LSTM.py

import pandas as pd
import numpy as numpy
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten,Reshape
from keras.layers import Conv1D, MaxPooling1D
from keras.utils import np_utils
from keras.layers import LSTM, LeakyReLU, CuDNNLSTM
from keras.callbacks import CSVLogger, ModelCheckpoint
import h5py
import os
import tensorflow as tf
from keras.backend.tensorflow_backend import set_session
from keras import regularizers


os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
os.environ['CUDA_VISIBLE_DEVICES'] = '1'
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'

config = tf.ConfigProto()
config.gpu_options.allow_growth = True
set_session(tf.Session(config=config))

with h5py.File(''.join(['data/bitcoin2015to2017_close.h5']), 'r') as hf:
    datas = hf['inputs'].value
    labels = hf['outputs'].value




step_size = datas.shape[1]
units= 50
second_units = 30
batch_size = 8
nb_features = datas.shape[2]
epochs = 50
output_size=16
reg = 1
output_file_name='bitcoin2015to2017_close_LSTM_1_tanh_leaky_areg_l1_'+ str(reg)
#split training validation
training_size = int(0.8* datas.shape[0])
training_datas = datas[:training_size,:]
training_labels = labels[:training_size,:,0]
validation_datas = datas[training_size:,:]
validation_labels = labels[training_size:,:,0]


#build model
model = Sequential()
model.add(CuDNNLSTM(units=units, activity_regularizer=regularizers.l1(reg), input_shape=(step_size,nb_features),return_sequences=False))
model.add(Activation('tanh'))
model.add(Dropout(0.2))
model.add(Dense(output_size))
model.add(LeakyReLU())
model.compile(loss='mse', optimizer='adam')
model.fit(training_datas, training_labels, batch_size=batch_size,validation_data=(validation_datas,validation_labels), epochs = epochs, callbacks=[CSVLogger(output_file_name+'.csv', append=True)])

# model.fit(datas,labels)
#model.save(output_file_name+'.h5')