deconv_ml_experiments/learning_gyroadapted.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "50dcc865-d26d-452e-8e9f-353bb4c88ed9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import sys\n",
    "#!{sys.executable} -m pip install tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e1a1f43-6314-42d9-bf81-424bee5b72e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/500\n",
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      "Epoch 108/500\n",
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      "Epoch 109/500\n",
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      "Epoch 110/500\n",
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      "Epoch 111/500\n",
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      "Epoch 112/500\n",
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      "Epoch 113/500\n",
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      "Epoch 114/500\n",
      "40/40 [==============================] - 65s 2s/step - loss: 0.0011\n",
      "Epoch 115/500\n",
      "40/40 [==============================] - 63s 2s/step - loss: 0.0013\n",
      "Epoch 116/500\n",
      "40/40 [==============================] - 63s 2s/step - loss: 0.0012\n",
      "Epoch 117/500\n",
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      "Epoch 118/500\n",
      "13/40 [========>.....................] - ETA: 43s - loss: 0.0011"
     ]
    }
   ],
   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline\n",
    "if 'NOTEBOOK_INITIATED_FLAG' not in globals():\n",
    "    NOTEBOOK_INITIATED_FLAG = True\n",
    "    %cd ..\n",
    "import os \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import image\n",
    "\n",
    "import examplesetup\n",
    "import lib.deconvolution as deconvolution\n",
    "import lib.utils as utils\n",
    "import lib.xtrace as xtrace\n",
    "import lib.datagen as datagen\n",
    "import lib.mlmodels as mlmodels\n",
    "\n",
    "from numpy.random import default_rng\n",
    "rng = default_rng()\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import numpy as np\n",
    "\n",
    "img_shape = (100, 100)\n",
    "batch_size = 32\n",
    "batches = 40\n",
    "train_gen = datagen.SyntheticDepthBlurPositional(batch_size, batches, img_shape, 0)\n",
    "# Free up RAM in case the model definition cells were run multiple times\n",
    "keras.backend.clear_session()\n",
    "\n",
    "\n",
    "\n",
    "# Build model\n",
    "models = mlmodels.modelsClass(*img_shape)\n",
    "model = models.getDeepGyro()\n",
    "\n",
    "#model_name = \"pos_batch_deconvolve\"\n",
    "#model = keras.models.load_model(f'trained_networks/{model_name}.h5')\n",
    "\n",
    "model.compile(\n",
    "    optimizer=tf.keras.optimizers.Adam(learning_rate=0.00005, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False),\n",
    "    loss=tf.keras.losses.MeanAbsoluteError()\n",
    ")\n",
    "\n",
    "callbacks = [\n",
    "    keras.callbacks.ModelCheckpoint(\"trained_networks/pos_batch_deconvolve.h5\", monitor=\"loss\", save_best_only=True)\n",
    "]\n",
    "\n",
    "epochs = 500\n",
    "model.fit(train_gen, epochs=epochs, callbacks=callbacks)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "48a01f10-6c2f-4459-82a4-15324cda70ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  }
 ],
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