From 0af6e5cefee75a9fc00eea8a0a83de30919dbbc9 Mon Sep 17 00:00:00 2001 From: euisuk-chung Date: Tue, 23 Nov 2021 06:19:53 +0000 Subject: [PATCH] debugging complete --- VRAE_example.ipynb | 577 +++---------------------- model/org_vrae.py | 491 ---------------------- model/vrae.py | 40 +- saved_model/vrae.pth | Bin 0 -> 393415 bytes test.ipynb | 976 ------------------------------------------- 5 files changed, 66 insertions(+), 2018 deletions(-) delete mode 100644 model/org_vrae.py create mode 100644 saved_model/vrae.pth delete mode 100644 test.ipynb diff --git a/VRAE_example.ipynb b/VRAE_example.ipynb index 860e312..dd7de3e 100644 --- a/VRAE_example.ipynb +++ b/VRAE_example.ipynb @@ -22,12 +22,11 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ - "# from model.vrae import VRAE\n", - "from model.org_vrae import VRAE\n", + "from model.vrae import VRAE\n", "\n", "from model.utils import *\n", "import numpy as np\n", @@ -46,7 +45,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -87,17 +86,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(23195128, 56)\n" - ] - } - ], + "outputs": [], "source": [ "# params\n", "folder = 'data'\n", @@ -112,72 +103,52 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "class HamonDataset(Dataset):\n", - " def __init__(self, data, window, stride):\n", + " def __init__(self, data, window):\n", " self.data = torch.Tensor(data)\n", " self.window = window\n", " \n", " def __len__(self):\n", - " return len(self.data) - self.window \n", + " df_len = len(self.data)\n", + " left = df_len%window\n", + "# return len(self.data)-left\n", + " return len(self.data) // self.window -1\n", " \n", " def __getitem__(self, index):\n", - " x_index = index*self.window\n", - " x = self.data[x_index:x_index+self.window]\n", + "# x = self.data[index*self.window:index*(self.window+1)]\n", + " x = self.data[index*self.window:(index+1)*(self.window)]\n", " return x" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data = df_total\n", - "stride = 10\n", "window = 100" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "<__main__.HamonDataset at 0x7f8662f866d8>" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "train_dataset = HamonDataset(data, window, stride)\n", + "train_dataset = HamonDataset(data, window)\n", "train_dataset" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([100, 56])" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "train_dataset[0].shape" ] @@ -191,20 +162,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "100" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "sequence_length = train_dataset[0].shape[0]\n", "sequence_length" @@ -221,20 +181,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "56" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "number_of_features = train_dataset[0].shape[1]\n", "number_of_features" @@ -249,11 +198,11 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "n_epochs = 1\n", + "n_epochs = 50\n", "hidden_size = 90\n", "hidden_layer_depth = 1\n", "latent_length = 20\n", @@ -269,145 +218,6 @@ "block = 'LSTM' # options: LSTM, GRU" ] }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "train_loader = DataLoader(dataset = train_dataset,\n", - " batch_size = batch_size,\n", - " shuffle = False,\n", - " drop_last=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([[[2.7220e+03, 1.2400e+02, 1.8431e+05, ..., 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00],\n", - " [2.7220e+03, 1.2400e+02, 3.8349e+05, ..., 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00],\n", - " [2.7220e+03, 1.2400e+02, 2.3519e+05, ..., 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00],\n", - " ...,\n", - " [2.8500e+03, 1.2400e+02, 2.3200e+02, ..., 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00],\n", - " [2.8500e+03, 1.2400e+02, 2.4000e+02, ..., 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00],\n", - " [2.8500e+03, 1.2400e+02, 2.4000e+02, ..., 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00]],\n", - "\n", - " [[2.8500e+03, 1.2400e+02, 2.4000e+02, ..., 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00],\n", - " [2.8500e+03, 1.2400e+02, 2.4000e+02, ..., 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00],\n", - " [2.8500e+03, 1.2400e+02, 2.4000e+02, ..., 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00],\n", - " ...,\n", - " [2.8630e+03, 1.2400e+02, 1.8664e+04, ..., 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00],\n", - " [2.8630e+03, 1.2400e+02, 1.9056e+04, ..., 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00],\n", - " [2.8630e+03, 1.2400e+02, 1.8104e+04, ..., 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00]],\n", - "\n", - " [[2.8630e+03, 1.2400e+02, 1.8096e+04, ..., 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00],\n", - " [2.8630e+03, 1.2400e+02, 1.8640e+04, ..., 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00],\n", - " [2.8630e+03, 1.2400e+02, 1.9448e+04, ..., 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00],\n", - " ...,\n", - " [2.8730e+03, 1.2400e+02, 3.3920e+03, ..., 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00],\n", - " [2.8730e+03, 1.2400e+02, 3.4480e+03, ..., 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00],\n", - " [2.8730e+03, 1.2400e+02, 3.3840e+03, ..., 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00]],\n", - "\n", - " ...,\n", - "\n", - " [[3.7730e+03, 1.2400e+02, 2.0880e+03, ..., 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00],\n", - " [3.7730e+03, 1.2400e+02, 1.9360e+03, ..., 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00],\n", - " [3.7730e+03, 1.2400e+02, 1.9840e+03, ..., 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00],\n", - " ...,\n", - " [3.7810e+03, 1.2400e+02, 1.7760e+03, ..., 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00],\n", - " [3.7810e+03, 1.2400e+02, 1.6800e+03, ..., 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00],\n", - " [3.7810e+03, 1.2400e+02, 1.7600e+03, ..., 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00]],\n", - "\n", - " [[3.7820e+03, 1.2400e+02, 5.1096e+04, ..., 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00],\n", - " [3.7820e+03, 1.2400e+02, 1.2566e+06, ..., 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00],\n", - " [3.7820e+03, 1.2400e+02, 5.2016e+04, ..., 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00],\n", - " ...,\n", - " [3.7900e+03, 1.2400e+02, 1.6496e+04, ..., 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00],\n", - " [3.7900e+03, 1.2400e+02, 1.6416e+04, ..., 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00],\n", - " [3.7900e+03, 1.2400e+02, 1.6776e+04, ..., 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00]],\n", - "\n", - " [[3.7900e+03, 1.2400e+02, 1.6032e+04, ..., 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00],\n", - " [3.7900e+03, 1.2400e+02, 1.6528e+04, ..., 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00],\n", - " [3.7900e+03, 1.2400e+02, 1.7032e+04, ..., 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00],\n", - " ...,\n", - " [3.7980e+03, 1.2400e+02, 6.1760e+03, ..., 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00],\n", - " [3.7980e+03, 1.2400e+02, 6.1920e+03, ..., 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00],\n", - " [3.7980e+03, 1.2400e+02, 5.9840e+03, ..., 0.0000e+00,\n", - " 0.0000e+00, 0.0000e+00]]])" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X = iter(train_loader).next()\n", - "X" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([32, 100, 56])" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X.shape" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -419,20 +229,9 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/rnn.py:65: UserWarning: dropout option adds dropout after all but last recurrent layer, so non-zero dropout expects num_layers greater than 1, but got dropout=0.2 and num_layers=1\n", - " \"num_layers={}\".format(dropout, num_layers))\n", - "/usr/local/lib/python3.6/dist-packages/torch/nn/_reduction.py:42: UserWarning: size_average and reduce args will be deprecated, please use reduction='sum' instead.