From bdfef613ae58f3556663f7426d224b291f7d5f0d Mon Sep 17 00:00:00 2001
From: "Jean-Luc Parouty Jean-Luc.Parouty@simap.grenoble-inp.fr"
<paroutyj@f-dahu.u-ga.fr>
Date: Tue, 4 Feb 2020 19:02:23 +0100
Subject: [PATCH] Add save/load functionality to VAE model
---
VAE/01-VAE with MNIST.ipynb | 400 -------------------------------
VAE/01-VAE-with-MNIST.ipynb | 269 +++++++++++++++++++++
VAE/02-VAE-with-MNIST-post.ipynb | 208 ++++++++++++++++
VAE/modules/vae.py | 107 ++++++---
4 files changed, 554 insertions(+), 430 deletions(-)
delete mode 100644 VAE/01-VAE with MNIST.ipynb
create mode 100644 VAE/01-VAE-with-MNIST.ipynb
create mode 100644 VAE/02-VAE-with-MNIST-post.ipynb
diff --git a/VAE/01-VAE with MNIST.ipynb b/VAE/01-VAE with MNIST.ipynb
deleted file mode 100644
index 8eb9c45..0000000
--- a/VAE/01-VAE with MNIST.ipynb
+++ /dev/null
@@ -1,400 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Variational AutoEncoder\n",
- "=======================\n",
- "---\n",
- "Formation Introduction au Deep Learning (FIDLE) - S. Arias, E. Maldonado, JL. Parouty - CNRS/SARI/DEVLOG - 2020 \n",
- "\n",
- "## Variational AutoEncoder (VAE), with MNIST Dataset\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 1 - Init python stuff"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "IDLE 2020 - Practical Work Module\n",
- " Version : 0.2.5\n",
- " Run time : Tuesday 4 February 2020, 00:10:15\n",
- " Matplotlib style : ../fidle/talk.mplstyle\n",
- " TensorFlow version : 2.0.0\n",
- " Keras version : 2.2.4-tf\n"
- ]
- }
- ],
- "source": [
- "import numpy as np\n",
- "\n",
- "import tensorflow as tf\n",
- "import tensorflow.keras as keras\n",
- "import tensorflow.keras.datasets.mnist as mnist\n",
- "\n",
- "import modules.vae\n",
- "\n",
- "import matplotlib.pyplot as plt\n",
- "import matplotlib\n",
- "import seaborn as sns\n",
- "\n",
- "import os,sys,h5py,json\n",
- "\n",
- "from importlib import reload\n",
- "\n",
- "sys.path.append('..')\n",
- "import fidle.pwk as ooo\n",
- "\n",
- "ooo.init()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 2 - Get data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "(60000, 28, 28, 1)\n",
- "(10000, 28, 28, 1)\n"
- ]
- }
- ],
- "source": [
- "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
- "\n",
- "x_train = x_train.astype('float32') / 255.\n",
- "x_train = np.expand_dims(x_train, axis=3)\n",
- "x_test = x_test.astype('float32') / 255.\n",
- "x_test = np.expand_dims(x_test, axis=3)\n",
- "print(x_train.shape)\n",
- "print(x_test.shape)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 3 - Get VAE model"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Model initialized.\n",
- "Outputs will be in : ./run/001\n"
- ]
- }
- ],
- "source": [
- "# reload(modules.vae)\n",
- "# reload(modules.callbacks)\n",
- "\n",
- "tag = '001'\n",
- "\n",
- "input_shape = (28,28,1)\n",
- "z_dim = 2\n",
- "verbose = 0\n",
- "\n",
- "encoder= [ {'type':'Conv2D', 'filters':32, 'kernel_size':(3,3), 'strides':1, 'padding':'same', 'activation':'relu'},\n",
- " {'type':'Conv2D', 'filters':64, 'kernel_size':(3,3), 'strides':2, 'padding':'same', 'activation':'relu'},\n",
- " {'type':'Conv2D', 'filters':64, 'kernel_size':(3,3), 'strides':2, 'padding':'same', 'activation':'relu'},\n",
- " {'type':'Conv2D', 'filters':64, 'kernel_size':(3,3), 'strides':1, 'padding':'same', 'activation':'relu'}\n",
- " ]\n",
- "\n",
- "decoder= [ {'type':'Conv2DT', 'filters':64, 'kernel_size':(3,3), 'strides':1, 'padding':'same', 'activation':'relu'},\n",
- " {'type':'Conv2DT', 'filters':64, 'kernel_size':(3,3), 'strides':2, 'padding':'same', 'activation':'relu'},\n",
- " {'type':'Conv2DT', 'filters':32, 'kernel_size':(3,3), 'strides':2, 'padding':'same', 'activation':'relu'},\n",
- " {'type':'Conv2DT', 'filters':1, 'kernel_size':(3,3), 'strides':1, 'padding':'same', 'activation':'sigmoid'}\n",
- " ]\n",
- "\n",
- "vae = modules.vae.VariationalAutoencoder(input_shape = input_shape, \n",
- " encoder_layers = encoder, \n",
- " decoder_layers = decoder,\n",
- " z_dim = z_dim, \n",
- " verbose = verbose,\n",
- " run_tag = tag)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 4 - Compile it"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [],
- "source": [
- "learning_rate = 0.0005\n",
- "r_loss_factor = 1000\n",
- "\n",
- "vae.compile(learning_rate, r_loss_factor)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 5 - Train"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [],
- "source": [
- "batch_size = 100\n",
- "epochs = 200\n",
