def objective(params):
encode_l1, encode_dropout, decode_l1, decode_dropout = params
# load model
vae, enc, gen = create_lstm_vae(input_dim,
timesteps=timesteps,
batch_size=batch_size,
intermediate_dim=intermediate_dim,
latent_dim=latent_dim,
epsilon_std=epsilon_std)
# train model
vae.fit(x, x, epochs=epochs, callbacks=[history])
return np.mean(history.losses)
def main():
x_input = []
gen = get_coord_drawings_z_axis()
for i in range(0, 2048):
x, y = next(gen)
x_input.extend(x)
x_input = np.asarray(x_input)
print(x_input.shape)
y_input = x_input
input_dim = 5 # 13
vae, enc, gen = create_lstm_vae(input_dim,
timesteps=timesteps,
batch_size=batch_size,
intermediate_dim=32,
latent_dim=100,
epsilon_std=1.)
for i in range(4):
vae.fit(x_input, x_input, epochs=4, batch_size=batch_size)
preds = vae.predict(x_input, batch_size=batch_size)
# pick a column to plot.
print("[plotting...]")
print("x: %s, preds: %s" % (x_input.shape, preds.shape))
tt = preds
l = np.exp(tt[:, :, 2:5])
ld = np.sum(np.exp(tt[:, :, 2:5]), axis=-1, keepdims=True)
l = l / ld
print(l[0])
print(tt[0])
print(y_input[0])
tot = sketcher.save_batch_diff_z_axis(list((1 + tt[0:16]) * 128),
list((1 + y_input[0:16]) * 128),
"./",
str(i) + "FINAL")
dataX = []
for i in range(len(data) - timesteps - 1):
x = data[i:(i+timesteps), :]
dataX.append(x)
return np.array(dataX) # samples x time-steps x features
if __name__ == "__main__":
x = get_data()
input_dim = x.shape[-1] # 13 features
timesteps = x.shape[1] # 3 timesteps
batch_size = 1
vae, enc, gen = create_lstm_vae(input_dim,
timesteps=timesteps,
batch_size=batch_size,
intermediate_dim=32,
latent_dim=100,
epsilon_std=1.)
vae.fit(x, x, epochs=20)
preds = vae.predict(x, batch_size=batch_size)
# pick a column to plot.
print("[plotting...]")
print("x: %s, preds: %s" % (x.shape, preds.shape))
plt.plot(x[:,0,3], label='data')
plt.plot(preds[:,0,3], label='predict')
plt.legend()
plt.show()
if __name__ == "__main__":
timesteps_max, enc_tokens, characters, char2id, id2char, x, x_decoder = get_text_data(
num_samples=3000, data_path="data/fra.txt")
print(x.shape, "Creating model...")
input_dim = x.shape[-1]
timesteps = x.shape[-2]
batch_size = 1
latent_dim = 191
intermediate_dim = 353
epochs = 40
vae, enc, gen, stepper = create_lstm_vae(input_dim,
batch_size=batch_size,
intermediate_dim=intermediate_dim,
latent_dim=latent_dim)
print("Training model...")
vae.fit([x, x_decoder], x, epochs=epochs, verbose=1)
print("Fitted, predicting...")
def decode(s):
return inference.decode_sequence(s, gen, stepper, input_dim, char2id,
id2char, timesteps_max)
for _ in range(5):
id_from = np.random.randint(0, x.shape[0] - 1)
id_to = np.random.randint(0, x.shape[0] - 1)
batch_size = 1
penalty = 0.001
lr = 0.001
dr = 0.5
period = 10 # checkpoint period
if dataname == 'germany':
penalty = 0
lr = 0.00005
vae, enc, gen = create_lstm_vae(nb_features,
n_pre=n_pre,
n_post=n_post,
batch_size=batch_size,
intermediate_dim=32,
latent_dim=200,
initialization='glorot_normal',
activation='linear',
lr=lr,
penalty=penalty,
dropout=dr,
epsilon_std=1.)
filepath = "results/{}/{}".format(
dataname, analysis) + "/weights.{epoch:02d}-{val_loss:.3f}.hdf5"
checkpointer = ModelCheckpoint(filepath=filepath,
monitor='val_loss',
verbose=1,
period=period,
save_best_only=True)
csv_logger = CSVLogger('results/{}/{}/training_log_{}_{}.csv'.format(
