def FLVAE(epochs, latent_dim, X_train, X_test, train_dataset):
model = VAE(latent_dim)
for i in range(0, epochs):
models = []
elbo = 0
for j in range(0, SPLIT_SIZE):
print('Running data on node ', j + 1)
current_model = model
for train_x in X_train[j]:
model.train_step(current_model, train_x, model.optimizer)
loss = tf.keras.metrics.Mean()
for test_x in X_test[j]:
loss(model.compute_loss(current_model, test_x))
elbo = -loss.result()
display.clear_output(wait=False)
print('Epoch: {}, Test set ELBO: {}, clien-num: {}'.format(i, elbo, j))
# generate_and_save_images(current_model, i, j, test_sample)
models.append(current_model)
weights = [model.get_weights() for model in models]
new_weights = list()
for weights_list_tuple in zip(*weights):
new_weights.append(np.array([np.array(w).mean(axis=0) for w in zip(*weights_list_tuple)]))
model.set_weights(new_weights)
# write code for accuracy
loss = tf.keras.metrics.Mean()
for train_x in train_dataset:
loss(model.compute_loss(model, train_x))
print(loss.result())
from RNN import RNN
from VAE import VAE
from keras.preprocessing.sequence import pad_sequences
import matplotlib.pyplot as plt
import sys
sys.path.append("..")
import config
dir_name = config.dir_name
episode = config.episode
frame = config.frame
np.set_printoptions(precision=4, edgeitems=6, linewidth=100, suppress=True)
vae = VAE()
vae.set_weights(config.vae_weight)
rnn = RNN()
rnn.set_weights(config.rnn_weight)
rnn_input = np.load(dir_name + '/rnn_input_0.npy')
rnn_output = np.load(dir_name + '/rnn_output_0.npy')
rnn_input = pad_sequences(rnn_input,
maxlen=30,
dtype='float32',
padding='pre',
truncating='pre')
rnn_output = pad_sequences(rnn_output,
maxlen=30,
dtype='float32',
padding='pre',