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main_dsprites_scriterion.py
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
seed = 124
import os
import random
import numpy as np
import pandas as pd
import tensorflow as tf
import tensorflow_datasets as tfds
from tensorflow import keras
from keras.models import load_model
import tensorflow_datasets as tfds
from sklearn.model_selection import train_test_split
from train_gcvae_2d_ds import train_gcvae as gcvae
#----import trainer for distributed training
from train_gcvae_distrib import train_gcvae_distrib
from utils import (plot_latent_space, compute_metric,
model_saver, model_saver_dsprites,
model_saver_scriterion_sc)
import matplotlib.pyplot as plt
plt.rcParams.update({'font.size': 10})
plt.rc('text', usetex=False)
plt.rc('font', family='serif')
plt.rcParams['figure.dpi'] = 120
import seaborn as sns
#set random seed
random.seed(seed)
np.random.seed(seed)
tf.random.set_seed(seed)
#declare path
path = os.getcwd()
#import data
datatype = "dsprites"
batch_size = 64
#%%% Datasets
dataset_zip = np.load(os.path.join(path, 'dsprites_ndarray_co1sh3sc6or40x32y32_64x64.npz'), allow_pickle=True, encoding = 'latin1' )
imgs = dataset_zip['imgs']
latents_values = dataset_zip['latents_values']
latents_classes = dataset_zip['latents_classes']
metadata = dataset_zip['metadata'][()]
#%%
x_train, x_test, y_train, y_test = train_test_split(imgs, latents_values, test_size = 0.999, random_state = 42)
x_test, _, y_test, _ = train_test_split(x_test, y_test, test_size = 0.992, random_state = 42)
N, L, M = x_train.shape
x_train = x_train.reshape(-1, L, M, 1).astype('float32')
x_test = x_test.reshape(-1, L, M, 1).astype('float32')
train_dataset = tf.data.Dataset.from_tensor_slices(x_train)
train_dataset = train_dataset.shuffle(buffer_size = 1024).batch(batch_size)
#test data
test_dataset = tf.data.Dataset.from_tensor_slices(x_test)
test_dataset = test_dataset.shuffle(buffer_size = 1024).batch(batch_size)
#%% Train Model
loss_index = 4
# vae_type = 'gcvae' #else infovae
inp_shape = x_train.shape[1:]
num_features = inp_shape[0]
#the parameters are only to change fixed weights
params = { #beta, gamma
'elbo': (1, 0),
'betavae': ((1, 10), 0),
'controlvae': (0, 0),
'infovae': (0, 500),
'gcvae': (1, 1), #not necessarily useful inside algo
}
for ii in [2, 10, 50, 100, 200, 500]:
for ij in ['mmd', 'mah', 'mah_gcvae']:
lr = 1e-3
epochs = 250000
hidden_dim = 200
latent_dims = ii
loss_type = list(params.keys())[loss_index] #elbo -> 0; betavae -> 1; controlvae -> 2; infovae -> 3; gcvae -> 4
archi_type = 'v2'
#params
distrib_type = 'b'
beta, gamma = params[f'{loss_type}']
mmd_typ = ij #['mmd', 'mah', 'mah_rkhs', 'mah_gcvae']
save_model_arg = False
save_model_after = 100
#-----------------------------Ignore this section if no stopping criterion is needed------------------------------------------
stop_criterion = 'useStop' #'useStop' or 'igstop' --> useStop --> Use stopping criterion or igstop --> Ignore stopping
stopping = True if stop_criterion == 'useStop' else False
pid_a = True if stopping == True else False
pid_b = True if stopping == True else False
#-----------------------------------------End stopping criterion params ------------------------------------------------------
model = gcvae(inp_shape = inp_shape,
num_features = num_features,
hidden_dim = hidden_dim,
latent_dim = latent_dims,
batch_size = batch_size,
beta = beta,
gamma = gamma,
dist = distrib_type,
vloss = loss_type,
lr = lr,
epochs = epochs,
architecture = archi_type,
mmd_type = mmd_typ).fit(train_dataset, x_test,
datatype, stopping = stopping,
save_model = save_model_arg,
save_model_iter = save_model_after,
pid_a = pid_a,
pid_b = pid_b,
epsilon_a = 1e-5,
epsilon_b = 1e-4)
if stop_criterion == 'igstop':
#save model....
model_saver_dsprites(model,\
x_test,\
y_test,\
hidden_dim,\
latent_dims,\
batch_size,\
beta,\
gamma,\
distrib_type,\
loss_type,\
lr,\
epochs,\
archi_type,\
mmd_typ,\
datatype)
else:
model_saver_scriterion_sc(model,\
x_test,\
y_test,\
hidden_dim,\
latent_dims,\
batch_size,\
beta,\
gamma,\
distrib_type,\
loss_type,\
lr,\
model.epoch,\
archi_type,\
mmd_typ,\
datatype
)