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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)
target_scaler = preprocessing.MinMaxScaler()
target = target_scaler.fit_transform(target)
# 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'):
# TODO use name scopes
sess = tf.InteractiveSession()
vecInput = tf.placeholder(tf.float32, [13], name="input")
numTarget = tf.placeholder(tf.float32, [1], name="target")
vecBias = tf.Variable(tf.zeros([13]), name="bias")
# vecWeights is not a true vector, it is turned by 90 degrees
vecWeights = tf.Variable(tf.zeros([1, 13]), name="weight")
# sum((i + b) * w)
matWeighted = tf.mul(tf.add(vecInput, vecBias), vecWeights)
vecNetinput = tf.reduce_sum(matWeighted, 1)
vecLayerOutput = tf.sigmoid(vecNetinput)
# since this is the last layer, vecOutput is a numOutput
numOutput = tf.reduce_sum(vecLayerOutput) # TODO needed?
numDifference = tf.sub(numOutput, numTarget)
numMSE = tf.square(numDifference)
summaryBias = tf.histogram_summary("bias", vecBias)
summaryWeights = tf.histogram_summary("weigths", vecWeights)
summaryDifference = tf.scalar_summary(["difference"], 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())
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(numMSE)
for count in range(0, len(x_train)):
trainsteps = 100
print("count " + str(count))
for i in range(0, trainsteps): # 100 epochs
if i % 10 == 9:
# TODO mean over test data
feed = {
vecInput: x_test[0],
numTarget: [y_test[0]]
}
result = sess.run([summaries, numMSE], feed_dict=feed)
writer.add_summary(result[0], count * trainsteps + i)
# TODO run a complete set
sess.run(train_step,
feed_dict={
vecInput: x_train[count],
numTarget: [y_train[count]]
})
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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