timesteps.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "\n",
    "sys.path.append(os.path.join(os.getcwd(), 'modules'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from token_predictor import TokenPredictor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "timesteps = [3, 10, 50, 100, 150, 200]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1118, 3, 24)\n",
      "(1118, 1)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-05-09 12:31:28.180671: I metal_plugin/src/device/metal_device.cc:1154] Metal device set to: Apple M2 Max\n",
      "2024-05-09 12:31:28.180689: I metal_plugin/src/device/metal_device.cc:296] systemMemory: 32.00 GB\n",
      "2024-05-09 12:31:28.180693: I metal_plugin/src/device/metal_device.cc:313] maxCacheSize: 10.67 GB\n",
      "2024-05-09 12:31:28.180721: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:306] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n",
      "2024-05-09 12:31:28.180735: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:272] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\n",
      "/Users/bharxhav/.pyenv/versions/3.11.5/lib/python3.11/site-packages/keras/src/layers/rnn/rnn.py:204: 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__(**kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-05-09 12:31:28.923885: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:117] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7/7 - 4s - 604ms/step - loss: nan - val_loss: nan\n",
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      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 326ms/step\n",
      "(1111, 10, 24)\n",
      "(1111, 1)\n",
      "\n",
      "Epoch 1/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bharxhav/.pyenv/versions/3.11.5/lib/python3.11/site-packages/keras/src/layers/rnn/rnn.py:204: 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__(**kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7/7 - 3s - 387ms/step - loss: nan - val_loss: nan\n",
      "Epoch 2/100\n",
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      "(1071, 50, 24)\n",
      "(1071, 1)\n",
      "\n",
      "Epoch 1/100\n"
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     "output_type": "stream",
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      "/Users/bharxhav/.pyenv/versions/3.11.5/lib/python3.11/site-packages/keras/src/layers/rnn/rnn.py:204: 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__(**kwargs)\n"
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      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 261ms/step\n",
      "(1021, 100, 24)\n",
      "(1021, 1)\n",
      "\n",
      "Epoch 1/100\n"
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     "text": [
      "/Users/bharxhav/.pyenv/versions/3.11.5/lib/python3.11/site-packages/keras/src/layers/rnn/rnn.py:204: 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__(**kwargs)\n"
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      "(971, 150, 24)\n",
      "(971, 1)\n",
      "\n",
      "Epoch 1/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bharxhav/.pyenv/versions/3.11.5/lib/python3.11/site-packages/keras/src/layers/rnn/rnn.py:204: 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__(**kwargs)\n"
     ]
    },
    {
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      "WARNING:tensorflow:5 out of the last 5 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x39850aca0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 285ms/step\n",
      "(921, 200, 24)\n",