- "image_periodicity = 1 # for each epoch\n",
- "chkpt_periodicity = 2 # for each epoch\n",
- "initial_epoch = 0\n",
- "dataset_size = 1"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Train on 60000 samples, validate on 10000 samples\n",
- "Epoch 1/200\n",
- " 100/60000 [..............................] - ETA: 23:40 - loss: 231.4378 - vae_r_loss: 231.4373 - vae_kl_loss: 5.3801e-04WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.251492). Check your callbacks.\n",
- "60000/60000 [==============================] - 6s 101us/sample - loss: 67.7431 - vae_r_loss: 65.0691 - vae_kl_loss: 2.6740 - val_loss: 55.6598 - val_vae_r_loss: 52.4039 - val_vae_kl_loss: 3.2560\n",
- "Epoch 2/200\n",
- "60000/60000 [==============================] - 3s 58us/sample - loss: 54.0334 - vae_r_loss: 50.4695 - vae_kl_loss: 3.5639 - val_loss: 52.9105 - val_vae_r_loss: 49.1433 - val_vae_kl_loss: 3.7672\n",
- "Epoch 3/200\n",
- "60000/60000 [==============================] - 3s 57us/sample - loss: 51.8937 - vae_r_loss: 47.9195 - vae_kl_loss: 3.9743 - val_loss: 51.1775 - val_vae_r_loss: 47.0874 - val_vae_kl_loss: 4.0901\n",
- "Epoch 4/200\n",
- "60000/60000 [==============================] - 4s 59us/sample - loss: 50.4622 - vae_r_loss: 46.1359 - vae_kl_loss: 4.3264 - val_loss: 49.8507 - val_vae_r_loss: 45.2015 - val_vae_kl_loss: 4.6492\n",
- "Epoch 5/200\n",
- "60000/60000 [==============================] - 3s 57us/sample - loss: 49.3577 - vae_r_loss: 44.8123 - vae_kl_loss: 4.5454 - val_loss: 48.9416 - val_vae_r_loss: 44.3832 - val_vae_kl_loss: 4.5584\n",
- "Epoch 6/200\n",
- "60000/60000 [==============================] - 3s 58us/sample - loss: 48.5603 - vae_r_loss: 43.8800 - vae_kl_loss: 4.6803 - val_loss: 48.1800 - val_vae_r_loss: 43.5046 - val_vae_kl_loss: 4.6754\n",
- "Epoch 7/200\n",
- "60000/60000 [==============================] - 3s 57us/sample - loss: 48.0286 - vae_r_loss: 43.2646 - vae_kl_loss: 4.7640 - val_loss: 47.9362 - val_vae_r_loss: 43.2833 - val_vae_kl_loss: 4.6529\n",
- "Epoch 8/200\n",
- "60000/60000 [==============================] - 3s 58us/sample - loss: 47.6163 - vae_r_loss: 42.7828 - vae_kl_loss: 4.8336 - val_loss: 47.6161 - val_vae_r_loss: 42.7176 - val_vae_kl_loss: 4.8985\n",
- "Epoch 9/200\n",
- "60000/60000 [==============================] - 3s 58us/sample - loss: 47.2654 - vae_r_loss: 42.3804 - vae_kl_loss: 4.8850 - val_loss: 47.1385 - val_vae_r_loss: 42.2280 - val_vae_kl_loss: 4.9105\n",
- "Epoch 10/200\n",
- "WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.732872). Check your callbacks.\n",
- " 100/60000 [..............................] - ETA: 7:23 - loss: 47.8688 - vae_r_loss: 43.0966 - vae_kl_loss: 4.7722WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.366450). Check your callbacks.\n",
- "60000/60000 [==============================] - 4s 70us/sample - loss: 46.9698 - vae_r_loss: 42.0353 - vae_kl_loss: 4.9345 - val_loss: 47.0246 - val_vae_r_loss: 42.1103 - val_vae_kl_loss: 4.9143\n",
- "Epoch 11/200\n",
- "60000/60000 [==============================] - 3s 57us/sample - loss: 46.7538 - vae_r_loss: 41.7733 - vae_kl_loss: 4.9805 - val_loss: 46.9033 - val_vae_r_loss: 41.9019 - val_vae_kl_loss: 5.0014\n",
- "Epoch 12/200\n",
- "60000/60000 [==============================] - 3s 58us/sample - loss: 46.4962 - vae_r_loss: 41.4867 - vae_kl_loss: 5.0095 - val_loss: 46.6990 - val_vae_r_loss: 41.8006 - val_vae_kl_loss: 4.8985\n",
- "Epoch 13/200\n",
- "60000/60000 [==============================] - 3s 57us/sample - loss: 46.3232 - vae_r_loss: 41.2603 - vae_kl_loss: 5.0629 - val_loss: 46.6737 - val_vae_r_loss: 41.4675 - val_vae_kl_loss: 5.2061\n",
- "Epoch 14/200\n",
- "60000/60000 [==============================] - 3s 58us/sample - loss: 46.1505 - vae_r_loss: 41.0678 - vae_kl_loss: 5.0828 - val_loss: 46.3871 - val_vae_r_loss: 41.4687 - val_vae_kl_loss: 4.9184\n",
- "Epoch 15/200\n",
- "60000/60000 [==============================] - 3s 57us/sample - loss: 45.9750 - vae_r_loss: 40.8533 - vae_kl_loss: 5.1217 - val_loss: 46.1730 - val_vae_r_loss: 41.0982 - val_vae_kl_loss: 5.0748\n",
- "Epoch 16/200\n",
- "60000/60000 [==============================] - 3s 57us/sample - loss: 45.8053 - vae_r_loss: 40.6467 - vae_kl_loss: 5.1586 - val_loss: 46.2439 - val_vae_r_loss: 41.1142 - val_vae_kl_loss: 5.1297\n",
- "Epoch 17/200\n",
- "60000/60000 [==============================] - 3s 57us/sample - loss: 45.6415 - vae_r_loss: 40.4657 - vae_kl_loss: 5.1758 - val_loss: 46.0754 - val_vae_r_loss: 41.0632 - val_vae_kl_loss: 5.0122\n",
- "Epoch 18/200\n",
