Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "# import tensorflow as tf\n",
    "import os\n",
    "from keras.preprocessing.image import img_to_array, ImageDataGenerator\n",
    "from keras.utils import to_categorical\n",
    "from keras.models import Sequential\n",
    "from keras.layers.convolutional import Conv2D, MaxPooling2D\n",
    "from keras.layers.core import Dense, Dropout, Flatten, Activation\n",
    "from keras.layers import BatchNormalization\n",
    "from keras.regularizers import l2\n",
    "from keras.optimizers import Adam\n",
    "from keras.initializers import glorot_normal\n",
    "from keras import backend as K\n",
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from plotly import __version__\n",
    "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
    "import plotly.graph_objs as go\n",
    "init_notebook_mode(connected=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading eastern russell's viper\n",
      "80 loaded\n",
      "\n",
      "loading Malayan Krait\n",
      "77 loaded\n",
      "\n",
      "loading Malayan Pit Viper\n",
      "72 loaded\n",
      "\n",
      "loading Sumatra Spitting Cobra\n",
      "91 loaded\n",
      "\n",
      "loading Whtie-lipped pit Viper\n",
      "95 loaded\n",
      "\n"
     ]
    }
   ],
   "source": [
    "data_path = \"E:\\Isa\\kehidupan\\dataset_focused\"\n",
    "specieces = os.listdir(data_path)\n",
    "data = []\n",
    "label = []\n",
    "counts = []\n",
    "for i, path in enumerate(specieces):\n",
    "    count = 0\n",
    "    print(\"loading\",path)\n",
    "    for file in os.listdir(\"%s\\%s\"%(data_path,path)):\n",
    "        image = cv2.imread(\"%s\\%s\\%s\"%(data_path,path,file))\n",
    "        image = cv2.resize(image, (250, 250))\n",
    "        image = img_to_array(image)\n",
    "        data.append(image)\n",
    "        label.append(i)\n",
    "        count += 1\n",
    "    counts.append(count)\n",
    "    print (count, \"loaded\\n\")\n",
    "\n",
    "# plt.figure()\n",
    "# plt.plot(specieces,counts)\n",
    "# plt.show()\n",
    "\n",
    "data = np.array(data)/255.0\n",
    "labes = np.array(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "labes = to_categorical(labes, num_classes=len(specieces))\n",
    "(trainX, testX, trainY, testY) = train_test_split(data, labes, test_size = 0.25, shuffle = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "aug = ImageDataGenerator(rotation_range=30, width_shift_range=0.2,height_shift_range=0.2, shear_range=0.2, zoom_range=0.1, horizontal_flip=True, fill_mode='nearest')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class LeNet:\n",
    "    @staticmethod\n",
    "    def build(w, h, d, classes):\n",
    "        model = Sequential()\n",
    "        input_shape = (h, w, d)\n",
    "        \n",
    "        if K.image_data_format() == \"channels_first\":\n",
    "            input_shape = (h, w, d)\n",
    "            \n",
    "        model.add(Conv2D(35, (5, 5), padding=\"same\", input_shape = input_shape, kernel_initializer=glorot_normal()))\n",
    "        model.add(Activation('relu'))\n",
    "        \n",
    "        model.add(Conv2D(40, (5, 5), padding=\"same\", kernel_initializer=glorot_normal()))\n",
    "        model.add(Activation('relu'))\n",
    "        \n",
    "        model.add(Conv2D(75, (3, 3), padding=\"same\", kernel_initializer=glorot_normal()))\n",
    "        model.add(Activation('relu'))\n",
    "        \n",
    "        model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))\n",
    "        \n",
    "        model.add(Conv2D(40, (3, 3), padding=\"same\", kernel_initializer=glorot_normal()))\n",
    "        model.add(Activation('relu'))\n",
    "        \n",
    "        model.add(Conv2D(40, (3, 3), padding=\"same\", kernel_initializer=glorot_normal()))\n",
    "        model.add(Activation('relu'))\n",
    "        \n",
    "        model.add(Conv2D(40, (3, 3), padding=\"same\", kernel_initializer=glorot_normal()))\n",
    "        model.add(Activation('relu'))\n",
    "        \n",
    "        model.add(Conv2D(40, (3, 3), padding=\"same\", kernel_initializer=glorot_normal()))\n",
    "        model.add(Activation('relu'))\n",
    "        \n",
    "        model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))\n",
    "        \n",
    "        model.add(Conv2D(40, (3, 3), padding=\"same\", kernel_initializer=glorot_normal()))\n",
    "        model.add(Activation('relu'))\n",
    "        \n",
    "        model.add(Conv2D(40, (3, 3), padding=\"same\", kernel_initializer=glorot_normal()))\n",
    "        model.add(Activation('relu'))\n",
    "        \n",
    "        model.add(Conv2D(40, (3, 3), padding=\"same\", kernel_initializer=glorot_normal()))\n",
    "        model.add(Activation('relu'))\n",
    "        \n",
    "        model.add(Conv2D(40, (3, 3), padding=\"same\", kernel_initializer=glorot_normal()))\n",
    "        model.add(Activation('relu'))\n",
    "        \n",
    "        model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))\n",
    "        \n",
    "#         model.add(Conv2D(10, (3, 3), padding=\"same\", kernel_initializer=glorot_normal()))\n",
    "#         model.add(Activation('relu'))\n",
    "#         model.add(MaxPooling2D(pool_size=(3,3), strides=(2,2)))\n",
    "        \n",
    "        model.add(Flatten())\n",
