model_trial.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from keras.layers import Conv2D, Conv2DTranspose, Concatenate\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.layers import Conv2D, Conv2DTranspose, Concatenate, Activation\n",
    "\n",
    "def self_attention(x, filters):\n",
    "    input_shape = tf.shape(x)\n",
    "    height, width = input_shape[1], input_shape[2]\n",
    "\n",
    "    query = Conv2D(filters, 1)(x)\n",
    "    query = tf.reshape(query, [-1, height * width, filters])\n",
    "\n",
    "    key = Conv2D(filters, 1)(x)\n",
    "    key = tf.reshape(key, [-1, height * width, filters])\n",
    "\n",
    "    value = Conv2D(filters, 1)(x)\n",
    "    value = tf.reshape(value, [-1, height * width, filters])\n",
    "\n",
    "    attention = tf.keras.layers.Attention()([query, key, value])\n",
    "\n",
    "    attention = tf.reshape(attention, [-1, height, width, filters])\n",
    "    return attention\n",
    "\n",
    "def sidnet_with_attention():\n",
    "    inputs = tf.keras.Input(shape=(108, 160, 3), name='InputLayer')\n",
    "    conv1 = Conv2D(32, 3, activation='relu', padding='same', name='FirstConv')(inputs)\n",
    "    conv2 = Conv2D(64, 3, activation='relu', padding='same', strides=2, name='SecondConv')(conv1)\n",
    "    conv3 = Conv2D(128, 3, activation='relu', padding='same', strides=2, name='ThirdConv')(conv2)\n",
    "\n",
    "    attention = self_attention(conv3, 256)\n",
    "\n",
    "    deconv1 = Conv2DTranspose(128, 3, activation='relu', padding='same', strides=2, name='FirstDeConv')(attention)\n",
    "    skip1 = Concatenate()([deconv1, conv2])\n",
    "    deconv2 = Conv2DTranspose(64, 3, activation='relu', padding='same', strides=2, name='SecondDeConv')(skip1)\n",
    "    skip2 = Concatenate()([deconv2, conv1])\n",
    "    deconv2 = Conv2DTranspose(32, 3, activation='relu', padding='same', strides=2, name='SecondDeConv')(skip2)\n",
    "    output = Conv2D(3, 3, activation='sigmoid', padding='same', name='OutputLayer')(deconv2)\n",
    "\n",
    "    model = tf.keras.Model(inputs=inputs, outputs=output, name='DHaRT')\n",
    "    return model\n",
    "\n",
    "model = sidnet_with_attention()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "def self_attention(x, filters):\n",
    "    # Get the shape of the input tensor\n",
    "    input_shape = tf.shape(x)\n",
    "    height, width = input_shape[1], input_shape[2]\n",
    "    print(height,width)\n",
    "\n",
    "    # Reshape query, key, and value tensors\n",
    "    query = Conv2D(filters // 8, 1)(x)\n",
    "    query = tf.reshape(query, [-1, height * width, filters // 8])\n",
    "\n",
    "    key = Conv2D(filters // 8, 1)(x)\n",
    "    key = tf.reshape(key, [-1, height * width, filters // 8])\n",
    "\n",
    "    value = Conv2D(filters, 1)(x)\n",
    "    value = tf.reshape(value, [-1, height * width,  filters // 8],name='reshaping_here')\n",
    "\n",
    "    # Apply attention mechanism\n",
    "    attention = tf.keras.layers.Attention(name='Attention_layer')([query, key, value])\n",
    "\n",
    "    # Reshape attention tensor\n",
    "    attention = tf.reshape(attention, [-1, height, width, filters])\n",
    "    return attention\n",
    "\n",
    "def sidnet_with_attention():\n",
    "    # Encoder\n",
    "    inputs = tf.keras.Input(shape=(460, 620,3),name='InputLayer')\n",
    "    conv1 = Conv2D(64, 3, activation='relu', padding='same',name='FirstConv')(inputs)\n",
    "    conv2 = Conv2D(128, 3, activation='relu', padding='same', strides=2,name='SecondConv')(conv1)\n",
    "    conv3 = Conv2D(256, 3, activation='relu', padding='same', strides=2,name='ThirdConv')(conv2)\n",
    "\n",
    "    # Self-attention applied to the encoder output\n",
    "    attention = self_attention(conv3, 256)\n",
    "\n",
    "    # Decoder\n",
    "    deconv1 = Conv2DTranspose(128, 3, activation='relu', padding='same', strides=2,name='FirstDeConv')(attention)\n",
    "    skip1 = Concatenate(name='FirstSkip')([deconv1, conv2])\n",
    "    deconv2 = Conv2DTranspose(64, 3, activation='relu', padding='same', strides=2,name='SecondDeConv')(skip1)\n",
    "    skip2 = Concatenate(name='SecondSkip')([deconv2, conv1])\n",
    "    output = Conv2D(3, 3, activation='sigmoid', padding='same',name='OutputLayer')(skip2)\n",
    "\n",
    "    model = tf.keras.Model(inputs=inputs, outputs=output,name='DHaRT')\n",
    "    return model\n",
    "\n",
    "# Create an instance of the SIDNet model with attention\n",
    "model = sidnet_with_attention()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keras.utils.plot_model(model,show_shapes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"DHaRT\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " InputLayer (InputLayer)        [(None, 108, 160, 3  0           []                               \n",
      "                                )]                                                                \n",
      "                                                                                                  \n",
      " FirstConv (Conv2D)             (None, 108, 160, 32  896         ['InputLayer[0][0]']             \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " SecondConv (Conv2D)            (None, 54, 80, 64)   18496       ['FirstConv[0][0]']              \n",
      "                                                                                                  \n",
      " ThirdConv (Conv2D)             (None, 27, 40, 128)  73856       ['SecondConv[0][0]']             \n",
      "                                                                                                  \n",