- "60000/60000 [==============================] - 3s 58us/sample - loss: 45.5121 - vae_r_loss: 40.3147 - vae_kl_loss: 5.1974 - val_loss: 45.8663 - val_vae_r_loss: 40.5329 - val_vae_kl_loss: 5.3334\n",
- "Epoch 19/200\n",
- "60000/60000 [==============================] - 3s 56us/sample - loss: 45.3686 - vae_r_loss: 40.1475 - vae_kl_loss: 5.2211 - val_loss: 46.2054 - val_vae_r_loss: 41.1238 - val_vae_kl_loss: 5.0816\n",
- "Epoch 20/200\n",
- "60000/60000 [==============================] - 3s 58us/sample - loss: 45.2161 - vae_r_loss: 39.9703 - vae_kl_loss: 5.2458 - val_loss: 45.7448 - val_vae_r_loss: 40.6166 - val_vae_kl_loss: 5.1283\n",
- "Epoch 21/200\n",
- "60000/60000 [==============================] - 3s 56us/sample - loss: 45.1159 - vae_r_loss: 39.8419 - vae_kl_loss: 5.2740 - val_loss: 45.8612 - val_vae_r_loss: 40.8692 - val_vae_kl_loss: 4.9920\n",
- "Epoch 22/200\n",
- "60000/60000 [==============================] - 3s 57us/sample - loss: 44.9881 - vae_r_loss: 39.7023 - vae_kl_loss: 5.2857 - val_loss: 45.8085 - val_vae_r_loss: 40.2675 - val_vae_kl_loss: 5.5410\n",
- "Epoch 23/200\n",
- "60000/60000 [==============================] - 3s 57us/sample - loss: 44.8471 - vae_r_loss: 39.5384 - vae_kl_loss: 5.3087 - val_loss: 45.4330 - val_vae_r_loss: 40.0743 - val_vae_kl_loss: 5.3587\n",
- "Epoch 24/200\n",
- "60000/60000 [==============================] - 3s 58us/sample - loss: 44.7550 - vae_r_loss: 39.4362 - vae_kl_loss: 5.3188 - val_loss: 45.3320 - val_vae_r_loss: 39.9992 - val_vae_kl_loss: 5.3328\n",
- "Epoch 25/200\n",
- "60000/60000 [==============================] - 3s 57us/sample - loss: 44.6692 - vae_r_loss: 39.3461 - vae_kl_loss: 5.3232 - val_loss: 45.3552 - val_vae_r_loss: 40.0258 - val_vae_kl_loss: 5.3294\n",
- "Epoch 26/200\n",
- "60000/60000 [==============================] - 3s 58us/sample - loss: 44.5891 - vae_r_loss: 39.2333 - vae_kl_loss: 5.3558 - val_loss: 45.2681 - val_vae_r_loss: 39.9015 - val_vae_kl_loss: 5.3666\n",
- "Epoch 27/200\n",
- "60000/60000 [==============================] - 3s 56us/sample - loss: 44.5072 - vae_r_loss: 39.1374 - vae_kl_loss: 5.3698 - val_loss: 45.3209 - val_vae_r_loss: 39.9636 - val_vae_kl_loss: 5.3574\n",
- "Epoch 28/200\n",
- "60000/60000 [==============================] - 3s 58us/sample - loss: 44.4180 - vae_r_loss: 39.0149 - vae_kl_loss: 5.4031 - val_loss: 45.2435 - val_vae_r_loss: 39.7765 - val_vae_kl_loss: 5.4671\n",
- "Epoch 29/200\n",
- "60000/60000 [==============================] - 3s 57us/sample - loss: 44.3102 - vae_r_loss: 38.9046 - vae_kl_loss: 5.4057 - val_loss: 45.2258 - val_vae_r_loss: 39.8441 - val_vae_kl_loss: 5.3817\n",
- "Epoch 30/200\n",
- "60000/60000 [==============================] - 3s 58us/sample - loss: 44.2489 - vae_r_loss: 38.8299 - vae_kl_loss: 5.4190 - val_loss: 45.0044 - val_vae_r_loss: 39.6516 - val_vae_kl_loss: 5.3528\n",
- "Epoch 31/200\n",
- "60000/60000 [==============================] - 3s 57us/sample - loss: 44.1732 - vae_r_loss: 38.7482 - vae_kl_loss: 5.4249 - val_loss: 45.0000 - val_vae_r_loss: 39.5609 - val_vae_kl_loss: 5.4391\n",
- "Epoch 32/200\n",
- "60000/60000 [==============================] - 3s 58us/sample - loss: 44.0894 - vae_r_loss: 38.6580 - vae_kl_loss: 5.4314 - val_loss: 44.9769 - val_vae_r_loss: 39.5384 - val_vae_kl_loss: 5.4385\n",
- "Epoch 33/200\n",
- "60000/60000 [==============================] - 3s 57us/sample - loss: 44.0582 - vae_r_loss: 38.6092 - vae_kl_loss: 5.4490 - val_loss: 44.9346 - val_vae_r_loss: 39.3805 - val_vae_kl_loss: 5.5541\n",
- "Epoch 34/200\n",
- "60000/60000 [==============================] - 3s 57us/sample - loss: 43.9458 - vae_r_loss: 38.4818 - vae_kl_loss: 5.4640 - val_loss: 45.0624 - val_vae_r_loss: 39.5811 - val_vae_kl_loss: 5.4813\n",
- "Epoch 35/200\n",
- "60000/60000 [==============================] - 3s 56us/sample - loss: 43.8850 - vae_r_loss: 38.4031 - vae_kl_loss: 5.4819 - val_loss: 45.0285 - val_vae_r_loss: 39.5350 - val_vae_kl_loss: 5.4935\n",
- "Epoch 36/200\n",
- "60000/60000 [==============================] - 3s 58us/sample - loss: 43.8698 - vae_r_loss: 38.3779 - vae_kl_loss: 5.4918 - val_loss: 44.9170 - val_vae_r_loss: 39.5714 - val_vae_kl_loss: 5.3456\n",
- "Epoch 37/200\n",
- "60000/60000 [==============================] - 3s 57us/sample - loss: 43.7739 - vae_r_loss: 38.2723 - vae_kl_loss: 5.5016 - val_loss: 44.8441 - val_vae_r_loss: 39.3665 - val_vae_kl_loss: 5.4776\n",
- "Epoch 38/200\n",
- "60000/60000 [==============================] - 3s 57us/sample - loss: 43.7084 - vae_r_loss: 38.1933 - vae_kl_loss: 5.5151 - val_loss: 44.9233 - val_vae_r_loss: 39.5526 - val_vae_kl_loss: 5.3706\n",