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      "Epoch 1/100\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "/Users/bharxhav/.pyenv/versions/3.11.5/lib/python3.11/site-packages/keras/src/layers/rnn/rnn.py:204: 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__(**kwargs)\n"
     ]
    },
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      "6/6 - 0s - 46ms/step - loss: nan - val_loss: nan\n",
      "Epoch 33/100\n",
      "6/6 - 0s - 46ms/step - loss: nan - val_loss: nan\n",
      "Epoch 34/100\n",
      "6/6 - 0s - 47ms/step - loss: nan - val_loss: nan\n",
      "Epoch 35/100\n",
      "6/6 - 0s - 46ms/step - loss: nan - val_loss: nan\n",
      "Epoch 36/100\n",
      "6/6 - 0s - 47ms/step - loss: nan - val_loss: nan\n",
      "Epoch 37/100\n",
      "6/6 - 0s - 46ms/step - loss: nan - val_loss: nan\n",
      "Epoch 38/100\n",
      "6/6 - 0s - 46ms/step - loss: nan - val_loss: nan\n",
      "Epoch 39/100\n",
      "6/6 - 0s - 46ms/step - loss: nan - val_loss: nan\n",
      "Epoch 40/100\n",
      "6/6 - 0s - 46ms/step - loss: nan - val_loss: nan\n",
      "Epoch 41/100\n",
      "6/6 - 0s - 48ms/step - loss: nan - val_loss: nan\n",
      "Epoch 42/100\n",
      "6/6 - 0s - 48ms/step - loss: nan - val_loss: nan\n",
      "Epoch 43/100\n",
      "6/6 - 0s - 49ms/step - loss: nan - val_loss: nan\n",
      "Epoch 44/100\n",
      "6/6 - 0s - 49ms/step - loss: nan - val_loss: nan\n",
      "Epoch 45/100\n",
      "6/6 - 0s - 50ms/step - loss: nan - val_loss: nan\n",
      "Epoch 46/100\n",
      "6/6 - 0s - 49ms/step - loss: nan - val_loss: nan\n",
      "Epoch 47/100\n",
      "6/6 - 0s - 48ms/step - loss: nan - val_loss: nan\n",
      "Epoch 48/100\n",
      "6/6 - 0s - 49ms/step - loss: nan - val_loss: nan\n",
      "Epoch 49/100\n",
      "6/6 - 0s - 48ms/step - loss: nan - val_loss: nan\n",
      "Epoch 50/100\n",
      "6/6 - 0s - 52ms/step - loss: nan - val_loss: nan\n",
      "Epoch 51/100\n",
      "6/6 - 0s - 49ms/step - loss: nan - val_loss: nan\n",
      "Epoch 52/100\n",
      "6/6 - 0s - 49ms/step - loss: nan - val_loss: nan\n",
      "Epoch 53/100\n",
      "6/6 - 0s - 49ms/step - loss: nan - val_loss: nan\n",
      "Epoch 54/100\n",
      "6/6 - 0s - 48ms/step - loss: nan - val_loss: nan\n",
      "Epoch 55/100\n",
      "6/6 - 0s - 49ms/step - loss: nan - val_loss: nan\n",
      "Epoch 56/100\n",
      "6/6 - 0s - 52ms/step - loss: nan - val_loss: nan\n",
      "Epoch 57/100\n",
      "6/6 - 0s - 49ms/step - loss: nan - val_loss: nan\n",
      "Epoch 58/100\n",
      "6/6 - 0s - 50ms/step - loss: nan - val_loss: nan\n",
      "Epoch 59/100\n",
      "6/6 - 0s - 47ms/step - loss: nan - val_loss: nan\n",
      "Epoch 60/100\n",
      "6/6 - 0s - 47ms/step - loss: nan - val_loss: nan\n",
      "Epoch 61/100\n",
      "6/6 - 0s - 49ms/step - loss: nan - val_loss: nan\n",
      "Epoch 62/100\n",
      "6/6 - 0s - 50ms/step - loss: nan - val_loss: nan\n",
      "Epoch 63/100\n",
      "6/6 - 0s - 48ms/step - loss: nan - val_loss: nan\n",
      "Epoch 64/100\n",
      "6/6 - 0s - 48ms/step - loss: nan - val_loss: nan\n",
      "Epoch 65/100\n",
      "6/6 - 0s - 48ms/step - loss: nan - val_loss: nan\n",
      "Epoch 66/100\n",
      "6/6 - 0s - 48ms/step - loss: nan - val_loss: nan\n",
      "Epoch 67/100\n",
      "6/6 - 0s - 49ms/step - loss: nan - val_loss: nan\n",
      "Epoch 68/100\n",
      "6/6 - 0s - 49ms/step - loss: nan - val_loss: nan\n",
      "Epoch 69/100\n",
      "6/6 - 0s - 49ms/step - loss: nan - val_loss: nan\n",
      "Epoch 70/100\n",
