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#!/usr/bin/python3
# _*_ coding=utf-8 _*_
import argparse
import code
import readline
import signal
import sys
import keras
from keras import Input, Model
from keras import layers
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_list = [keras.callbacks.EarlyStopping(monitor="acc", patience=1,), keras.callbacks.ModelCheckpoint(filepath="mymodel.h5", monitor="val_loss", save_best_only=True,)]
input_tensor = Input(shape=(64,))
x = layers.Dense(32, activation="relu")(input_tensor)
x = layers.Dense(32, activation="relu")(x)
output_tensor = layers.Dense(10, activation="softmax")(x)
model = Model(input_tensor, output_tensor)
model.summary()
model.compile(optimizer="rmsprop", loss="categorical_crossentropy", metrics=["acc"])
import numpy as np
x_train = np.random.random((1000, 64))
y_train = np.random.random((1000, 10))
x_val = np.random.random((1000, 64))
y_val = np.random.random((1000, 10))
model.fit(x_train, y_train, epochs=10, batch_size=128, callbacks=callbacks_list, validation_data=(x_val, y_val))
score = model.evaluate(x_train, y_train)
print(score)
'''
text_vocabulary_size = 10000
question_vocabulary_size = 10000
answer_vocabulary_size = 500
text_input = Input(shape=(None, ), dtype="int32", name="text")
embedded_text = layers.Embedding(64, text_vocabulary_size)(text_input)
encoded_text = layers.LSTM(32)(embedded_text)
question_input = Input(shape=(None,), dtype="int32", name="question")
embedded_question = layers.Embedding(32, question_vocabulary_size)(question_input)
encoded_question = layers.LSTM(16)(embedded_question)
concatenated = layers.concatenate([encoded_text, encoded_question], axis=-1)
answer = layers.Dense(answer_vocabulary_size, activatoin="softmax")(concatenated)
model = Model([text_input, question_input], answer)
model.compile(optimizer="rmsprop", loss="categorical_crossentropy", metrics=["acc"])
import numpy as np
num_samples = 1000
max_length = 100
text = np.random.randint(1, text_vocabulary_size, size=(num_samples, max_length))
question = np.random.randint(1, question_vocabulary_size, size=(num_samples, max_length))
answers = np.random.randint(0, 1, size=(num_samples, answer_vocabulary_size))
model.fit([text, question], answers, epochs=10, batch_size=128)
'''
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()
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