v2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "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": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# clean_session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import Datasets.OCR.CNN_Letters.loader as CNN_Letters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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(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(Dropout(0.3))\n",
    "    model.add(Dense(len(COMBINED_CHARS), activation='softmax'))\n",
    "\n",
    "    model.summary()\n",
    "\n",
    "    return model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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",
    "    X_data = CX_data\n",
    "    Y_data = CY_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": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/evangelospro/Downloads/Final-Project/.venv/lib/python3.11/site-packages/keras/src/layers/convolutional/base_conv.py:99: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(\n",
      "2024-03-23 18:01:39.071326: E external/local_xla/xla/stream_executor/cuda/cuda_driver.cc:282] failed call to cuInit: CUDA_ERROR_UNKNOWN: unknown error\n",
      "2024-03-23 18:01:39.071903: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:134] retrieving CUDA diagnostic information for host: ELARCH-F15\n",
      "2024-03-23 18:01:39.071968: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:141] hostname: ELARCH-F15\n",
      "2024-03-23 18:01:39.072538: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:165] libcuda reported version is: NOT_FOUND: was unable to find libcuda.so DSO loaded into this program\n",
      "2024-03-23 18:01:39.072782: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:169] kernel reported version is: 550.67.0\n"
     ]
    },
    {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
      ]
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">98</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">96</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">46</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">94</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">44</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">22</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">47</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">22</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">132352</span>)         │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │    <span style=\"color: #00af00; text-decoration-color: #00af00\">16,941,184</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">35</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">4,515</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m98\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │           \u001b[38;5;34m320\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m96\u001b[0m, \u001b[38;5;34m46\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │        \u001b[38;5;34m18,496\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m94\u001b[0m, \u001b[38;5;34m44\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │        \u001b[38;5;34m73,856\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m22\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout (\u001b[38;5;33mDropout\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m47\u001b[0m, \u001b[38;5;34m22\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m132352\u001b[0m)         │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │    \u001b[38;5;34m16,941,184\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m35\u001b[0m)             │         \u001b[38;5;34m4,515\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">17,038,371</span> (65.00 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m17,038,371\u001b[0m (65.00 MB)\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">17,038,371</span> (65.00 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m17,038,371\u001b[0m (65.00 MB)\n"
      ]
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
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       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
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    {
     "ename": "TypeError",
     "evalue": "split_dataset() missing 2 required positional arguments: 'test_percent' and 'random_state'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[10], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m model \u001b[38;5;241m=\u001b[39m create_model()\n\u001b[0;32m----> 2\u001b[0m history \u001b[38;5;241m=\u001b[39m \u001b[43mtrain_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlearning_rate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.001\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m32\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m50\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpatience\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      3\u001b[0m plot_model(history)\n\u001b[1;32m      4\u001b[0m save_model(model, \u001b[38;5;241m2\u001b[39m)\n",
      "Cell \u001b[0;32mIn[9], line 8\u001b[0m, in \u001b[0;36mtrain_model\u001b[0;34m(model, learning_rate, batch_size, epochs, patience)\u001b[0m\n\u001b[1;32m      5\u001b[0m callbacks \u001b[38;5;241m=\u001b[39m [keras\u001b[38;5;241m.\u001b[39mcallbacks\u001b[38;5;241m.\u001b[39mEarlyStopping(monitor\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mval_loss\u001b[39m\u001b[38;5;124m'\u001b[39m, patience\u001b[38;5;241m=\u001b[39mpatience, restore_best_weights\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)]\n\u001b[1;32m      7\u001b[0m X_data, Y_data \u001b[38;5;241m=\u001b[39m load_dataset()\n\u001b[0;32m----> 8\u001b[0m X_train, X_test, X_val, Y_train, Y_test, Y_val \u001b[38;5;241m=\u001b[39m \u001b[43msplit_dataset\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_data\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mY_data\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      9\u001b[0m X_train, X_test \u001b[38;5;241m=\u001b[39m shape_dataset(X_train, X_test)\n\u001b[1;32m     10\u001b[0m X_train, X_test \u001b[38;5;241m=\u001b[39m scale_dataset(X_train, X_test)\n",
      "\u001b[0;31mTypeError\u001b[0m: split_dataset() missing 2 required positional arguments: 'test_percent' and 'random_state'"
     ]
    }
   ],
   "source": [
    "model = create_model()\n",
    "history = train_model(model, learning_rate=0.001, batch_size=32, epochs=50, patience=5)\n",
    "plot_model(history, version=2, save=True)\n",
    "save_model(model, version=2)"
   ]
  }
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