def _main():
options = cmdargv.parse_argv()
statistics = pd.read_csv(options.statistics)
_remap_logpath(statistics, options.statistics)
with _open_output(options.output) as output:
output.write(statistics.to_csv())
VALIDATION_SPLIT = 0.2
import os, sys, time
import numpy as np
import pandas as pd
import encoder as enc
import common as common
import cmdargv as cmdargv
from keras.models import Sequential, load_model
from keras.layers.core import Dense
from keras.layers.recurrent import SimpleRNN
from keras.callbacks import EarlyStopping, ModelCheckpoint
from sklearn.model_selection import train_test_split
from sklearn import metrics
options = cmdargv.parse_argv(sys.argv, ANN_NAME)
# read file
print('===== read file =====')
df = pd.read_csv(options.dataset)
print(df.info())
common.dropp_columns_regex(df, options.exclude)
# dealing with: NaN, ∞, -∞
print('===== cleanup =====')
dropped_columns = common.cleanup(df)
print('dropped_columns: {}'.format(dropped_columns))
# encode
print('===== encode =====')
from keras.models import Sequential
from keras.layers import Dense, Flatten
from keras.datasets import fashion_mnist
from keras.losses import SparseCategoricalCrossentropy
from sklearn import metrics
import cmdargv
import save_result
from CustomLogger import CustomLogger
import tf_tricks
batch_size = 128
epochs = 12
# read commandline arguments
options = cmdargv.parse_argv()
# TensorFlow wizardry
if options.allow_growth:
tf_tricks.allow_growth()
if options.fp16:
tf_tricks.mixed_precision()
start_time = time.time() # -------------------------------------------------┐
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()
train_images = train_images / 255.0
test_images = test_images / 255.0
preprocess_time = time.time() - start_time # --------------------------------┘
start_time = time.time() # -------------------------------------------------┐