    "#         model.add(Dropout(0.1))\n",
    "        model.add(Dense(70,kernel_initializer=glorot_normal()))\n",
    "        model.add(Activation(\"relu\"))\n",
    "        \n",
    "#         model.add(Dropout(0.3))\n",
    "        model.add(Dense(50, kernel_initializer=glorot_normal()))\n",
    "        model.add(Activation(\"relu\"))\n",
    "        \n",
    "#         model.add(Dropout(0.4))\n",
    "        model.add(Dense(50,kernel_initializer=glorot_normal()))\n",
    "        model.add(Activation(\"relu\"))\n",
    "        \n",
    "        model.add(Dense(classes))\n",
    "        model.add(Activation(\"softmax\"))\n",
    "        \n",
    "        return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 250, 250, 35)      2660      \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 250, 250, 35)      0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 250, 250, 40)      35040     \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 250, 250, 40)      0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 250, 250, 75)      27075     \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 250, 250, 75)      0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 125, 125, 75)      0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 125, 125, 40)      27040     \n",
      "_________________________________________________________________\n",
      "activation_4 (Activation)    (None, 125, 125, 40)      0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 125, 125, 40)      14440     \n",
      "_________________________________________________________________\n",
      "activation_5 (Activation)    (None, 125, 125, 40)      0         \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 125, 125, 40)      14440     \n",
      "_________________________________________________________________\n",
      "activation_6 (Activation)    (None, 125, 125, 40)      0         \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 125, 125, 40)      14440     \n",
      "_________________________________________________________________\n",
      "activation_7 (Activation)    (None, 125, 125, 40)      0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 62, 62, 40)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_8 (Conv2D)            (None, 62, 62, 40)        14440     \n",
      "_________________________________________________________________\n",
      "activation_8 (Activation)    (None, 62, 62, 40)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_9 (Conv2D)            (None, 62, 62, 40)        14440     \n",
      "_________________________________________________________________\n",
      "activation_9 (Activation)    (None, 62, 62, 40)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_10 (Conv2D)           (None, 62, 62, 40)        14440     \n",
      "_________________________________________________________________\n",
      "activation_10 (Activation)   (None, 62, 62, 40)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_11 (Conv2D)           (None, 62, 62, 40)        14440     \n",
      "_________________________________________________________________\n",
      "activation_11 (Activation)   (None, 62, 62, 40)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 31, 31, 40)        0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 38440)             0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 70)                2690870   \n",
      "_________________________________________________________________\n",
      "activation_12 (Activation)   (None, 70)                0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 50)                3550      \n",
      "_________________________________________________________________\n",
      "activation_13 (Activation)   (None, 50)                0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 50)                2550      \n",
      "_________________________________________________________________\n",
      "activation_14 (Activation)   (None, 50)                0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 5)                 255       \n",
      "_________________________________________________________________\n",
      "activation_15 (Activation)   (None, 5)                 0         \n",
      "=================================================================\n",
      "Total params: 2,890,120\n",
      "Trainable params: 2,890,120\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "epoch = 50\n",
    "learning_rate = 1e-6\n",
    "batch_size = 20\n",
    "\n",
    "# model = vgg16.VGG16(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000)\n",
    "model = LeNet.build(250, 250, 3, len(specieces))\n",
    "opt = Adam(lr=learning_rate, decay=learning_rate/epoch)\n",
    "model.compile(loss=\"categorical_crossentropy\", optimizer=opt, metrics=['accuracy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Variable *= will be deprecated. Use variable.assign_mul if you want assignment to the variable value or 'x = x * y' if you want a new python Tensor object.\n",
      "Train on 311 samples, validate on 104 samples\n",
      "Epoch 1/50\n"
     ]
    },
    {
     "ename": "ResourceExhaustedError",