      " tf.compat.v1.shape_11 (TFOpLam  (4,)                0           ['ThirdConv[0][0]']              \n",
      " bda)                                                                                             \n",
      "                                                                                                  \n",
      " tf.__operators__.getitem_22 (S  ()                  0           ['tf.compat.v1.shape_11[0][0]']  \n",
      " licingOpLambda)                                                                                  \n",
      "                                                                                                  \n",
      " tf.__operators__.getitem_23 (S  ()                  0           ['tf.compat.v1.shape_11[0][0]']  \n",
      " licingOpLambda)                                                                                  \n",
      "                                                                                                  \n",
      " conv2d_33 (Conv2D)             (None, 27, 40, 256)  33024       ['ThirdConv[0][0]']              \n",
      "                                                                                                  \n",
      " tf.math.multiply_33 (TFOpLambd  ()                  0           ['tf.__operators__.getitem_22[0][\n",
      " a)                                                              0]',                             \n",
      "                                                                  'tf.__operators__.getitem_23[0][\n",
      "                                                                 0]']                             \n",
      "                                                                                                  \n",
      " conv2d_34 (Conv2D)             (None, 27, 40, 256)  33024       ['ThirdConv[0][0]']              \n",
      "                                                                                                  \n",
      " tf.math.multiply_34 (TFOpLambd  ()                  0           ['tf.__operators__.getitem_22[0][\n",
      " a)                                                              0]',                             \n",
      "                                                                  'tf.__operators__.getitem_23[0][\n",
      "                                                                 0]']                             \n",
      "                                                                                                  \n",
      " conv2d_35 (Conv2D)             (None, 27, 40, 256)  33024       ['ThirdConv[0][0]']              \n",
      "                                                                                                  \n",
      " tf.math.multiply_35 (TFOpLambd  ()                  0           ['tf.__operators__.getitem_22[0][\n",
      " a)                                                              0]',                             \n",
      "                                                                  'tf.__operators__.getitem_23[0][\n",
      "                                                                 0]']                             \n",
      "                                                                                                  \n",
      " tf.reshape_42 (TFOpLambda)     (None, 1080, 256)    0           ['conv2d_33[0][0]',              \n",
      "                                                                  'tf.math.multiply_33[0][0]']    \n",
      "                                                                                                  \n",
      " tf.reshape_43 (TFOpLambda)     (None, 1080, 256)    0           ['conv2d_34[0][0]',              \n",
      "                                                                  'tf.math.multiply_34[0][0]']    \n",
      "                                                                                                  \n",
      " tf.reshape_44 (TFOpLambda)     (None, 1080, 256)    0           ['conv2d_35[0][0]',              \n",
      "                                                                  'tf.math.multiply_35[0][0]']    \n",
      "                                                                                                  \n",
      " attention_6 (Attention)        (None, 1080, 256)    0           ['tf.reshape_42[0][0]',          \n",
      "                                                                  'tf.reshape_43[0][0]',          \n",
      "                                                                  'tf.reshape_44[0][0]']          \n",
      "                                                                                                  \n",
      " tf.reshape_45 (TFOpLambda)     (None, 27, 40, 256)  0           ['attention_6[0][0]',            \n",
      "                                                                  'tf.__operators__.getitem_22[0][\n",
      "                                                                 0]',                             \n",
      "                                                                  'tf.__operators__.getitem_23[0][\n",
      "                                                                 0]']                             \n",
      "                                                                                                  \n",
      " FirstDeConv (Conv2DTranspose)  (None, 54, 80, 128)  295040      ['tf.reshape_45[0][0]']          \n",
      "                                                                                                  \n",
