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AWS_multiFile_1_2.py
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AWS_multiFile_1_2.py
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# -*- coding: utf-8 -*-
"""
Created on Mon May 23 19:13:57 2016
@author: kezhili
"""
#from keras.models import Model
from keras.models import Sequential
from keras.layers.core import Dense, Activation, Dropout
from keras.layers.recurrent import LSTM
import pandas as pd
import time
import h5py
import numpy as np
import os
model = Sequential()
model.add(LSTM(7, 12, return_sequences=True))
model.add(LSTM(12, 12, return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(12, 12, return_sequences=False))
model.add(Dropout(0.2))
model.add(Dense(12, 7))
model.add(Activation("linear"))
model.compile(loss="mean_squared_error", optimizer="rmsprop")
def _load_data(data, n_prev = 50):
"""
data should be pd.DataFrame()
"""
docX, docY = [], []
for i in range(len(data)-n_prev):
docX.append(data.iloc[i:i+n_prev].as_matrix())
docY.append(data.iloc[i+n_prev].as_matrix())
alsX = np.array(docX)
alsY = np.array(docY)
return alsX, alsY
def train_test_split(df, test_size=0.1):
"""
This just splits data to training and testing parts
"""
ntrn = int(round(len(df) * (1 - test_size)))
X_train, y_train = _load_data(df.iloc[0:ntrn])
X_test, y_test = _load_data(df.iloc[ntrn:])
return (X_train, y_train), (X_test, y_test)
t0 = time.time()
file_no = 0
root = 'Z:/DLWeights/nas207-1/experimentBackup/from pc207-7/!worm_videos/copied_from_pc207-8/Andre/03-03-11/'
#root = 'Z:\DLWeights\nas207-1\experimentBackup\from pc207-7\!worm_videos\copied_from_pc207-8\Andre\03-03-11\'
for path, subdirs, files in os.walk(root):
for name in files:
print os.path.join(path, name)
file_no = file_no + 1
with h5py.File(os.path.join(path, name), 'r') as fid:
eig_coef = fid['/eig_coef'][:]
columns = ['a', 'b','c','d','e','f','g']
data = pd.DataFrame(eig_coef, columns = columns)
(X_train_curr, y_train_curr), (X_test, y_test) = train_test_split(data) # retrieve data
del data
if file_no == 1:
X_train = X_train_curr
y_train = y_train_curr
else:
X_train = np.concatenate((X_train,X_train_curr), axis=0)
y_train = np.concatenate((y_train,y_train_curr), axis=0)
del X_train_curr,y_train_curr
# and now train the model
# batch_size should be appropriate to your memory size
# number of epochs should be higher for real world problems
model.fit(X_train, y_train, batch_size=450, nb_epoch=1500, validation_split=0.05)
#predicted = model.predict(X_test)
#rmse = np.sqrt(((predicted - y_test) ** 2).mean(axis=0))
#
## and maybe plot it
##pd.DataFrame(predicted[:50]).plot()
##pd.DataFrame(y_test[:50]).plot()
#
## save to csv
#np.savetxt("./DLWeights/nas207-1/experimentBackup/from pc207-7/!worm_videos/copied_from_pc207-8/Andre/03-03-11/predicted.csv", predicted, delimiter=",")
#np.savetxt("./DLWeights/nas207-1/experimentBackup/from pc207-7/!worm_videos/copied_from_pc207-8/Andre/03-03-11/y_test2.csv", y_test, delimiter=",")
#
sentence = X_test[0,:,:]
eig_generated1 = sentence[-1,]
x_prev = np.zeros(sentence.shape)
x_prev[1:,] = sentence[0:-1,]
next_ske = sentence[-1,:]
for ii in range(400):
if ii % 50 == 1:
print "loop = %d / 400 ." % ii
x_now = np.zeros((1,sentence.shape[0],sentence.shape[1]))
#x_now[0:-2,] = x_prev[1:,]
x_now[0,] = np.concatenate((x_prev[1:,],[next_ske.T]))
next_ske = model.predict(x_now, verbose=0)[0]
eig_generated1 = np.vstack((eig_generated1, next_ske))
x_prev = np.copy(x_now[0,])
print time.time() - t0
name_generated = "generated.csv"
## save model weights
#model.save_weights('./DLWeights/nas207-1/experimentBackup/from pc207-7/!worm_videos/copied_from_pc207-8/Andre/03-03-11/multiFile_weights.h5')
## save to csv
#np.savetxt(os.path.join(root, name_generated), eig_generated1, delimiter=",")