\n", - " warnings.warn(warning.format(ret))\n" - ] - } - ], + "outputs": [], "source": [ "vrae = VRAE(sequence_length=sequence_length,\n", " number_of_features = number_of_features,\n", @@ -462,277 +261,11 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch: 0\n", - "Batch 30, loss = 19379261806739456.0000, recon_loss = 19379261806739456.0000, kl_loss = 0.2303\n", - "Batch 60, loss = 91916457662742528.0000, recon_loss = 91916457662742528.0000, kl_loss = 0.3164\n", - "Batch 90, loss = 199138993357455360.0000, recon_loss = 199138993357455360.0000, kl_loss = 0.4391\n", - "Batch 120, loss = 94359520960053248.0000, recon_loss = 94359520960053248.0000, kl_loss = 0.6790\n", - "Batch 150, loss = 3174933777612800.0000, recon_loss = 3174933777612800.0000, kl_loss = 0.9729\n", - "Batch 180, loss = 79180144463314944.0000, recon_loss = 79180144463314944.0000, kl_loss = 1.2229\n", - "Batch 210, loss = 83308741906137088.0000, recon_loss = 83308741906137088.0000, kl_loss = 1.4732\n", - "Batch 240, loss = 1843474051629056.0000, recon_loss = 1843474051629056.0000, kl_loss = 1.8257\n", - "Batch 270, loss = 37535862388424704.0000, recon_loss = 37535862388424704.0000, kl_loss = 1.6207\n", - "Batch 300, loss = 98044018784468992.0000, recon_loss = 98044018784468992.0000, kl_loss = 2.0940\n", - "Batch 330, loss = 14806790231293952.0000, recon_loss = 14806790231293952.0000, kl_loss = 2.3544\n", - "Batch 360, loss = 2522978948481024.0000, recon_loss = 2522978948481024.0000, kl_loss = 1.5472\n", - "Batch 390, loss = 87295141571723264.0000, recon_loss = 87295141571723264.0000, kl_loss = 2.5580\n", - "Batch 420, loss = 2241480897058570240.0000, recon_loss = 2241480897058570240.0000, kl_loss = 2.3133\n", - "Batch 450, loss = 3360706682421248.0000, recon_loss = 3360706682421248.0000, kl_loss = 1.7848\n", - "Batch 480, loss = 234995098331381760.0000, recon_loss = 234995098331381760.0000, kl_loss = 2.5119\n", - "Batch 510, loss = 213598636414599168.0000, recon_loss = 213598636414599168.0000, kl_loss = 2.1470\n", - "Batch 540, loss = 136755057239523328.0000, recon_loss = 136755057239523328.0000, kl_loss = 1.8140\n", - "Batch 570, loss = 1136399968009977856.0000, recon_loss = 1136399968009977856.0000, kl_loss = 2.7262\n", - "Batch 600, loss = 41889005016449024.0000, recon_loss = 41889005016449024.0000, kl_loss = 2.9700\n", - "Batch 630, loss = 93917534465556480.0000, recon_loss = 93917534465556480.0000, kl_loss = 3.0759\n", - "Batch 660, loss = 1300890618377011200.0000, recon_loss = 1300890618377011200.0000, kl_loss = 2.6904\n", - "Batch 690, loss = 132702008271437824.0000, recon_loss = 132702008271437824.0000, kl_loss = 3.0196\n", - "Batch 720, loss = 41139730791792640.0000, recon_loss = 41139730791792640.0000, kl_loss = 3.2321\n", - "Batch 750, loss = 549135861128626176.0000, recon_loss = 549135861128626176.0000, kl_loss = 3.1368\n", - "Batch 780, loss = 1613814238982176768.0000, recon_loss = 1613814238982176768.0000, kl_loss = 3.3273\n", - "Batch 810, loss = 642778414608023552.0000, recon_loss = 642778414608023552.0000, kl_loss = 3.0262\n", - "Batch 840, loss = 300460312706940928.0000, recon_loss = 300460312706940928.0000, kl_loss = 3.2334\n", - "Batch 870, loss = 258167391786106880.0000, recon_loss = 258167391786106880.0000, kl_loss = 3.3916\n", - "Batch 900, loss = 4611922997542912.0000, recon_loss = 4611922997542912.0000, kl_loss = 3.4092\n", - "Batch 930, loss = 985828035338960896.0000, recon_loss = 985828035338960896.0000, kl_loss = 3.1072\n", - "Batch 960, loss = 281314774430515200.0000, recon_loss = 281314774430515200.0000, kl_loss = 3.3649\n", - "Batch 990, loss = 1010036051567181824.0000, recon_loss = 1010036051567181824.0000, kl_loss = 3.5526\n", - "Batch 1020, loss = 625583358419664896.0000, recon_loss = 625583358419664896.0000, kl_loss = 3.0276\n", - "Batch 1050, loss = 3345548351264260096.0000, recon_loss = 3345548351264260096.0000, kl_loss = 3.4595\n", - "Batch 1080, loss = 707813221720915968.0000, recon_loss = 707813221720915968.0000, kl_loss = 3.3172\n", - "Batch 1110, loss = 448382869031616512.0000, recon_loss = 448382869031616512.0000, kl_loss = 3.5179\n", - "Batch 1140, loss = 621316703548080128.0000, recon_loss = 621316703548080128.0000, kl_loss = 3.4117\n", - "Batch 1170, loss = 649576969880469504.0000, recon_loss = 649576969880469504.0000, kl_loss = 3.5944\n", - "Batch 1200, loss = 262826331790639104.0000, recon_loss = 262826331790639104.0000, kl_loss = 3.6796\n", - "Batch 1230, loss = 637669877426946048.0000, recon_loss = 637669877426946048.0000, kl_loss = 3.7638\n", - "Batch 1260, loss = 80988377234538496.0000, recon_loss = 80988377234538496.0000, kl_loss = 3.9598\n", - "Batch 1290, loss = 168746705259331584.0000, recon_loss = 168746705259331584.0000, kl_loss = 3.7831\n", - "Batch 1320, loss = 286936147886604288.0000, recon_loss = 286936147886604288.0000, kl_loss = 3.4293\n", - "Batch 1350, loss = 71298561647575040.0000, recon_loss = 71298561647575040.0000, kl_loss = 3.5952\n", - "Batch 1380, loss = 876040699793899520.0000, recon_loss = 876040699793899520.0000, kl_loss = 3.2339\n", - "Batch 1410, loss = 9273483378802294784.0000, recon_loss = 9273483378802294784.0000, kl_loss = 3.2680\n", - "Batch 1440, loss = 41132426529890893824.0000, recon_loss = 41132426529890893824.0000, kl_loss = 2.6957\n", - "Batch 1470, loss = 33044045162017193984.0000, recon_loss = 33044045162017193984.0000, kl_loss = 2.8820\n", - "Batch 1500, loss = 31619603658977574912.0000, recon_loss = 31619603658977574912.0000, kl_loss = 2.7427\n", - "Batch 1530, loss = 38652803508883947520.0000, recon_loss = 38652803508883947520.0000, kl_loss = 2.7473\n", - "Batch 1560, loss = 32683682425036865536.0000, recon_loss = 32683682425036865536.0000, kl_loss = 2.7802\n", - "Batch 1590, loss = 25223335501879050240.0000, recon_loss = 25223335501879050240.0000, kl_loss = 2.8415\n", - "Batch 1620, loss = 33390400120859656192.0000, recon_loss = 33390400120859656192.0000, kl_loss = 2.8633\n", - "Batch 1650, loss = 47161906404056367104.0000, recon_loss = 47161906404056367104.0000, kl_loss = 3.1276\n", - "Batch 1680, loss = 43688496395353849856.0000, recon_loss = 43688496395353849856.0000, kl_loss = 2.9582\n", - "Batch 1710, loss = 33433166725133631488.0000, recon_loss = 33433166725133631488.0000, kl_loss = 2.9795\n", - "Batch 1740, loss = 23964753128866185216.0000, recon_loss = 23964753128866185216.0000, kl_loss = 2.9744\n", - "Batch 1770, loss = 16878229164035932160.0000, recon_loss = 16878229164035932160.0000, kl_loss = 3.0740\n", - "Batch 1800, loss = 5926930623666061312.0000, recon_loss = 5926930623666061312.0000, kl_loss = 3.0954\n", - "Batch 1830, loss = 4814176477845127168.0000, recon_loss = 4814176477845127168.0000, kl_loss = 3.2625\n", - "Batch 1860, loss = 4582059402739056640.0000, recon_loss = 4582059402739056640.0000, kl_loss = 3.4350\n", - "Batch 1890, loss = 4817600357054021632.0000, recon_loss = 4817600357054021632.0000, kl_loss = 3.7322\n", - "Batch 1920, loss = 4375878432542687232.0000, recon_loss = 4375878432542687232.0000, kl_loss = 3.8269\n", - "Batch 1950, loss = 4619853740554321920.0000, recon_loss = 4619853740554321920.0000, kl_loss = 4.0293\n", - "Batch 1980, loss = 2280669140739948544.0000, recon_loss = 2280669140739948544.0000, kl_loss = 3.8210\n", - "Batch 2010, loss = 2447821846196781056.0000, recon_loss = 2447821846196781056.0000, kl_loss = 3.5923\n", - "Batch 2040, loss = 1861221122784100352.0000, recon_loss = 1861221122784100352.0000, kl_loss = 3.5020\n", - "Batch 2070, loss = 1222147581325344768.0000, recon_loss = 1222147581325344768.0000, kl_loss = 3.5126\n", - "Batch 