diff options
Diffstat (limited to 'boston.py')
| -rw-r--r-- | boston.py | 89 |
1 files changed, 38 insertions, 51 deletions
@@ -18,58 +18,47 @@ x_train, x_test, y_train, y_test = train_test_split( ) -sess = tf.InteractiveSession() +with tf.device('/cpu:0'): + sess = tf.InteractiveSession() -x = tf.placeholder(tf.float32, [None, 13], name="input") -y_ = tf.placeholder(tf.float32, [1, None], name="target") -fact = tf.placeholder(tf.float32, [1, None], name="factor") # max(x) - min(x) -offs = tf.placeholder(tf.float32, [1, None], name="offset") # min(x) + input_matrix = tf.placeholder(tf.float32, [None, 13], name="input") + real = tf.placeholder(tf.float32, [1, None], name="target") + fact = tf.placeholder(tf.float32, [1, None], name="factor") + # ^ max(input_matrix) - min(input_matrix) + offs = tf.placeholder(tf.float32, [1, None], name="offset") + # ^ min(input_matrix) -fitted_y_ = tf.div(tf.sub(y_, offs), fact) # input scaled + fitted_real = tf.div(tf.sub(real, offs), fact) # input scaled -W = tf.Variable(tf.zeros([13]), name="weight") -b = tf.Variable(0.0, name="bias") + weights = tf.Variable(tf.truncated_normal([13], stddev=0.1), name="weight") + bias = tf.Variable(0.0, name="bias") -weighted = tf.mul(x, W) -combined_weights = tf.reduce_sum(weighted, 1) # sum w_n*x -y = tf.nn.sigmoid(tf.add(combined_weights, b)) -unfitted_y = tf.add(tf.mul(y, fact), offs) # un-scale prediction + weighted = tf.reduce_sum(tf.mul(input_matrix, weights), 1) + # ^ sum ([13]weights*input_matrix) + fitted_prediction = tf.sigmoid(tf.add(weighted, bias)) + prediction = tf.add(tf.mul(fitted_prediction, fact), offs) + # ^ un-scale prediction -# debug -#unfitted_y = tf.Print(unfitted_y, [unfitted_y], "est: ") -#foo = tf.add(y_, 0) -#foo = tf.Print(foo, [foo], "real: ") -# -# debug -#y = tf.Print(y, [y], "est: ") -#foo = tf.add(fitted_y_, 0) -#fitted_y_ = tf.Print(fitted_y_, [fitted_y_], "real: ") -# - -sqr = tf.square(tf.sub(unfitted_y, y_)) - -# debug -#sqr = tf.Print(sqr, [sqr], "sqr: ") -# - -mean = tf.reduce_mean(sqr) -rmse = tf.sqrt(mean) + diff = tf.sub(prediction, real) # for testing -difference = tf.abs(tf.reduce_sum(tf.sub(y, fitted_y_))) + sqr = tf.square(diff) + mean = tf.reduce_mean(sqr) + mse = mean -# debug -#rmse = tf.Print(rmse, [rmse], "rmse: ") -# + difference = tf.abs( + tf.reduce_sum(tf.sub(fitted_prediction, fitted_real))) -rmse_summary = tf.scalar_summary("rmse", rmse) -diff_summary = tf.scalar_summary("diff", difference) -merged = tf.merge_all_summaries() -writer = tf.train.SummaryWriter("/tmp/boston_logs", sess.graph_def) + mse_summary = tf.scalar_summary("mse", mse) + diff_summary = tf.scalar_summary("max diff", tf.reduce_max(diff)) + diff_summary = tf.scalar_summary("mean diff", tf.reduce_mean(diff)) + merged = 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()) + # all variables have to be specified here + sess.run(tf.initialize_all_variables()) -train_step = tf.train.GradientDescentOptimizer(0.005).minimize(rmse) + mse = tf.Print(mse, [mse], "mse: ") + train_step = tf.train.GradientDescentOptimizer(0.0001).minimize(mse) last = 0 @@ -90,25 +79,23 @@ while next_last < len(x_train): xt = x_train[last:next_last] yt = [y_train[last:next_last]] - for i in range(0, 1000): # 100 epochs + for i in range(0, 100): # 100 epochs if i % 100 == 0: - feed = {x: x_test, y_: [y_test], fact: factor, offs: offset} - result = sess.run([merged, rmse, difference], feed_dict=feed) + feed = {input_matrix: x_test, real: [y_test], fact: factor, offs: offset} + result = sess.run([merged, mse, difference], feed_dict=feed) writer.add_summary(result[0], i) sess.run(train_step, - feed_dict={x: xt, y_: yt, fact: factor, offs: offset}) + feed_dict={input_matrix: xt, real: yt, fact: factor, offs: offset}) last = next_last print("finished training") -#rdiff = tf.reduce_mean(tf.sub(y, y_)) -diff = tf.sub(unfitted_y, y_) yt = [y_test] -# debug? +# debug print(" --------- ") print("mean difference to test data: ") -print(sess.run(rmse, feed_dict={x: x_test, y_: yt, fact: factor, offs: offset})) -print(sess.run(diff, feed_dict={x: x_test, y_: yt, fact: factor, offs: offset})) +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})) |