v4.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-04-03 18:37:24.350678: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2024-04-03 18:37:24.727953: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2024-04-03 18:37:24.728095: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2024-04-03 18:37:24.773970: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "2024-04-03 18:37:24.905332: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-04-03 18:37:27.460382: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Conv2D, MaxPooling2D, Flatten, Dropout, BatchNormalization\n",
    "from keras.losses import categorical_crossentropy\n",
    "from keras.optimizers import Adam\n",
    "import keras\n",
    "\n",
    "from settings import *\n",
    "\n",
    "from utils.general import clean_session, plot_model, split_dataset, scale_dataset, shape_dataset, print_dataset_info\n",
    "from utils.models.ocr import save_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import Datasets.OCR.CNN_Letters.loader as CNN_Letters\n",
    "import Datasets.OCR.manual.loader as manual\n",
    "import Datasets.OCR.EnglishFnt.loader as EnglishFnt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# clean_session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_model():\n",
    "    model = Sequential()\n",
    "\n",
    "    # CNN\n",
    "    model.add(Conv2D(32, kernel_size=(3, 3), activation='relu',input_shape=INPUT_SHAPE))\n",
    "    model.add(BatchNormalization())\n",
    "    model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))\n",
    "    model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))\n",
    "\n",
    "    # Pooling\n",
    "    model.add(MaxPooling2D(pool_size=(2, 2), strides=2, padding='same'))\n",
    "\n",
    "    # Drop some data points randomly (mainly for caligraphy reasons)\n",
    "    model.add(Dropout(0.25))\n",
    "\n",
    "    # Flattening before feeding to FCNN\n",
    "    model.add(Flatten())\n",
    "\n",
    "    # FCNN\n",
    "    # use softmax since we are doing classification (1 node for each possible output)\n",
    "    model.add(Dense(128, activation='relu'))\n",
    "    model.add(Dense(64, activation='relu'))\n",
    "    # model.add(Dropout(0.3))\n",
    "    model.add(Dense(len(COMBINED_CHARS), activation='softmax'))\n",
    "\n",
    "    model.summary()\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_model(model, learning_rate, batch_size, epochs, patience):\n",
    "    optimizer = Adam(learning_rate=learning_rate)\n",
    "    model.compile(loss=categorical_crossentropy, optimizer=optimizer, metrics=['accuracy'])\n",
    "\n",
    "    callbacks = [keras.callbacks.EarlyStopping(monitor='val_loss', patience=patience, restore_best_weights=True, verbose=1)]\n",
    "\n",
    "    CX_data, CY_data = CNN_Letters.load_dataset()\n",
    "    EX_data, EY_data = EnglishFnt.load_dataset(include_fonts=ENGLISH_FNT_FONTS)\n",
    "    MX_data, MY_data = manual.load_dataset()\n",
    "    X_data = np.concatenate((CX_data, MX_data, EX_data))\n",
    "    Y_data = np.concatenate((CY_data, MY_data, EY_data))\n",
    "\n",
    "    X_train, X_test, Y_train, Y_test = split_dataset(X_data, Y_data, test_percent=TEST_PERCENT, random_state=RANDOM_STATE, shuffle=True)\n",
    "    X_train, X_test = shape_dataset(X_train, X_test, input_shape=INPUT_SHAPE)\n",
    "    X_train, X_test = scale_dataset(X_train, X_test, batch_size=batch_size)\n",
    "\n",
    "    print_dataset_info(X_data, X_train, Y_train, X_test, Y_test)\n",
    "\n",
    "    history = model.fit(X_train, Y_train, batch_size=batch_size, epochs=epochs, validation_split=VALIDATION_PERCENT, callbacks=callbacks)\n",
    "\n",
    "    return history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-04-03 18:37:32.100964: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2024-04-03 18:37:32.291484: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2024-04-03 18:37:32.292165: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2024-04-03 18:37:32.294835: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2024-04-03 18:37:32.295282: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2024-04-03 18:37:32.295572: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2024-04-03 18:37:32.399916: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2024-04-03 18:37:32.400287: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2024-04-03 18:37:32.400539: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2024-04-03 18:37:32.400730: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1929] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 2051 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 3050 Ti Laptop GPU, pci bus id: 0000:01:00.0, compute capability: 8.6\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 98, 48, 32)        320       \n",
      "                                                                 \n",
      " batch_normalization (Batch  (None, 98, 48, 32)        128       \n",
      " Normalization)                                                  \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 96, 46, 64)        18496     \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 94, 44, 128)       73856     \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2  (None, 47, 22, 128)       0         \n",
      " D)                                                              \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 47, 22, 128)       0         \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 132352)            0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 128)               16941184  \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 64)                8256      \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 35)                2275      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 17044515 (65.02 MB)\n",
      "Trainable params: 17044451 (65.02 MB)\n",
      "Non-trainable params: 64 (256.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    },
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     "text": [
      "Data samples: 180840\n",
      "X_train shape: 162756\n",
      "Y_train shape: 162756\n",
      "X_test shape: 18084\n",
      "Y_test shape: 18084\n",
      "Epoch 1/25\n"
