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#!/usr/bin/python3
from sklearn import datasets
from sklearn import preprocessing
from sklearn.cross_validation import train_test_split
import tensorflow as tf
dataset = datasets.load_boston()
data, target = dataset.data, dataset.target
data_scaler = preprocessing.MinMaxScaler()
data = data_scaler.fit_transform(data)
# use sklearn to fit the data values between 0 and 1
x_train, x_test, y_train, y_test = train_test_split(
data, target, train_size=0.9
)
with tf.device('/cpu:0'):
sess = tf.InteractiveSession()
vecUnfittedInput = tf.placeholder(tf.float32, [13], name="input")
numTarget = tf.placeholder(tf.float32, [1], name="target")
# numbers to scale the output between 0-1 and back to prices
numFactor = tf.placeholder(tf.float32, [1], name="factor")
# ^ max(input_matrix) - min(input_matrix)
numOffset = tf.placeholder(tf.float32, [1], name="offset")
# ^ min(input_matrix)
# scale the unscaled input
vecInput = tf.div(tf.sub(vecUnfittedInput, numOffset), numFactor)
vecBias = tf.Variable(tf.truncated_normal([13], stddev=0.1), name="bias")
vecWeights = tf.Variable(tf.truncated_normal([1, 13], stddev=0.1), name="weight")
matWeighted = tf.mul(tf.add(vecInput, vecBias), vecWeights)
vecNetinput = tf.reduce_sum(matWeighted)
vecNetinput = tf.Print(vecNetinput, [vecNetinput], "netinput: ") # DEBUG
vecUnfittedOutput = tf.sigmoid(vecNetinput)
# unscale the output
vecOutput = tf.add(tf.mul(vecUnfittedOutput, numFactor), numOffset)
# since this is the last layer, vecOutput is a numOutput
numOutput = vecOutput
numDifference = tf.sub(numOutput, numTarget)
numMSE = tf.reduce_mean(tf.square(numDifference))
summaryMSE = tf.scalar_summary("MSE", numMSE)
summaryDifference = tf.scalar_summary("Mean difference", numDifference)
summaries = tf.merge_all_summaries()
writer = tf.train.SummaryWriter("/tmp/boston_logs", sess.graph_def)
# all variables have to be specified here
sess.run(tf.initialize_all_variables())
# mse = tf.Print(mse, [mse], "mse: ")
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(numMSE)
factor = 0 #max(target) - min(target)
offset = 0 #min(target)
for count in range(0, len(x_train)):
trainsteps = 100
for i in range(0, trainsteps): # 100 epochs
#if i % 10 == 9:
#feed = {
#"input": x_test[testcount], # x_test,
#"target": y_test[testcount], # [y_test],
#"factor": factor,
#"offset": offset
#}
#result = sess.run([summaries, numMSE], feed_dict=feed)
#writer.add_summary(result[0], count * trainsteps + i)
y_t = 1 #float(y_train[count].tolist())
print(y_t)
sess.run(train_step,
feed_dict={
"input": 1, #x_train[count].tolist(),
"target": 1, #y_t,
"factor": 1, #[[factor]],
"offset": 1, #[[offset]]
})
print("finished training")
#yt = [y_test]
## debug
#print(" --------- ")
#print("mean difference to test data: ")
#print(sess.run(mse, feed_dict={input_matrix: x_test, real: yt, fact: factor, offs: offset}))
#print(sess.run(diff, feed_dict={input_matrix: x_test, real: yt, fact: factor, offs: offset}))
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