MXnet查看参数的权值

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　　我们用MXnet训练好模型之后，有时想看看其中参数的权值，可以用
model.get_params()函数，具体的操作见下面的例子。

import mxnet as mximport numpy as npimport logginglogging.getLogger().setLevel(logging.DEBUG)# Training datatrain_data = np.random.uniform(0, 1, [100, 2])train_label = np.array([train_data[i][0] + 2 * train_data[i][1] for i in range(100)])batch_size = 3# Evaluation Dataeval_data = np.array([[7,2],[6,10],[12,2]])eval_label = np.array([11,26,16])train_iter = mx.io.NDArrayIter(train_data,train_label, batch_size, shuffle=True,label_name='lin_reg_label')eval_iter = mx.io.NDArrayIter(eval_data, eval_label, batch_size, shuffle=False)X = mx.sym.Variable('data')Y = mx.symbol.Variable('lin_reg_label')fully_connected_layer  = mx.sym.FullyConnected(data=X, name='fc1', num_hidden = 1)lro = mx.sym.LinearRegressionOutput(data=fully_connected_layer, label=Y, name="lro")model = mx.mod.Module(    symbol = lro ,    data_names=['data'],    label_names = ['lin_reg_label']  # network structure)mx.viz.plot_network(symbol=lro)model.fit(train_iter, eval_iter,            optimizer_params={'learning_rate':0.01, 'momentum': 0.9},            num_epoch=50,            eval_metric='mse',            batch_end_callback = mx.callback.Speedometer(batch_size, 2))model.predict(eval_iter).asnumpy()metric = mx.metric.MSE()model.score(eval_iter, metric)keys = model.get_params()[0].keys() # 列出所有权重名称print(keys)conv_w = model.get_params()[0]['fc1_weight'] # 获取想要查看的权重信息bias = model.get_params()[0]['fc1_bias']print(conv_w.asnumpy()) # 查看具体数值print(bias.asnumpy())

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