From 9e4e9e88fa44d5d96ba5ab3e0f537effbb1d437c 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: Mon, 3 Feb 2020 20:45:11 +0100
Subject: [PATCH] Change VAE
Former-commit-id: 7d55dedb383b84d4bfa6e2817f955b8daaf099b1
---
VAE/01-VAE with MNIST.ipynb | 14 +++++++-------
VAE/modules/callbacks.py | 9 ++++++---
VAE/modules/vae.py | 16 +++++++++++-----
3 files changed, 24 insertions(+), 15 deletions(-)
diff --git a/VAE/01-VAE with MNIST.ipynb b/VAE/01-VAE with MNIST.ipynb
index 5c0c2b9..5c74509 100644
--- a/VAE/01-VAE with MNIST.ipynb
+++ b/VAE/01-VAE with MNIST.ipynb
@@ -33,7 +33,6 @@
"import tensorflow.keras.datasets.mnist as mnist\n",
"\n",
"import modules.vae\n",
- "# from modules.vae import VariationalAutoencoder\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib\n",
@@ -46,7 +45,6 @@
"sys.path.append('..')\n",
"import fidle.pwk as ooo\n",
"\n",
- "reload(ooo)\n",
"ooo.init()"
]
},
@@ -86,8 +84,8 @@
"metadata": {},
"outputs": [],
"source": [
- "reload(modules.vae)\n",
- "reload(modules.callbacks)\n",
+ "# reload(modules.vae)\n",
+ "# reload(modules.callbacks)\n",
"\n",
"tag = '000'\n",
"\n",
@@ -149,9 +147,10 @@
"source": [
"batch_size = 100\n",
"epochs = 200\n",
- "batch_periodicity = 1000\n",
+ "image_periodicity = 1 # in epoch\n",
+ "chkpt_periodicity = 2 # in epoch\n",
"initial_epoch = 0\n",
- "dataset_size = 0.1"
+ "dataset_size = 1"
]
},
{
@@ -164,7 +163,8 @@
" x_test,\n",
" batch_size = batch_size, \n",
" epochs = epochs,\n",
- " batch_periodicity = batch_periodicity,\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",
diff --git a/VAE/modules/callbacks.py b/VAE/modules/callbacks.py
index a7bbe1a..63adb23 100644
--- a/VAE/modules/callbacks.py
+++ b/VAE/modules/callbacks.py
@@ -5,12 +5,15 @@ import os
class ImagesCallback(Callback):
- def __init__(self, initial_epoch=0, batch_periodicity=1000, vae=None):
+ def __init__(self, initial_epoch=0, image_periodicity=1, vae=None):
self.epoch = initial_epoch
- self.batch_periodicity = batch_periodicity
+ self.image_periodicity = image_periodicity
self.vae = vae
self.images_dir = vae.run_directory+'/images'
-
+ batch_per_epochs = int(vae.n_train / vae.batch_size)
+ self.batch_periodicity = batch_per_epochs*image_periodicity
+
+
def on_train_batch_end(self, batch, logs={}):
if batch % self.batch_periodicity == 0:
diff --git a/VAE/modules/vae.py b/VAE/modules/vae.py
index 8f09c47..f9efa3b 100644
--- a/VAE/modules/vae.py
+++ b/VAE/modules/vae.py
@@ -144,8 +144,10 @@ class VariationalAutoencoder():
def train(self,
x_train,x_test,
- batch_size=32, epochs=200,
- batch_periodicity=100,
+ batch_size=32,
+ epochs=200,
+ image_periodicity=1,
+ chkpt_periodicity=2,
initial_epoch=0,
dataset_size=1,
lr_decay=1):
@@ -154,14 +156,18 @@ class VariationalAutoencoder():
n_train = int(x_train.shape[0] * dataset_size)
n_test = int(x_test.shape[0] * dataset_size)
+ # ---- Need by callbacks
+ self.n_train = n_train
+ self.n_test = n_test
+ self.batch_size = batch_size
+
# ---- Callbacks
- images_callback = modules.callbacks.ImagesCallback(initial_epoch, batch_periodicity, self)
+ images_callback = modules.callbacks.ImagesCallback(initial_epoch, image_periodicity, self)
# lr_sched = step_decay_schedule(initial_lr=self.learning_rate, decay_factor=lr_decay, step_size=1)
filename1 = self.run_directory+"/models/model-{epoch:03d}-{loss:.2f}.h5"
- batch_per_epoch = int(len(x_train)/batch_size)
- checkpoint1 = ModelCheckpoint(filename1, save_freq=batch_per_epoch*5,verbose=0)
+ checkpoint1 = ModelCheckpoint(filename1, save_freq=n_train*chkpt_periodicity ,verbose=0)
filename2 = self.run_directory+"/models/best_model.h5"
checkpoint2 = ModelCheckpoint(filename2, save_best_only=True, mode='min',monitor='val_loss',verbose=0)
--
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