      "6/6 - 0s - 52ms/step - loss: nan - val_loss: nan\n",
      "Epoch 71/100\n",
      "6/6 - 0s - 49ms/step - loss: nan - val_loss: nan\n",
      "Epoch 72/100\n",
      "6/6 - 0s - 49ms/step - loss: nan - val_loss: nan\n",
      "Epoch 73/100\n",
      "6/6 - 0s - 48ms/step - loss: nan - val_loss: nan\n",
      "Epoch 74/100\n",
      "6/6 - 0s - 48ms/step - loss: nan - val_loss: nan\n",
      "Epoch 75/100\n",
      "6/6 - 0s - 48ms/step - loss: nan - val_loss: nan\n",
      "Epoch 76/100\n",
      "6/6 - 0s - 48ms/step - loss: nan - val_loss: nan\n",
      "Epoch 77/100\n",
      "6/6 - 0s - 48ms/step - loss: nan - val_loss: nan\n",
      "Epoch 78/100\n",
      "6/6 - 0s - 48ms/step - loss: nan - val_loss: nan\n",
      "Epoch 79/100\n",
      "6/6 - 0s - 48ms/step - loss: nan - val_loss: nan\n",
      "Epoch 80/100\n",
      "6/6 - 0s - 50ms/step - loss: nan - val_loss: nan\n",
      "Epoch 81/100\n",
      "6/6 - 0s - 49ms/step - loss: nan - val_loss: nan\n",
      "Epoch 82/100\n",
      "6/6 - 0s - 47ms/step - loss: nan - val_loss: nan\n",
      "Epoch 83/100\n",
      "6/6 - 0s - 48ms/step - loss: nan - val_loss: nan\n",
      "Epoch 84/100\n",
      "6/6 - 0s - 52ms/step - loss: nan - val_loss: nan\n",
      "Epoch 85/100\n",
      "6/6 - 0s - 50ms/step - loss: nan - val_loss: nan\n",
      "Epoch 86/100\n",
      "6/6 - 0s - 51ms/step - loss: nan - val_loss: nan\n",
      "Epoch 87/100\n",
      "6/6 - 0s - 60ms/step - loss: nan - val_loss: nan\n",
      "Epoch 88/100\n",
      "6/6 - 0s - 66ms/step - loss: nan - val_loss: nan\n",
      "Epoch 89/100\n",
      "6/6 - 0s - 53ms/step - loss: nan - val_loss: nan\n",
      "Epoch 90/100\n",
      "6/6 - 0s - 53ms/step - loss: nan - val_loss: nan\n",
      "Epoch 91/100\n",
      "6/6 - 0s - 50ms/step - loss: nan - val_loss: nan\n",
      "Epoch 92/100\n",
      "6/6 - 0s - 49ms/step - loss: nan - val_loss: nan\n",
      "Epoch 93/100\n",
      "6/6 - 0s - 49ms/step - loss: nan - val_loss: nan\n",
      "Epoch 94/100\n",
      "6/6 - 0s - 49ms/step - loss: nan - val_loss: nan\n",
      "Epoch 95/100\n",
      "6/6 - 0s - 48ms/step - loss: nan - val_loss: nan\n",
      "Epoch 96/100\n",
      "6/6 - 0s - 48ms/step - loss: nan - val_loss: nan\n",
      "Epoch 97/100\n",
      "6/6 - 0s - 49ms/step - loss: nan - val_loss: nan\n",
      "Epoch 98/100\n",
      "6/6 - 0s - 49ms/step - loss: nan - val_loss: nan\n",
      "Epoch 99/100\n",
      "6/6 - 0s - 48ms/step - loss: nan - val_loss: nan\n",
      "Epoch 100/100\n",
      "6/6 - 0s - 47ms/step - loss: nan - val_loss: nan\n",
      "WARNING:tensorflow:6 out of the last 6 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x407719940> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 322ms/step\n"
     ]
    }
   ],
   "source": [
    "predictions = []\n",
    "\n",
    "for timestep in timesteps:\n",
    "    tp = TokenPredictor()\n",
    "    tp.timesteps = timestep\n",
    "    tp.train()\n",
    "    predictions.append(tp.predict())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[nan, nan, nan, nan, 54.6383, nan]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# My plotting configs\n",
    "plt.rcParams['figure.dpi'] = 600\n",
    "plt.rcParams['savefig.dpi'] = 600\n",
    "\n",
    "sns.set(rc={\"figure.dpi\": 600, 'savefig.dpi': 600})\n",
    "sns.set(rc={'figure.figsize': (11.7, 8.27)})\n",
    "sns.set_style('darkgrid')\n",
    "sns.set_palette('viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 7020x4962 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}