      " concatenate_8 (Concatenate)    (None, 54, 80, 192)  0           ['FirstDeConv[0][0]',            \n",
      "                                                                  'SecondConv[0][0]']             \n",
      "                                                                                                  \n",
      " SecondDeConv (Conv2DTranspose)  (None, 108, 160, 64  110656     ['concatenate_8[0][0]']          \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " concatenate_9 (Concatenate)    (None, 108, 160, 96  0           ['SecondDeConv[0][0]',           \n",
      "                                )                                 'FirstConv[0][0]']              \n",
      "                                                                                                  \n",
      " ThirdDeConv (Conv2DTranspose)  (None, 216, 320, 32  27680       ['concatenate_9[0][0]']          \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " OutputLayer (Conv2D)           (None, 216, 320, 3)  867         ['ThirdDeConv[0][0]']            \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 626,563\n",
      "Trainable params: 626,563\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam', loss='mean_squared_error',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 500 images belonging to 1 classes.\n",
      "Found 500 images belonging to 1 classes.\n"
     ]
    }
   ],
   "source": [
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "path=os.getcwd()\n",
    "\n",
    "# Set the paths to the directories\n",
    "hazy_dir = os.path.join(path,'indoor','hazy')\n",
    "clear_dir = os.path.join(path,'indoor','clear')\n",
    "\n",
    "# Calculate the number of steps per epoch\n",
    "batch_size = 32\n",
    "hazy_images = len(os.listdir(hazy_dir))\n",
    "clear_images = len(os.listdir(clear_dir))\n",
    "steps_per_epoch = clear_images // batch_size\n",
    "\n",
    "# Create the ImageDataGenerator\n",
    "datagen = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "# Load the images and create the dataset\n",
    "train_generator = datagen.flow_from_directory(directory=hazy_dir,\n",
    "                                              target_size=(620, 460),\n",
    "                                              batch_size=batch_size,\n",
    "                                              class_mode=None,\n",
    "                                              shuffle=False\n",
    "                                              )\n",
    "\n",
    "train_labels_generator = datagen.flow_from_directory(directory=clear_dir,\n",
    "                                                     target_size=(620, 460),\n",
    "                                                     batch_size=batch_size,\n",
    "                                                     class_mode=None,\n",
    "                                                     shuffle=False\n",
    "                                                     )\n",
    "\n",
    "# Combine the hazy and clear images into a dataset\n",
    "train_dataset = zip(train_generator, train_labels_generator)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2\n"
     ]
    },
    {
     "ename": "ResourceExhaustedError",
     "evalue": "Graph execution error:\n\nDetected at node 'DHaRT/attention_3/MatMul' defined at (most recent call last):\n    File \"c:\\Users\\user\\anaconda3\\lib\\runpy.py\", line 197, in _run_module_as_main\n      return _run_code(code, main_globals, None,\n    File \"c:\\Users\\user\\anaconda3\\lib\\runpy.py\", line 87, in _run_code\n      exec(code, run_globals)\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py\", line 17, in <module>\n      app.launch_new_instance()\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\traitlets\\config\\application.py\", line 846, in launch_instance\n      app.start()\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\ipykernel\\kernelapp.py\", line 712, in start\n      self.io_loop.start()\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\tornado\\platform\\asyncio.py\", line 199, in start\n      self.asyncio_loop.run_forever()\n    File \"c:\\Users\\user\\anaconda3\\lib\\asyncio\\base_events.py\", line 601, in run_forever\n      self._run_once()\n    File \"c:\\Users\\user\\anaconda3\\lib\\asyncio\\base_events.py\", line 1905, in _run_once\n      handle._run()\n    File \"c:\\Users\\user\\anaconda3\\lib\\asyncio\\events.py\", line 80, in _run\n      self._context.run(self._callback, *self._args)\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 510, in dispatch_queue\n      await self.process_one()\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 499, in process_one\n      await dispatch(*args)\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 406, in dispatch_shell\n      await result\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 730, in execute_request\n      reply_content = await reply_content\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\ipykernel\\ipkernel.py\", line 390, in do_execute\n      res = shell.run_cell(code, store_history=store_history, silent=silent)\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\ipykernel\\zmqshell.py\", line 528, in run_cell\n      return super().run_cell(*args, **kwargs)\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2914, in run_cell\n      result = self._run_cell(\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2960, in _run_cell\n      return runner(coro)\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\IPython\\core\\async_helpers.py\", line 78, in _pseudo_sync_runner\n      coro.send(None)\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3185, in