import tensorflow as tf import pandas as pd import models print 'Reading evaluation data...' eval_df = pd.read_csv('data/merged_eval_2016_total.csv', parse_dates=['transactiondate']) models.fillna_df(eval_df) eval_df = models.add_outlier_column(eval_df) eval_df = models.add_sign_column(eval_df) model = models.logsign_classifier results = model.evaluate(input_fn=lambda: models.input_fn(eval_df, 'logsign'), steps=1) # results = model.evaluate(input_fn=lambda: models.input_fn(eval_df_outl), steps=1) # results2 = model.evaluate(input_fn=lambda: models.input_fn(eval_df), steps=1) print 'Results:' print results print model.get_variable_names() # models.print_dnn(model) # print 'Logits:' # for weight in model.get_variable_value('dnn/logits/weights').flatten(): # print ' {: .3f}x + {: .3f}'.format(weight, model.get_variable_value('dnn/logits/biases')[0]) # print model.get_variable_value('dnn/hiddenlayer_0/weights') # print model.get_variable_value('dnn/hiddenlayer_0/biases') input_samples = eval_df.sample(n=20)
import tensorflow as tf import pandas as pd import numpy as np import models tf.logging.set_verbosity(tf.logging.ERROR) print 'Reading training data...' train_df = pd.read_csv('data/merged_train_2016_total.csv', parse_dates=['transactiondate']) models.fillna_df(train_df) err_std = train_df['logerror'].std() err_mean = train_df['logerror'].mean() query_outl = '(logerror >= ' + str( err_std + err_mean) + ') or (logerror <= ' + str(err_mean - err_std) + ')' query_norm = '(logerror < ' + str( err_std + err_mean) + ') or (logerror > ' + str(err_mean - err_std) + ')' train_df_outl = train_df.query(query_outl) train_df_norm = train_df.query(query_norm) #feature_columns = [ # tf.contrib.layers.real_valued_column('taxamount', dtype=tf.float64), # tf.contrib.layers.real_valued_column('yearbuilt', dtype=tf.float64) #] model = models.dnn_regressor print 'Training...' for _ in range(1): print 'Iteration: %f' % (_ + 1) model.fit(input_fn=lambda: models.input_fn(train_df_outl), steps=50000)