keras.preprocessing.timeseries_dataset_from_array(data,targets,sequence_length,# 窗口大小 sequence_stride=1,#连续输出序列之间的周期。对于步幅s,输出采样将开始索引data[i],data[i+s],data[i+2*s],等。 sampling_rate=1,# 序列中连续的各个时间步之间的时间间隔。对于rate r,时间步 用于创建样本序列。
补0之后,测试数据比原来数据多past-1个,2个。 #输出数据forbatchindataset_val.take(1): inputs, targets=batchprint("val Input shape:", inputs.numpy().shape)print("val Target shape:", targets.numpy().shape)
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我有一个pandas数据基,为了生成一个tensorflow数据集,我将其转换为一个numpy数组: dataset = tf.keras.utils.timeseries_dataset_from_array当我试图训练这个模型时,我会遇到像ValueError: Cannot convert a partially known TensorShape (None, None) to a Tensor.这样的错误我怀疑这是由于我的数据集</e ...
DAYS_FOR_TRAIN =5defcreate_dataset(data, days_for_train=5) -> (np.array, np.array):""" 根据给定的序列data,生成数据集 数据集分为输入和输出,每一个输入的长度为days_for_train,每一个输出的长度为1。 也就是说用days_for_train天的数据,对应下一天的数据。
from netCDF4 import Dataset import numpy as np # Define x & y index for grid cell of interest # Pittsburgh is 37,89 yindex = 37 #first number xindex = 89 #second number # Path path = '/Users/LMC/Research Data/NARCCAP/'
The CESNET-TimeSeries24 dataset14 is a complex real-world dataset that will finally bring insights into the evaluation of forecasting models in real-world environments. Methods The dataset was obtained from the network traffic of the CESNET3 network—an ISP-like network providing internet access to...
return np.array(X), np.array(y) time_step = 60 X_train, y_train = create_dataset(train_data, time_step) X_test, y_test = create_dataset(test_data, time_step) 接着,调整数据的形状,以适应 LSTM 输入格式: X_train = X_train.reshape(X_train.shape[0], X_train.shape[1], 1) ...
Make a time-series call This call gets the WTI Crude Oil Price , which has a code of EIA/PET_RWTC_D from the US Department of Energy dataset: data = nasdaqdatalink.get("EIA/PET_RWTC_D") Change formats You can get the same data in a NumPy array: data = na
1 结果 先看一下最后的结果，使用的是手写数字MNIST数据集，上面一行是添加噪音的图像，下面一 ...