run_cell_async\n      has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3377, in run_ast_nodes\n      if (await self.run_code(code, result,  async_=asy)):\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3457, in run_code\n      exec(code_obj, self.user_global_ns, self.user_ns)\n    File \"C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_5692\\2197217471.py\", line 1, in <module>\n      model.fit(train_dataset,epochs=2)\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\keras\\utils\\traceback_utils.py\", line 65, in error_handler\n      return fn(*args, **kwargs)\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\keras\\engine\\training.py\", line 1650, in fit\n      tmp_logs = self.train_function(iterator)\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\keras\\engine\\training.py\", line 1249, in train_function\n      return step_function(self, iterator)\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\keras\\engine\\training.py\", line 1233, in step_function\n      outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\keras\\engine\\training.py\", line 1222, in run_step\n      outputs = model.train_step(data)\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\keras\\engine\\training.py\", line 1023, in train_step\n      y_pred = self(x, training=True)\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\keras\\utils\\traceback_utils.py\", line 65, in error_handler\n      return fn(*args, **kwargs)\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\keras\\engine\\training.py\", line 561, in __call__\n      return super().__call__(*args, **kwargs)\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\keras\\utils\\traceback_utils.py\", line 65, in error_handler\n      return fn(*args, **kwargs)\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\keras\\engine\\base_layer.py\", line 1132, in __call__\n      outputs = call_fn(inputs, *args, **kwargs)\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\keras\\utils\\traceback_utils.py\", line 96, in error_handler\n      return fn(*args, **kwargs)\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\keras\\engine\\functional.py\", line 511, in call\n      return self._run_internal_graph(inputs, training=training, mask=mask)\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\keras\\engine\\functional.py\", line 668, in _run_internal_graph\n      outputs = node.layer(*args, **kwargs)\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\keras\\utils\\traceback_utils.py\", line 65, in error_handler\n      return fn(*args, **kwargs)\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\keras\\engine\\base_layer.py\", line 1132, in __call__\n      outputs = call_fn(inputs, *args, **kwargs)\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\keras\\utils\\traceback_utils.py\", line 96, in error_handler\n      return fn(*args, **kwargs)\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\keras\\layers\\attention\\base_dense_attention.py\", line 165, in call\n      scores = self._calculate_scores(query=q, key=k)\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\keras\\layers\\attention\\attention.py\", line 181, in _calculate_scores\n      scores = tf.matmul(query, key, transpose_b=True)\nNode: 'DHaRT/attention_3/MatMul'\nOOM when allocating tensor with shape[32,17825,17825] and type float on /job:localhost/replica:0/task:0/device:CPU:0 by allocator cpu\n\t [[{{node DHaRT/attention_3/MatMul}}]]\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. This isn't available when running in Eager mode.\n [Op:__inference_train_function_12918]",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mResourceExhaustedError\u001b[0m                    Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_5692\\2197217471.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_dataset\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\Users\\user\\anaconda3\\lib\\site-packages\\keras\\utils\\traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     68\u001b[0m             \u001b[1;31m# To get the full stack trace, call:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     69\u001b[0m             \u001b[1;31m# `tf.debugging.disable_traceback_filtering()`\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 70\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     71\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     72\u001b[0m             \u001b[1;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\user\\anaconda3\\lib\\site-packages\\tensorflow\\python\\eager\\execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m     50\u001b[0m   \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     51\u001b[0m     \u001b[0mctx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mensure_initialized\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---> 52\u001b[1;33m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0m\u001b[0;32m     53\u001b[0m                                         inputs, attrs, num_outputs)\n\u001b[0;32m     54\u001b[0m   \u001b[1;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\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: Graph execution error:\n\nDetected at node 'DHaRT/attention_3/MatMul' defined at (most recent call last):\n    File \"c:\\Users\\user\\anaconda3\\lib\\runpy.py\", line 197, in _run_module_as_main\n      return _run_code(code, main_globals, None,\n    File \"c:\\Users\\user\\anaconda3\\lib\\runpy.py\", line 87, in _run_code\n      exec(code, run_globals)\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py\", line 17, in <module>\n      