ExploreGradientDescent.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How does the gradient evolve during training ?\n",
    "\n",
    "How does the size of the gradient and its direction evolve during training? \n",
    "\n",
    "Note: WORK IN PROGRESS (UNFINISHED)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import tensorflow.keras as tfk\n",
    "import numpy as np\n",
    "from tqdm import trange"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# switch off annoying tensorflow autograph warning\n",
    "# (seems a known issue, may depend on tf version; try without it\n",
    "# and see what happens!)\n",
    "tf.autograph.set_verbosity(0)\n",
    "import logging\n",
    "logging.getLogger(\"tensorflow\").setLevel(logging.ERROR)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# our target function:\n",
    "\n",
    "def F(x):\n",
    "    return np.sin(4*(x[...,0]+x[...,1]))/(1+np.sum(x**2,axis=-1))\n",
    "\n",
    "# defining the network:\n",
    "\n",
    "def get_network(layers,learning_rate=0.01,optimizer=None):\n",
    "    network=tfk.Sequential([tfk.layers.Dense(n,activation='relu') \n",
    "        for n in layers] + [tfk.layers.Dense(1)])\n",
    "    if optimizer is None:\n",
    "        optimizer=tfk.optimizers.SGD(learning_rate=learning_rate)\n",
    "    network.compile(loss='mse', optimizer=optimizer)\n",
    "    return network\n",
    "\n",
    "# running the training:\n",
    "\n",
    "def run_training(network,n_batches=1000,batchsize=30,\n",
    "    fixed_batch=False, test_batch=None):\n",
    "    losses=[]\n",
    "    grad_lengths=[]\n",
    "    loss_fn=tfk.losses.MeanSquaredError()\n",
    "    if test_batch is not None:\n",
    "        test_batch=np.array(test_batch) # if user passes a list, convert to array\n",
    "        test_results=np.zeros([n_batches]+[test_batch.shape[0],1])\n",
    "    else:\n",
    "        test_results=None\n",
    "    if fixed_batch:\n",
    "        x=3.0*np.random.randn(batchsize,2)\n",
    "    for sample in trange(n_batches):\n",
    "        if not fixed_batch:\n",
    "            x=3.0*np.random.randn(batchsize,2)\n",
    "        with tf.GradientTape() as tape:\n",
    "            output=network(x) # get output of network\n",
    "            loss=loss_fn(output,F(x)) # get loss\n",
    "# note that reduce_mean sums over all samples in the batch\n",
    "# and then divides the result by the batchsize (number of samples)\n",
    "\n",
    "# now: calculate gradients!\n",
    "# this will produce a list 'grads', containing gradients of the loss\n",
    "# with respect to the parameters in each layer\n",
    "# (each weights and bias set is a separate entry\n",
    "# in the list, so 2 entries per layer in the list)\n",
    "        grads=tape.gradient(loss,network.trainable_variables)\n",
    "\n",
    "# calculate total length of gradient vector (summing Pythagoras)\n",
    "        grad_length=np.sqrt(sum([tf.reduce_sum(g**2) for g in grads]))\n",
    "        network.optimizer.apply_gradients(zip(grads,network.trainable_variables))\n",
    "        if test_batch is not None:\n",
    "            test_results[sample,...]=network(test_batch) # evaluate on test batch\n",
    "        losses.append(loss.numpy()) # append loss as number (not tensor)\n",
    "        grad_lengths.append(grad_length)\n",
    "    return losses, grad_lengths, test_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# plot loss curves, gradient length, test results,\n",
    "# and the network approximation to F(x):\n",
    "def plot_losses_and_fit(loss,grad_lengths,test_results,\n",
    "        network,title):\n",
    "    fig,ax=plt.subplots(ncols=4,figsize=[10,3],dpi=200)\n",
    "    ax[0].plot(loss,alpha=0.5)\n",
    "    ax[0].set_yscale(\"log\", base=10)\n",
    "    ax[0].set_ylim([np.amin(loss[300:])/1.1,loss[300]*1.1]) # reasonable scale\n",
    "    ax[0].set_title(title)\n",
    "    ax[1].plot(grad_lengths,alpha=0.5)\n",
    "    ax[1].set_ylim([0.,grad_lengths[300]*2])\n",
    "    ax[1].set_title(\"gradient length\")\n",
    "    npixels=100\n",
    "    extent=[-3.0,3.0]\n",
    "    vals=np.linspace(*extent,npixels)\n",
    "    x=np.zeros([npixels,npixels,2])\n",
    "    x[...,0],x[...,1]=vals[:,None],vals[None,:]\n",
    "    n=test_results.shape[0]\n",
    "    n_results=test_results.shape[1]*test_results.shape[2]\n",
    "    flattened_test_results=np.reshape(test_results,[n,n_results])\n",
    "    ax[2].plot(flattened_test_results)\n",
    "    ax[2].set_ylim([np.amin(flattened_test_results[200:,:]),\n",
    "        np.amax(flattened_test_results[200:,:])])\n",
    "    ax[2].set_title(\"test results\")\n",
    "    ax[3].imshow(network(x).numpy()[...,0],origin='lower', extent=extent + extent)\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# run several different layer configurations, each of them several times (for statistics)\n",
    "def try_layers(layers,n_batches=5000,batchsize=30,\n",
    "    print_summary=False,num_trials=1,fixed_batch=False,\n",
    "    test_batch=None,\n",
    "    learning_rate=0.1, optimizer=None):\n",
    "    network=get_network(layers,learning_rate=learning_rate, optimizer=optimizer)\n",
    "    losses,grad_lengths,test_results=run_training(network=network,\n",