     ]
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    {
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     "text": [
      "2024-04-03 18:38:31.865441: E tensorflow/core/grappler/optimizers/meta_optimizer.cc:961] layout failed: INVALID_ARGUMENT: Size of values 0 does not match size of permutation 4 @ fanin shape insequential/dropout/dropout/SelectV2-2-TransposeNHWCToNCHW-LayoutOptimizer\n",
      "2024-04-03 18:38:33.471032: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:454] Loaded cuDNN version 8906\n",
      "2024-04-03 18:38:33.750529: I external/local_tsl/tsl/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n",
      "2024-04-03 18:38:35.083618: I external/local_tsl/tsl/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n",
      "2024-04-03 18:38:36.025689: I external/local_xla/xla/service/service.cc:168] XLA service 0x76c4511ad5c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
      "2024-04-03 18:38:36.025729: I external/local_xla/xla/service/service.cc:176]   StreamExecutor device (0): NVIDIA GeForce RTX 3050 Ti Laptop GPU, Compute Capability 8.6\n",
      "2024-04-03 18:38:36.045030: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n",
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "I0000 00:00:1712158716.209820   41987 device_compiler.h:186] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\n",
      "2024-04-03 18:38:38.237937: W external/local_tsl/tsl/framework/bfc_allocator.cc:296] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.86GiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.\n",
      "2024-04-03 18:38:38.598156: W external/local_tsl/tsl/framework/bfc_allocator.cc:296] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.86GiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.\n",
      "2024-04-03 18:38:39.755100: W external/local_tsl/tsl/framework/bfc_allocator.cc:296] Allocator (GPU_0_bfc) ran out of memory trying to allocate 970.82MiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.\n",
      "2024-04-03 18:38:41.912988: W external/local_tsl/tsl/framework/bfc_allocator.cc:296] Allocator (GPU_0_bfc) ran out of memory trying to allocate 970.82MiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2034/2035 [============================>.] - ETA: 0s - loss: 1.2013 - accuracy: 0.6714"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-04-03 18:40:56.189991: W external/local_tsl/tsl/framework/bfc_allocator.cc:296] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.17GiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.\n",
      "2024-04-03 18:40:56.363502: W external/local_tsl/tsl/framework/bfc_allocator.cc:296] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.17GiB with freed_by_count=0. The caller indicates that this is not a failure, but this may mean that there could be performance gains if more memory were available.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2035/2035 [==============================] - 159s 71ms/step - loss: 1.2010 - accuracy: 0.6715 - val_loss: 0.1691 - val_accuracy: 0.9523\n",
      "Epoch 2/25\n",
      "2035/2035 [==============================] - 141s 69ms/step - loss: 0.1277 - accuracy: 0.9620 - val_loss: 0.0892 - val_accuracy: 0.9744\n",
      "Epoch 3/25\n",
      "2035/2035 [==============================] - 141s 69ms/step - loss: 0.0773 - accuracy: 0.9760 - val_loss: 0.0794 - val_accuracy: 0.9773\n",
      "Epoch 4/25\n",
      "2035/2035 [==============================] - 141s 69ms/step - loss: 0.0596 - accuracy: 0.9815 - val_loss: 0.0801 - val_accuracy: 0.9770\n",
      "Epoch 5/25\n",
      "2035/2035 [==============================] - 141s 69ms/step - loss: 0.0454 - accuracy: 0.9854 - val_loss: 0.0600 - val_accuracy: 0.9836\n",
      "Epoch 6/25\n",
      "2035/2035 [==============================] - 141s 69ms/step - loss: 0.0377 - accuracy: 0.9879 - val_loss: 0.0480 - val_accuracy: 0.9873\n",
      "Epoch 7/25\n",
      "2035/2035 [==============================] - 141s 69ms/step - loss: 0.0318 - accuracy: 0.9896 - val_loss: 0.0491 - val_accuracy: 0.9868\n",
      "Epoch 8/25\n",
      "2035/2035 [==============================] - 141s 69ms/step - loss: 0.0276 - accuracy: 0.9911 - val_loss: 0.0500 - val_accuracy: 0.9865\n",
      "Epoch 9/25\n",
      "2035/2035 [==============================] - 141s 69ms/step - loss: 0.0222 - accuracy: 0.9924 - val_loss: 0.0663 - val_accuracy: 0.9854\n",
      "Epoch 10/25\n",
      "2035/2035 [==============================] - 141s 69ms/step - loss: 0.0204 - accuracy: 0.9935 - val_loss: 0.0471 - val_accuracy: 0.9888\n",
      "Epoch 11/25\n",
      "2035/2035 [==============================] - 140s 69ms/step - loss: 0.0179 - accuracy: 0.9941 - val_loss: 0.0567 - val_accuracy: 0.9879\n",
      "Epoch 12/25\n",
      "2035/2035 [==============================] - 141s 69ms/step - loss: 0.0161 - accuracy: 0.9948 - val_loss: 0.0594 - val_accuracy: 0.9878\n",
      "Epoch 13/25\n",
      "2035/2035 [==============================] - 141s 69ms/step - loss: 0.0143 - accuracy: 0.9956 - val_loss: 0.0571 - val_accuracy: 0.9881\n",
      "Epoch 14/25\n",
      "2035/2035 [==============================] - 141s 69ms/step - loss: 0.0136 - accuracy: 0.9959 - val_loss: 0.0602 - val_accuracy: 0.9876\n",
      "Epoch 15/25\n",
      "2034/2035 [============================>.] - ETA: 0s - loss: 0.0119 - accuracy: 0.9963Restoring model weights from the end of the best epoch: 10.\n",
      "2035/2035 [==============================] - 141s 69ms/step - loss: 0.0119 - accuracy: 0.9963 - val_loss: 0.0708 - val_accuracy: 0.9848\n",
      "Epoch 15: early stopping\n"
     ]
    },
    {
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",
      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved model v4 to disk\n"
     ]
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n",
      "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n",
      "\u001b[1;31mClick <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "model = create_model()\n",
    "history = train_model(model, learning_rate=0.001, batch_size=64, epochs=25, patience=5)\n",
    "plot_model(history, 4, save=True)\n",
    "save_model(model, 4)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.11.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}