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