- "Epoch 39/200\n",
- "60000/60000 [==============================] - 3s 57us/sample - loss: 43.6626 - vae_r_loss: 38.1320 - vae_kl_loss: 5.5306 - val_loss: 44.6793 - val_vae_r_loss: 39.2304 - val_vae_kl_loss: 5.4489\n",
- "Epoch 40/200\n",
- "60000/60000 [==============================] - 3s 58us/sample - loss: 43.5838 - vae_r_loss: 38.0592 - vae_kl_loss: 5.5246 - val_loss: 44.6130 - val_vae_r_loss: 39.0715 - val_vae_kl_loss: 5.5415\n",
- "Epoch 41/200\n",
- "60000/60000 [==============================] - 3s 56us/sample - loss: 43.5194 - vae_r_loss: 37.9840 - vae_kl_loss: 5.5354 - val_loss: 44.8512 - val_vae_r_loss: 39.6158 - val_vae_kl_loss: 5.2354\n",
- "Epoch 42/200\n",
- "60000/60000 [==============================] - 3s 57us/sample - loss: 43.5129 - vae_r_loss: 37.9786 - vae_kl_loss: 5.5343 - val_loss: 44.6991 - val_vae_r_loss: 39.2098 - val_vae_kl_loss: 5.4894\n",
- "Epoch 43/200\n",
- "60000/60000 [==============================] - 3s 56us/sample - loss: 43.4707 - vae_r_loss: 37.9237 - vae_kl_loss: 5.5470 - val_loss: 44.7121 - val_vae_r_loss: 39.2446 - val_vae_kl_loss: 5.4675\n",
- "Epoch 44/200\n",
- "60000/60000 [==============================] - 3s 57us/sample - loss: 43.3832 - vae_r_loss: 37.8227 - vae_kl_loss: 5.5604 - val_loss: 44.9172 - val_vae_r_loss: 39.3446 - val_vae_kl_loss: 5.5726\n",
- "Epoch 45/200\n",
- "60000/60000 [==============================] - 3s 57us/sample - loss: 43.3868 - vae_r_loss: 37.8075 - vae_kl_loss: 5.5793 - val_loss: 44.5718 - val_vae_r_loss: 39.0284 - val_vae_kl_loss: 5.5434\n",
- "Epoch 46/200\n",
- "60000/60000 [==============================] - 3s 57us/sample - loss: 43.2774 - vae_r_loss: 37.6953 - vae_kl_loss: 5.5821 - val_loss: 44.6954 - val_vae_r_loss: 39.1276 - val_vae_kl_loss: 5.5678\n",
- "Epoch 47/200\n",
- "60000/60000 [==============================] - 3s 56us/sample - loss: 43.2765 - vae_r_loss: 37.6813 - vae_kl_loss: 5.5952 - val_loss: 44.6153 - val_vae_r_loss: 38.9606 - val_vae_kl_loss: 5.6547\n",
- "Epoch 48/200\n",
- "60000/60000 [==============================] - 3s 58us/sample - loss: 43.2385 - vae_r_loss: 37.6431 - vae_kl_loss: 5.5954 - val_loss: 44.5508 - val_vae_r_loss: 39.0830 - val_vae_kl_loss: 5.4678\n",
- "Epoch 49/200\n",
- "60000/60000 [==============================] - 3s 56us/sample - loss: 43.1847 - vae_r_loss: 37.5822 - vae_kl_loss: 5.6025 - val_loss: 44.8277 - val_vae_r_loss: 39.1688 - val_vae_kl_loss: 5.6589\n",
- "Epoch 50/200\n",
- "60000/60000 [==============================] - 4s 58us/sample - loss: 43.1557 - vae_r_loss: 37.5533 - vae_kl_loss: 5.6024 - val_loss: 44.5082 - val_vae_r_loss: 38.9529 - val_vae_kl_loss: 5.5553\n",
- "Epoch 51/200\n",
- "60000/60000 [==============================] - 3s 56us/sample - loss: 43.0726 - vae_r_loss: 37.4533 - vae_kl_loss: 5.6193 - val_loss: 44.6332 - val_vae_r_loss: 38.9104 - val_vae_kl_loss: 5.7228\n",
- "Epoch 52/200\n",
- "60000/60000 [==============================] - 3s 57us/sample - loss: 43.1003 - vae_r_loss: 37.4708 - vae_kl_loss: 5.6295 - val_loss: 44.5279 - val_vae_r_loss: 39.0846 - val_vae_kl_loss: 5.4433\n",
- "Epoch 53/200\n",
- "60000/60000 [==============================] - 3s 56us/sample - loss: 43.0121 - vae_r_loss: 37.3923 - vae_kl_loss: 5.6198 - val_loss: 44.5675 - val_vae_r_loss: 38.9651 - val_vae_kl_loss: 5.6024\n",
- "Epoch 54/200\n",
- "60000/60000 [==============================] - 3s 57us/sample - loss: 42.9750 - vae_r_loss: 37.3273 - vae_kl_loss: 5.6477 - val_loss: 44.6084 - val_vae_r_loss: 39.0057 - val_vae_kl_loss: 5.6027\n",
- "Epoch 55/200\n",
- "60000/60000 [==============================] - 3s 57us/sample - loss: 42.9669 - vae_r_loss: 37.3124 - vae_kl_loss: 5.6545 - val_loss: 44.4369 - val_vae_r_loss: 38.7499 - val_vae_kl_loss: 5.6870\n",
- "Epoch 56/200\n",
- "60000/60000 [==============================] - 3s 57us/sample - loss: 42.9172 - vae_r_loss: 37.2666 - vae_kl_loss: 5.6506 - val_loss: 44.4817 - val_vae_r_loss: 38.8071 - val_vae_kl_loss: 5.6747\n",
- "Epoch 57/200\n",
- "60000/60000 [==============================] - 3s 56us/sample - loss: 42.8719 - vae_r_loss: 37.2088 - vae_kl_loss: 5.6630 - val_loss: 44.7545 - val_vae_r_loss: 39.1340 - val_vae_kl_loss: 5.6205\n",
- "Epoch 58/200\n",
- "60000/60000 [==============================] - 3s 57us/sample - loss: 42.8724 - vae_r_loss: 37.2070 - vae_kl_loss: 5.6654 - val_loss: 44.4428 - val_vae_r_loss: 38.8374 - val_vae_kl_loss: 5.6054\n",
- "Epoch 59/200\n",