2100, loss = 909030515393167360.0000, recon_loss = 909030515393167360.0000, kl_loss = 4.1649\n", - "Batch 2130, loss = 431430530035089408.0000, recon_loss = 431430530035089408.0000, kl_loss = 4.1230\n", - "Batch 2160, loss = 543629197659078656.0000, recon_loss = 543629197659078656.0000, kl_loss = 4.0491\n", - "Batch 2190, loss = 263748701787258880.0000, recon_loss = 263748701787258880.0000, kl_loss = 3.8632\n", - "Batch 2220, loss = 185978114050883584.0000, recon_loss = 185978114050883584.0000, kl_loss = 4.0508\n", - "Batch 2250, loss = 114880943419817984.0000, recon_loss = 114880943419817984.0000, kl_loss = 4.1684\n", - "Batch 2280, loss = 126620695357554688.0000, recon_loss = 126620695357554688.0000, kl_loss = 4.2360\n", - "Batch 2310, loss = 95448836335468544.0000, recon_loss = 95448836335468544.0000, kl_loss = 3.9150\n", - "Batch 2340, loss = 333798536053260288.0000, recon_loss = 333798536053260288.0000, kl_loss = 3.7986\n", - "Batch 2370, loss = 143422951016890368.0000, recon_loss = 143422951016890368.0000, kl_loss = 4.5492\n", - "Batch 2400, loss = 193812289017610240.0000, recon_loss = 193812289017610240.0000, kl_loss = 4.2353\n", - "Batch 2430, loss = 474188475654995968.0000, recon_loss = 474188475654995968.0000, kl_loss = 3.9627\n", - "Batch 2460, loss = 5450960286339039232.0000, recon_loss = 5450960286339039232.0000, kl_loss = 3.4399\n", - "Batch 2490, loss = 22902910769457790976.0000, recon_loss = 22902910769457790976.0000, kl_loss = 3.1981\n", - "Batch 2520, loss = 37486828602232143872.0000, recon_loss = 37486828602232143872.0000, kl_loss = 3.2248\n", - "Batch 2550, loss = 44153347921344987136.0000, recon_loss = 44153347921344987136.0000, kl_loss = 3.1029\n", - "Batch 2580, loss = 40896519713081786368.0000, recon_loss = 40896519713081786368.0000, kl_loss = 3.2632\n", - "Batch 2610, loss = 39113375735619780608.0000, recon_loss = 39113375735619780608.0000, kl_loss = 3.1936\n", - "Batch 2640, loss = 31401695847515160576.0000, recon_loss = 31401695847515160576.0000, kl_loss = 3.1836\n", - "Batch 2670, loss = 20910998121183444992.0000, recon_loss = 20910998121183444992.0000, kl_loss = 2.9999\n", - "Batch 2700, loss = 35076782677030862848.0000, recon_loss = 35076782677030862848.0000, kl_loss = 3.1315\n", - "Batch 2730, loss = 39445318296045355008.0000, recon_loss = 39445318296045355008.0000, kl_loss = 3.1214\n", - "Batch 2760, loss = 38884008813972684800.0000, recon_loss = 38884008813972684800.0000, kl_loss = 3.1407\n", - "Batch 2790, loss = 30149294928873652224.0000, recon_loss = 30149294928873652224.0000, kl_loss = 3.1564\n", - "Batch 2820, loss = 21874143918836154368.0000, recon_loss = 21874143918836154368.0000, kl_loss = 3.5414\n", - "Batch 2850, loss = 13545975557618925568.0000, recon_loss = 13545975557618925568.0000, kl_loss = 3.5089\n", - "Batch 2880, loss = 6872026288681385984.0000, recon_loss = 6872026288681385984.0000, kl_loss = 3.6535\n", - "Batch 2910, loss = 4224240385909587968.0000, recon_loss = 4224240385909587968.0000, kl_loss = 3.8963\n", - "Batch 2940, loss = 4729494841052889088.0000, recon_loss = 4729494841052889088.0000, kl_loss = 4.1375\n", - "Batch 2970, loss = 3564959195121844224.0000, recon_loss = 3564959195121844224.0000, kl_loss = 4.2535\n", - "Batch 3000, loss = 5526420319109120000.0000, recon_loss = 5526420319109120000.0000, kl_loss = 4.2485\n", - "Batch 3030, loss = 4557787408677994496.0000, recon_loss = 4557787408677994496.0000, kl_loss = 4.1133\n", - "Batch 3060, loss = 2583350398215520256.0000, recon_loss = 2583350398215520256.0000, kl_loss = 4.1987\n", - "Batch 3090, loss = 2375521809844928512.0000, recon_loss = 2375521809844928512.0000, kl_loss = 4.2191\n", - "Batch 3120, loss = 2253456365391446016.0000, recon_loss = 2253456365391446016.0000, kl_loss = 3.7260\n", - "Batch 3150, loss = 650683422175395840.0000, recon_loss = 650683422175395840.0000, kl_loss = 3.8122\n", - "Batch 3180, loss = 1173110462238162944.0000, recon_loss = 1173110462238162944.0000, kl_loss = 3.8877\n", - "Batch 3210, loss = 369838328188502016.0000, recon_loss = 369838328188502016.0000, kl_loss = 4.1504\n", - "Batch 3240, loss = 467194791428358144.0000, recon_loss = 467194791428358144.0000, kl_loss = 4.5172\n", - "Batch 3270, loss = 447052322523054080.0000, recon_loss = 447052322523054080.0000, kl_loss = 4.2178\n", - "Batch 3300, loss = 144394962245517312.0000, recon_loss = 144394962245517312.0000, kl_loss = 4.3927\n", - "Batch 3330, loss = 306539271978745856.0000, recon_loss = 306539271978745856.0000, kl_loss = 4.4975\n", - "Batch 3360, loss = 239573550648786944.0000, recon_loss = 239573550648786944.0000, kl_loss = 4.3327\n", - "Batch 3390, loss = 168214215213973504.0000, recon_loss = 168214215213973504.0000, kl_loss = 4.2009\n", - "Batch 3420, loss = 100749882540687360.0000, recon_loss = 100749882540687360.0000, kl_loss = 4.0564\n", - "Batch 3450, loss = 160620403797000192.0000, recon_loss = 160620403797000192.0000, kl_loss = 3.9211\n", - "Batch 3480, loss = 239976126523375616.0000, recon_loss = 239976126523375616.0000, kl_loss = 3.6577\n", - "Batch 3510, loss = 5052528608514408448.0000, recon_loss = 5052528608514408448.0000, kl_loss = 3.8262\n", - "Batch 3540, loss = 26966639775020220416.0000, recon_loss = 26966639775020220416.0000, kl_loss = 3.2408\n", - "Batch 3570, loss = 51464722014055432192.0000, recon_loss = 51464722014055432192.0000, kl_loss = 3.1268\n", - "Batch 3600, loss = 39488463132319285248.0000, recon_loss = 39488463132319285248.0000, kl_loss = 3.2909\n", - "Batch 3630, loss = 41163146884770955264.0000, recon_loss = 41163146884770955264.0000, kl_loss = 3.2126\n", - "Batch 3660, loss = 36849833137749884928.0000, recon_loss = 36849833137749884928.0000, kl_loss = 3.1998\n", - "Batch 3690, loss = 36693726675861504000.0000, recon_loss = 36693726675861504000.0000, kl_loss = 3.4663\n", - "Batch 3720, loss = 27259246207450480640.0000, recon_loss = 27259246207450480640.0000, kl_loss = 3.4073\n", - "Batch 3750, loss = 43833860628638859264.0000, recon_loss = 43833860628638859264.0000, kl_loss = 3.6197\n", - "Batch 3780, loss = 29025050886542458880.0000, recon_loss = 29025050886542458880.0000, kl_loss = 3.3380\n", - "Batch 3810, loss = 29348852662875979776.0000, recon_loss = 29348852662875979776.0000, kl_loss = 3.4324\n", - "Batch 3840, loss = 29194448244987396096.0000, recon_loss = 29194448244987396096.0000, kl_loss = 3.4032\n", - "Batch 3870, loss = 23986545449328705536.0000, recon_loss = 23986545449328705536.0000, kl_loss = 3.4741\n", - "Batch 3900, loss = 11338648790253961216.0000, recon_loss = 11338648790253961216.0000, kl_loss = 3.4304\n", - "Batch 3930, loss = 10976542227831980032.0000, recon_loss = 10976542227831980032.0000, kl_loss = 3.9526\n", - "Batch 3960, loss = 4114279877283151872.0000, recon_loss = 4114279877283151872.0000, kl_loss = 4.0535\n", - "Batch 3990, loss = 5390341461520678912.0000, recon_loss = 5390341461520678912.0000, kl_loss = 4.4559\n", - "Batch 4020, loss = 3178166947388850176.0000, recon_loss = 3178166947388850176.0000, kl_loss = 4.4672\n", - "Batch 4050, loss = 3608196390372507648.0000, recon_loss = 3608196390372507648.0000, kl_loss = 4.3946\n", - "Batch 4080, loss = 2878138811471101952.0000, recon_loss = 2878138811471101952.0000, kl_loss = 3.9146\n", - "Batch 4110, loss = 1231594035475382272.0000, recon_loss = 1231594035475382272.0000, kl_loss = 3.8930\n", - "Batch 4140, loss = 716228883719913472.0000, recon_loss = 716228883719913472.0000, kl_loss = 3.9441\n", - "Batch 4170, loss = 446832901233836032.0000, recon_loss = 446832901233836032.0000, kl_loss = 4.1965\n", - "Batch 4200, loss = 366228768953466880.0000, recon_loss = 366228768953466880.0000, kl_loss = 4.2730\n", - "Batch 4230, loss = 703739393701052416.0000, recon_loss = 703739393701052416.0000, kl_loss = 4.0138\n", - "Batch 4260, loss = 486687414601908224.0000, recon_loss = 