def GetConvRBMModel(X):
inp = Input(shape=X.shape)
rbm_model = Sequential()
rbm_model.add(Convolution1D(K,132,activation=activations.linear,padding='same',input_shape=X.shape))
rbm_model.add(LeakyReLU(alpha=0.1))
dropout.rate = prob
rbm_model.add(dropout)
rbm_model.add(MaxPooling1D(50,10))
model = rbm_model.Model(inputs=inp, outputs=inp)
opt = optimizers.Adam(beta_1=b1,beta_2=b2)
model.compile(optimizer=opt, loss=losses.sparse_categorical_crossentropy, metrics=['acc'])
model.summary()
return model
