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import tensorflow as tf | |
import numpy as np | |
import keras | |
import keras.backend as K | |
from functools import reduce | |
from keras.models import Model | |
import keras.callbacks as cbks | |
from keras.applications import ResNet50 | |
from keras.layers import Conv2D | |
import threading |
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from keras.layers.core import Layer | |
from keras import initializers, regularizers, constraints | |
from keras import backend as K | |
class Attention(Layer): | |
def __init__(self, | |
kernel_regularizer=None, bias_regularizer=None, | |
kernel_constraint=None, bias_constraint=None, | |
use_bias=True, **kwargs): | |
""" |
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""" | |
A keras attention layer that wraps RNN layers. | |
Based on tensorflows [attention_decoder](https://github.com/tensorflow/tensorflow/blob/c8a45a8e236776bed1d14fd71f3b6755bd63cc58/tensorflow/python/ops/seq2seq.py#L506) | |
and [Grammar as a Foreign Language](https://arxiv.org/abs/1412.7449). | |
date: 20161101 | |
author: wassname | |
url: https://gist.github.com/wassname/5292f95000e409e239b9dc973295327a | |
""" |
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"""A stripped-down MLP example, using Theano. | |
Based on the tutorial here: http://deeplearning.net/tutorial/mlp.html | |
This example trims away some complexities, and makes it easier to see how Theano works. | |
Design changes: | |
* Model compiled in a distinct function, so that symbolic variables are not in run-time scope. | |
* No classes. Network shown by chained function calls. |
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package quickcheck | |
import common._ | |
import org.scalacheck._ | |
import Arbitrary._ | |
import Gen._ | |
import Prop._ | |
import Math._ |
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# 1. Delete all existing rules | |
iptables -F | |
# 2. Set default chain policies | |
iptables -P INPUT DROP | |
iptables -P FORWARD DROP | |
iptables -P OUTPUT DROP | |
# 4. Allow ALL incoming SSH | |
iptables -A INPUT -i eth0 -p tcp --dport 22 -m state --state NEW,ESTABLISHED -j ACCEPT |