From fbdd5118f62b7c2ad41e50a01c3d8bce8d2d5ea3 Mon Sep 17 00:00:00 2001 From: beckynevin Date: Wed, 8 Nov 2023 16:41:18 -0700 Subject: [PATCH] separate notebook with epsilon --- notebooks/DeepEnsemble.ipynb | 1775 ++++++++++++++++++++++++++++++++++ 1 file changed, 1775 insertions(+) create mode 100644 notebooks/DeepEnsemble.ipynb diff --git a/notebooks/DeepEnsemble.ipynb b/notebooks/DeepEnsemble.ipynb new file mode 100644 index 0000000..a4356fb --- /dev/null +++ b/notebooks/DeepEnsemble.ipynb @@ -0,0 +1,1775 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f774193d", + "metadata": {}, + "source": [ + "# Pendulum Deep Ensemble" + ] + }, + { + "cell_type": "markdown", + "id": "fe040c0f", + "metadata": {}, + "source": [ + "## The dataset: simple static pendulum\n", + "Using the position of a pendulum at one point in time from a collection of pendulums on two different planets, determine the position associated with L, $\\theta$, and $a_g$ using a deep ensemble. Additionally, predict $\\sigma$, which is the error introduced." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "5e8c8f57", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import StandardScaler\n", + "import torch\n", + "import torch.nn as nn\n", + "from torch.utils.data import DataLoader, TensorDataset" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2fa611df", + "metadata": {}, + "outputs": [], + "source": [ + "## first, import all the necessary modules\n", + "import arviz as az\n", + "import corner\n", + "import graphviz\n", + "import jax\n", + "from jax import random\n", + "import jax.numpy as jnp # yes i know this is confusing\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import numpyro\n", + "\n", + "## in numpyro, you must specify number of sampling chains you will use upfront\n", + "\n", + "# words of wisdom from Tian Li and crew:\n", + "# on gpu, don't use conda, use pip install\n", + "# HMC after SBI to look at degeneracies between params\n", + "# different guides (some are slower but better at showing degeneracies)\n", + "\n", + "## define the platform and number of cores (one chain per core)\n", + "numpyro.set_platform('cpu')\n", + "core_num = 4\n", + "numpyro.set_host_device_count(core_num)\n", + "\n", + "import numpyro.distributions as dist\n", + "from numpyro.infer import MCMC, NUTS\n", + "import pandas as pd\n", + "from sklearn.preprocessing import LabelEncoder" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "df535a12-c8bf-46eb-a575-12deafb1109b", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.append('..')\n", + "from src.scripts import train, models, analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "e420b77a", + "metadata": {}, + "outputs": [], + "source": [ + "loss_type = 'var_loss'\n", + "# options are 'no_var_loss' or 'var_loss'" + ] + }, + { + "cell_type": "markdown", + "id": "d478548e", + "metadata": {}, + "source": [ + "## Generate pendulum data\n", + "To do this make a dataframe and replicate a bunch of columns. There are 8 pendulums on two different planets. The planet_id and pendulum_id are integers denoting which pendulum and which planet each row of the dataframe belongs." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "4a76432d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0. 0.02020202 0.04040404 0.06060606 0.08080808 0.1010101\n", + " 0.12121212 0.14141414 0.16161616 0.18181818 0.2020202 0.22222222\n", + " 0.24242424 0.26262626 0.28282828 0.3030303 0.32323232 0.34343434\n", + " 0.36363636 0.38383838 0.4040404 0.42424242 0.44444444 0.46464646\n", + " 0.48484848 0.50505051 0.52525253 0.54545455 0.56565657 0.58585859\n", + " 0.60606061 0.62626263 0.64646465 0.66666667 0.68686869 0.70707071\n", + " 0.72727273 0.74747475 0.76767677 0.78787879 0.80808081 0.82828283\n", + " 0.84848485 0.86868687 0.88888889 0.90909091 0.92929293 0.94949495\n", + " 0.96969697 0.98989899 1.01010101 1.03030303 1.05050505 1.07070707\n", + " 1.09090909 1.11111111 1.13131313 1.15151515 1.17171717 1.19191919\n", + " 1.21212121 1.23232323 1.25252525 1.27272727 1.29292929 1.31313131\n", + " 1.33333333 1.35353535 1.37373737 1.39393939 1.41414141 1.43434343\n", + " 1.45454545 1.47474747 1.49494949 1.51515152 1.53535354 1.55555556\n", + " 1.57575758 1.5959596 1.61616162 1.63636364 1.65656566 1.67676768\n", + " 1.6969697 1.71717172 1.73737374 1.75757576 1.77777778 1.7979798\n", + " 1.81818182 1.83838384 1.85858586 1.87878788 1.8989899 1.91919192\n", + " 1.93939394 1.95959596 1.97979798 2. ]\n" + ] + }, + { + "data": { + "image/png": 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0.01477607852854518, 0.01891108010031174, 0.020386007865249807, 0.02147491018448422, 0.0231136587979831, 0.022788697524834603, 0.02440339768353371, 0.025611983513776408, 0.026823808240271575, 0.028410904137982917, 0.028434567273839424, 0.02930878632035886, 0.030261377108566107, 0.02902444507149441, 0.03189699630982238, 0.03135391919526903, 0.03275928420989218]\n", + "[ 0.03141076 0.03134799 0.03115992 0.0308473 0.03041138 0.0298539\n", + " 0.02917708 0.02838362 0.02747668 0.02645989 0.0253373 0.02411339\n", + " 0.02279304 0.02138154 0.01988452 0.01830796 0.01665816 0.01494171\n", + " 0.01316548 0.01133657 0.0094623 0.00755017 0.00560782 0.00364302\n", + " 0.00166365 -0.00032238 -0.00230712 -0.00428263 -0.006241 -0.00817439\n", + " -0.01007507 -0.01193543 -0.01374803 -0.01550563 -0.01720119 -0.01882793\n", + " -0.02037935 -0.02184926 -0.02323178 -0.02452139 -0.02571293 -0.02680166\n", + " -0.02778323 -0.02865371 -0.02940965 -0.03004801 -0.03056626 -0.03096233\n", + " -0.03123465 -0.03138211 -0.03140415 -0.03130066 -0.03107206 -0.03071927\n", + " -0.03024369 -0.02964721 -0.02893222 -0.02810156 -0.02715856 -0.02610697\n", + " -0.02495098 -0.02369522 -0.0223447 -0.02090481 -0.01938131 -0.01778027\n", + " -0.0161081 -0.01437149 -0.01257738 -0.01073294 -0.00884555 -0.00692277\n", + " -0.00497228 -0.00300189 -0.00101948 0.000967 0.00294962 0.00492043\n", + " 0.00687154 0.00879516 0.01068358 0.01252925 0.01432479 0.01606301\n", + " 0.01773697 0.01933997 0.0208656 0.02230778 0.02366073 0.02491906\n", + " 0.02607774 0.02713215 0.02807807 0.02891174 0.02962983 0.03022947\n", + " 0.03070828 0.03106433 0.03129622 0.03140303]\n" + ] + } + ], + "source": [ + "import deepbench\n", + "from deepbench.physics_object import Pendulum\n", + "\n", + "true_L = 1.0\n", + "true_theta = np.pi / 100\n", + "true_a = 9.8\n", + "percent_error = 0.1\n", + "dL = percent_error * true_L\n", + "\n", + "pendulum = Pendulum(\n", + " pendulum_arm_length=true_L,\n", + " starting_angle_radians=true_theta,\n", + " acceleration_due_to_gravity=true_a,\n", + " noise_std_percent={\n", + " \"pendulum_arm_length\": percent_error,\n", + " \"starting_angle_radians\": 0.0,\n", + " \"acceleration_due_to_gravity\": 0.0,\n", + " },\n", + " )\n", + "\n", + "time = np.linspace(0,2,100)\n", + "print(time)\n", + "one_time = 0.5\n", + "\n", + "pendulum_noiseless = pendulum.create_object(time, noiseless=True)\n", + "# for every moment in time\n", + "true_sigma = 0.001\n", + "pendulum_noisy = []\n", + "for p in pendulum_noiseless:\n", + " rs = np.random.RandomState()#2147483648)# \n", + " attribute = rs.normal(loc=0, scale=true_sigma)\n", + " pendulum_noisy.append(p + attribute)\n", + "\n", + "'''\n", + "for r in range(100):\n", + " rs = np.random.RandomState()#2147483648)# \n", + " attribute = rs.normal(loc=0, scale=1)\n", + " #print('random number', attribute)\n", + " plt.scatter(one_time, attribute * dx_dtheta_0 + pendulum_noiseless_one, color = 'red', s= 0.1)#, label = 'Expected error plus gaussian random noise')\n", + "\n", + "'''\n", + "\n", + "pendulum_noisy_one = pendulum.create_object(one_time, noiseless=False)\n", + "pendulum_noiseless_one = pendulum.create_object(one_time, noiseless=True)\n", + "rs = np.random.RandomState()#2147483648)# \n", + "attribute = rs.normal(loc=0, scale=true_sigma)\n", + "pendulum_noisy_one = pendulum_noiseless_one + attribute\n", + "\n", + "\n", + "plt.clf()\n", + "plt.plot(time, pendulum_noisy, color = '#832161')\n", + "plt.scatter(time, pendulum_noisy, label = 'Noisy draws', color = '#832161')\n", + "plt.scatter(one_time, pendulum_noisy_one, label = 'Noisy draws one', color = '#ED474A')\n", + "plt.plot(time, pendulum_noiseless, color = '#EDCBB1')\n", + "plt.scatter(time, pendulum_noiseless, label = 'Noise free draws', \n", + " color = '#EDCBB1')\n", + "plt.scatter(one_time, pendulum_noiseless_one, label = 'Noise free draws one', \n", + " color = '#6883BA')\n", + "legend = plt.legend(loc=\"upper right\", edgecolor=\"black\")\n", + "legend.get_frame().set_alpha(1.0)\n", + "plt.xlabel('time [s]')\n", + "plt.ylabel('x position of pendulum')\n", + "plt.show()\n", + "\n", + "print(pendulum_noisy)\n", + "print(pendulum_noiseless)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "63760eba", + "metadata": {}, + "outputs": [], + "source": [ + "# okay now make a dataframe with a bunch of different options for the parameters\n", + "# generate the L, theta, a_g values somewhat randomly between ranges\n", + "length_percent_error_all = 0.0\n", + "theta_percent_error_all = 0.0 # was 0.1\n", + "a_g_percent_error_all = 0.0\n", + "pos_err = 0.0\n", + "\n", + "time = 0.5\n", + "\n", + "length_df = 1000\n", + "xs = np.zeros((length_df, 4))\n", + "y_noisy = []\n", + "#true_sigmas = []\n", + "\n", + "for r in range(length_df):\n", + " rs = np.random.RandomState()#2147483648)# \n", + " length = abs(rs.normal(loc=5, scale=1))\n", + " theta = abs(rs.normal(loc=jnp.pi/100, scale=jnp.pi/200))\n", + " a_g = abs(rs.normal(loc=10, scale=2))\n", + " epsilon = rs.normal(loc=0, scale=true_sigma)\n", + " xs[r,:] = [length, theta, a_g, epsilon]\n", + " \n", + " pendulum = Pendulum(\n", + " pendulum_arm_length=length,\n", + " starting_angle_radians=theta,\n", + " acceleration_due_to_gravity=a_g,\n", + " noise_std_percent={\n", + " \"pendulum_arm_length\": length_percent_error_all,\n", + " \"starting_angle_radians\": theta_percent_error_all,\n", + " \"acceleration_due_to_gravity\": a_g_percent_error_all,\n", + " },\n", + " )\n", + " \n", + " #true_sigmas.append(attribute)\n", + " y_noisy.append(pendulum.create_object(time, noiseless=True) + attribute)\n", + " del pendulum" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "b18cea86", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.clf()\n", + "fig = plt.figure(figsize = (10,4))\n", + "ax = fig.add_subplot(151)\n", + "ax.hist(y_noisy, bins=50)\n", + "ax.set_xlabel('x pos')\n", + "ax1 = fig.add_subplot(152)\n", + "ax1.hist(xs[:,0], bins=50)\n", + "ax1.set_xlabel('length')\n", + "ax2 = fig.add_subplot(153)\n", + "ax2.hist(xs[:,1], bins=50)\n", + "ax2.set_xlabel('theta')\n", + "ax3 = fig.add_subplot(154)\n", + "ax3.hist(xs[:,2], bins=50)\n", + "ax3.set_xlabel('a_g')\n", + "ax4 = fig.add_subplot(155)\n", + "ax4.hist(xs[:,3], bins=50)\n", + "ax4.set_xlabel('epsilon')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "278994cc", + "metadata": {}, + "outputs": [], + "source": [ + "# we need to normalize everything\n", + "xmin = np.min(xs, axis = 0)\n", + "xmax = np.max(xs, axis = 0)\n", + "ymin = np.min(y_noisy)\n", + "ymax = np.max(y_noisy)\n", + "\n", + "norm_xs = (xs - xmin) / (xmax - xmin)\n", + "# normal_value = (og_value - xmin) / (xmax - xmin)\n", + "# og_value = normal_value * (xmax - xmin) + xmin\n", + "norm_ys = (y_noisy - ymin) / (ymax - ymin)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "0e7c1c4a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(900, 4) (900,)\n", + "(4,)\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "'\\nSTOP\\n#\\n\\nscaler = StandardScaler()\\nscaler.fit(data)\\nStandardScaler()\\n>>> print(scaler.mean_)\\n[0.5 0.5]\\n>>> print(scaler.transform(data))\\n\\nx_scaler, x_train, x_val = StandardScaler(x_train, x_val)\\ny_scaler, y_train, y_val = StandardScaler(y_train, y_val)\\n'" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# prepare the training, validation, and test set split:\n", + "\n", + "val_proportion = 0.1\n", + "x_train, x_val, y_train, y_val = train_test_split(norm_xs, norm_ys, test_size=val_proportion, random_state=42)\n", + "\n", + "print(np.shape(x_train), np.shape(y_train))\n", + "print(np.shape(x_train[:][0]))\n", + "\n", + "\n", + "plt.clf()\n", + "plt.scatter(x_train[:,0], y_train, label = 'training', color = '#B5DDA4', s = 10)\n", + "plt.scatter(x_val[:,0], y_val, label = 'val', color = '#FAA381', s = 10)\n", + "plt.legend()\n", + "plt.xlabel('length')\n", + "plt.ylabel('x position')\n", + "plt.show()\n", + "\n", + "plt.clf()\n", + "plt.scatter(x_train[:,1], y_train, label = 'training', color = '#B5DDA4', s = 10)\n", + "plt.scatter(x_val[:,1], y_val, label = 'val', color = '#FAA381', s = 10)\n", + "plt.legend()\n", + "plt.xlabel('theta')\n", + "plt.ylabel('x position')\n", + "plt.show()\n", + "\n", + "plt.clf()\n", + "plt.scatter(x_train[:,2], y_train, label = 'training', color = '#B5DDA4', s = 10)\n", + "plt.scatter(x_val[:,2], y_val, label = 'val', color = '#FAA381', s = 10)\n", + "plt.legend()\n", + "plt.xlabel('a_g')\n", + "plt.ylabel('x position')\n", + "plt.show()\n", + "\n", + "plt.clf()\n", + "plt.scatter(x_train[:,3], y_train, label = 'training', color = '#B5DDA4', s = 10)\n", + "plt.scatter(x_val[:,3], y_val, label = 'val', color = '#FAA381', s = 10)\n", + "plt.legend()\n", + "plt.xlabel('epsilon')\n", + "plt.ylabel('x position')\n", + "plt.show()\n", + "\n", + "\n", + "\n", + "'''\n", + "STOP\n", + "#\n", + "\n", + "scaler = StandardScaler()\n", + "scaler.fit(data)\n", + "StandardScaler()\n", + ">>> print(scaler.mean_)\n", + "[0.5 0.5]\n", + ">>> print(scaler.transform(data))\n", + "\n", + "x_scaler, x_train, x_val = StandardScaler(x_train, x_val)\n", + "y_scaler, y_train, y_val = StandardScaler(y_train, y_val)\n", + "'''" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "b49925a4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape of training set (900, 4)\n" + ] + } + ], + "source": [ + "# okay now train the thing\n", + "BATCH_SIZE = 128\n", + "\n", + "# add a dimension so that xs have a one channel input\n", + "x_train = x_train.reshape(x_train.shape[0], x_train.shape[1]) # 1 was the middle dimension\n", + "x_val = x_val.reshape(x_val.shape[0], x_val.shape[1])\n", + "#x_test = x_test.reshape(x_test.shape[0], 1, x_test.shape[1], x_test.shape[1])\n", + "\n", + "trainData = TensorDataset(torch.Tensor(x_train), torch.Tensor(y_train))\n", + "trainDataLoader = DataLoader(trainData, batch_size=BATCH_SIZE, shuffle=True)\n", + "\n", + "valData = TensorDataset(torch.Tensor(x_val), torch.Tensor(y_val))\n", + "valDataLoader = DataLoader(valData, batch_size=BATCH_SIZE)\n", + "\n", + "# calculate steps per epoch for training and validation set\n", + "trainSteps = len(trainDataLoader.dataset) // BATCH_SIZE\n", + "valSteps = len(valDataLoader.dataset) // BATCH_SIZE\n", + "\n", + "print('shape of training set', np.shape(x_train))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "472a52ae", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[INFO] initializing the gal model...\n" + ] + } + ], + "source": [ + "# initialize the simple model\n", + "INIT_LR = 0.001\n", + "print(\"[INFO] initializing the gal model...\")\n", + "# set the device we will be using to train the model\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "\n", + "\n", + "if loss_type == 'no_var_loss':\n", + " model = models.de_no_var().to(device)\n", + " # initialize our optimizer and loss function\n", + " opt = torch.optim.Adam(model.parameters(), lr=INIT_LR)\n", + " lossFn = torch.nn.MSELoss(reduction=\"mean\")\n", + "else:\n", + " model = models.de_var().to(device)\n", + " # initialize our optimizer and loss function\n", + " opt = torch.optim.Adam(model.parameters(), lr=INIT_LR)\n", + " lossFn = torch.nn.GaussianNLLLoss(full=False, eps=1e-06, reduction=\"mean\")\n", + "#nn.MSELoss(reduction = \"mean\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "bbd9a6bc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "de_var(\n", + " (ln_1): Linear(in_features=4, out_features=100, bias=True)\n", + " (act1): ReLU()\n", + " (drop1): Dropout(p=0.1, inplace=False)\n", + " (ln_2): Linear(in_features=100, out_features=100, bias=True)\n", + " (act2): ReLU()\n", + " (drop2): Dropout(p=0.1, inplace=False)\n", + " (ln_3): Linear(in_features=100, out_features=100, bias=True)\n", + " (act3): ReLU()\n", + " (drop3): Dropout(p=0.1, inplace=False)\n", + " (ln_4): Linear(in_features=100, out_features=2, bias=True)\n", + ")\n" + ] + } + ], + "source": [ + "print(model)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "e6218eca", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([128, 4])\n", + "torch.Size([128, 4])\n", + "torch.Size([128, 4])\n", + "torch.Size([128, 4])\n", + "torch.Size([128, 4])\n", + "torch.Size([128, 4])\n", + "torch.Size([128, 4])\n", + "torch.Size([4, 4])\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot what we're trying to predict\n", + "counter = 0\n", + "for (x, y) in trainDataLoader: # loading it up in batches\n", + " #print('batch', counter, 'length', len(y))\n", + " # send the input to the device\n", + " (x, y) = (x.to(device), y.to(device))\n", + " print(np.shape(x))\n", + " pred_tensor = model(x)\n", + " pred = pred_tensor.detach().numpy()\n", + " #print(y.shape, pred.shape)\n", + " if loss_type == 'no_var_loss':\n", + " print(np.shape(y), np.shape(pred))\n", + " plt.scatter(y, pred, linestyle='None')\n", + " #var = torch.ones(5, 1, requires_grad=True) # homoscedastic\n", + " #print('var', pred[:,1])\n", + " loss = lossFn(pred_tensor, y)\n", + " #print('loss', loss.item())\n", + " else:\n", + " plt.errorbar(y, pred[:,0], yerr = abs(pred[:,1]), linestyle='None')\n", + " plt.scatter(y, pred[:,0], linestyle='None')\n", + " #var = torch.ones(5, 1, requires_grad=True) # homoscedastic\n", + " #print('var', pred[:,1])\n", + " loss = lossFn(pred_tensor[:,0], y, pred_tensor[:,1]**2)\n", + " #print('loss', loss.item())\n", + " \n", + " counter += 1\n", + "plt.xlabel('predicted')\n", + "plt.ylabel('true')\n", + "#plt.xlim([0,1])\n", + "#plt.ylim([0,1])\n", + "plt.show()\n", + "# Interesting, before the model is trained it basically predicts the mean?" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "260722fc-e794-4fd3-89f0-5d22d331215d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[INFO] training the network...\n", + "saving checkpoints?\n", + "False\n", + "starting here 0\n", + "epoch 0 0.0\n", + "(100, 4)\n", + "new best mse -0.5615105032920837 in epoch 0\n", + "epoch 1 0.03\n", + "(100, 4)\n", + "new best mse -0.8148034811019897 in epoch 1\n", + "epoch 2 0.05\n", + "(100, 4)\n", + "new best mse -0.8844935894012451 in epoch 2\n", + "epoch 3 0.07\n", + "(100, 4)\n", + "new best mse -0.9681487083435059 in epoch 3\n", + "epoch 4 0.1\n", + "(100, 4)\n", + "new best mse -1.1171536445617676 in epoch 4\n", + "epoch 5 0.12\n", + "(100, 4)\n", + "new best mse -1.3846325874328613 in epoch 5\n", + "epoch 6 0.15\n", + "(100, 4)\n", + "new best mse -1.7928428649902344 in epoch 6\n", + "epoch 7 0.17\n", + "(100, 4)\n", + "epoch 8 0.2\n", + "(100, 4)\n", + "epoch 9 0.23\n", + "(100, 4)\n", + "epoch 10 0.25\n", + "(100, 4)\n", + "epoch 11 0.28\n", + "(100, 4)\n", + "epoch 12 0.3\n", + "(100, 4)\n", + "new best mse -2.2076315879821777 in epoch 12\n", + "epoch 13 0.33\n", + "(100, 4)\n", + "epoch 14 0.35\n", + "(100, 4)\n", + "epoch 15 0.38\n", + "(100, 4)\n", + "epoch 16 0.4\n", + "(100, 4)\n", + "epoch 17 0.42\n", + "(100, 4)\n", + "epoch 18 0.45\n", + "(100, 4)\n", + "epoch 19 0.47\n", + "(100, 4)\n", + "epoch 20 0.5\n", + "(100, 4)\n", + "epoch 21 0.53\n", + "(100, 4)\n", + "new best mse -2.2450973987579346 in epoch 21\n", + "epoch 22 0.55\n", + "(100, 4)\n", + "epoch 23 0.57\n", + "(100, 4)\n", + "epoch 24 0.6\n", + "(100, 4)\n", + "epoch 25 0.62\n", + "(100, 4)\n", + "epoch 26 0.65\n", + "(100, 4)\n", + "new best mse -2.297891616821289 in epoch 26\n", + "epoch 27 0.68\n", + "(100, 4)\n", + "epoch 28 0.7\n", + "(100, 4)\n", + "epoch 29 0.72\n", + "(100, 4)\n", + "epoch 30 0.75\n", + "(100, 4)\n", + "epoch 31 0.78\n", + "(100, 4)\n", + "epoch 32 0.8\n", + "(100, 4)\n", + "epoch 33 0.82\n", + "(100, 4)\n", + "epoch 34 0.85\n", + "(100, 4)\n", + "epoch 35 0.88\n", + "(100, 4)\n", + "epoch 36 0.9\n", + "(100, 4)\n", + "epoch 37 0.93\n", + "(100, 4)\n", + "epoch 38 0.95\n", + "(100, 4)\n", + "epoch 39 0.97\n", + "(100, 4)\n", + "epoch 0 0.0\n", + "(100, 4)\n", + "epoch 1 0.03\n", + "(100, 4)\n", + "epoch 2 0.05\n", + "(100, 4)\n", + "epoch 3 0.07\n", + "(100, 4)\n", + "epoch 4 0.1\n", + "(100, 4)\n", + "epoch 5 0.12\n", + "(100, 4)\n", + "epoch 6 0.15\n", + "(100, 4)\n", + "epoch 7 0.17\n", + "(100, 4)\n", + "epoch 8 0.2\n", + "(100, 4)\n", + "epoch 9 0.23\n", + "(100, 4)\n", + "epoch 10 0.25\n", + "(100, 4)\n", + "epoch 11 0.28\n", + "(100, 4)\n", + "epoch 12 0.3\n", + "(100, 4)\n", + "epoch 13 0.33\n", + "(100, 4)\n", + "epoch 14 0.35\n", + "(100, 4)\n", + "epoch 15 0.38\n", + "(100, 4)\n", + "epoch 16 0.4\n", + "(100, 4)\n", + "new best mse -2.4166929721832275 in epoch 16\n", + "epoch 17 0.42\n", + "(100, 4)\n", + "epoch 18 0.45\n", + "(100, 4)\n", + "epoch 19 0.47\n", + "(100, 4)\n", + "epoch 20 0.5\n", + "(100, 4)\n", + "epoch 21 0.53\n", + "(100, 4)\n", + "epoch 22 0.55\n", + "(100, 4)\n", + "epoch 23 0.57\n", + "(100, 4)\n", + "epoch 24 0.6\n", + "(100, 4)\n", + "epoch 25 0.62\n", + "(100, 4)\n", + "epoch 26 0.65\n", + "(100, 4)\n", + "epoch 27 0.68\n", + "(100, 4)\n", + "epoch 28 0.7\n", + "(100, 4)\n", + "epoch 29 0.72\n", + "(100, 4)\n", + "epoch 30 0.75\n", + "(100, 4)\n", + "epoch 31 0.78\n", + "(100, 4)\n", + "epoch 