maml_test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python version:  3.6.10 |Anaconda, Inc.| (default, May  7 2020, 19:46:08) [MSC v.1916 64 bit (AMD64)]\n",
      "TensorFlow version:  2.3.1\n",
      "GPU found at: /device:GPU:0\n",
      "1 Physical GPUs, 1 Logical GPUs\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import tensorflow.keras as keras\n",
    "import tensorflow.keras.backend as keras_backend\n",
    "\n",
    "from MAML import MAMLmodel, normalize, forward_func\n",
    "\n",
    "from SinusoidDs import meta_sinusoid_ds, SinusoidGenerator, SineModel, eval_sinewave_for_test\n",
    "from utils import plot\n",
    "\n",
    "# Other dependencies\n",
    "import random\n",
    "import sys\n",
    "import timeit\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import logging\n",
    "\n",
    "dtype=\"float32\"\n",
    "tf.keras.backend.set_floatx(dtype)\n",
    "print('Python version: ', sys.version)\n",
    "print('TensorFlow version: ', tf.__version__)\n",
    "device_name = tf.test.gpu_device_name()\n",
    "if device_name != '/device:GPU:0':\n",
    "  raise SystemError('GPU device not found')\n",
    "print('GPU found at: {}'.format(device_name))\n",
    "plt.style.use('seaborn')\n",
    "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'  # FATAL\n",
    "logging.getLogger('tensorflow').setLevel(logging.FATAL)\n",
    "gpus = tf.config.experimental.list_physical_devices('GPU')\n",
    "if gpus:\n",
    "    try:\n",
    "        # Currently, memory growth needs to be the same across GPUs\n",
    "        for gpu in gpus:\n",
    "            tf.config.experimental.set_memory_growth(gpu, True)\n",
    "        logical_gpus = tf.config.experimental.list_logical_devices('GPU')\n",
    "        print(len(gpus), \"Physical GPUs,\", len(logical_gpus), \"Logical GPUs\")\n",
    "    except RuntimeError as e:\n",
    "        # Memory growth must be set before GPUs have been initialized\n",
    "        print(e)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fshot = 10\n",
    "meta_batch_size = 16\n",
    "ds =meta_sinusoid_ds(fshot, meta_batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "inp = next(iter(ds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure()\n",
    "for _ in range(3):\n",
    "    plt.title('Sinusoid example')\n",
    "    plot(SinusoidGenerator(K=100).equally_spaced_samples())\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "inner_update = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "maml_model = SineModel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "meta_batch_size=16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "meta_trainer = MAMLmodel(maml_model, inner_update, meta_batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Update 4922 : Outer loss: 0.12702783942222595   time: 0.03718190000000732\n",
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     ]
    }
   ],
   "source": [
    "meta_trainer.meta_training(ds)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_ds = meta_sinusoid_ds(fshot, meta_batch_size, train=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "title_temp = '{} , K = {}'\n",
    "title  = title_temp.format(\"MAML\", fshot)\n",
    "res = eval_sinewave_for_test(title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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