('dense1', DenseLayer), ('dropout1', DropoutLayer), ('dense2', DenseLayer), ('dropout2', DropoutLayer), ('dense3', DenseLayer), ('output', DenseLayer)], input_shape=(None, num_features), dense1_num_units=512, dropout1_p=0.5, dense2_num_units=512, dropout2_p=0.5, dense3_num_units=512, output_num_units=num_classes, output_nonlinearity=softmax, update=nesterov_momentum, eval_size=0.2, verbose=1, update_learning_rate=theano.shared(float32(0.01)), update_momentum=theano.shared(float32(0.9)), on_epoch_finished=[ AdjustVariable('update_learning_rate', start=0.01, stop=0.00001), AdjustVariable('update_momentum', start=0.9, stop=0.999), EarlyStopping(), ], max_epochs=10000,) net0.initialize() # do_fit(net0, 'data/train_impu_norm_shuf.csv', n_iter=1) net0.load_weights_from('nn_weights') RainCompetition.do_predict(net0, RainCompetition.__data__['test_normalized'], 'data/rain_nn_pred.csv')