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from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Reshape
from keras.layers.core import Activation
from keras.layers.normalization import BatchNormalization
from keras.layers.convolutional import Convolution2D, MaxPooling2D, UpSampling2D
from keras.layers.convolutional import Convolution1D, Conv1D
from keras.layers.core import Flatten
from keras.optimizers import SGD
from keras.datasets import mnist

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from keras import layers, models

Nin = 784
Nh = 100
number_of_class = 10
Nout = number_of_class

class ANN(models.Model):
    def __init__(self, Nin, Nh, Nout):
        # Prepare network layers and activate functions

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'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats

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from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.layers.normalization import BatchNormalization

#AlexNet with batch normalization in Keras 
#input image is 224x224

model = Sequential()
model.add(Convolution2D(64, 3, 11, 11, border_mode='full'))

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