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sho_sr_l0.py
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sho_sr_l0.py
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import tensorflow as tf
import numpy as np
import os
import pickle
from utils.symbolic_network import SymbolicNetL0, SymbolicCell
from utils import functions, helpers, pretty_print
import argparse
def main(results_dir='results/sho/test', trials=20, learning_rate=1e-3, reg_weight=1e-3, timesteps=25, batch_size=128,
n_epochs1=10001, n_epochs2=10001):
# Hyperparameters
summary_step = 1000
primitive_funcs = [
*[functions.Constant()] * 2,
*[functions.Identity()] * 4,
*[functions.Square()] * 4,
*[functions.Sin()] * 2,
*[functions.Exp()] * 2,
*[functions.Sigmoid()] * 2,
*[functions.Product(norm=0.1)] * 2,
]
# Import parabola data
data = np.load('dataset/sho.npz')
x_d = np.asarray(data["x_d"])
x_v = np.asarray(data["x_v"])
y_d = np.asarray(data["y_d"])
y_v = np.asarray(data["y_v"])
omega2_data = data["omega2"]
N = data["N"]
# Prepare data
x = np.stack((x_d, x_v), axis=2) # Shape (N, NT, 2)
y0 = np.stack((y_d[:, 0], y_v[:, 0]), axis=1) # Initial conditions for prediction y, fed into propagator
y_data = np.stack((y_d[:, 1:timesteps + 1], y_v[:, 1:timesteps + 1]), axis=2) # shape(NG, timesteps, 2)
z_data = omega2_data[:, np.newaxis]
# Tensorflow placeholders for x, y0, y
x_input = tf.placeholder(shape=(None, x.shape[1], x.shape[2]), dtype=tf.float32, name="enc_input")
y0_input = tf.placeholder(shape=(None, 2), dtype=tf.float32, name="prop_input") # input is d, v
y_input = tf.placeholder(shape=(None, timesteps, 2), dtype=tf.float32, name="label_input")
length_input = tf.placeholder(dtype=tf.int32, shape=())
# Dynamics encoder
encoder = helpers.Encoder(n_filters=[16, 16, 16, 16])
training = tf.placeholder_with_default(False, [])
z = encoder(x_input, training=training)
# Propagating decoders
prop_d = SymbolicNetL0(2, funcs=primitive_funcs)
prop_v = SymbolicNetL0(2, funcs=primitive_funcs)
prop_d.build(4)
prop_v.build(4)
# Building recurrent structure
rnn = tf.keras.layers.RNN(SymbolicCell(prop_d, prop_v), return_sequences=True)
y0_rnn = tf.concat([tf.expand_dims(y0_input, axis=1), tf.zeros((tf.shape(y0_input)[0], length_input - 1, 2))], axis=1)
prop_input = tf.concat([y0_rnn, tf.keras.backend.repeat(z, length_input),
tf.ones((tf.shape(y0_input)[0], length_input, 1))], axis=2)
y_hat = rnn(prop_input)
length_list = [1, 2, 3, 4, 5, 7, 10, 15, 25] # Slowly increase the length of propagation
# Training
learning_rate_ph = tf.placeholder(tf.float32)
opt = tf.train.RMSPropOptimizer(learning_rate=learning_rate_ph)
reg_weight_ph = tf.placeholder(tf.float32)
reg_loss = prop_d.get_loss() + prop_v.get_loss()
error = tf.losses.mean_squared_error(labels=y_input[:, :length_input, :], predictions=y_hat)
loss = error + reg_weight_ph * reg_loss
train = tf.group([opt.minimize(loss), encoder.bn.updates])
batch = helpers.batch_generator([x, y_data, y0, z_data], N=N, batch_size=batch_size)
# Training session
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
for _ in range(trials):
loss_i = np.nan
while np.isnan(loss_i):
loss_list = []
error_list = []
reg_list = []
sess.run(tf.global_variables_initializer())
length_i = 1
for i in range(n_epochs1 + n_epochs2):
if i < n_epochs1:
lr_i = learning_rate
else:
lr_i = learning_rate/10
x_batch, y_batch, y0_batch, z_batch = next(batch)
feed_dict = {x_input: x_batch, y0_input: y0_batch, y_input: y_batch,
learning_rate_ph: lr_i, training: True, reg_weight_ph: reg_weight,
length_input: length_i}
_ = sess.run(train, feed_dict=feed_dict)
if i % summary_step == 0:
feed_dict[training] = False
loss_i, error_i, reg_i, z_arr = sess.run((loss, error, reg_loss, z), feed_dict=feed_dict)
r = np.corrcoef(z_batch[:, 0], z_arr[:, 0])[1, 0]
loss_list.append(loss_i)
error_list.append(error_i)
reg_list.append(reg_i)
print("Epoch %d\tTotal loss: %f\tError: %f\tReg loss: %f\tCorrelation: %f"
% (i, loss_i, error_i, reg_i, r))
if np.isnan(loss_i):
break
i_length = min(i // 1000, len(length_list)-1)
length_i = length_list[i_length]
weights_d = sess.run(prop_d.get_weights())
expr_d = pretty_print.network(weights_d, primitive_funcs, ["d", "v", "z", 1])
print(expr_d)
weights_v = sess.run(prop_v.get_weights())
expr_v = pretty_print.network(weights_v, primitive_funcs, ["d", "v", "z", 1])
print(expr_v)
print("Done. Saving results.")
# z_arr = sess.run(z, feed_dict=feed_dict)
# Save results
results = {
"summary_step": summary_step,
"learning_rate": learning_rate,
"n_epochs1": n_epochs1,
"reg_weight": reg_weight,
"timesteps": timesteps,
"weights_d": weights_d,
"weights_v": weights_v,
"loss_plot": loss_list,
"error_plot": error_list,
"reg_plot": reg_list,
"expr_d": expr_d,
"expr_v": expr_v
}
trial_dir = helpers.get_trial_path(results_dir) # Get directory in which to save trial results
tf.saved_model.simple_save(sess, trial_dir, inputs={"x": x_input, "y0": y0_input, "training": training},
outputs={"z": z, "y": y_hat})
# Save a summary of the parameters and results
with open(os.path.join(trial_dir, 'summary.pickle'), "wb+") as f:
pickle.dump(results, f)
with open(os.path.join(results_dir, 'eq_summary.txt'), 'a') as f:
f.write(str(expr_d) + "\n")
f.write(str(expr_v) + "\n")
f.write("Error: %f\n\n" % error_list[-1])
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--results-dir', type=str, default='results/sho/test_l0')
parser.add_argument('--learning-rate', type=float, default=1e-3)
parser.add_argument('--reg-weight', type=float, default=1e-2)
parser.add_argument('--batch-size', type=int, default=128)
parser.add_argument('--n-epochs1', type=int, default=10001)
parser.add_argument('--n-epochs2', type=int, default=10001)
parser.add_argument("--timesteps", type=int, default=25, help="Number of time steps to predict")
parser.add_argument('--trials', type=int, default=1, help="Number of trials to train.")
args = parser.parse_args()
kwargs = vars(args)
print(kwargs)
if not os.path.exists(kwargs['results_dir']):
os.makedirs(kwargs['results_dir'])
meta = open(os.path.join(kwargs['results_dir'], 'args.txt'), 'a')
import json
meta.write(json.dumps(kwargs))
meta.close()
main(**kwargs)