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from keras.models import Model
from keras.layers import Input
from keras.layers.convolutional import Convolution2D
from keras.layers.convolutional import Deconvolution2D
from keras.layers import merge
import numpy as np

'''
Attempt at implementing a smaller version of "Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections"
(http://arxiv.org/abs/1606.08921)

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from scipy.misc import imread, imresize, imsave
from scipy.optimize import fmin_l_bfgs_b
import numpy as np
import time
import os
import argparse
import h5py

from keras.models import Sequential
from keras.layers.convolutional import Convolution2D, ZeroPadding2D, AveragePooling2D, MaxPooling2D

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from keras import backend as K
from keras.regularizers import ActivityRegularizer


dummy_loss_val = K.variable(0.0)

# Dummy loss function which simply returns 0
# This is because we will be training the network using regularizers.
def dummy_loss(y_true, y_pred):
    return dummy_loss_val

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from __future__ import print_function

import densenet
import numpy as np
import sklearn.metrics as metrics

from keras.datasets import cifar10
from keras.utils import np_utils
from keras.preprocessing.image import ImageDataGenerator
from keras.optimizers import Adam

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# Copyright 2017 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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'''Trains an SRU model on the IMDB sentiment classification task.
The dataset is actually too small for LSTM to be of any advantage
compared to simpler, much faster methods such as TF-IDF + LogReg.
Notes:
- RNNs are tricky. Choice of batch size is important,
choice of loss and optimizer is critical, etc.
Some configurations won't converge.
- LSTM loss decrease patterns during training can be quite different
from what you see with CNNs/MLPs/etc.
'''

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# Copyright (c) 2020, NVIDIA CORPORATION.  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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