- "60000/60000 [==============================] - 3s 57us/sample - loss: 42.8085 - vae_r_loss: 37.1356 - vae_kl_loss: 5.6729 - val_loss: 44.3657 - val_vae_r_loss: 38.8973 - val_vae_kl_loss: 5.4684\n",
- "Epoch 60/200\n",
- "60000/60000 [==============================] - 3s 57us/sample - loss: 42.7711 - vae_r_loss: 37.1025 - vae_kl_loss: 5.6687 - val_loss: 44.5526 - val_vae_r_loss: 38.7923 - val_vae_kl_loss: 5.7603\n",
- "Epoch 61/200\n",
- "60000/60000 [==============================] - 3s 56us/sample - loss: 42.7549 - vae_r_loss: 37.0712 - vae_kl_loss: 5.6837 - val_loss: 44.6274 - val_vae_r_loss: 39.1211 - val_vae_kl_loss: 5.5063\n",
- "Epoch 62/200\n",
- "60000/60000 [==============================] - 3s 57us/sample - loss: 42.7314 - vae_r_loss: 37.0368 - vae_kl_loss: 5.6946 - val_loss: 44.3828 - val_vae_r_loss: 38.8327 - val_vae_kl_loss: 5.5502\n",
- "Epoch 63/200\n",
- "60000/60000 [==============================] - 3s 56us/sample - loss: 42.6688 - vae_r_loss: 36.9835 - vae_kl_loss: 5.6853 - val_loss: 44.4869 - val_vae_r_loss: 38.8497 - val_vae_kl_loss: 5.6372\n",
- "Epoch 64/200\n",
- "60000/60000 [==============================] - 3s 57us/sample - loss: 42.6714 - vae_r_loss: 36.9633 - vae_kl_loss: 5.7080 - val_loss: 44.4562 - val_vae_r_loss: 38.7178 - val_vae_kl_loss: 5.7384\n",
- "Epoch 65/200\n",
- "60000/60000 [==============================] - 3s 56us/sample - loss: 42.6547 - vae_r_loss: 36.9360 - vae_kl_loss: 5.7187 - val_loss: 44.4947 - val_vae_r_loss: 38.8561 - val_vae_kl_loss: 5.6386\n",
- "Epoch 66/200\n",
- "60000/60000 [==============================] - 3s 57us/sample - loss: 42.5807 - vae_r_loss: 36.8625 - vae_kl_loss: 5.7182 - val_loss: 44.4270 - val_vae_r_loss: 38.7251 - val_vae_kl_loss: 5.7019\n",
- "Epoch 67/200\n",
- "60000/60000 [==============================] - 3s 56us/sample - loss: 42.5664 - vae_r_loss: 36.8466 - vae_kl_loss: 5.7197 - val_loss: 44.5878 - val_vae_r_loss: 38.8787 - val_vae_kl_loss: 5.7091\n",
- "Epoch 68/200\n",
- "60000/60000 [==============================] - 3s 57us/sample - loss: 42.5503 - vae_r_loss: 36.8269 - vae_kl_loss: 5.7235 - val_loss: 44.6236 - val_vae_r_loss: 38.8846 - val_vae_kl_loss: 5.7390\n",
- "Epoch 69/200\n",
- "60000/60000 [==============================] - 3s 56us/sample - loss: 42.5057 - vae_r_loss: 36.7706 - vae_kl_loss: 5.7352 - val_loss: 44.5720 - val_vae_r_loss: 38.9196 - val_vae_kl_loss: 5.6525\n",
- "Epoch 70/200\n",
- "60000/60000 [==============================] - 3s 57us/sample - loss: 42.4955 - vae_r_loss: 36.7553 - vae_kl_loss: 5.7402 - val_loss: 44.4059 - val_vae_r_loss: 38.8886 - val_vae_kl_loss: 5.5173\n",
- "Epoch 71/200\n",
- "60000/60000 [==============================] - 3s 56us/sample - loss: 42.4649 - vae_r_loss: 36.7251 - vae_kl_loss: 5.7398 - val_loss: 44.5864 - val_vae_r_loss: 38.8203 - val_vae_kl_loss: 5.7661\n",
- "Epoch 72/200\n",
- "60000/60000 [==============================] - 3s 58us/sample - loss: 42.4907 - vae_r_loss: 36.7440 - vae_kl_loss: 5.7467 - val_loss: 44.3493 - val_vae_r_loss: 38.6765 - val_vae_kl_loss: 5.6727\n",
- "Epoch 73/200\n",
- "60000/60000 [==============================] - 3s 56us/sample - loss: 42.4224 - vae_r_loss: 36.6558 - vae_kl_loss: 5.7666 - val_loss: 44.5477 - val_vae_r_loss: 38.7588 - val_vae_kl_loss: 5.7889\n",
- "Epoch 74/200\n",
- "43100/60000 [====================>.........] - ETA: 0s - loss: 42.3141 - vae_r_loss: 36.5576 - vae_kl_loss: 5.7565"
- ]
- }
- ],
- "source": [
- "vae.train(x_train,\n",
- " x_test,\n",
- " batch_size = batch_size, \n",
- " epochs = epochs,\n",
- " image_periodicity = image_periodicity,\n",
- " chkpt_periodicity = chkpt_periodicity,\n",
- " initial_epoch = initial_epoch,\n",
- " dataset_size = dataset_size,\n",
- " lr_decay = 1\n",
- " )"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "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": "3.7.5"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/VAE/01-VAE-with-MNIST.ipynb b/VAE/01-VAE-with-MNIST.ipynb
new file mode 100644
index 0000000..ae68bca
--- /dev/null
+++ b/VAE/01-VAE-with-MNIST.ipynb
@@ -0,0 +1,269 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Variational AutoEncoder (VAE) with MNIST\n",
+ "========================================\n",
+ "---\n",
+ "Formation Introduction au Deep Learning (FIDLE) - S. Arias, E. Maldonado, JL. Parouty - CNRS/SARI/DEVLOG - 2020 \n",
+ "\n",
+ "## Episode 1 - Train a model\n",
+ " - Defining a VAE model\n",
+ " - Build the model\n",
+ " - Train it\n",
