import keras
from keras.models import Sequential
from keras.optimizers import SGD
from keras.layers.core import Dense, Activation, Lambda, Reshape,Flatten
from keras.layers import Conv1D,Conv2D,MaxPooling2D, MaxPooling1D, Reshape
from keras.utils import np_utils
from keras.models import Model
from keras.layers import Input, Dense
from keras.layers import Dropout
from keras import backend as K
from keras.callbacks import ReduceLROnPlateau
from keras.callbacks import CSVLogger
from keras.callbacks import EarlyStopping
from keras.layers.merge import Concatenate
from keras.callbacks import ModelCheckpoint
import random
import numpy as np
window_height = 8
filter_size=window_height
pooling_size = 28
stride_step = 2
def fws():
np.random.seed(100)
input = Input(5,window_height,1)
shared_conv = Conv2D(filters = 1, kernel_size = (0,window_height,1))
output = shared_conv(input)
print output.shape
fws()