32 0.8\n", + "(100, 4)\n", + "epoch 33 0.82\n", + "(100, 4)\n", + "epoch 34 0.85\n", + "(100, 4)\n", + "epoch 35 0.88\n", + "(100, 4)\n", + "epoch 36 0.9\n", + "(100, 4)\n", + "epoch 37 0.93\n", + "(100, 4)\n", + "epoch 38 0.95\n", + "(100, 4)\n", + "epoch 39 0.97\n", + "(100, 4)\n", + "epoch 0 0.0\n", + "(100, 4)\n", + "epoch 1 0.03\n", + "(100, 4)\n", + "epoch 2 0.05\n", + "(100, 4)\n", + "epoch 3 0.07\n", + "(100, 4)\n", + "epoch 4 0.1\n", + "(100, 4)\n", + "epoch 5 0.12\n", + "(100, 4)\n", + "epoch 6 0.15\n", + "(100, 4)\n", + "epoch 7 0.17\n", + "(100, 4)\n", + "epoch 8 0.2\n", + "(100, 4)\n", + "epoch 9 0.23\n", + "(100, 4)\n", + "epoch 10 0.25\n", + "(100, 4)\n", + "epoch 11 0.28\n", + "(100, 4)\n", + "epoch 12 0.3\n", + "(100, 4)\n", + "epoch 13 0.33\n", + "(100, 4)\n", + "epoch 14 0.35\n", + "(100, 4)\n", + "epoch 15 0.38\n", + "(100, 4)\n", + "epoch 16 0.4\n", + "(100, 4)\n", + "epoch 17 0.42\n", + "(100, 4)\n", + "epoch 18 0.45\n", + "(100, 4)\n", + "epoch 19 0.47\n", + "(100, 4)\n", + "epoch 20 0.5\n", + "(100, 4)\n", + "epoch 21 0.53\n", + "(100, 4)\n", + "epoch 22 0.55\n", + "(100, 4)\n", + "epoch 23 0.57\n", + "(100, 4)\n", + "epoch 24 0.6\n", + "(100, 4)\n", + "epoch 25 0.62\n", + "(100, 4)\n", + "epoch 26 0.65\n", + "(100, 4)\n", + "epoch 27 0.68\n", + "(100, 4)\n", + "epoch 28 0.7\n", + "(100, 4)\n", + "epoch 29 0.72\n", + "(100, 4)\n", + "epoch 30 0.75\n", + "(100, 4)\n", + "epoch 31 0.78\n", + "(100, 4)\n", + "epoch 32 0.8\n", + "(100, 4)\n", + "epoch 33 0.82\n", + "(100, 4)\n", + "epoch 34 0.85\n", + "(100, 4)\n", + "epoch 35 0.88\n", + "(100, 4)\n", + "epoch 36 0.9\n", + "(100, 4)\n", + "epoch 37 0.93\n", + "(100, 4)\n", + "epoch 38 0.95\n", + "(100, 4)\n", + "epoch 39 0.97\n", + "(100, 4)\n", + "epoch 0 0.0\n", + "(100, 4)\n", + "epoch 1 0.03\n", + "(100, 4)\n", + "epoch 2 0.05\n", + "(100, 4)\n", + "epoch 3 0.07\n", + "(100, 4)\n", + "epoch 4 0.1\n", + "(100, 4)\n", + "epoch 5 0.12\n", + "(100, 4)\n", + "epoch 6 0.15\n", + "(100, 4)\n", + "epoch 7 0.17\n", + "(100, 4)\n", + "epoch 8 0.2\n", + "(100, 4)\n", + "epoch 9 0.23\n", + "(100, 4)\n", + "epoch 10 0.25\n", + "(100, 4)\n", + "epoch 11 0.28\n", + "(100, 4)\n", + "epoch 12 0.3\n", + "(100, 4)\n", + "epoch 13 0.33\n", + "(100, 4)\n", + "epoch 14 0.35\n", + "(100, 4)\n", + "epoch 15 0.38\n", + "(100, 4)\n", + "epoch 16 0.4\n", + "(100, 4)\n", + "epoch 17 0.42\n", + "(100, 4)\n", + "epoch 18 0.45\n", + "(100, 4)\n", + "epoch 19 0.47\n", + "(100, 4)\n", + "epoch 20 0.5\n", + "(100, 4)\n", + "epoch 21 0.53\n", + "(100, 4)\n", + "epoch 22 0.55\n", + "(100, 4)\n", + "epoch 23 0.57\n", + "(100, 4)\n", + "epoch 24 0.6\n", + "(100, 4)\n", + "epoch 25 0.62\n", + "(100, 4)\n", + "epoch 26 0.65\n", + "(100, 4)\n", + "epoch 27 0.68\n", + "(100, 4)\n", + "epoch 28 0.7\n", + "(100, 4)\n", + "epoch 29 0.72\n", + "(100, 4)\n", + "epoch 30 0.75\n", + "(100, 4)\n", + "epoch 31 0.78\n", + "(100, 4)\n", + "epoch 32 0.8\n", + "(100, 4)\n", + "epoch 33 0.82\n", + "(100, 4)\n", + "epoch 34 0.85\n", + "(100, 4)\n", + "epoch 35 0.88\n", + "(100, 4)\n", + "epoch 36 0.9\n", + "(100, 4)\n", + "epoch 37 0.93\n", + "(100, 4)\n", + "epoch 38 0.95\n", + "(100, 4)\n", + "epoch 39 0.97\n", + "(100, 4)\n", + "epoch 0 0.0\n", + "(100, 4)\n", + "epoch 1 0.03\n", + "(100, 4)\n", + "epoch 2 0.05\n", + "(100, 4)\n", + "epoch 3 0.07\n", + "(100, 4)\n", + "epoch 4 0.1\n", + "(100, 4)\n", + "epoch 5 0.12\n", + "(100, 4)\n", + "epoch 6 0.15\n", + "(100, 4)\n", + "epoch 7 0.17\n", + "(100, 4)\n", + "epoch 8 0.2\n", + "(100, 4)\n", + "epoch 9 0.23\n", + "(100, 4)\n", + "epoch 10 0.25\n", + "(100, 4)\n", + "epoch 11 0.28\n", + "(100, 4)\n", + "epoch 12 0.3\n", + "(100, 4)\n", + "epoch 13 0.33\n", + "(100, 4)\n", + "epoch 14 0.35\n", + "(100, 4)\n", + "epoch 15 0.38\n", + "(100, 4)\n", + "epoch 16 0.4\n", + "(100, 4)\n", + "epoch 17 0.42\n", + "(100, 4)\n", + "epoch 18 0.45\n", + "(100, 4)\n", + "epoch 19 0.47\n", + "(100, 4)\n", + "epoch 20 0.5\n", + "(100, 4)\n", + "epoch 21 0.53\n", + "(100, 4)\n", + "epoch 22 0.55\n", + "(100, 4)\n", + "epoch 23 0.57\n", + "(100, 4)\n", + "epoch 24 0.6\n", + "(100, 4)\n", + "epoch 25 0.62\n", + "(100, 4)\n", + "epoch 26 0.65\n", + "(100, 4)\n", + "epoch 27 0.68\n", + "(100, 4)\n", + "epoch 28 0.7\n", + "(100, 4)\n", + "epoch 29 0.72\n", + "(100, 4)\n", + "epoch 30 0.75\n", + "(100, 4)\n", + "epoch 31 0.78\n", + "(100, 4)\n", + "epoch 32 0.8\n", + "(100, 4)\n", + "epoch 33 0.82\n", + "(100, 4)\n", + "epoch 34 0.85\n", + "(100, 4)\n", + "epoch 35 0.88\n", + "(100, 4)\n", + "epoch 36 0.9\n", + "(100, 4)\n", + "epoch 37 0.93\n", + "(100, 4)\n", + "epoch 38 0.95\n", + "(100, 4)\n", + "epoch 39 0.97\n", + "(100, 4)\n", + "epoch 0 0.0\n", + "(100, 4)\n", + "epoch 1 0.03\n", + "(100, 4)\n", + "epoch 2 0.05\n", + "(100, 4)\n", + "epoch 3 0.07\n", + "(100, 4)\n", + "epoch 4 0.1\n", + "(100, 4)\n", + "epoch 5 0.12\n", + "(100, 4)\n", + "epoch 6 0.15\n", + "(100, 4)\n", + "epoch 7 0.17\n", + "(100, 4)\n", + "epoch 8 0.2\n", + "(100, 4)\n", + "epoch 9 0.23\n", + "(100, 4)\n", + "epoch 10 0.25\n", + "(100, 4)\n", + "epoch 11 0.28\n", + "(100, 4)\n", + "epoch 12 0.3\n", + "(100, 4)\n", + "epoch 13 0.33\n", + "(100, 4)\n", + "epoch 14 0.35\n", + "(100, 4)\n", + "epoch 15 0.38\n", + "(100, 4)\n", + "epoch 16 0.4\n", + "(100, 4)\n", + "epoch 17 0.42\n", + "(100, 4)\n", + "epoch 18 0.45\n", + "(100, 4)\n", + "epoch 19 0.47\n", + "(100, 