+ " - Follow the learning process with Tensorboard\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 1 - Init python stuff"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 65,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "FIDLE 2020 - Variational AutoEncoder (VAE)\n",
+ "TensorFlow version : 2.0.0\n"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "import tensorflow as tf\n",
+ "import tensorflow.keras.datasets.mnist as mnist\n",
+ "import sys, importlib\n",
+ "\n",
+ "import modules.vae\n",
+ "importlib.reload(modules.vae)\n",
+ "\n",
+ "print('FIDLE 2020 - Variational AutoEncoder (VAE)')\n",
+ "print('TensorFlow version :',tf.__version__)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 2 - Get data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 66,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Dataset loaded.\n",
+ "x_train shape : (60000, 28, 28, 1)\n",
+ "x_test_shape : (10000, 28, 28, 1)\n"
+ ]
+ }
+ ],
+ "source": [
+ "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
+ "\n",
+ "x_train = x_train.astype('float32') / 255.\n",
+ "x_train = np.expand_dims(x_train, axis=3)\n",
+ "x_test = x_test.astype('float32') / 255.\n",
+ "x_test = np.expand_dims(x_test, axis=3)\n",
+ "print('Dataset loaded.')\n",
+ "print(f'x_train shape : {x_train.shape}\\nx_test_shape : {x_test.shape}')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 3 - Get VAE model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 67,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model initialized.\n",
+ "Outputs will be in : ./run/004\n"
+ ]
+ }
+ ],
+ "source": [
+ "tag = '004'\n",
+ "\n",
+ "input_shape = (28,28,1)\n",
+ "z_dim = 2\n",
+ "verbose = 0\n",
+ "\n",
+ "encoder= [ {'type':'Conv2D', 'filters':32, 'kernel_size':(3,3), 'strides':1, 'padding':'same', 'activation':'relu'},\n",
+ " {'type':'Conv2D', 'filters':64, 'kernel_size':(3,3), 'strides':2, 'padding':'same', 'activation':'relu'},\n",
+ " {'type':'Conv2D', 'filters':64, 'kernel_size':(3,3), 'strides':2, 'padding':'same', 'activation':'relu'},\n",
+ " {'type':'Conv2D', 'filters':64, 'kernel_size':(3,3), 'strides':1, 'padding':'same', 'activation':'relu'}\n",
+ " ]\n",
+ "\n",
+ "decoder= [ {'type':'Conv2DT', 'filters':64, 'kernel_size':(3,3), 'strides':1, 'padding':'same', 'activation':'relu'},\n",
+ " {'type':'Conv2DT', 'filters':64, 'kernel_size':(3,3), 'strides':2, 'padding':'same', 'activation':'relu'},\n",
+ " {'type':'Conv2DT', 'filters':32, 'kernel_size':(3,3), 'strides':2, 'padding':'same', 'activation':'relu'},\n",
+ " {'type':'Conv2DT', 'filters':1, 'kernel_size':(3,3), 'strides':1, 'padding':'same', 'activation':'sigmoid'}\n",
+ " ]\n",
+ "\n",
+ "vae = modules.vae.VariationalAutoencoder(input_shape = input_shape, \n",
+ " encoder_layers = encoder, \n",
+ " decoder_layers = decoder,\n",
+ " z_dim = z_dim, \n",
+ " verbose = verbose,\n",
+ " run_tag = tag)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 68,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Config saved in : ./run/004/models/vae_config.json\n",
+ "Model saved in : ./run/004/models/model.h5\n"
+ ]
+ }
+ ],
+ "source": [
+ "vae.save()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 69,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "dict_keys(['input_shape', 'encoder_layers', 'decoder_layers', 'z_dim', 'run_tag', 'verbose'])\n",
+ "Model initialized.\n",
+ "Outputs will be in : ./run/004\n",
+ "Weights loaded from : ./run/004/models/model.h5\n"
+ ]
+ }
+ ],
+ "source": [
+ "vae=modules.vae.VariationalAutoencoder.load('004')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 4 - Compile it"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 70,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Compiled.\n",
+ "Optimizer is Adam with learning_rate=0.0005\n"
+ ]
+ }
+ ],
+ "source": [
+ "learning_rate = 0.0005\n",
+ "r_loss_factor = 1000\n",
+ "\n",
+ "vae.compile(learning_rate, r_loss_factor)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 5 - Train"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "batch_size = 100\n",
+ "epochs = 100\n",
+ "image_periodicity = 1 # for each epoch\n",
+ "chkpt_periodicity = 2 # for each epoch\n",
+ "initial_epoch = 0\n",
+ "dataset_size = 1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "vae.train(x_train,\n",
+ " x_test,\n",
+ " batch_size = batch_size, \n",
+ " epochs = epochs,\n",
+ " image_periodicity = image_periodicity,\n",
+ " chkpt_periodicity = chkpt_periodicity,\n",
+ " initial_epoch = initial_epoch,\n",
+ " dataset_size = dataset_size\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "vae."
