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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
import random
import numpy.random # TODO get rid of these
SEED = 42
random.seed(SEED)
numpy.random.seed(SEED)
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
)
# TODO weights typc
BATCH = 10 # 10 data sets
STEPS = 100 # 100 epochs
HIDDEN = 9 # 9 hidden nodes in 1 layer
IN = 13 # 13 input nodes
OUT = 1 # 1 output node
with tf.device('/cpu:0'):
tf.set_random_seed(SEED)
sess = tf.InteractiveSession()
with tf.name_scope('input_layer'):
matInput = tf.placeholder(tf.float32, [None, IN], name='input')
vecTarget = tf.placeholder(tf.float32, [None], name='target')
scalarNumelements = tf.shape(vecTarget, name='batch_length')
vecBias = tf.Variable(tf.random_uniform([IN], -1.0, 1.0), name='bias')
summaryBias = tf.histogram_summary("bias", vecBias)
vecsBias = tf.tile(vecBias, scalarNumelements)
vecsBias = tf.reshape(vecsBias, [-1, IN])
matBiasedInput = tf.add(matInput, vecsBias, name='biased_input')
with tf.name_scope('hidden_layer'):
matWeights = tf.Variable(tf.random_uniform([IN, HIDDEN], -1.0, 1.0),
name='weights')
summaryWeights = tf.histogram_summary('weigths', matWeights)
matLayerin = tf.matmul(matBiasedInput, matWeights, name='layer_input')
matLayerout = tf.sigmoid(matLayerin, name='layer_output')
with tf.name_scope('output_layer'):
vecTarget = vecTarget
matWeigthsHidden = tf.Variable(
tf.random_uniform([HIDDEN, OUT], -1.0, 1.0),
name='hidden-weigths')
summaryWeightsHidden = tf.histogram_summary('hiddenweigths',
matWeigthsHidden)
# only vec because OUT=1
vecLayerinHidden = tf.matmul(matLayerout, matWeigthsHidden,
name='hidden_input')
vecLayeroutHidden = tf.sigmoid(vecLayerinHidden, name='hidden_output')
# TODO in Facharbeit Matrixprodukt erwähnen
with tf.name_scope('mse'):
vecDifference = tf.sub(vecTarget, vecLayeroutHidden, name='diff')
vecMSE = tf.square(vecDifference, name='mse')
scalarMeanMSE = tf.reduce_mean(vecMSE, name='meanmse')
summaryMeanMSE = tf.scalar_summary('MSE', scalarMeanMSE)
with tf.name_scope('train'):
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(
scalarMeanMSE)
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) / BATCH)):
print("count " + str(count))
for i in range(0, STEPS):
result = sess.run([summaries, train_step],
feed_dict={
matInput: x_train[count*BATCH:(count+1)*BATCH],
vecTarget: y_train[count*BATCH:(count+1)*BATCH]
})
writer.add_summary(result[0], count * STEPS + i)
print("finished training")
print("Test results:")
result = sess.run([vecDifference],
feed_dict={
matInput: x_test,
vecTarget: y_test
})
print(result[0])
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