     "evalue": "OOM when allocating tensor with shape[20,75,250,250] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc\n\t [[Node: conv2d_3/convolution = Conv2D[T=DT_FLOAT, data_format=\"NCHW\", dilations=[1, 1, 1, 1], padding=\"SAME\", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true, _device=\"/job:localhost/replica:0/task:0/device:GPU:0\"](activation_2/Relu, conv2d_3/kernel/read)]]\nHint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.\n\n\t [[Node: loss/mul/_307 = _Recv[client_terminated=false, recv_device=\"/job:localhost/replica:0/task:0/device:CPU:0\", send_device=\"/job:localhost/replica:0/task:0/device:GPU:0\", send_device_incarnation=1, tensor_name=\"edge_1759_loss/mul\", tensor_type=DT_FLOAT, _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"]()]]\nHint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.\n\n\nCaused by op 'conv2d_3/convolution', defined at:\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\runpy.py\", line 193, in _run_module_as_main\n    \"__main__\", mod_spec)\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\runpy.py\", line 85, in _run_code\n    exec(code, run_globals)\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py\", line 16, in <module>\n    app.launch_new_instance()\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\traitlets\\config\\application.py\", line 658, in launch_instance\n    app.start()\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\ipykernel\\kernelapp.py\", line 486, in start\n    self.io_loop.start()\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\tornado\\platform\\asyncio.py\", line 112, in start\n    self.asyncio_loop.run_forever()\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\asyncio\\base_events.py\", line 421, in run_forever\n    self._run_once()\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\asyncio\\base_events.py\", line 1425, in _run_once\n    handle._run()\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\asyncio\\events.py\", line 127, in _run\n    self._callback(*self._args)\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\tornado\\platform\\asyncio.py\", line 102, in _handle_events\n    handler_func(fileobj, events)\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\tornado\\stack_context.py\", line 276, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 450, in _handle_events\n    self._handle_recv()\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 480, in _handle_recv\n    self._run_callback(callback, msg)\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 432, in _run_callback\n    callback(*args, **kwargs)\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\tornado\\stack_context.py\", line 276, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 283, in dispatcher\n    return self.dispatch_shell(stream, msg)\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 233, in dispatch_shell\n    handler(stream, idents, msg)\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 399, in execute_request\n    user_expressions, allow_stdin)\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\ipykernel\\ipkernel.py\", line 208, in do_execute\n    res = shell.run_cell(code, store_history=store_history, silent=silent)\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\ipykernel\\zmqshell.py\", line 537, in run_cell\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2662, in run_cell\n    raw_cell, store_history, silent, shell_futures)\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2785, in _run_cell\n    interactivity=interactivity, compiler=compiler, result=result)\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2903, in run_ast_nodes\n    if self.run_code(code, result):\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2963, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)\n  File \"<ipython-input-7-17636a51af5d>\", line 6, in <module>\n    model = LeNet.build(250, 250, 3, len(specieces))\n  File \"<ipython-input-6-fad2fdf977cb>\", line 16, in build\n    model.add(Conv2D(75, (3, 3), padding=\"same\", kernel_initializer=glorot_normal()))\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\keras\\models.py\", line 492, in add\n    output_tensor = layer(self.outputs[0])\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\keras\\engine\\topology.py\", line 619, in __call__\n    output = self.call(inputs, **kwargs)\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\keras\\layers\\convolutional.py\", line 168, in call\n    dilation_rate=self.dilation_rate)\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py\", line 3335, in conv2d\n    data_format=tf_data_format)\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\ops\\nn_ops.py\", line 782, in convolution\n    return op(input, filter)\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\ops\\nn_ops.py\", line 870, in __call__\n    return self.conv_op(inp, filter)\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\ops\\nn_ops.py\", line 522, in __call__\n    return self.call(inp, filter)\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\ops\\nn_ops.py\", line 206, in __call__\n    name=self.name)\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\ops\\gen_nn_ops.py\", line 1039, in conv2d\n    data_format=data_format, dilations=dilations, name=name)\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py\", line 787, in _apply_op_helper\n    op_def=op_def)\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 3290, in create_op\n    op_def=op_def)\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 1654, in __init__\n    self._traceback = self._graph._extract_stack()  # pylint: disable=protected-access\n\nResourceExhaustedError (see above for traceback): OOM when allocating tensor with shape[20,75,250,250] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc\n\t [[Node: conv2d_3/convolution = Conv2D[T=DT_FLOAT, data_format=\"NCHW\", dilations=[1, 1, 1, 1], padding=\"SAME\", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true, _device=\"/job:localhost/replica:0/task:0/device:GPU:0\"](activation_2/Relu, conv2d_3/kernel/read)]]\nHint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.