4)\n", + "epoch 20 0.5\n", + "(100, 4)\n", + "epoch 21 0.53\n", + "(100, 4)\n", + "epoch 22 0.55\n", + "(100, 4)\n", + "epoch 23 0.57\n", + "(100, 4)\n", + "epoch 24 0.6\n", + "(100, 4)\n", + "epoch 25 0.62\n", + "(100, 4)\n", + "epoch 26 0.65\n", + "(100, 4)\n", + "epoch 27 0.68\n", + "(100, 4)\n", + "epoch 28 0.7\n", + "(100, 4)\n", + "epoch 29 0.72\n", + "(100, 4)\n", + "epoch 30 0.75\n", + "(100, 4)\n", + "epoch 31 0.78\n", + "(100, 4)\n", + "epoch 32 0.8\n", + "(100, 4)\n", + "epoch 33 0.82\n", + "(100, 4)\n", + "epoch 34 0.85\n", + "(100, 4)\n", + "epoch 35 0.88\n", + "(100, 4)\n", + "epoch 36 0.9\n", + "(100, 4)\n", + "epoch 37 0.93\n", + "(100, 4)\n", + "epoch 38 0.95\n", + "(100, 4)\n", + "new best mse -2.5409891605377197 in epoch 38\n", + "epoch 39 0.97\n", + "(100, 4)\n", + "epoch 0 0.0\n", + "(100, 4)\n", + "epoch 1 0.03\n", + "(100, 4)\n", + "epoch 2 0.05\n", + "(100, 4)\n", + "epoch 3 0.07\n", + "(100, 4)\n", + "epoch 4 0.1\n", + "(100, 4)\n", + "epoch 5 0.12\n", + "(100, 4)\n", + "epoch 6 0.15\n", + "(100, 4)\n", + "epoch 7 0.17\n", + "(100, 4)\n", + "epoch 8 0.2\n", + "(100, 4)\n", + "epoch 9 0.23\n", + "(100, 4)\n", + "epoch 10 0.25\n", + "(100, 4)\n", + "epoch 11 0.28\n", + "(100, 4)\n", + "epoch 12 0.3\n", + "(100, 4)\n", + "epoch 13 0.33\n", + "(100, 4)\n", + "epoch 14 0.35\n", + "(100, 4)\n", + "epoch 15 0.38\n", + "(100, 4)\n", + "epoch 16 0.4\n", + "(100, 4)\n", + "epoch 17 0.42\n", + "(100, 4)\n", + "epoch 18 0.45\n", + "(100, 4)\n", + "epoch 19 0.47\n", + "(100, 4)\n", + "epoch 20 0.5\n", + "(100, 4)\n", + "epoch 21 0.53\n", + "(100, 4)\n", + "epoch 22 0.55\n", + "(100, 4)\n", + "epoch 23 0.57\n", + "(100, 4)\n", + "epoch 24 0.6\n", + "(100, 4)\n", + "epoch 25 0.62\n", + "(100, 4)\n", + "epoch 26 0.65\n", + "(100, 4)\n", + "epoch 27 0.68\n", + "(100, 4)\n", + "epoch 28 0.7\n", + "(100, 4)\n", + "epoch 29 0.72\n", + "(100, 4)\n", + "epoch 30 0.75\n", + "(100, 4)\n", + "epoch 31 0.78\n", + "(100, 4)\n", + "epoch 32 0.8\n", + "(100, 4)\n", + "epoch 33 0.82\n", + "(100, 4)\n", + "epoch 34 0.85\n", + "(100, 4)\n", + "epoch 35 0.88\n", + "(100, 4)\n", + "epoch 36 0.9\n", + "(100, 4)\n", + "epoch 37 0.93\n", + "(100, 4)\n", + "epoch 38 0.95\n", + "(100, 4)\n", + "epoch 39 0.97\n", + "(100, 4)\n", + "epoch 0 0.0\n", + "(100, 4)\n", + "epoch 1 0.03\n", + "(100, 4)\n", + "epoch 2 0.05\n", + "(100, 4)\n", + "epoch 3 0.07\n", + "(100, 4)\n", + "epoch 4 0.1\n", + "(100, 4)\n", + "epoch 5 0.12\n", + "(100, 4)\n", + "epoch 6 0.15\n", + "(100, 4)\n", + "epoch 7 0.17\n", + "(100, 4)\n", + "epoch 8 0.2\n", + "(100, 4)\n", + "epoch 9 0.23\n", + "(100, 4)\n", + "epoch 10 0.25\n", + "(100, 4)\n", + "epoch 11 0.28\n", + "(100, 4)\n", + "epoch 12 0.3\n", + "(100, 4)\n", + "epoch 13 0.33\n", + "(100, 4)\n", + "epoch 14 0.35\n", + "(100, 4)\n", + "epoch 15 0.38\n", + "(100, 4)\n", + "epoch 16 0.4\n", + "(100, 4)\n", + "epoch 17 0.42\n", + "(100, 4)\n", + "epoch 18 0.45\n", + "(100, 4)\n", + "epoch 19 0.47\n", + "(100, 4)\n", + "epoch 20 0.5\n", + "(100, 4)\n", + "epoch 21 0.53\n", + "(100, 4)\n", + "epoch 22 0.55\n", + "(100, 4)\n", + "epoch 23 0.57\n", + "(100, 4)\n", + "epoch 24 0.6\n", + "(100, 4)\n", + "epoch 25 0.62\n", + "(100, 4)\n", + "epoch 26 0.65\n", + "(100, 4)\n", + "epoch 27 0.68\n", + "(100, 4)\n", + "epoch 28 0.7\n", + "(100, 4)\n", + "epoch 29 0.72\n", + "(100, 4)\n", + "epoch 30 0.75\n", + "(100, 4)\n", + "epoch 31 0.78\n", + "(100, 4)\n", + "epoch 32 0.8\n", + "(100, 4)\n", + "epoch 33 0.82\n", + "(100, 4)\n", + "epoch 34 0.85\n", + "(100, 4)\n", + "epoch 35 0.88\n", + "(100, 4)\n", + "epoch 36 0.9\n", + "(100, 4)\n", + "epoch 37 0.93\n", + "(100, 4)\n", + "epoch 38 0.95\n", + "(100, 4)\n", + "epoch 39 0.97\n", + "(100, 4)\n", + "epoch 0 0.0\n", + "(100, 4)\n", + "epoch 1 0.03\n", + "(100, 4)\n", + "epoch 2 0.05\n", + "(100, 4)\n", + "epoch 3 0.07\n", + "(100, 4)\n", + "epoch 4 0.1\n", + "(100, 4)\n", + "epoch 5 0.12\n", + "(100, 4)\n", + "epoch 6 0.15\n", + "(100, 4)\n", + "epoch 7 0.17\n", + "(100, 4)\n", + "epoch 8 0.2\n", + "(100, 4)\n", + "epoch 9 0.23\n", + "(100, 4)\n", + "epoch 10 0.25\n", + "(100, 4)\n", + "epoch 11 0.28\n", + "(100, 4)\n", + "epoch 12 0.3\n", + "(100, 4)\n", + "epoch 13 0.33\n", + "(100, 4)\n", + "epoch 14 0.35\n", + "(100, 4)\n", + "epoch 15 0.38\n", + "(100, 4)\n", + "epoch 16 0.4\n", + "(100, 4)\n", + "epoch 17 0.42\n", + "(100, 4)\n", + "epoch 18 0.45\n", + "(100, 4)\n", + "epoch 19 0.47\n", + "(100, 4)\n", + "epoch 20 0.5\n", + "(100, 4)\n", + "new best mse -2.612260341644287 in epoch 20\n", + "epoch 21 0.53\n", + "(100, 4)\n", + "new best mse -2.6678833961486816 in epoch 21\n", + "epoch 22 0.55\n", + "(100, 4)\n", + "new best mse -2.694044828414917 in epoch 22\n", + "epoch 23 0.57\n", + "(100, 4)\n", + "epoch 24 0.6\n", + "(100, 4)\n", + "epoch 25 0.62\n", + "(100, 4)\n", + "epoch 26 0.65\n", + "(100, 4)\n", + "epoch 27 0.68\n", + "(100, 4)\n", + "epoch 28 0.7\n", + "(100, 4)\n", + "epoch 29 0.72\n", + "(100, 4)\n", + "epoch 30 0.75\n", + "(100, 4)\n", + "epoch 31 0.78\n", + "(100, 4)\n", + "epoch 32 0.8\n", + "(100, 4)\n", + "epoch 33 0.82\n", + "(100, 4)\n", + "epoch 34 0.85\n", + "(100, 4)\n", + "epoch 35 0.88\n", + "(100, 4)\n", + "epoch 36 0.9\n", + "(100, 4)\n", + "epoch 37 0.93\n", + "(100, 4)\n", + "epoch 38 0.95\n", + "(100, 4)\n", + "epoch 39 0.97\n", + "(100, 4)\n", + "epoch 0 0.0\n", + "(100, 4)\n", + "epoch 1 0.03\n", + "(100, 