486687414601908224.0000, kl_loss = 4.2183\n", - "Batch 4290, loss = 186581488236494848.0000, recon_loss = 186581488236494848.0000, kl_loss = 4.3824\n", - "Batch 4320, loss = 44344970395516928.0000, recon_loss = 44344970395516928.0000, kl_loss = 4.1209\n", - "Batch 4350, loss = 623474495117590528.0000, recon_loss = 623474495117590528.0000, kl_loss = 4.1445\n", - "Batch 4380, loss = 5871937016299520.0000, recon_loss = 5871937016299520.0000, kl_loss = 4.4229\n", - "Batch 4410, loss = 20847337463087104.0000, recon_loss = 20847337463087104.0000, kl_loss = 2.6880\n", - "Batch 4440, loss = 133099404415467520.0000, recon_loss = 133099404415467520.0000, kl_loss = 4.1744\n", - "Batch 4470, loss = 133374505660710912.0000, recon_loss = 133374505660710912.0000, kl_loss = 4.3157\n", - "Batch 4500, loss = 540973602200092672.0000, recon_loss = 540973602200092672.0000, kl_loss = 2.9365\n", - "Batch 4530, loss = 7689827094502572032.0000, recon_loss = 7689827094502572032.0000, kl_loss = 3.9271\n", - "Batch 4560, loss = 28747166914808119296.0000, recon_loss = 28747166914808119296.0000, kl_loss = 2.9858\n", - "Batch 4590, loss = 22590240448843874304.0000, recon_loss = 22590240448843874304.0000, kl_loss = 3.0301\n", - "Batch 4620, loss = 52246100947450724352.0000, recon_loss = 52246100947450724352.0000, kl_loss = 2.5943\n", - "Batch 4650, loss = 82701148069776326656.0000, recon_loss = 82701148069776326656.0000, kl_loss = 2.7881\n", - "Batch 4680, loss = 29883054785277460480.0000, recon_loss = 29883054785277460480.0000, kl_loss = 2.6741\n", - "Batch 4710, loss = 37036961220704337920.0000, recon_loss = 37036961220704337920.0000, kl_loss = 2.5181\n", - "Batch 4740, loss = 35516464182838951936.0000, recon_loss = 35516464182838951936.0000, kl_loss = 2.7543\n", - "Batch 4770, loss = 18620068887688904704.0000, recon_loss = 18620068887688904704.0000, kl_loss = 2.4356\n", - "Batch 4800, loss = 22021608219330215936.0000, recon_loss = 22021608219330215936.0000, kl_loss = 2.6314\n", - "Batch 4830, loss = 34739850532838440960.0000, recon_loss = 34739850532838440960.0000, kl_loss = 2.4388\n", - "Batch 4860, loss = 25963619088674586624.0000, recon_loss = 25963619088674586624.0000, kl_loss = 2.7116\n", - "Batch 4890, loss = 14638418425139953664.0000, recon_loss = 14638418425139953664.0000, kl_loss = 3.6878\n", - "Batch 4920, loss = 22355164862825365504.0000, recon_loss = 22355164862825365504.0000, kl_loss = 3.1402\n", - "Batch 4950, loss = 3773681411567910912.0000, recon_loss = 3773681411567910912.0000, kl_loss = 4.2819\n", - "Batch 4980, loss = 5482600382695735296.0000, recon_loss = 5482600382695735296.0000, kl_loss = 4.0357\n", - "Batch 5010, loss = 4636795015715094528.0000, recon_loss = 4636795015715094528.0000, kl_loss = 3.9072\n", - "Batch 5040, loss = 2525967985873518592.0000, recon_loss = 2525967985873518592.0000, kl_loss = 4.3734\n", - "Batch 5070, loss = 4398653441522532352.0000, recon_loss = 4398653441522532352.0000, kl_loss = 4.0022\n", - "Batch 5100, loss = 1753061064448147456.0000, recon_loss = 1753061064448147456.0000, kl_loss = 4.0580\n", - "Batch 5130, loss = 23056926338187264.0000, recon_loss = 23056926338187264.0000, kl_loss = 4.4220\n", - "Batch 5160, loss = 1363283092889927680.0000, recon_loss = 1363283092889927680.0000, kl_loss = 4.3436\n", - "Batch 5190, loss = 5498536154472906752.0000, recon_loss = 5498536154472906752.0000, kl_loss = 4.4744\n", - "Batch 5220, loss = 16755965575561216.0000, recon_loss = 16755965575561216.0000, kl_loss = 4.4271\n", - "Batch 5250, loss = 1270923841278836736.0000, recon_loss = 1270923841278836736.0000, kl_loss = 4.3068\n", - "Batch 5280, loss = 442062841835421696.0000, recon_loss = 442062841835421696.0000, kl_loss = 4.0832\n", - "Batch 5310, loss = 298905190948405248.0000, recon_loss = 298905190948405248.0000, kl_loss = 4.1592\n", - "Batch 5340, loss = 238996891159756800.0000, recon_loss = 238996891159756800.0000, kl_loss = 4.3411\n", - "Batch 5370, loss = 247322015248154624.0000, recon_loss = 247322015248154624.0000, kl_loss = 4.3920\n", - "Batch 5400, loss = 154758649611288576.0000, recon_loss = 154758649611288576.0000, kl_loss = 4.6549\n", - "Batch 5430, loss = 206606034519719936.0000, recon_loss = 206606034519719936.0000, kl_loss = 4.1851\n", - "Batch 5460, loss = 167260131178840064.0000, recon_loss = 167260131178840064.0000, kl_loss = 4.6311\n", - "Batch 5490, loss = 38085407748915200.0000, recon_loss = 38085407748915200.0000, kl_loss = 4.5142\n", - "Batch 5520, loss = 229590637903609856.0000, recon_loss = 229590637903609856.0000, kl_loss = 4.4933\n", - "Batch 5550, loss = 598970916419928064.0000, recon_loss = 598970916419928064.0000, kl_loss = 4.5246\n", - "Batch 5580, loss = 9162174319164391424.0000, recon_loss = 9162174319164391424.0000, kl_loss = 4.0459\n", - "Batch 5610, loss = 18752768946045190144.0000, recon_loss = 18752768946045190144.0000, kl_loss = 3.1402\n", - 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"Batch 6540, loss = 80322141907582976.0000, recon_loss = 80322141907582976.0000, kl_loss = 4.3196\n", - "Batch 6570, loss = 77846574297776128.0000, recon_loss = 77846574297776128.0000, kl_loss = 4.4622\n", - "Batch 6600, loss = 775562448164880384.0000, recon_loss = 775562448164880384.0000, kl_loss = 4.3608\n", - "Batch 6630, loss = 2675430648751063040.0000, recon_loss = 2675430648751063040.0000, kl_loss = 4.4058\n", - "Batch 6660, loss = 1017110446819246080.0000, recon_loss = 1017110446819246080.0000, kl_loss = 4.5588\n", - "Batch 6690, loss = 1179096478417682432.0000, recon_loss = 1179096478417682432.0000, kl_loss = 4.0587\n", - "Batch 6720, loss = 1428724375707713536.0000, recon_loss = 1428724375707713536.0000, kl_loss = 4.2508\n", - "Batch 6750, loss = 1859639750185451520.0000, recon_loss = 1859639750185451520.0000, kl_loss = 4.0286\n", - "Batch 6780, loss = 651870070099673088.0000, recon_loss = 651870070099673088.0000, kl_loss = 4.3571\n", - "Batch 6810, loss = 629231538000625664.0000, recon_loss = 629231538000625664.0000, kl_loss = 4.4887\n", - "Batch 6840, loss = 1266330494014849024.0000, recon_loss = 1266330494014849024.0000, kl_loss = 4.4959\n", - "Batch 6870, loss = 1128287908659200000.0000, recon_loss = 1128287908659200000.0000, kl_loss = 4.5587\n", - "Batch 6900, loss = 1153398005458534400.0000, recon_loss = 1153398005458534400.0000, kl_loss = 4.3432\n", - "Batch 6930, loss = 461640230483525632.0000, recon_loss = 461640230483525632.0000, kl_loss = 4.5066\n", - "Batch 6960, loss = 820025186563653632.0000, recon_loss = 820025186563653632.0000, kl_loss = 4.6680\n", - "Batch 6990, loss = 422697555852263424.0000, recon_loss = 422697555852263424.0000, kl_loss = 4.6864\n", - "Batch 7020, loss = 373239529970073600.0000, recon_loss = 373239529970073600.0000, kl_loss = 4.6832\n", - "Batch 7050, loss = 316716489044393984.0000, recon_loss = 316716489044393984.0000, kl_loss = 4.5528\n", - "Batch 7080, loss = 410488303859531776.0000, recon_loss = 410488303859531776.0000, kl_loss = 3.8536\n", - "Batch 7110, loss = 261555605586575360.0000, recon_loss = 261555605586575360.0000, kl_loss = 4.6350\n", - "Batch 7140, loss = 601974782187012096.0000, recon_loss = 601974782187012096.0000, kl_loss = 3.9559\n", - "Batch 7170, loss = 205541209047826432.0000, recon_loss = 205541209047826432.0000, kl_loss = 3.9270\n", - "Batch 7200, loss = 219844412216180736.0000, recon_loss = 219844412216180736.0000, kl_loss = 4.4065\n", - "Batch 7230, loss = 385543855358869504.0000, recon_loss = 385543855358869504.0000, kl_loss = 4.3305\n" - ] - }, - { - "ename": "RuntimeError", - "evalue": "stack expects each tensor to be equal size, but got [100, 56] at entry 0 and [28, 56] at entry 15", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------\u001b[0m", - "\u001b[0;31mRuntimeError\u001b[0mTraceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mvrae\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_dataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;31m#If the model has to be saved, with the learnt parameters use:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m# vrae.fit(dataset, save = True)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/repo/projects/timeseries-generation/model/org_vrae.