\n\n\t [[Node: loss/mul/_307 = _Recv[client_terminated=false, recv_device=\"/job:localhost/replica:0/task:0/device:CPU:0\", send_device=\"/job:localhost/replica:0/task:0/device:GPU:0\", send_device_incarnation=1, tensor_name=\"edge_1759_loss/mul\", tensor_type=DT_FLOAT, _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"]()]]\nHint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.\n\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mResourceExhaustedError\u001b[0m                    Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_call\u001b[1;34m(self, fn, *args)\u001b[0m\n\u001b[0;32m   1326\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1327\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1328\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[1;34m(feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[0;32m   1311\u001b[0m       return self._call_tf_sessionrun(\n\u001b[1;32m-> 1312\u001b[1;33m           options, feed_dict, fetch_list, target_list, run_metadata)\n\u001b[0m\u001b[0;32m   1313\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_call_tf_sessionrun\u001b[1;34m(self, options, feed_dict, fetch_list, target_list, run_metadata)\u001b[0m\n\u001b[0;32m   1419\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1420\u001b[1;33m             status, run_metadata)\n\u001b[0m\u001b[0;32m   1421\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\framework\\errors_impl.py\u001b[0m in \u001b[0;36m__exit__\u001b[1;34m(self, type_arg, value_arg, traceback_arg)\u001b[0m\n\u001b[0;32m    515\u001b[0m             \u001b[0mcompat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc_api\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_Message\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstatus\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstatus\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 516\u001b[1;33m             c_api.TF_GetCode(self.status.status))\n\u001b[0m\u001b[0;32m    517\u001b[0m     \u001b[1;31m# Delete the underlying status object from memory otherwise it stays alive\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mResourceExhaustedError\u001b[0m: OOM when allocating tensor with shape[20,75,250,250] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc\n\t [[Node: conv2d_3/convolution = Conv2D[T=DT_FLOAT, data_format=\"NCHW\", dilations=[1, 1, 1, 1], padding=\"SAME\", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true, _device=\"/job:localhost/replica:0/task:0/device:GPU:0\"](activation_2/Relu, conv2d_3/kernel/read)]]\nHint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.\n\n\t [[Node: loss/mul/_307 = _Recv[client_terminated=false, recv_device=\"/job:localhost/replica:0/task:0/device:CPU:0\", send_device=\"/job:localhost/replica:0/task:0/device:GPU:0\", send_device_incarnation=1, tensor_name=\"edge_1759_loss/mul\", tensor_type=DT_FLOAT, _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"]()]]\nHint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.\n",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mResourceExhaustedError\u001b[0m                    Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-8-44c4ccbc64e5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mh\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtrainX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtrainY\u001b[0m\u001b[1;33m,\u001b[0m  \u001b[0mbatch_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mepoch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtestX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtestY\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\keras\\models.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[0;32m    961\u001b[0m                               \u001b[0minitial_epoch\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    962\u001b[0m                               \u001b[0msteps_per_epoch\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 963\u001b[1;33m                               validation_steps=validation_steps)\n\u001b[0m\u001b[0;32m    964\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    965\u001b[0m     def evaluate(self, x=None, y=None,\n",
      "\u001b[1;32m~\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[0;32m   1703\u001b[0m                               \u001b[0minitial_epoch\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1704\u001b[0m                               \u001b[0msteps_per_epoch\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1705\u001b[1;33m                               validation_steps=validation_steps)\n\u001b[0m\u001b[0;32m   1706\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1707\u001b[0m     def evaluate(self, x=None, y=None,\n",
