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from __future__ import division
from __future__ import print_function
import argparse

import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from sklearn.model_selection import train_test_split

from utils import get_minibatches_idx

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import time

import numpy as np
import tensorflow as tf
from keras.datasets import mnist, cifar10, cifar100
import matplotlib.pyplot as plt

from utils import get_minibatches_idx

# Based on https://jmetzen.github.io/2015-11-27/vae.html

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	@tf.RegisterGradient("CustomRelu")
	def _custom_relu_grad(op, grad):
		#return gen_nn_ops._relu_grad(grad, op.outputs[0])
		return tf.where(tf.greater(op.outputs[0],0.0),grad,tf.zeros_like(grad))

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# Adapted from https://github.com/fchollet/keras/blob/master/examples/imdb_lstm.py

from __future__ import print_function
from __future__ import division

from keras.preprocessing import sequence
from keras.datasets import imdb
from keras.layers.core import Dense
from sklearn.cross_validation import train_test_split
from sklearn.preprocessing import LabelBinarizer

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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,

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import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation

N = 100
dt = 1/30 # 30fps
iterations = 600
bounds = [-2, 2, -2, 2]
init_state = [-0.5,-0.5]

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import tensorflow as tf

class AdamOptimizer(tf.train.Optimizer):
	def __init__(self, alpha=0.001, 
						beta1=0.9, 
						beta2=0.999,
						epsilon=1e-8):
		
		self.alpha = alpha
		self.beta1 = beta1