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, dataset, save)\u001b[0m\n\u001b[1;32m 345\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Epoch: %s'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 346\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 347\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_train\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_loader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 348\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 349\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_fitted\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/repo/projects/timeseries-generation/model/org_vrae.py\u001b[0m in \u001b[0;36m_train\u001b[0;34m(self, train_loader)\u001b[0m\n\u001b[1;32m 300\u001b[0m \u001b[0mt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 301\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 302\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_loader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 303\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 304\u001b[0m \u001b[0;31m# Index first element of array to return tensor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 519\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sampler_iter\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 520\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 521\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 522\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_yielded\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 523\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_kind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_DatasetKind\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIterable\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 559\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 560\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 561\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_fetcher\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 562\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pin_memory\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 563\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/fetch.py\u001b[0m in \u001b[0;36mfetch\u001b[0;34m(self, possibly_batched_index)\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 51\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 52\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcollate_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/collate.py\u001b[0m in \u001b[0;36mdefault_collate\u001b[0;34m(batch)\u001b[0m\n\u001b[1;32m 54\u001b[0m \u001b[0mstorage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0melem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstorage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_new_shared\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 55\u001b[0m \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0melem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnew\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstorage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 56\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 57\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0melem_type\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__module__\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'numpy'\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0melem_type\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'str_'\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 58\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0melem_type\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'string_'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mRuntimeError\u001b[0m: stack expects each tensor to be equal size, but got [100, 56] at entry 0 and [28, 56] at entry 15" - ] - } - ], + "outputs": [], "source": [ "vrae.fit(train_dataset)\n", "\n", @@ -740,61 +273,53 @@ "# vrae.fit(dataset, save = True)" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Transform the input timeseries to encoded latent vectors" - ] - }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "z_run = vrae.transform(test_dataset)\n", + "vrae.save('vrae.pth')\n", "\n", - "#If the latent vectors have to be saved, pass the parameter `save`\n", - "# z_run = vrae.transform(dataset, save = True)" + "# To load a presaved model, execute:\n", + "# vrae.load('vrae.pth')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Save the model to be fetched later" + "### Reconstruct" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "RuntimeError", + "evalue": "number of dims don't match in permute", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------\u001b[0m", + "\u001b[0;31mRuntimeError\u001b[0mTraceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mreconstruction\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvrae\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_dataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mreconstruction\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/repo/projects/timeseries-generation/model/org_vrae.py\u001b[0m in \u001b[0;36mtransform\u001b[0;34m(self, dataset, save)\u001b[0m\n\u001b[1;32m 440\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_loader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 441\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 442\u001b[0;31m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpermute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 443\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 444\u001b[0m \u001b[0mz_run_each\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_batch_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mRuntimeError\u001b[0m: number of dims don't match in permute" + ] + } + ], "source": [ - "vrae.save('vrae.pth')\n", - "\n", - "# To load a presaved model, execute:\n", - "# vrae.load('vrae.pth')" + "reconstruction = vrae.transform(train_dataset)\n", + "reconstruction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Visualize using PCA and tSNE" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plot_clustering(z_run, y_val, engine='matplotlib', download = False)\n", - "\n", - "# If plotly to be used as rendering engine, uncomment below line\n", - "#plot_clustering(z_run, y_val, engine='plotly', download = False)" + "### Save the model to be fetched later" ] } ], diff --git a/model/org_vrae.py b/model/org_vrae.py deleted file mode 100644 index 6d9bdab..0000000 --- a/model/org_vrae.py +++ /dev/null @@ -1,491 +0,0 @@ -import numpy as np -import torch -from torch import nn, optim -from torch import distributions -from sklearn.base import BaseEstimator -from torch.utils.data import DataLoader -from torch.autograd import Variable -import os - - -class Encoder(nn.Module): - """ - Encoder network containing enrolled LSTM/GRU - - :param number_of_features: number of input features - :param hidden_size: hidden size of the RNN - :param hidden_layer_depth: number of layers in RNN - :param latent_length: latent vector length - :param dropout: percentage of nodes to dropout - :param block: LSTM/GRU block - """ - def __init__(self, number_of_features, hidden_size, hidden_layer_depth, latent_length, dropout, block = 'LSTM'): - - super(Encoder, self).