+ ]
+ }
+ ],
+ "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": "3.7.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/VAE/02-VAE-with-MNIST-post.ipynb b/VAE/02-VAE-with-MNIST-post.ipynb
new file mode 100644
index 0000000..64e2a11
--- /dev/null
+++ b/VAE/02-VAE-with-MNIST-post.ipynb
@@ -0,0 +1,208 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Variational AutoEncoder (VAE) with MNIST\n",
+ "========================================\n",
+ "---\n",
+ "Formation Introduction au Deep Learning (FIDLE) - S. Arias, E. Maldonado, JL. Parouty - CNRS/SARI/DEVLOG - 2020 \n",
+ "\n",
+ "## Episode 2 - Analyse our trained model\n",
+ " - Defining a VAE model\n",
+ " - Build the model\n",
+ " - Train it\n",
+ " - Follow the learning process with Tensorboard\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 1 - Init python stuff"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "FIDLE 2020 - Variational AutoEncoder (VAE)\n",
+ "TensorFlow version : 2.0.0\n"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "import tensorflow as tf\n",
+ "import tensorflow.keras.datasets.mnist as mnist\n",
+ "import sys, importlib\n",
+ "\n",
+ "import modules.vae\n",
+ "importlib.reload(modules.vae)\n",
+ "\n",
+ "print('FIDLE 2020 - Variational AutoEncoder (VAE)')\n",
+ "print('TensorFlow version :',tf.__version__)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 2 - Get data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Dataset loaded.\n",
+ "x_train shape : (60000, 28, 28, 1)\n",
+ "x_test_shape : (10000, 28, 28, 1)\n"
+ ]
+ }
+ ],
+ "source": [
+ "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
+ "\n",
+ "x_train = x_train.astype('float32') / 255.\n",
+ "x_train = np.expand_dims(x_train, axis=3)\n",
+ "x_test = x_test.astype('float32') / 255.\n",
+ "x_test = np.expand_dims(x_test, axis=3)\n",
+ "print('Dataset loaded.')\n",
+ "print(f'x_train shape : {x_train.shape}\\nx_test_shape : {x_test.shape}')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 3 - Load best model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "vae\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "end_time = time.time()\n",
+ "dt = end_time-start_time\n",
+ "dth = str(datetime.timedelta(seconds=dt))\n",
+ "print(f'\\nTrain duration : {dt:.2f} sec. - {dth:}')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 4 - Compile it"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "learning_rate = 0.0005\n",
+ "r_loss_factor = 1000\n",
+ "\n",
+ "vae.compile(learning_rate, r_loss_factor)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 5 - Train"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "batch_size = 100\n",
+ "epochs = 200\n",
+ "image_periodicity = 1 # for each epoch\n",
+ "chkpt_periodicity = 2 # for each epoch\n",
+ "initial_epoch = 0\n",
+ "dataset_size = 1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "vae.train(x_train,\n",
+ " x_test,\n",
+ " batch_size = batch_size, \n",
+ " epochs = epochs,\n",
+ " image_periodicity = image_periodicity,\n",
+ " chkpt_periodicity = chkpt_periodicity,\n",
+ " initial_epoch = initial_epoch,\n",
+ " dataset_size = dataset_size,\n",
+ " lr_decay = 1\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "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": "3.7.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/VAE/modules/vae.py b/VAE/modules/vae.py
index e98409b..207cfc1 100644
--- a/VAE/modules/vae.py
+++ b/VAE/modules/vae.py
@@ -13,20 +13,21 @@ from tensorflow.keras.utils import plot_model
import tensorflow.keras.datasets.imdb as imdb
import modules.callbacks
-import os
+import os, json, time, datetime
class VariationalAutoencoder():
- def __init__(self, input_shape=None, encoder_layers=None, decoder_layers=None, z_dim=None, run_tag='default', verbose=0):
-
+ def __init__(self, input_shape=None, encoder_layers=None, decoder_layers=None, z_dim=None, run_tag='000', verbose=0):
+
self.name = 'Variational AutoEncoder'
- self.input_shape = input_shape
+ self.input_shape = list(input_shape)
self.encoder_layers = encoder_layers
self.decoder_layers = decoder_layers
self.z_dim = z_dim
+ self.run_tag = str(run_tag)
self.verbose = verbose
self.run_directory = f'./run/{run_tag}'
@@ -42,13 +43,14 @@ class VariationalAutoencoder():
# ---- Add next layers
i=1
- for params in encoder_layers:
