#!/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()