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run_tf_deep_lstm_seq2seq_faster_trainer.py
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#-*- coding: utf-8 -*-
#! /usr/bin/env python
'''
filename: run_tf_deep_lstm_seq2seq_faster_trainer.py
This script is for predicting time series
author: Jaewook Kang @ 2018 Sep
'''
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from datetime import datetime
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
model_config = \
{
'n_input' : 1,
'n_neurons' : 200,
'n_layers' : 1,
'n_output' : 1,
'num_steps' : 30,
'dtype' : tf.float32,
'shift_sample': 3
}
training_config = \
{
'learning_rate': 0.01,
'n_iteration':2000,
}
def get_lstm_dynamic_seq2seq_model(X,scope):
with tf.name_scope(name=scope,values=[X]):
layer = [tf.nn.rnn_cell.LSTMCell(num_units=model_config['n_neurons'],
activation= tf.nn.relu,
name='basic_lstm_cell')\
for layer in range(model_config['n_layers'])]
multi_layer_cell = tf.contrib.rnn.MultiRNNCell(layer)
rnn_outputs, states = tf.nn.dynamic_rnn(cell=multi_layer_cell,
inputs=X,
dtype=model_config['dtype'])
stacked_rnn_outputs = tf.reshape(rnn_outputs,
shape=[-1,
model_config['n_neurons']])
stacked_logits = tf.layers.dense(stacked_rnn_outputs,
model_config['n_output'])
logits = tf.reshape(stacked_logits,
shape=[-1,
model_config['num_steps'],
model_config['n_output']])
return logits
def gen_seq_data(shift_sample,sqe_sample_length):
data_step = 0.1
start_n = np.random.random_integers(low=0, high=30)
tx = np.arange(start=start_n, stop=start_n + sqe_sample_length*data_step, step=data_step)
ty = tx + shift_sample * data_step
x_batch = tx * np.sin(tx) / 3 + 2 * np.sin(5 * tx)
y_batch = ty * np.sin(ty) / 3 + 2 * np.sin(5 * ty)
return x_batch, y_batch, tx, ty
if __name__ == '__main__':
input_shape = [1,
model_config['num_steps'],
model_config['n_input']]
output_shape = [1,
model_config['num_steps'],
model_config['n_output']]
X = tf.placeholder(dtype = model_config['dtype'],
shape = input_shape,
name = 'X')
Y = tf.placeholder(dtype = model_config['dtype'],
shape = output_shape,
name = 'Y')
# build model
scope = 'lstm_seq2seq_faster_model'
pred_y = get_lstm_dynamic_seq2seq_model(X,scope)
loss = tf.reduce_mean(tf.square(pred_y - Y))
optimizer = tf.train.AdamOptimizer(learning_rate=training_config['learning_rate'])
training_op = optimizer.minimize(loss)
# tensorboard summary
now = datetime.utcnow().strftime("%Y%m%d%H%M%S")
root_logdir = 'tf_logs/lstm_basic_seq2seq_faster_trainer'
subdir = "{}/run-{}/".format(root_logdir, now)
logdir = './pb_and_ckpt/' + subdir
if not tf.gfile.Exists(logdir):
tf.gfile.MakeDirs(logdir)
summary_writer = tf.summary.FileWriter(logdir=logdir)
summary_writer.add_graph(graph=tf.get_default_graph())
init = tf.global_variables_initializer()
n_iteration = training_config['n_iteration']
with tf.Session() as sess:
sess.run(init)
shift_sample =2
for iteration in range(n_iteration):
x_batch,y_batch,tx_train,ty_train =gen_seq_data(shift_sample=shift_sample,
sqe_sample_length = model_config['num_steps'])
x_test, y_test, tx_test, ty_test = gen_seq_data(shift_sample=shift_sample,
sqe_sample_length=model_config['num_steps'])
x_batch = x_batch.reshape((-1,\
model_config['num_steps'],
model_config['n_input']))
y_batch = y_batch.reshape((-1, \
model_config['num_steps'],
model_config['n_output']))
x_test = x_test.reshape((-1,\
model_config['num_steps'],
model_config['n_input']))
y_test = y_test.reshape((-1, \
model_config['num_steps'],
model_config['n_output']))
_, mse_train,pred_y_train = sess.run([training_op,loss,pred_y],
feed_dict={X:x_batch,
Y:y_batch})
mse_test,pred_y_test = sess.run([loss,pred_y],
feed_dict={X:x_test,
Y:y_test})
print(iteration,"Train accuracy:", mse_train, "Test accuracy:", mse_test)
x_batch = x_batch.reshape((model_config['num_steps']))
y_batch = y_batch.reshape((model_config['num_steps']))
pred_y_train = pred_y_train.reshape((model_config['num_steps']))
x_test = x_test.reshape((model_config['num_steps']))
y_test = y_test.reshape((model_config['num_steps']))
pred_y_test = pred_y_test.reshape((model_config['num_steps']))
plt.figure(1)
plt.plot(tx_train,x_batch,color='b',marker='o',label='train_input')
plt.plot(ty_train,y_batch,color='r',marker='x',label='train_output')
plt.plot(ty_train,pred_y_train,color='m',marker='x',label='pred_output')
plt.legend()
plt.figure(2)
plt.plot(tx_test,x_test,color='b',marker='o',label='test_input')
plt.plot(ty_test,y_test,color='r',marker='x',label='test_output')
plt.plot(ty_test,pred_y_test,color='m',marker='x',label='pred_output')
plt.legend()
plt.show()
summary_writer.close()