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#!/usr/bin/python3
# _*_ coding=utf-8 _*_
import argparse
import code
import readline
import signal
import sys
from keras.datasets import imdb
import numpy as np
from keras import models
from keras import layers
from keras import regularizers
import matplotlib.pyplot as plt
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()
def vectorize_sequences(sequences, dimension=10000):
results = np.zeros((len(sequences), dimension))
for i, sequence in enumerate(sequences):
results[i, sequence] = 1.
return results
def plot_loss(history):
history_dic = history.history
loss_values = history_dic["loss"]
val_loss_values = history_dic["val_loss"]
epochs = range(1, len(history_dic["loss"]) + 1)
plt.plot(epochs, loss_values, "bo", label="Training Loss")
plt.plot(epochs, val_loss_values, "b", label="Validation Loss")
plt.title("training and validation loss")
plt.xlabel("Epochs")
plt.ylabel("Loss")
plt.legend()
plt.show()
def plot_acc(history):
history_dic = history.history
acc_values = history_dic["acc"]
val_acc_values = history_dic["val_acc"]
epochs = range(1, len(history_dic["acc"]) + 1)
plt.plot(epochs, acc_values, "bo", label="Training Acc")
plt.plot(epochs, val_acc_values, "b", label="Validation Acc")
plt.title("training and validation acc")
plt.xlabel("Epochs")
plt.ylabel("Acc")
plt.legend()
plt.show()
# write code here
def premain(argparser):
signal.signal(signal.SIGINT, SigHandler_SIGINT)
#here
(train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=10000)
x_train = vectorize_sequences(train_data)
x_test = vectorize_sequences(test_data)
y_train = np.asarray(train_labels).astype("float32")
y_test = np.asarray(test_labels).astype("float32")
model = models.Sequential()
model.add(layers.Dense(16, kernel_regularizer=regularizers.l2(0.001), activation="relu", input_shape=(10000,)))
model.add(layers.Dropout(0.5))
model.add(layers.Dense(16, kernel_regularizer=regularizers.l2(0.001), activation="relu"))
model.add(layers.Dropout(0.5))
model.add(layers.Dense(1, activation="sigmoid"))
x_val = x_train[:10000]
partial_x_train = x_train[10000:]
y_val = y_train[:10000]
partial_y_train = y_train[10000:]
model.compile(optimizer="rmsprop", loss="binary_crossentropy", metrics=["acc"])
'''
history = model.fit(partial_x_train, partial_y_train, epochs=20, batch_size=512, validation_data=(x_val, y_val))
plot_loss(history)
plt.clf()
plot_acc(history)
'''
model.fit(x_train, y_train, epochs=20, batch_size=512)
results = model.evaluate(x_test, y_test)
print(results)
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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