4)\n", + "epoch 2 0.05\n", + "(100, 4)\n", + "epoch 3 0.07\n", + "(100, 4)\n", + "epoch 4 0.1\n", + "(100, 4)\n", + "epoch 5 0.12\n", + "(100, 4)\n", + "epoch 6 0.15\n", + "(100, 4)\n", + "epoch 7 0.17\n", + "(100, 4)\n", + "epoch 8 0.2\n", + "(100, 4)\n", + "epoch 9 0.23\n", + "(100, 4)\n", + "epoch 10 0.25\n", + "(100, 4)\n", + "epoch 11 0.28\n", + "(100, 4)\n", + "epoch 12 0.3\n", + "(100, 4)\n", + "epoch 13 0.33\n", + "(100, 4)\n", + "epoch 14 0.35\n", + "(100, 4)\n", + "epoch 15 0.38\n", + "(100, 4)\n", + "epoch 16 0.4\n", + "(100, 4)\n", + "epoch 17 0.42\n", + "(100, 4)\n", + "epoch 18 0.45\n", + "(100, 4)\n", + "epoch 19 0.47\n", + "(100, 4)\n", + "epoch 20 0.5\n", + "(100, 4)\n", + "epoch 21 0.53\n", + "(100, 4)\n", + "epoch 22 0.55\n", + "(100, 4)\n", + "epoch 23 0.57\n", + "(100, 4)\n", + "epoch 24 0.6\n", + "(100, 4)\n", + "epoch 25 0.62\n", + "(100, 4)\n", + "epoch 26 0.65\n", + "(100, 4)\n", + "epoch 27 0.68\n", + "(100, 4)\n", + "epoch 28 0.7\n", + "(100, 4)\n", + "epoch 29 0.72\n", + "(100, 4)\n", + "epoch 30 0.75\n", + "(100, 4)\n", + "epoch 31 0.78\n", + "(100, 4)\n", + "epoch 32 0.8\n", + "(100, 4)\n", + "epoch 33 0.82\n", + "(100, 4)\n", + "epoch 34 0.85\n", + "(100, 4)\n", + "epoch 35 0.88\n", + "(100, 4)\n", + "epoch 36 0.9\n", + "(100, 4)\n", + "epoch 37 0.93\n", + "(100, 4)\n", + "epoch 38 0.95\n", + "(100, 4)\n", + "epoch 39 0.97\n", + "(100, 4)\n", + "start at 1699486680.923317 end at 1699486684.62006\n", + "3.6967430114746094\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "n_models = 10\n", + "model_ensemble = train.train_DE(trainDataLoader,\n", + " x_val,\n", + " y_val,\n", + " INIT_LR,\n", + " device,\n", + " loss_type,\n", + " n_models,\n", + " model_name='DE',\n", + " EPOCHS=40,\n", + " save_checkpoints=False,\n", + " plot=False)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "5689d17f-f1a6-4cde-8f65-459ef969b60d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(100, 4) (100,)\n", + "[0.52737029 0.45062146 0.1429688 0.50652642] 0.30948618607086154\n", + "og_xval [5.13117276e+00 3.47605067e-02 5.79065461e+00 3.69802671e-04]\n", + "og_yval 0.15557922609750496\n" + ] + }, + { + "ename": "NameError", + "evalue": "name 'STOP' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[16], line 11\u001b[0m\n\u001b[1;32m 9\u001b[0m og_yval \u001b[38;5;241m=\u001b[39m y_val[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m*\u001b[39m (ymax \u001b[38;5;241m-\u001b[39m ymin) \u001b[38;5;241m+\u001b[39m ymin\n\u001b[1;32m 10\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mog_yval\u001b[39m\u001b[38;5;124m'\u001b[39m, og_yval)\n\u001b[0;32m---> 11\u001b[0m \u001b[43mSTOP\u001b[49m\n\u001b[1;32m 12\u001b[0m color_list \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m#C7F2A7\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m#FF8360\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m#C7F2A7\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m#FF8360\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m#C7F2A7\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m#FF8360\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m#C7F2A7\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m#FF8360\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m#C7F2A7\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m#FF8360\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m 14\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(n_models):\n", + "\u001b[0;31mNameError\u001b[0m: name 'STOP' is not defined" + ] + } + ], + "source": [ + "# plot the \"posterior\" plot, which is gonna be a Gaussian\n", + "# for each individual data point\n", + "print(np.shape(x_val), np.shape(y_val))\n", + "print(x_val[0], y_val[0])\n", + "# is there a way to convert x_val[0][3] back to its true value?\n", + "# og_value = normal_value * (xmax - xmin) + xmin\n", + "og_xval = x_val[0] * (xmax - xmin) + xmin\n", + "print('og_xval', og_xval)\n", + "og_yval = y_val[0] * (ymax - ymin) + ymin\n", + "print('og_yval', og_yval)\n", + "STOP\n", + "color_list = ['#C7F2A7','#FF8360','#C7F2A7','#FF8360','#C7F2A7','#FF8360','#C7F2A7','#FF8360','#C7F2A7','#FF8360']\n", + "\n", + "for m in range(n_models):\n", + " model = model_ensemble[m]\n", + " model.eval()\n", + " y_pred = model(torch.Tensor(x_val[0]))\n", + " print(y_pred)\n", + " STOP\n", + " y_pred_list.append(y_pred[:,0].detach().numpy())\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a66ed474", + "metadata": {}, + "outputs": [], + "source": [ + "# there's gotta be a good way to plot the x and the y\n", + "plt.clf()\n", + "\n", + "color_list = ['#C7F2A7','#FF8360','#C7F2A7','#FF8360','#C7F2A7','#FF8360','#C7F2A7','#FF8360','#C7F2A7','#FF8360']\n", + "y_pred_list = []\n", + "for m in range(n_models):\n", + " print(m)\n", + " \n", + " \n", + " \n", + " model = model_ensemble[m]\n", + " model.eval()\n", + " y_pred = model(torch.Tensor(x_val))\n", + " y_pred_list.append(y_pred[:,0].detach().numpy())\n", + " if m > 0:\n", + " continue\n", + "\n", + " if loss_type == 'no_var_loss':\n", + " plt.scatter(x_val[:,1], y_pred.detach().numpy(),\n", + " label = 'predicted', s = 10, color = 'black')#color = color_list[m],\n", + " else:\n", + " plt.scatter(x_val[:,1], y_pred[:,0].detach().numpy(),\n", + " label = f'predicted model {m}', color = 'black', ls = 'None')#color = color_list[m],\n", + " plt.errorbar(x_val[:,1], y_pred[:,0].detach().numpy(),\n", + " yerr = abs(y_pred[:,1].detach().numpy()),\n", + " label = f'predicted var model {m}', color = 'black', ls = 'None')#color = color_list[m],\n", + " \n", + "dx_dtheta = analysis.calc_error_prop(x_val[:,0], x_val[:,1], x_val[:,2], 0.1, time = 0.5, wrt = 'theta_0')\n", + "#print(dx_dtheta)\n", + "plt.scatter(x_val[:,1], y_val, label = 'actual', color = 'red', s = 10)\n", + "plt.errorbar(x_val[:,1], y_val, yerr = dx_dtheta, label = 'actual', color = 'red', ls = 'None')\n", + "\n", + "plt.xlabel('theta')\n", + "plt.ylabel('x pos')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "0876c80d-04eb-4e9f-b98c-daf6a815d0c9", + "metadata": {}, + "source": [ + "## Now epistemic error" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8056e02a", + "metadata": {}, + "outputs": [], + "source": [ + "#print(y_pred_list)\n", + "#print(np.mean(y_pred_list, axis = 0))\n", + "\n", + "plt.clf()\n", + "plt.scatter(x_val[:,1], np.mean(y_pred_list, axis = 0), label = 'predicted mean', color = 'black')\n", + "plt.errorbar(x_val[:,1], np.mean(y_pred_list, axis = 0),\n", + " yerr = np.std(y_pred_list, axis = 0),\n", + " label = 'predicted std', color = 'black',\n", + " ls = 'None')\n", + "dx_dtheta = analysis.calc_error_prop(x_val[:,0], x_val[:,1], x_val[:,2], 0.1, time = 0.5, wrt = 'theta_0')\n", + "#print(dx_dtheta)\n", + "plt.scatter(x_val[:,1], y_val, label = 'actual', color = 'red', s = 10)\n", + "plt.errorbar(x_val[:,1], y_val, yerr = dx_dtheta, label = 'actual analytic expectation', color = 'red', ls = 'None')\n", + "plt.xlabel('theta')\n", + "plt.ylabel('x pos')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "312d0dd5-87f9-4a43-b944-2194814b40b0", + "metadata": {}, + "outputs": [], + "source": [ + "plt.clf()\n", + "plt.scatter(x_val[:,3], np.mean(y_pred_list, axis = 0), label = 'predicted mean', color = 'black')\n", + "plt.errorbar(x_val[:,3], np.mean(y_pred_list, axis = 0),\n", + " yerr = np.std(y_pred_list, axis = 0),\n", + " label = 'predicted std', color = 'black',\n", + " ls = 'None')\n", + "plt.scatter(x_val[:,3], y_val, label = 'actual', color = 'red', s = 10)\n", + "#plt.errorbar(x_val[:,3], y_val, yerr = dx_dtheta, label = 'actual analytic expectation', color = 'red', ls = 'None')\n", + "plt.xlabel('epsilon')\n", + "plt.ylabel('x pos')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "93e20250", + "metadata": {}, + "outputs": [], + "source": [ + "# there's gotta be a good way to plot the x and the y\n", + "plt.clf()\n", + "\n", + "y_pred_list_array = np.zeros((np.shape(x_val)[0], n_models))\n", + "for m in range(n_models):\n", + " model = model_ensemble[m]\n", + " model.eval()\n", + " y_pred = model(torch.Tensor(x_val))\n", + " y_pred_list_array[:,m] = y_pred.detach().numpy().flatten()\n", + "\n", + "sort_indices = np.argsort(x_val[:, 1])\n", + "x_val_sorted = x_val[sort_indices]\n", + "y_pred_list_sorted = y_pred_list_array[sort_indices] \n", + "medians = np.median(y_pred_list_sorted, axis = 1).flatten()\n", + "mins = np.min(y_pred_list_sorted, axis = 1).flatten()\n", + "maxes = np.max(y_pred_list_sorted, axis = 1).flatten()\n", + "\n", + "#plt.fill_between(x_val_sorted[:,1],\n", + "# mins,\n", + "# maxes,\n", + "# label = 'predicted')#color = color_list[m],\n", + "plt.errorbar(x_val_sorted[:,1], medians,\n", + " yerr = [medians - mins,maxes - medians], linestyle = 'None', capsize = 5, color = 'black')\n", + "\n", + "plt.scatter(x_val[:,1], y_val, label = 'actual', color = 'red', s = 10)\n", + "\n", + "plt.xlabel('theta')\n", + "plt.ylabel('x pos')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6287a786", + "metadata": {}, + "outputs": [], + "source": [ + "# also go through and plot the error prop uncertainty given x_val\n", + "plt.clf()\n", + "\n", + "\n", + "\n", + "y_pred_list_array = np.zeros((np.shape(x_val)[0], n_models))\n", + "for m in range(n_models):\n", + " model = model_ensemble[m]\n", + " model.eval()\n", + " y_pred = model(torch.Tensor(x_val))\n", + " y_pred_list_array[:,m] = y_pred.detach().numpy().flatten()\n", + "\n", + "sort_indices = np.argsort(x_val[:, 1])\n", + "x_val_sorted = x_val[sort_indices]\n", + "y_pred_list_sorted = y_pred_list_array[sort_indices] \n", + "medians = np.median(y_pred_list_sorted, axis = 1).flatten()\n", + "mins = np.min(y_pred_list_sorted, axis = 1).flatten()\n", + "maxes = np.max(y_pred_list_sorted, axis = 1).flatten()\n", + "\n", + "#plt.fill_between(x_val_sorted[:,1],\n", + "# mins,\n", + "# maxes,\n", + "# label = 'predicted')#color = color_list[m],\n", + "plt.errorbar(x_val_sorted[:,1], medians,\n", + " yerr = [medians - mins,maxes - medians], linestyle = 'None', capsize = 5, color = 'black',\n", + " label = 'prediction')\n", + "\n", + "plt.scatter(x_val[:,1], y_val, label = 'actual', color = 'red', s = 10)\n", + "plt.errorbar(x_val[:,1], y_val,\n", + " yerr = calc_error_prop(x_val[:,0], x_val[:,1], x_val[:,2], percent_error * x_val[:,1], time = 0.5, wrt = 'theta_0'),\n", + " color = 'red', ls = 'None', capsize = 5)\n", + "\n", + "\n", + "plt.xlabel('theta')\n", + "plt.ylabel('x pos')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b36675f9", + "metadata": {}, + "outputs": [], + "source": [ + "analysis.calc_error_prop(x_val[:,0], x_val[:,1], x_val[:,2], percent_error * x_val[:,1], time = 0.5, wrt = 'theta_0')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c7d0effa", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}