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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()
numElements = tf.placeholder(tf.int32, [1], name="numelements") # = w(matInputs)=h(vecTargets)
matInputs = tf.placeholder(tf.float32, [13, None], name="input")
vecTargets = tf.placeholder(tf.float32, [None], name="target")
vecBias = tf.Variable(tf.zeros([13]), name="bias")
vecWeights = tf.Variable(tf.zeros([13]), name="weight")
scalarElements = tf.reshape(numElements, [])
# 'vecs' means a (redundant) matrix
# tile vecBias and vecWeigths, flatten matInputs
vecsInputs = tf.transpose(matInputs)
vecsBias = tf.reshape(tf.tile(vecBias, numElements), [13, -1])
vecsBias = tf.transpose(vecsBias)
# vecsWeights is not a true vector, it is turned by 90 degrees
# so that i*w is a matrix
vecsWeigths = tf.reshape(tf.tile(vecWeights, numElements), [13, -1])
# now we can sum((i + b) * w)
matWeighted = tf.mul(tf.add(vecsInputs, vecsBias), vecWeights)
vecNetinput = tf.reduce_sum(matWeighted, 1)
vecLayerOutput = tf.sigmoid(vecNetinput)
# since this is the last layer, vecOutput is a numOutput
vecOutput = tf.reduce_sum(vecLayerOutput) # TODO needed?
vecDifference = tf.sub(vecOutput, vecTargets)
vecMSE = tf.square(vecDifference)
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(tf.reduce_sum(vecMSE))
# summaries
# TODO reshape to scalar: reshape(t, [])
summaryBias = tf.histogram_summary("bias", vecBias)
summaryWeights = tf.histogram_summary("weigths", vecWeights)
summaryMSE = tf.scalar_summary("MSE", tf.reduce_sum(vecMSE))
summaryDifference = tf.scalar_summary("Mean difference", tf.reduce_mean(vecDifference))
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())
for count in range(0, int(len(x_train) / 10)):
trainsteps = 100
print("count " + str(count))
for i in range(0, trainsteps): # 100 epochs
#result = sess.run([summaries, numMSE], feed_dict=feed)
#writer.add_summary(result[0], count * trainsteps + i)
# TODO run a complete set
xt = x_train[count*10:(count+1)*10]
xt = [list(i) for i in zip(*xt)] # transpose
result = sess.run([summaries, train_step],
feed_dict={
numElements: [10],
matInputs: xt,
vecTargets: y_train[count*10:(count+1)*10]
})
if i % 10 == 9:
writer.add_summary(result[0], count * trainsteps + i) # TODO this slows down
print("finished training")
# yt = [y_test]
# # debug
# print(" --------- ")
# print("mean difference to test data: ")
# TODO
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