    "        n_batches=n_batches,batchsize=batchsize,test_batch=test_batch,\n",
    "        fixed_batch=fixed_batch)\n",
    "    print(\"Layers: \", layers, \" Final loss: \", losses[-1])\n",
    "    plot_losses_and_fit(losses,grad_lengths,test_results,\n",
    "        network,str(layers))\n",
    "    if print_summary:\n",
    "        print(network.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 19%|█▉        | 943/5000 [00:03<00:13, 309.73it/s]2021-10-23 20:08:00.921475: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 960).\n",
      "2021-10-23 20:08:00.921681: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 960).\n",
      "2021-10-23 20:08:00.934444: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 964).\n",
      "2021-10-23 20:08:00.934645: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 964).\n",
      "2021-10-23 20:08:00.941115: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 966).\n",
      "2021-10-23 20:08:00.941319: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 966).\n",
      "2021-10-23 20:08:00.948202: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 968).\n",
      "2021-10-23 20:08:00.948415: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 968).\n",
      "2021-10-23 20:08:00.951685: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 969).\n",
      "2021-10-23 20:08:00.951890: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 969).\n",
      "2021-10-23 20:08:00.958090: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 971).\n",
      "2021-10-23 20:08:00.958287: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 971).\n",
      "2021-10-23 20:08:00.961530: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 972).\n",
      "2021-10-23 20:08:00.961816: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 972).\n",
      "2021-10-23 20:08:00.965242: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 973).\n",
      "2021-10-23 20:08:00.965444: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 973).\n",
      " 19%|█▉        | 974/5000 [00:03<00:13, 307.92it/s]2021-10-23 20:08:00.973196: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 975).\n",
      "2021-10-23 20:08:00.973415: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 975).\n",
      "2021-10-23 20:08:00.977114: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 976).\n",
      "2021-10-23 20:08:00.977358: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 976).\n",
      "2021-10-23 20:08:00.981508: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 977).\n",
      "2021-10-23 20:08:00.981925: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 977).\n",
      "2021-10-23 20:08:00.985642: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 978).\n",
      "2021-10-23 20:08:00.985865: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 978).\n",
      "2021-10-23 20:08:00.989362: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 979).\n",
      "2021-10-23 20:08:00.989578: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 979).\n",
      "2021-10-23 20:08:00.996629: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 981).\n",
      "2021-10-23 20:08:00.997498: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 981).\n",
      "2021-10-23 20:08:01.002879: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 982).\n",
      "2021-10-23 20:08:01.003483: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 982).\n",
      "2021-10-23 20:08:01.007872: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 983).\n",
      "2021-10-23 20:08:01.008252: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 983).\n",
      "2021-10-23 20:08:01.014019: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 984).\n",
      "2021-10-23 20:08:01.014261: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 984).\n",
      "2021-10-23 20:08:01.018202: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 985).\n",
      "2021-10-23 20:08:01.018432: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 985).\n",
      "2021-10-23 20:08:01.022454: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 986).\n",
      "2021-10-23 20:08:01.022943: W tensorflow/compiler/tf2mlcompute/convert/mlc_convert_utils.cc:690] ComputeTimeStepForAdam: Computing time_step from beta1_power and beta2_power gives different results, probably due to losing precision in pow or log. The time_step that comes from the larger beta_power is chosen (time_step = 986).\n",
      "100%|██████████| 5000/5000 [00:26<00:00, 191.60it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Layers:  [40]  Final loss:  0.03854987770318985\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": "<Figure size 2000x600 with 4 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "try_layers([40],batchsize=30,\n",
    "    n_batches=5000,fixed_batch=True,\n",
    "    test_batch=[[0.0,0.0],[1.0,0.0]],\n",
    "    optimizer='adam')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.10 64-bit ('tf_m1': conda)",
   "name": "python3810jvsc74a57bd067905617eea89501e813a344fd4dcbb609bed90dc054fc90507923f179c60e4d"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  },
  "orig_nbformat": 2
 },
 "nbformat": 4,
 "nbformat_minor": 2
}