- t=params['type']
- params.pop('type')
- if t=='Conv2D':
- layer = Conv2D(**params, name=f"Layer_{i}")
- if t=='Dropout':
- layer = Dropout(**params)
+ for l_config in encoder_layers:
+ l_type = l_config['type']
+ l_params = l_config.copy()
+ l_params.pop('type')
+ if l_type=='Conv2D':
+ layer = Conv2D(**l_params)
+ if l_type=='Dropout':
+ layer = Dropout(**l_params)
x = layer(x)
i+=1
@@ -83,13 +85,14 @@ class VariationalAutoencoder():
# ---- Add next layers
i=1
- for params in decoder_layers:
- t=params['type']
- params.pop('type')
- if t=='Conv2DT':
- layer = Conv2DTranspose(**params, name=f"Layer_{i}")
- if t=='Dropout':
- layer = Dropout(**params)
+ for l_config in decoder_layers:
+ l_type = l_config['type']
+ l_params = l_config.copy()
+ l_params.pop('type')
+ if l_type=='Conv2DT':
+ layer = Conv2DTranspose(**l_params)
+ if l_type=='Dropout':
+ layer = Dropout(**l_params)
x = layer(x)
i+=1
@@ -140,6 +143,8 @@ class VariationalAutoencoder():
loss = vae_loss,
metrics = [vae_r_loss, vae_kl_loss],
experimental_run_tf_function=False)
+ print('Compiled.')
+ print(f'Optimizer is Adam with learning_rate={learning_rate:}')
def train(self,
@@ -165,7 +170,7 @@ class VariationalAutoencoder():
callbacks_images = modules.callbacks.ImagesCallback(initial_epoch, image_periodicity, self)
# ---- Callback : Learning rate scheduler
- lr_sched = modules.callbacks.step_decay_schedule(initial_lr=self.learning_rate, decay_factor=lr_decay, step_size=1)
+ #lr_sched = modules.callbacks.step_decay_schedule(initial_lr=self.learning_rate, decay_factor=lr_decay, step_size=1)
# ---- Callback : Checkpoint
filename = self.run_directory+"/models/model-{epoch:03d}-{loss:.2f}.h5"
@@ -179,17 +184,23 @@ class VariationalAutoencoder():
dirname = self.run_directory+"/logs"
callback_tensorboard = TensorBoard(log_dir=dirname, histogram_freq=1)
- callbacks_list = [callbacks_images, callback_chkpts, callback_bestmodel, callback_tensorboard, lr_sched]
-
- self.model.fit(x_train[:n_train], x_train[:n_train],
- batch_size = batch_size,
- shuffle = True,
- epochs = epochs,
- initial_epoch = initial_epoch,
- callbacks = callbacks_list,
- validation_data = (x_test[:n_test], x_test[:n_test])
- )
-
+ callbacks_list = [callbacks_images, callback_chkpts, callback_bestmodel, callback_tensorboard]
+
+ # ---- Let's go...
+ start_time = time.time()
+ self.history = self.model.fit(x_train[:n_train], x_train[:n_train],
+ batch_size = batch_size,
+ shuffle = True,
+ epochs = epochs,
+ initial_epoch = initial_epoch,
+ callbacks = callbacks_list,
+ validation_data = (x_test[:n_test], x_test[:n_test])
+ )
+ end_time = time.time()
+ dt = end_time-start_time
+ dth = str(datetime.timedelta(seconds=int(dt)))
+ self.duration = dt
+ print(f'\nTrain duration : {dt:.2f} sec. - {dth:}')
def plot_model(self):
d=self.run_directory+'/figs'
@@ -198,3 +209,39 @@ class VariationalAutoencoder():
plot_model(self.decoder, to_file=f'{d}/decoder.png', show_shapes = True, show_layer_names = True)
+ def save(self,config='vae_config.json', model='model.h5'):
+ # ---- Save config in json
+ if config!=None:
+ to_save = ['input_shape', 'encoder_layers', 'decoder_layers', 'z_dim', 'run_tag', 'verbose']
+ data = { i:self.__dict__[i] for i in to_save }
+ filename = self.run_directory+'/models/'+config
+ with open(filename, 'w') as outfile:
+ json.dump(data, outfile)
+ print(f'Config saved in : {filename}')
+ # ---- Save model
+ if model!=None:
+ filename = self.run_directory+'/models/'+model
+ self.model.save(filename)
+ print(f'Model saved in : {filename}')
+
+
+ def load_weights(self,model='model.h5'):
+ filename = self.run_directory+'/models/'+model
+ self.model.load_weights(filename)
+ print(f'Weights loaded from : {filename}')
+
+
+ @classmethod
+ def load(cls, run_tag='000', config='vae_config.json', model='model.h5'):
+ # ---- Instantiate a new vae
+ filename = f'./run/{run_tag}/models/{config}'
+ with open(filename, 'r') as infile:
+ params=json.load(infile)
+ print(params.keys())
+# vae=cls( params['input_shape'], params['encoder_layers'], params['decoder_layers'], params['z_dim'], '004', 0)
+ vae=cls( **params)
+ # ---- model==None, just return it
+ if model==None: return vae
+ # ---- model!=None, get weight
+ vae.load_weights(model)
+ return vae
\ No newline at end of file
--
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