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Diffstat (limited to 'boston.py')
| -rw-r--r-- | boston.py | 142 |
1 files changed, 0 insertions, 142 deletions
diff --git a/boston.py b/boston.py deleted file mode 100644 index 079e587..0000000 --- a/boston.py +++ /dev/null @@ -1,142 +0,0 @@ -#!/usr/bin/python3 -from sklearn import datasets -from sklearn import preprocessing -from sklearn.cross_validation import train_test_split -import tensorflow as tf -import numpy.random -import numpy as np - -# Siehe Kommentar in dem Programm der linearen Regression -SEED = 42 -numpy.random.seed(SEED) -tf.set_random_seed(SEED) - -# Datenreihe laden -dataset = datasets.load_boston() -data, target = dataset.data, dataset.target - -# Datenwerte für ein effektiveres Lernen zwischen 0 und 1 skalieren -data_scaler = preprocessing.MinMaxScaler() -data = data_scaler.fit_transform(data) -target_scaler = preprocessing.MinMaxScaler() -target = target_scaler.fit_transform(target) - -# Datenwerte in Trainings- und Testreihe aufteilen -x_train, x_test, y_train, y_test = train_test_split( - data, target, train_size=0.85 -) - -# Die Netzkonfiguration -BATCH = 50 -STEPS = 300 -HIDDEN = 11 -IN = 13 -OUT = 1 -LEARNINGRATE = 0.01 -DECAYRATE = 0.96 -DECAYSTEPS = 50 - -# Der Lernverlauf wird in einem individuellem Dateinamen gespeichert, -# der je nach Konfiguration variiert. -RUNNAME = 'B'+str(BATCH)+'S'+str(STEPS)+'H'+str(HIDDEN)+'LR'+str(LEARNINGRATE)\ - + 'DR'+str(DECAYRATE)+'DS'+str(DECAYSTEPS)+'SD'+str(SEED) - -# Es wird ausschließlich auf der CPU gerechnet, -# da die GPU zu lange zum Initialisieren braucht und mehr RAM. -with tf.device('/cpu:0'): - sess = tf.InteractiveSession() - # Ausführliche Kommentare und ein vereinfachter Ablauf - # finden sich in Abschnitt 2.2.2 der Facharbeit. - - 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('weights', matWeights) - matLayerin = tf.matmul(matBiasedInput, matWeights, name='layer_input') - matLayerout = tf.sigmoid(matLayerin, name='layer_output') - - with tf.name_scope('output_layer'): - vecTarget = vecTarget - matWeightsHidden = tf.Variable( - tf.random_uniform([HIDDEN, OUT], -1.0, 1.0), - name='hidden-weights') - summaryWeightsHidden = tf.histogram_summary('hiddenweights', - matWeightsHidden) - matLayerinHidden = tf.matmul(matLayerout, matWeightsHidden, - name='hidden_input') - matLayeroutHidden = tf.sigmoid(matLayerinHidden, name='hidden_output') - # nur vec, wenn OUT=1 - vecLayeroutHidden = tf.reshape(matLayeroutHidden, [-1]) - - with tf.name_scope('cost'): - vecDifference = tf.sub(vecTarget, vecLayeroutHidden, name='diff') - scalarError = tf.reduce_sum(tf.abs(vecDifference), name='error') - summaryError = tf.scalar_summary('error', scalarError) - - with tf.name_scope('train'): - global_step = tf.Variable(0, trainable=False) - learning_rate = tf.train.exponential_decay(LEARNINGRATE, - global_step, DECAYSTEPS, - DECAYRATE, staircase=True) - train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize( - scalarError, global_step=global_step) - - summaries = tf.merge_all_summaries() - writer = tf.train.SummaryWriter("/tmp/boston_logs/%s" % RUNNAME, - sess.graph_def) - # Alle Tensorflow-Variablen müssen bis hier spezifiziert sein! - sess.run(tf.initialize_all_variables()) - - # Training - for count in range(0, int(len(x_train) / BATCH)): - print("batch " + 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("Training beendet") - - print("Testergebnisse - Differenz zu echten Daten:") - result = sess.run([vecLayeroutHidden], - feed_dict={ - matInput: x_test, - vecTarget: y_test - }) - print("================================") - print("erwartete Daten:") - print("--------------------------------") - # Ausgabedaten sind skaliert, zurückskalieren - print(target_scaler.inverse_transform(y_test)) - print("--------------------------------") - print("vorhergesagte Daten:") - print(target_scaler.inverse_transform(result[0])) - print("--------------------------------") - print("") - print("Differenz:") - print("--------------------------------") - # MSE aus der Differenz unskalierter Daten berechnen, - # nicht MSE aus Differenz skalierter Daten unskalieren: - # !!! (a - b) * f + o != (a * f + o) - (b * f + o) - # <=> af - bf + o != af - bf - diff = target_scaler.inverse_transform( - result[0]) - target_scaler.inverse_transform(y_test) - print(diff) - print("--------------------------------") - print("") - print("MSE") - # Entsprechend (af - bf + o)^2 != (af - bf)^2… - print(np.mean(diff ** 2)) |