Created
August 10, 2017 01:00
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import tensorflow as tf | |
import numpy as np | |
from tensorflow.contrib import rnn | |
def MinMaxScaler(data): | |
numerator = data - np.min(data, 0) | |
denominator = np.max(data, 0) - np.min(data, 0) | |
# noise term prevents the zero division | |
return numerator / (denominator + 1e-7) | |
timesteps=seq_length=1 | |
data_dim=400 | |
output_dim=3 | |
xy=np.loadtxt('data-ar-001.csv',delimiter=',') | |
x=xy[0:1000,100:500] | |
y=xy[0:1000,500:503] | |
x=MinMaxScaler(x) | |
#xy1=np.loadtxt('data-02-test-score.csv',delimiter=',') | |
x1=xy[9900:10000,100:500] | |
y1=xy[9900:10000,500:503] | |
x1=MinMaxScaler(x1) | |
dataX=[] | |
dataY=[] | |
dataXt=[] | |
dataYt=[] | |
for i in range(0,len(y)): | |
_x=x[i:i+1] | |
_y=y[i] | |
dataX.append(_x) | |
dataY.append(_y) | |
for j in range(0,len(y1)): | |
__x=x1[j:j+1] | |
__y=y1[j] | |
dataXt.append(__x) | |
dataYt.append(__y) | |
trainX=dataX | |
trainY=dataY | |
testX=dataXt | |
testY=dataYt | |
X=tf.placeholder(tf.float32,[None,seq_length,data_dim]) | |
Y=tf.placeholder(tf.float32,[None,3]) | |
hidden_dim=3 | |
output_dim=50 | |
def lstm_cell(): | |
cell = rnn.BasicLSTMCell(hidden_dim, state_is_tuple=True) | |
return cell | |
cell = rnn.MultiRNNCell([lstm_cell() for _ in range(3)], state_is_tuple=True) | |
outputs,_states=tf.nn.dynamic_rnn(cell,X,dtype=tf.float32) | |
Y_pred=tf.contrib.layers.fully_connected(outputs[:,-1],output_dim,activation_fn=None) | |
dropout_rate=tf.placeholder("float") | |
W1=tf.Variable(tf.random_normal([50,20])) | |
W2=tf.Variable(tf.random_normal([20,10])) | |
#W3=tf.Variable(tf.random_normal([30,20])) | |
#W4=tf.Variable(tf.random_normal([20,10])) | |
W5=tf.Variable(tf.random_normal([10,3])) | |
b1=tf.Variable(tf.zeros([20]), name="Bias1") | |
b2=tf.Variable(tf.zeros([10]), name="Bias2") | |
#b3=tf.Variable(tf.zeros([20]), name="Bias3") | |
#b4=tf.Variable(tf.zeros([10]), name="Bias4") | |
b5=tf.Variable(tf.zeros([3]), name="Bias5") | |
with tf.name_scope("layer1") as scope: | |
L1=tf.nn.softmax(tf.matmul(Y_pred,W1)+b1) | |
L1=tf.nn.dropout(L1,dropout_rate) | |
with tf.name_scope("layer2") as scope: | |
L2=tf.nn.sigmoid(tf.matmul(L1,W2)+b2) | |
L2=tf.nn.dropout(L2,dropout_rate) | |
#with tf.name_scope("layer3") as scope: | |
# L3=tf.nn.relu(tf.matmul(L2,W3)+b3) | |
# L3=tf.nn.dropout(L3,dropout_rate) | |
#with tf.name_scope("layer4") as scope: | |
# L4=tf.nn.sigmoid(tf.matmul(L3,W4)+b4) | |
# L4=tf.nn.dropout(L4,dropout_rate) | |
with tf.name_scope("last") as scope: | |
Y_pred=tf.nn.softmax(tf.matmul(L2,W5)+b5) | |
loss=-tf.reduce_mean(Y*tf.log(Y_pred)) | |
accuracy=tf.reduce_mean(tf.cast(tf.equal(tf.arg_max(Y_pred,1),tf.arg_max(Y,1)),dtype=tf.float32)) | |
optimizer=tf.train.AdamOptimizer(0.01) | |
train=optimizer.minimize(loss) | |
sess=tf.Session() | |
sess.run(tf.global_variables_initializer()) | |
for i in range(3000): | |
_,l=sess.run([train,loss],feed_dict={X:trainX,Y:trainY,dropout_rate:0.5}) | |
if i%200==0: | |
print(i,l) | |
testPredict=sess.run(Y_pred,feed_dict={X:testX,dropout_rate:1.0}) | |
acc=sess.run(accuracy,feed_dict={X:testX,Y:testY,dropout_rate:1.0}) | |
print("\n정확도: ", acc) | |
print(np.argmax(testPredict,1)) | |
print(np.argmax(testY,1)) |
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