Example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/eck/software/anaconda3/envs/hk2/lib/python3.8/site-packages/tensorflow_addons/utils/ensure_tf_install.py:54: UserWarning: Tensorflow Addons supports using Python ops for all Tensorflow versions above or equal to 2.2.0 and strictly below 2.4.0 (nightly versions are not supported). \n",
      " The versions of TensorFlow you are currently using is 2.4.0 and is not supported. \n",
      "Some things might work, some things might not.\n",
      "If you were to encounter a bug, do not file an issue.\n",
      "If you want to make sure you're using a tested and supported configuration, either change the TensorFlow version or the TensorFlow Addons's version. \n",
      "You can find the compatibility matrix in TensorFlow Addon's readme:\n",
      "https://github.com/tensorflow/addons\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "import unet3plus as u3p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = u3p.Unet3plus()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "toy_x = tf.random.normal((64, 256, 256, 3))\n",
    "toy_y = (\n",
    "            tf.cast(tf.random.uniform((64, 256, 256, 1), 0, 2, tf.int32), tf.uint8),\n",
    "            tf.cast(tf.random.uniform((64, 1), 0, 2, tf.int32), tf.uint8)\n",
    "        )\n",
    "         "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.build((None, 256, 256, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "opt = tf.keras.optimizers.Adam()\n",
    "loss = u3p.HybridLoss()\n",
    "\n",
    "model.compile(optimizer = opt, loss = loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32/32 [==============================] - 24s 159ms/step - loss: 12.4056 - hybrid_loss: 8.7966 - hybrid_loss1: 1.7488 - hybrid_loss2: 1.7528 - hybrid_loss3: 1.7470 - hybrid_loss4: 1.7515 - hybrid_loss5: 1.7964 - binary_crossentropy: 3.2098 - dice: 0.6480 - dice1: 0.6519 - dice2: 0.6521 - dice3: 0.6561 - dice4: 0.6582 - dice5: 0.6216 - accuracy: 0.6406\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f94d46ef340>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(toy_x, toy_y, batch_size = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:hk2]",
   "language": "python",
   "name": "conda-env-hk2-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}