__init__() - - self.number_of_features = number_of_features - self.hidden_size = hidden_size - self.hidden_layer_depth = hidden_layer_depth - self.latent_length = latent_length - - if block == 'LSTM': - self.model = nn.LSTM(self.number_of_features, self.hidden_size, self.hidden_layer_depth, dropout = dropout) - elif block == 'GRU': - self.model = nn.GRU(self.number_of_features, self.hidden_size, self.hidden_layer_depth, dropout = dropout) - else: - raise NotImplementedError - - def forward(self, x): - """Forward propagation of encoder. Given input, outputs the last hidden state of encoder - - :param x: input to the encoder, of shape (sequence_length, batch_size, number_of_features) - :return: last hidden state of encoder, of shape (batch_size, hidden_size) - """ - - _, (h_end, c_end) = self.model(x) - - h_end = h_end[-1, :, :] - return h_end - - -class Lambda(nn.Module): - """Lambda module converts output of encoder to latent vector - - :param hidden_size: hidden size of the encoder - :param latent_length: latent vector length - """ - def __init__(self, hidden_size, latent_length): - super(Lambda, self).__init__() - - self.hidden_size = hidden_size - self.latent_length = latent_length - - self.hidden_to_mean = nn.Linear(self.hidden_size, self.latent_length) - self.hidden_to_logvar = nn.Linear(self.hidden_size, self.latent_length) - - nn.init.xavier_uniform_(self.hidden_to_mean.weight) - nn.init.xavier_uniform_(self.hidden_to_logvar.weight) - - def forward(self, cell_output): - """Given last hidden state of encoder, passes through a linear layer, and finds the mean and variance - - :param cell_output: last hidden state of encoder - :return: latent vector - """ - - self.latent_mean = self.hidden_to_mean(cell_output) - self.latent_logvar = self.hidden_to_logvar(cell_output) - - if self.training: - std = torch.exp(0.5 * self.latent_logvar) - eps = torch.randn_like(std) - return eps.mul(std).add_(self.latent_mean) - else: - return self.latent_mean - -class Decoder(nn.Module): - """Converts latent vector into output - - :param sequence_length: length of the input sequence - :param batch_size: batch size of the input sequence - :param hidden_size: hidden size of the RNN - :param hidden_layer_depth: number of layers in RNN - :param latent_length: latent vector length - :param output_size: 2, one representing the mean, other log std dev of the output - :param block: GRU/LSTM - use the same which you've used in the encoder - :param dtype: Depending on cuda enabled/disabled, create the tensor - """ - def __init__(self, sequence_length, batch_size, hidden_size, hidden_layer_depth, latent_length, output_size, dtype, block='LSTM'): - - super(Decoder, self).__init__() - - self.hidden_size = hidden_size - self.batch_size = batch_size - self.sequence_length = sequence_length - self.hidden_layer_depth = hidden_layer_depth - self.latent_length = latent_length - self.output_size = output_size - self.dtype = dtype - - if block == 'LSTM': - self.model = nn.LSTM(1, self.hidden_size, self.hidden_layer_depth) - elif block == 'GRU': - self.model = nn.GRU(1, self.hidden_size, self.hidden_layer_depth) - else: - raise NotImplementedError - - self.latent_to_hidden = nn.Linear(self.latent_length, self.hidden_size) - self.hidden_to_output = nn.Linear(self.hidden_size, self.output_size) - - self.decoder_inputs = torch.zeros(self.sequence_length, self.batch_size, 1, requires_grad=True).type(self.dtype) - self.c_0 = torch.zeros(self.hidden_layer_depth, self.batch_size, self.hidden_size, requires_grad=True).type(self.dtype) - - nn.init.xavier_uniform_(self.latent_to_hidden.weight) - nn.init.xavier_uniform_(self.hidden_to_output.weight) - - def forward(self, latent): - """Converts latent to hidden to output - - :param latent: latent vector - :return: outputs consisting of mean and std dev of vector - """ - h_state = self.latent_to_hidden(latent) - - if isinstance(self.model, nn.LSTM): - h_0 = torch.stack([h_state for _ in range(self.hidden_layer_depth)]) - decoder_output, _ = self.model(self.decoder_inputs, (h_0, self.c_0)) - elif isinstance(self.model, nn.GRU): - h_0 = torch.stack([h_state for _ in range(self.hidden_layer_depth)]) - decoder_output, _ = self.model(self.decoder_inputs, h_0) - else: - raise NotImplementedError - - out = self.hidden_to_output(decoder_output) - return out - -def _assert_no_grad(tensor): - assert not tensor.requires_grad, \ - "nn criterions don't compute the gradient w.r.t. targets - please " \ - "mark these tensors as not requiring gradients" - -class VRAE(BaseEstimator, nn.Module): - """Variational recurrent auto-encoder. This module is used for dimensionality reduction of timeseries - - :param sequence_length: length of the input sequence - :param number_of_features: number of input features - :param hidden_size: hidden size of the RNN - :param hidden_layer_depth: number of layers in RNN - :param latent_length: latent vector length - :param batch_size: number of timeseries in a single batch - :param learning_rate: the learning rate of the module - :param block: GRU/LSTM to be used as a basic building block - :param n_epochs: Number of iterations/epochs - :param dropout_rate: The probability of a node being dropped-out - :param optimizer: ADAM/ SGD optimizer to reduce the loss function - :param loss: SmoothL1Loss / MSELoss / ReconLoss / any custom loss which inherits from `_Loss` class - :param boolean cuda: to be run on GPU or not - :param print_every: The number of iterations after which loss should be printed - :param boolean clip: Gradient clipping to overcome explosion - :param max_grad_norm: The grad-norm to be clipped - :param dload: Download directory where models are to be dumped - """ - def __init__(self, sequence_length, number_of_features, hidden_size=90, hidden_layer_depth=2, latent_length=20, - batch_size=32, learning_rate=0.005, block='LSTM', - n_epochs=5, dropout_rate=0., optimizer='Adam', loss='MSELoss', - cuda=False, print_every=100, clip=True, max_grad_norm=5, dload='.'): - - super(VRAE, self).__init__() - - - self.dtype = torch.FloatTensor - self.use_cuda = cuda - - if not torch.cuda.is_available() and self.use_cuda: - self.use_cuda = False - - - if self.use_cuda: - self.dtype = torch.cuda.FloatTensor - - - self.encoder = Encoder(number_of_features = number_of_features, - hidden_size=hidden_size, - hidden_layer_depth=hidden_layer_depth, - latent_length=latent_length, - dropout=dropout_rate, - block=block) - - self.lmbd = Lambda(hidden_size=hidden_size, - latent_length=latent_length) - - self.decoder = Decoder(sequence_length=sequence_length, - batch_size = batch_size, - hidden_size=hidden_size, - hidden_layer_depth=hidden_layer_depth, - latent_length=latent_length, - output_size=number_of_features, - block=block, - dtype=self.dtype) - - self.sequence_length = sequence_length - self.hidden_size = hidden_size - self.hidden_layer_depth = hidden_layer_depth - self.latent_length = latent_length - self.batch_size = batch_size - self.learning_rate = learning_rate - self.n_epochs = n_epochs - - self.print_every = print_every - self.clip = clip - self.max_grad_norm = max_grad_norm - self.is_fitted = False - self.dload = dload - - if self.use_cuda: - self.cuda() - - if optimizer == 'Adam': - self.optimizer = optim.Adam(self.parameters(), lr=learning_rate) - elif optimizer == 'SGD': - self.optimizer = optim.SGD(self.parameters(), lr=learning_rate) - else: - raise ValueError('Not a recognized optimizer') - - if loss == 'SmoothL1Loss': - self.loss_fn = nn.SmoothL1Loss(size_average=False) - elif loss == 'MSELoss': - self.loss_fn = nn.MSELoss(size_average=False) - - def __repr__(self): - return """VRAE(n_epochs={n_epochs},batch_size={batch_size},cuda={cuda})""".format( - n_epochs=self.n_epochs, - batch_size=self.batch_size, - cuda=self.use_cuda) - - def forward(self, x): - """ - Forward propagation which involves one pass from inputs to encoder to lambda to decoder - - :param x:input tensor - :return: the decoded output, latent vector - """ - cell_output = self.encoder(x) - latent = self.lmbd(cell_output) - x_decoded = self.decoder(latent) - - return x_decoded, latent - - def _rec(self, x_decoded, x, loss_fn): - """ - Compute the loss given output x decoded, input x and the specified loss function - - :param x_decoded: output of the decoder - :param x: input to the encoder - :param loss_fn: loss function specified - :return: joint loss, reconstruction loss and kl-divergence loss - """ - latent_mean, latent_logvar = self.lmbd.latent_mean, self.lmbd.latent_logvar - - kl_loss = -0.5 * torch.mean(1 + latent_logvar - latent_mean.pow(2) - latent_logvar.exp()) - recon_loss = loss_fn(x_decoded, x) - - return kl_loss + recon_loss, recon_loss, kl_loss - - def compute_loss(self, X): - """ - Given input tensor, forward propagate, compute the loss, and backward propagate. - Represents the lifecycle of a single iteration - - :param X: Input tensor - :return: total loss, reconstruction loss, kl-divergence loss and original input - """ - x = Variable(X[:,:,:].type(self.dtype), requires_grad = True) - - x_decoded, _ = self(x) - loss, recon_loss, kl_loss = self._rec(x_decoded, x.detach(), self.loss_fn) - - return loss, recon_loss, kl_loss, x - - - def _train(self, train_loader): - """ - For each epoch, given the batch_size, run this function batch_size * num_of_batches number of times - - :param train_loader:input train loader with shuffle - :return: - """ - self.train() - - epoch_loss = 0 - t = 0 - - for t, X in enumerate(train_loader): - - # Index first element of array to return tensor - #X = X[0] - #print(X.shape) - # required to swap axes, since dataloader gives output in (batch_size x seq_len x num_of_features) - X = X.permute(1,0,2) - #print(X.shape) - - self.optimizer.zero_grad() - loss, recon_loss, kl_loss, _ = self.compute_loss(X) - loss.backward() - - if self.clip: - torch.nn.utils.clip_grad_norm_(self.parameters(), max_norm = self.max_grad_norm) - - # accumulator - epoch_loss += loss.item() - - self.optimizer.step() - - if (t + 1) % self.print_every == 0: - print('Batch %d, loss = %.4f, recon_loss = %.4f, kl_loss = %.4f' % (t + 1, loss.item(), - recon_loss.item(), kl_loss.item())) - - print('Average loss: {:.4f}'.format(epoch_loss / t)) - - - def fit(self, dataset, save = False): - """ - Calls `_train` function over a fixed number of epochs, specified by `n_epochs` - - :param dataset: `Dataset` object - :param bool save: If true, dumps the trained model parameters as pickle file at `dload` directory - :return: - """ - - train_loader = DataLoader(dataset = dataset, - batch_size = self.batch_size, - shuffle = False, - drop_last=True) - - for i in range(self.n_epochs): - print('Epoch: %s' % i) - - self._train(train_loader) - - self.is_fitted = True - if save: - self.save('model.pth') - - - def _batch_transform(self, x): - """ - Passes the given input tensor into encoder and lambda function - - :param x: input batch tensor - :return: intermediate latent vector - """ - return self.lmbd( - self.encoder( - Variable(x.type(self.dtype), requires_grad = False) - ) - ).cpu().data.numpy() - - def _batch_reconstruct(self, x): - """ - Passes the given input tensor into encoder, lambda and decoder function - - :param x: input batch tensor - :return: reconstructed output tensor - """ - - x = Variable(x.type(self.dtype), requires_grad = False) - x_decoded, _ = self(x) - - return x_decoded.cpu().data.numpy() - - def reconstruct(self, dataset, save = False): - """ - Given input dataset, creates dataloader, runs dataloader on `_batch_reconstruct` - Prerequisite is that model has to be fit - - :param dataset: input dataset who's output vectors are to be obtained - :param bool save: If true, dumps the output vector dataframe as a pickle file - :return: - """ - - self.eval() - - test_loader = DataLoader(dataset = dataset, - batch_size = self.batch_size, - shuffle = False, - drop_last=True) # Don't shuffle for test_loader - - if self.is_fitted: - with torch.no_grad(): - x_decoded = [] - - for t, x in enumerate(test_loader): - x = x[0] - x = x.permute(1, 0, 2) - - x_decoded_each = self._batch_reconstruct(x) - x_decoded.append(x_decoded_each) - - x_decoded = np.concatenate(x_decoded, axis=1) - - if save: - if os.path.exists(self.dload): - pass - else: - os.mkdir(self.dload) - x_decoded.dump(self.dload + '/z_run.pkl') - return x_decoded - - raise RuntimeError('Model needs to be fit') - - - def transform(self, dataset, save = False): - """ - Given input dataset, creates dataloader, runs dataloader on `_batch_transform` - Prerequisite is that model has to be fit - - :param dataset: input dataset who's latent vectors are to be obtained - :param bool save: If true, dumps the latent vector dataframe as a pickle file - :return: - """ - self.eval() - - test_loader = DataLoader(dataset = dataset, - batch_size = self.batch_size, - shuffle = False, - drop_last=True) # Don't shuffle for test_loader - if self.is_fitted: - with torch.no_grad(): - z_run = [] - - for t, x in enumerate(test_loader): - x = x[0] - x = x.permute(1, 0, 2) - - z_run_each = self._batch_transform(x) - z_run.append(z_run_each) - - z_run = np.concatenate(z_run, axis=0) - if save: - if os.path.exists(self.dload): - pass - else: - os.mkdir(self.dload) - z_run.dump(self.dload + '/z_run.pkl') - return z_run - - raise RuntimeError('Model needs to be fit') - - def fit_transform(self, dataset, save = False): - """ - Combines the `fit` and `transform` functions above - - :param dataset: Dataset on which fit and transform have to be performed - :param bool save: If true, dumps the model and latent vectors as pickle file - :return: latent vectors for input dataset - """ - self.fit(dataset, save = save) - return self.transform(dataset, save = save) - - def save(self, file_name): - """ - Pickles the model parameters to be retrieved later - - :param file_name: the filename to be saved as,`dload` serves as the download directory - :return: None - """ - PATH = self.dload + '/' + file_name - if os.path.exists(self.dload): - pass - else: - os.mkdir(self.dload) - torch.save(self.state_dict(), PATH) - - def load(self, PATH): - """ - Loads the model's parameters from the path mentioned - - :param PATH: Should contain pickle file - :return: None - """ - self.is_fitted = True - self.load_state_dict(torch.load(PATH)) \ No newline at end of file diff --git a/model/vrae.py b/model/vrae.py index 671b49e..1a9491b 100644 --- a/model/vrae.py +++ b/model/vrae.py @@ -41,16 +41,10 @@ def forward(self, x): :param x: input to the encoder, of shape (sequence_length, batch_size, number_of_features) :return: last hidden state of encoder, of shape (batch_size, hidden_size) """ - - print('--------------------------') - print('DEBUGGING') - print(x.shape) - print('--------------------------') - + _, (h_end, c_end) = self.model(x) - - h_end = h_end[:, -1, :] -# h_end = h_end[-1, :, :] + + h_end = h_end[-1, :, :] return h_end @@ -136,18 +130,13 @@ def forward(self, latent): :return: outputs consisting of mean and std dev of vector """ h_state = self.latent_to_hidden(latent) - print(h_state.shape) + if isinstance(self.model, nn.LSTM): h_0 = torch.stack([h_state for _ in range(self.hidden_layer_depth)]) - print(h_0.shape) - # TODO : Need for debugging decoder_output, _ = self.model(self.decoder_inputs, (h_0, self.c_0)) - elif isinstance(self.model, nn.GRU): - # TODO : Need for debugging h_0 = torch.stack([h_state for _ in range(self.hidden_layer_depth)]) decoder_output, _ = self.model(self.decoder_inputs, h_0) - else: raise NotImplementedError @@ -311,6 +300,12 @@ def _train(self, train_loader): t = 0 for t, X in enumerate(train_loader): + + # required to swap axes, since dataloader gives output as follows: + # (batch_size x seq_len x num_of_features) + + X = X.permute(1,0,2) + #print(X.shape) self.optimizer.zero_grad() loss, recon_loss, kl_loss, _ = self.compute_loss(X) @@ -344,16 +339,7 @@ def fit(self, dataset, save = False): batch_size = self.batch_size, shuffle = False, drop_last=True) - - #################### - print('debugging') - print('fit result') - print(dataset) - print(dataset[0]) - tmp = iter(train_loader).next() - print(tmp.shape) - #################### - + for i in range(self.n_epochs): print('Epoch: %s' % i) @@ -412,6 +398,8 @@ def reconstruct(self, dataset, save = False): x_decoded = [] for t, x in enumerate(test_loader): + x = x.permute(1, 0, 2) + x_decoded_each = self._batch_reconstruct(x) x_decoded.append(x_decoded_each) @@ -448,6 +436,8 @@ def transform(self, dataset, save = False): z_run = [] for t, x in 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KKl}gheg79797}8f literal 0 HcmV?d00001 diff --git a/test.ipynb b/test.ipynb deleted file mode 100644 index f8dabbd..0000000 --- a/test.ipynb +++ /dev/null @@ -1,976 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Timeseries clustering\n", - "\n", - "Time series clustering is to partition time series data into groups based on similarity or distance, so that time series in the same cluster are similar.