_time": "2020-06-15T21:07:03.500463Z"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "composite_phase = np.zeros((tst_side*point_size+overlap,tst_side*point_size+overlap),float)\n",
    "ctr = np.zeros_like(composite_phase)\n",
    "data_reshaped = preds_intens[1].reshape(tst_side,tst_side,64,64)[:,:,32-int(overlap/2):32+int(overlap/2),\n",
    "                                                     32-int(overlap/2):32+int(overlap/2)]\n",
    "\n",
    "for i in range(tst_side):\n",
    "    for j in range(tst_side):\n",
    "        composite_phase[point_size*i:point_size*i+overlap, point_size*j:point_size*j+overlap] += data_reshaped[i,j]\n",
    "        ctr[point_size*i:point_size*i+overlap, point_size*j:point_size*j+overlap] +=1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T21:07:03.579871Z",
     "start_time": "2020-06-15T21:07:03.566861Z"
    }
   },
   "outputs": [],
   "source": [
    "stitched_phase = composite_phase[int(overlap/2):-int(overlap/2), int(overlap/2):-int(overlap/2)]/ctr[int(overlap/2)\n",
    "                                                                    :-int(overlap/2), int(overlap/2):-int(overlap/2)]\n",
    "\n",
    "stitched_amp = composite_amp[int(overlap/2):-int(overlap/2), int(overlap/2):-int(overlap/2)]/ctr[int(overlap/2)\n",
    "                                                                    :-int(overlap/2), int(overlap/2):-int(overlap/2)]\n",
    "\n",
    "stitched_amp_down = resize(stitched_amp, (60,60), preserve_range=True, anti_aliasing=True)\n",
    "stitched_phase_down = resize(stitched_phase, (60,60), preserve_range=True, anti_aliasing=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the ePIE reconstruction, the PtychoNN prediction and the difference between the two"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T21:07:03.627399Z",
     "start_time": "2020-06-15T21:07:03.582386Z"
    }
   },
   "outputs": [],
   "source": [
    "amp = Y_I_test.reshape(nltest,nltest,64,64)\n",
    "ph = Y_phi_test.reshape(nltest,nltest,64,64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T21:07:04.332319Z",
     "start_time": "2020-06-15T21:07:03.630191Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5, 59.5, 59.5, -0.5)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0615 21:07:03.823880 140129710683968 font_manager.py:1282] findfont: Font family ['Times New Roman'] not found. Falling back to DejaVu Sans.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x864 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams.update({'font.size': 26})\n",
    "plt.viridis()\n",
    "fig, ax = plt.subplots(1,3,figsize=(20,12))\n",
    "\n",
    "im=ax[0].imshow(amp[:,:,32,32])#,vmin=minc, vmax=maxc)\n",
    "ax[0].axis('off')\n",
    "cb = plt.colorbar(im,ax=ax[0], fraction=0.046, pad=0.04)\n",
    "\n",
    "im=ax[1].imshow(stitched_amp_down,vmin=0.0605,vmax=0.105)\n",
    "plt.colorbar(im,ax=ax[1], fraction=0.046, pad=0.04)\n",
    "ax[1].axis('off')\n",
    "\n",
    "im=ax[2].imshow(stitched_amp_down-amp[:,:,32,32], vmin=-0.027,vmax=0.037)\n",
    "plt.colorbar(im,ax=ax[2], fraction=0.046, pad=0.04)\n",
    "ax[2].axis('off')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T21:07:04.881634Z",
     "start_time": "2020-06-15T21:07:04.334515Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5, 59.5, 59.5, -0.5)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,3,figsize=(20,10))\n",
    "\n",
    "im=ax[0].imshow(ph[:,:,32,32])#,vmin=minc, vmax=maxc)\n",
    "plt.colorbar(im,ax=ax[0], fraction=0.046, pad=0.04)\n",
    "ax[0].axis('off')\n",
    "\n",
    "im=ax[1].imshow(stitched_phase_down,vmin=-1.4, vmax=1.5)\n",
    "plt.colorbar(im,ax=ax[1], fraction=0.046, pad=0.04)\n",
    "ax[1].axis('off')\n",
    "\n",
    "im=ax[2].imshow(stitched_phase_down-ph[:,:,32,32],vmin=-1.35, vmax=1.2)\n",
    "plt.colorbar(im,ax=ax[2], fraction=0.046, pad=0.04)\n",
    "ax[2].axis('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T18:44:58.356993Z",
     "start_time": "2020-06-15T18:44:58.352585Z"
    }
   },
   "source": [
    "### Quantify error between phase retrieval and PtychoNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T21:07:04.890113Z",
     "start_time": "2020-06-15T21:07:04.883863Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE in amplitude:  5.150312583645702e-05\n",
      "MSE in phase:  0.10273966426298471\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error as mse\n",
    "print (\"MSE in amplitude: \", mse(stitched_amp_down, amp[:,:,32,32]))\n",
    "print (\"MSE in phase: \", mse(stitched_phase_down, ph[:,:,32,32]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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