      "\u001b[1;32m~\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36m_fit_loop\u001b[1;34m(self, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)\u001b[0m\n\u001b[0;32m   1233\u001b[0m                         \u001b[0mins_batch\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mins_batch\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1234\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1235\u001b[1;33m                     \u001b[0mouts\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1236\u001b[0m                     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1237\u001b[0m                         \u001b[0mouts\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mouts\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, inputs)\u001b[0m\n\u001b[0;32m   2476\u001b[0m         \u001b[0msession\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_session\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2477\u001b[0m         updated = session.run(fetches=fetches, feed_dict=feed_dict,\n\u001b[1;32m-> 2478\u001b[1;33m                               **self.session_kwargs)\n\u001b[0m\u001b[0;32m   2479\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mupdated\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2480\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m    903\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    904\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[1;32m--> 905\u001b[1;33m                          run_metadata_ptr)\n\u001b[0m\u001b[0;32m    906\u001b[0m       \u001b[1;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    907\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_run\u001b[1;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m   1138\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1139\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[1;32m-> 1140\u001b[1;33m                              feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[0;32m   1141\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1142\u001b[0m       \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_run\u001b[1;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m   1319\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1320\u001b[0m       return self._do_call(_run_fn, feeds, fetches, targets, options,\n\u001b[1;32m-> 1321\u001b[1;33m                            run_metadata)\n\u001b[0m\u001b[0;32m   1322\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1323\u001b[0m       \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_call\u001b[1;34m(self, fn, *args)\u001b[0m\n\u001b[0;32m   1338\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1339\u001b[0m           \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1340\u001b[1;33m       \u001b[1;32mraise\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnode_def\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mop\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1341\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1342\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_extend_graph\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mResourceExhaustedError\u001b[0m: OOM when allocating tensor with shape[20,75,250,250] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc\n\t [[Node: conv2d_3/convolution = Conv2D[T=DT_FLOAT, data_format=\"NCHW\", dilations=[1, 1, 1, 1], padding=\"SAME\", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true, _device=\"/job:localhost/replica:0/task:0/device:GPU:0\"](activation_2/Relu, conv2d_3/kernel/read)]]\nHint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.\n\n\t [[Node: loss/mul/_307 = _Recv[client_terminated=false, recv_device=\"/job:localhost/replica:0/task:0/device:CPU:0\", send_device=\"/job:localhost/replica:0/task:0/device:GPU:0\", send_device_incarnation=1, tensor_name=\"edge_1759_loss/mul\", tensor_type=DT_FLOAT, _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"]()]]\nHint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.\n\n\nCaused by op 'conv2d_3/convolution', defined at:\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\runpy.py\", line 193, in _run_module_as_main\n    \"__main__\", mod_spec)\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\runpy.py\", line 85, in _run_code\n    exec(code, run_globals)\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py\", line 16, in <module>\n    app.launch_new_instance()\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\traitlets\\config\\application.py\", line 658, in launch_instance\n    app.start()\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\ipykernel\\kernelapp.py\", line 486, in start\n    self.io_loop.start()\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\tornado\\platform\\asyncio.py\", line 112, in start\n    self.asyncio_loop.run_forever()\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\asyncio\\base_events.py\", line 421, in run_forever\n    self._run_once()\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\asyncio\\base_events.py\", line 1425, in _run_once\n    handle._run()\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\asyncio\\events.py\", line 127, in _run\n    self._callback(*self._args)\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\tornado\\platform\\asyncio.py\", line 102, in _handle_events\n    