\n", - "\n", - "Methodology followed:\n", - "* Use Variational Recurrent AutoEncoder (VRAE) for dimensionality reduction of the timeseries\n", - "* To visualize the clusters, PCA and t-sne are used\n", - "\n", - "Paper:\n", - "https://arxiv.org/pdf/1412.6581.pdf" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Contents\n", - "\n", - "0. [Load data and preprocess](#Load-data-and-preprocess)\n", - "1. [Initialize VRAE object](#Initialize-VRAE-object)\n", - "2. [Fit the model onto dataset](#Fit-the-model-onto-dataset)\n", - "3. [Transform the input timeseries to encoded latent vectors](#Transform-the-input-timeseries-to-encoded-latent-vectors)\n", - "4. [Save the model to be fetched later](#Save-the-model-to-be-fetched-later)\n", - "5. [Visualize using PCA and tSNE](#Visualize-using-PCA-and-tSNE)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Import required modules" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - " \n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from model.org_vrae import VRAE\n", - "from model.utils import *\n", - "import numpy as np\n", - "import torch\n", - "\n", - "import plotly\n", - "from torch.utils.data import DataLoader, TensorDataset\n", - "plotly.offline.init_notebook_mode()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Input parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "dload = './model_dir' #download directory" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Hyper parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "hidden_size = 90\n", - "hidden_layer_depth = 1\n", - "latent_length = 20\n", - "batch_size = 32\n", - "learning_rate = 0.0005\n", - "n_epochs = 1\n", - "dropout_rate = 0.2\n", - "optimizer = 'Adam' # options: ADAM, SGD\n", - "cuda = True # options: True, False\n", - "print_every=30\n", - "clip = True # options: True, False\n", - "max_grad_norm=5\n", - "loss = 'MSELoss' # options: SmoothL1Loss, MSELoss\n", - "block = 'LSTM' # options: LSTM, GRU" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load data and preprocess" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "X_train, X_val, y_train, y_val = open_data('data', ratio_train=0.9)\n", - "\n", - "num_classes = len(np.unique(y_train))\n", - "base = np.min(y_train) # Check if data is 0-based\n", - "if base != 0:\n", - " y_train -= base\n", - "y_val -= base" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "train_dataset = TensorDataset(torch.from_numpy(X_train))\n", - "test_dataset = TensorDataset(torch.from_numpy(X_val))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Fetch `sequence_length` from dataset**" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "140" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sequence_length = X_train.shape[1]\n", - "sequence_length" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Fetch `number_of_features` from dataset**\n", - "\n", - "This config corresponds to number of input features" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "number_of_features = X_train.shape[2]\n", - "number_of_features" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Initialize VRAE object\n", - "\n", - "VRAE inherits from `sklearn.base.BaseEstimator` and overrides `fit`, `transform` and `fit_transform` functions, similar to sklearn modules" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/rnn.py:65: UserWarning:\n", - "\n", - "dropout option adds dropout after all but last recurrent layer, so non-zero dropout expects num_layers greater than 1, but got dropout=0.2 and num_layers=1\n", - "\n", - "/usr/local/lib/python3.6/dist-packages/torch/nn/_reduction.py:42: UserWarning:\n", - "\n", - "size_average and reduce args will be deprecated, please use reduction='sum' instead.\n", - "\n" - ] - } - ], - "source": [ - "vrae = VRAE(sequence_length=sequence_length,\n", - " number_of_features = number_of_features,\n", - " hidden_size = hidden_size, \n", - " hidden_layer_depth = hidden_layer_depth,\n", - " latent_length = latent_length,\n", - " batch_size = batch_size,\n", - " learning_rate = learning_rate,\n", - " n_epochs = n_epochs,\n", - " dropout_rate = dropout_rate,\n", - " optimizer = optimizer, \n", - " cuda = cuda,\n", - " print_every=print_every, \n", - " clip=clip, \n", - " max_grad_norm=max_grad_norm,\n", - " loss = loss,\n", - " block = block,\n", - " dload = dload)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Fit the model onto dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(8549, 140, 1)\n" - ] - } - ], - "source": [ - "print(X_train.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch: 0\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - 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"Batch 30, loss = 4247.7686, recon_loss = 4247.6963, kl_loss = 0.0722\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "Batch 60, loss = 2994.4768, recon_loss = 2990.8428, kl_loss = 3.6341\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "Batch 90, loss = 3010.1553, recon_loss = 3006.1887, kl_loss = 3.9666\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "Batch 120, loss = 2636.3860, recon_loss = 2632.7612, kl_loss = 3.6248\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "Batch 150, loss = 2260.1436, recon_loss = 2256.7227, kl_loss = 3.4209\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "Batch 180, loss = 2926.3557, recon_loss = 2923.4617, kl_loss = 2.8939\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "Batch 210, loss = 3008.5190, recon_loss = 3005.9363, kl_loss = 2.5828\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "Batch 240, loss = 2375.7429, recon_loss = 2373.2915, kl_loss = 2.4513\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "torch.Size([32, 140, 1])\n", - "torch.Size([140, 32, 1])\n", - "Average loss: 2912.1796\n" - ] - } - ], - "source": [ - "vrae.fit(train_dataset)\n", - "\n", - "#If the model has to be saved, with the learnt parameters use:\n", - "# vrae.fit(dataset, save = True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Transform the input timeseries to encoded latent vectors" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "z_run = vrae.transform(test_dataset)\n", - "\n", - "#If the latent vectors have to be saved, pass the parameter `save`\n", - "# z_run = vrae.transform(dataset, save = True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Save the model to be fetched later" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "vrae.save('vrae.pth')\n", - "\n", - "# To load a presaved model, execute:\n", - "# vrae.load('vrae.pth')" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "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": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -}