if __name__ == "__main__": x_test = np.genfromtxt('X_test', delimiter=',')[1:] x_test = preprocess(x_test) x_test = preprocess(x_test) model = Sequential() model.add(Dense(1000, input_dim=x_train.shape[1], activation='relu')) model.add(Dense(500, activation='relu')) model.add(Dense(1)) model.add(BatchNormalization()) model.add(Activation('sigmoid')) adam = optimizers.Adam(lr=5e-4) model.checkpoint = ModelCheckpoint('best_29.h5', monitor = 'val_loss', verbose = 1, save_best_only = True, mode = 'min') model.compile(loss='binary_crossentropy', optimizer=adam, metrics=['accuracy']) print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1]*100)) # try: # gradient_decent(x_train, y_train) # except (KeyboardInterrupt): # np.save('result/w_'+str(lr)+'_'+str(epoch)+'_'+str(batch)+'_l='+str(loss), w) # np.save('result/b_'+str(lr)+'_'+str(epoch)+'_'+str(batch)+'_l='+str(loss), b) # np.save('w',w) # np.save('b',b) # np.save('w_v',w_var) # np.save('b_v',b_var)