handler_func(fileobj, events)\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\tornado\\stack_context.py\", line 276, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 450, in _handle_events\n    self._handle_recv()\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 480, in _handle_recv\n    self._run_callback(callback, msg)\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 432, in _run_callback\n    callback(*args, **kwargs)\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\tornado\\stack_context.py\", line 276, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 283, in dispatcher\n    return self.dispatch_shell(stream, msg)\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 233, in dispatch_shell\n    handler(stream, idents, msg)\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 399, in execute_request\n    user_expressions, allow_stdin)\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\ipykernel\\ipkernel.py\", line 208, in do_execute\n    res = shell.run_cell(code, store_history=store_history, silent=silent)\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\ipykernel\\zmqshell.py\", line 537, in run_cell\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2662, in run_cell\n    raw_cell, store_history, silent, shell_futures)\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2785, in _run_cell\n    interactivity=interactivity, compiler=compiler, result=result)\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2903, in run_ast_nodes\n    if self.run_code(code, result):\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2963, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)\n  File \"<ipython-input-7-17636a51af5d>\", line 6, in <module>\n    model = LeNet.build(250, 250, 3, len(specieces))\n  File \"<ipython-input-6-fad2fdf977cb>\", line 16, in build\n    model.add(Conv2D(75, (3, 3), padding=\"same\", kernel_initializer=glorot_normal()))\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\keras\\models.py\", line 492, in add\n    output_tensor = layer(self.outputs[0])\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\keras\\engine\\topology.py\", line 619, in __call__\n    output = self.call(inputs, **kwargs)\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\keras\\layers\\convolutional.py\", line 168, in call\n    dilation_rate=self.dilation_rate)\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py\", line 3335, in conv2d\n    data_format=tf_data_format)\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\ops\\nn_ops.py\", line 782, in convolution\n    return op(input, filter)\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\ops\\nn_ops.py\", line 870, in __call__\n    return self.conv_op(inp, filter)\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\ops\\nn_ops.py\", line 522, in __call__\n    return self.call(inp, filter)\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\ops\\nn_ops.py\", line 206, in __call__\n    name=self.name)\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\ops\\gen_nn_ops.py\", line 1039, in conv2d\n    data_format=data_format, dilations=dilations, name=name)\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py\", line 787, in _apply_op_helper\n    op_def=op_def)\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 3290, in create_op\n    op_def=op_def)\n  File \"C:\\Users\\Computing Laboratory\\AppData\\Local\\conda\\conda\\envs\\tensorflow\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 1654, in __init__\n    self._traceback = self._graph._extract_stack()  # pylint: disable=protected-access\n\nResourceExhaustedError (see above for traceback): OOM when allocating tensor with shape[20,75,250,250] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc\n\t [[Node: conv2d_3/convolution = Conv2D[T=DT_FLOAT, data_format=\"NCHW\", dilations=[1, 1, 1, 1], padding=\"SAME\", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true, _device=\"/job:localhost/replica:0/task:0/device:GPU:0\"](activation_2/Relu, conv2d_3/kernel/read)]]\nHint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.\n\n\t [[Node: loss/mul/_307 = _Recv[client_terminated=false, recv_device=\"/job:localhost/replica:0/task:0/device:CPU:0\", send_device=\"/job:localhost/replica:0/task:0/device:GPU:0\", send_device_incarnation=1, tensor_name=\"edge_1759_loss/mul\", tensor_type=DT_FLOAT, _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"]()]]\nHint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.\n\n"
     ]
    }
   ],
   "source": [
    "h = model.fit(x = trainX, y = trainY,  batch_size=batch_size, epochs=epoch, verbose=1, validation_data=(testX, testY))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "trace1 = go.Scatter(name = \"train loss\", x = np.arange(0, epoch), y = h.history[\"loss\"])\n",
    "trace2 = go.Scatter(name = \"val loss\", x = np.arange(0, epoch), y = h.history[\"val_loss\"])\n",
    "trace3 = go.Scatter(name = \"train acc\", x = np.arange(0, epoch), y = h.history[\"acc\"])\n",
    "trace4 = go.Scatter(name = \"val acc\", x = np.arange(0, epoch), y = h.history[\"val_acc\"])\n",
    "iplot([trace1, trace2, trace3, trace4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.save(\"overfit.h5\")"
   ]
  }
 ],
 "metadata": {
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  "language_info": {
   "codemirror_mode": {
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