python保存log日志,实现用log日志画图-创新互联
在神经网络训练中,我们常常需要画出loss function的变化图,log日志里会显示每一次迭代的loss function的值,于是我们先把log日志保存为log.txt文档,再利用这个文档来画图。
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import mxnet as mx import numpy as np import os import logging logging.getLogger().setLevel(logging.DEBUG) # Training data logging.basicConfig(filename = os.path.join(os.getcwd(), 'log.txt'), level = logging.DEBUG) # 把log日志保存为log.txt train_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 = 1 num_epoch=5 # Evaluation Data eval_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.sym.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 ) model.fit(train_iter, eval_iter, optimizer_params={'learning_rate':0.005, 'momentum': 0.9}, num_epoch=20, eval_metric='mse',) model.predict(eval_iter).asnumpy() metric = mx.metric.MSE() model.score(eval_iter, metric)
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