app.launch_new_instance()\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\traitlets\\config\\application.py\", line 846, in launch_instance\n      app.start()\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\ipykernel\\kernelapp.py\", line 712, in start\n      self.io_loop.start()\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\tornado\\platform\\asyncio.py\", line 199, in start\n      self.asyncio_loop.run_forever()\n    File \"c:\\Users\\user\\anaconda3\\lib\\asyncio\\base_events.py\", line 601, in run_forever\n      self._run_once()\n    File \"c:\\Users\\user\\anaconda3\\lib\\asyncio\\base_events.py\", line 1905, in _run_once\n      handle._run()\n    File \"c:\\Users\\user\\anaconda3\\lib\\asyncio\\events.py\", line 80, in _run\n      self._context.run(self._callback, *self._args)\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 510, in dispatch_queue\n      await self.process_one()\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 499, in process_one\n      await dispatch(*args)\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 406, in dispatch_shell\n      await result\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 730, in execute_request\n      reply_content = await reply_content\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\ipykernel\\ipkernel.py\", line 390, in do_execute\n      res = shell.run_cell(code, store_history=store_history, silent=silent)\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\ipykernel\\zmqshell.py\", line 528, in run_cell\n      return super().run_cell(*args, **kwargs)\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2914, in run_cell\n      result = self._run_cell(\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2960, in _run_cell\n      return runner(coro)\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\IPython\\core\\async_helpers.py\", line 78, in _pseudo_sync_runner\n      coro.send(None)\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3185, in run_cell_async\n      has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3377, in run_ast_nodes\n      if (await self.run_code(code, result,  async_=asy)):\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3457, in run_code\n      exec(code_obj, self.user_global_ns, self.user_ns)\n    File \"C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_5692\\2197217471.py\", line 1, in <module>\n      model.fit(train_dataset,epochs=2)\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\keras\\utils\\traceback_utils.py\", line 65, in error_handler\n      return fn(*args, **kwargs)\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\keras\\engine\\training.py\", line 1650, in fit\n      tmp_logs = self.train_function(iterator)\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\keras\\engine\\training.py\", line 1249, in train_function\n      return step_function(self, iterator)\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\keras\\engine\\training.py\", line 1233, in step_function\n      outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\keras\\engine\\training.py\", line 1222, in run_step\n      outputs = model.train_step(data)\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\keras\\engine\\training.py\", line 1023, in train_step\n      y_pred = self(x, training=True)\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\keras\\utils\\traceback_utils.py\", line 65, in error_handler\n      return fn(*args, **kwargs)\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\keras\\engine\\training.py\", line 561, in __call__\n      return super().__call__(*args, **kwargs)\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\keras\\utils\\traceback_utils.py\", line 65, in error_handler\n      return fn(*args, **kwargs)\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\keras\\engine\\base_layer.py\", line 1132, in __call__\n      outputs = call_fn(inputs, *args, **kwargs)\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\keras\\utils\\traceback_utils.py\", line 96, in error_handler\n      return fn(*args, **kwargs)\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\keras\\engine\\functional.py\", line 511, in call\n      return self._run_internal_graph(inputs, training=training, mask=mask)\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\keras\\engine\\functional.py\", line 668, in _run_internal_graph\n      outputs = node.layer(*args, **kwargs)\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\keras\\utils\\traceback_utils.py\", line 65, in error_handler\n      return fn(*args, **kwargs)\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\keras\\engine\\base_layer.py\", line 1132, in __call__\n      outputs = call_fn(inputs, *args, **kwargs)\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\keras\\utils\\traceback_utils.py\", line 96, in error_handler\n      return fn(*args, **kwargs)\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\keras\\layers\\attention\\base_dense_attention.py\", line 165, in call\n      scores = self._calculate_scores(query=q, key=k)\n    File \"c:\\Users\\user\\anaconda3\\lib\\site-packages\\keras\\layers\\attention\\attention.py\", line 181, in _calculate_scores\n      scores = tf.matmul(query, key, transpose_b=True)\nNode: 'DHaRT/attention_3/MatMul'\nOOM when allocating tensor with shape[32,17825,17825] and type float on /job:localhost/replica:0/task:0/device:CPU:0 by allocator cpu\n\t [[{{node DHaRT/attention_3/MatMul}}]]\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. This isn't available when running in Eager mode.\n [Op:__inference_train_function_12918]"
     ]
    }
   ],
   "source": [
    "model.fit(train_dataset,epochs=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}