#!/usr/bin/python3
# _*_ coding=utf-8 _*_
import argparse
import code
import readline
import signal
import sys
import keras
from keras import layers
from keras.datasets import imdb
from keras.preprocessing import sequence
def SigHandler_SIGINT(signum, frame):
print()
sys.exit(0)
class Argparser(object):
def __init__(self):
parser = argparse.ArgumentParser()
parser.add_argument("--string", type=str, help="string")
parser.add_argument("--bool", action="store_true", help="bool", default=False)
parser.add_argument("--dbg", action="store_true", help="debug", default=False)
self.args = parser.parse_args()
# write code here
def premain(argparser):
signal.signal(signal.SIGINT, SigHandler_SIGINT)
#here
callbacks = [keras.callbacks.TensorBoard(log_dir="logfiles", histogram_freq=1, embeddings_freq=1,)]
max_features = 2000
max_len = 500
(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)
x_train = sequence.pad_sequences(x_train, maxlen=max_len)
x_test = sequence.pad_sequences(x_test, maxlen=max_len)
model = keras.models.Sequential()
model.add(layers.Embedding(max_features, 128, input_length=max_len, name="embed"))
model.add(layers.Conv1D(32, 7, activation="relu"))
model.add(layers.MaxPooling1D(5))
model.add(layers.Conv1D(32, 7, activation="relu"))
model.add(layers.GlobalMaxPooling1D())
model.add(layers.Dense(1))
summary = model.summary()
print(summary)
model.compile(optimizer="rmsprop", loss="binary_crossentropy", metrics=["acc"])
history = model.fit(x_train, y_train, epochs=20, batch_size=128, validation_split=0.2, callbacks=callbacks)
def main():
argparser = Argparser()
if argparser.args.dbg:
try:
premain(argparser)
except Exception as e:
print(e.__doc__)
if e.message: print(e.message)
variables = globals().copy()
variables.update(locals())
shell = code.InteractiveConsole(variables)
shell.interact(banner="DEBUG REPL")
else:
premain(argparser)
if __name__ == "__main__":
main()
