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#!/usr/bin/python3
# _*_ coding=utf-8 _*_
import argparse
import code
import readline
import signal
import sys
import numpy as np
from keras.datasets import boston_housing
from keras import models
from keras import layers
import matplotlib.pyplot as plt
from keras.utils.np_utils import to_categorical
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 build_model(train_data):
model = models.Sequential()
model.add(layers.Dense(64, activation="relu", input_shape=(train_data.shape[1],)))
model.add(layers.Dense(64, activation="relu"))
model.add(layers.Dense(1))
model.compile(optimizer="rmsprop", loss="mse", metrics=["mae"])
return model
def smooth_curve(points, factor=0.9):
smoothed_points = []
for point in points:
if smoothed_points:
previous = smoothed_points[-1]
smoothed_points.append(previous*factor+point*(1-factor))
else:
smoothed_points.append(point)
return smoothed_points
# write code here
def premain(argparser):
signal.signal(signal.SIGINT, SigHandler_SIGINT)
#here
(train_data, train_targets), (test_data, test_targets) = boston_housing.load_data()
mean = train_data.mean(axis=0)
train_data -= mean
std = train_data.std(axis=0)
train_data /= std
test_data -= mean
test_data /= std
k = 4
num_epochs = 500
num_val_samples = len(train_data) // k
num_epochs = 100
all_scores = []
all_mae_histories = []
for i in range(k):
print("processing fold #", i)
val_data = train_data[i*num_val_samples:(i+1)*num_val_samples]
val_targets = train_targets[i*num_val_samples:(i+1)*num_val_samples]
partial_train_data = np.concatenate(
[train_data[:i*num_val_samples],
train_data[(i+1)*num_val_samples:]], axis=0)
partial_train_targets = np.concatenate(
[train_targets[:i*num_val_samples],
train_targets[(i+1)*num_val_samples:]], axis=0)
model = build_model(train_data)
history = model.fit(partial_train_data, partial_train_targets, validation_data=(val_data, val_targets), epochs=num_epochs, batch_size=1, verbose=0)
val_mse, val_mae = model.evaluate(val_data, val_targets, verbose=0)
mae_history = history.history["val_mean_absolute_error"]
all_mae_histories.append(mae_history)
all_scores.append(val_mae)
average_mae_history = [np.mean([x[i] for x in all_mae_histories]) for i in range(num_epochs)]
smoothed_mae_history = smooth_curve(average_mae_history[10:])
plt.plot(range(1, len(smoothed_mae_history) + 1), smoothed_mae_history)
plt.xlabel("Epochs")
plt.ylabel("Validation MAE")
plt.show()
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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