timestep

问题 I am a newbie to LSTM and RNN as a whole, I've been racking my brain to understand what exactly is a timestep. I would really appreciate an intuitive explanation to this 回答1: Let's start with a great image from Chris Olah's blog (a highly recommended read btw): In a recurrent neural network you have multiple repetitions of the same cell. The way inference goes is - you take some input (x 0 ), pass it through the cell to get some output 1 (depicted with black arrow to the right on the picture)

问题 I am a newbie to LSTM and RNN as a whole, I've been racking my brain to understand what exactly is a timestep. I would really appreciate an intuitive explanation to this 回答1: Let's start with a great image from Chris Olah's blog (a highly recommended read btw): In a recurrent neural network you have multiple repetitions of the same cell. The way inference goes is - you take some input (x 0 ), pass it through the cell to get some output 1 (depicted with black arrow to the right on the picture)

问题 I am a newbie to LSTM and RNN as a whole, I've been racking my brain to understand what exactly is a timestep. I would really appreciate an intuitive explanation to this 回答1: Let's start with a great image from Chris Olah's blog (a highly recommended read btw): In a recurrent neural network you have multiple repetitions of the same cell. The way inference goes is - you take some input (x 0 ), pass it through the cell to get some output 1 (depicted with black arrow to the right on the picture)

问题 This answer explains how to convert integers to hourly timesteps in Pandas. I need to do the opposite. My dataframe df1 : A 0 02:00:00 1 01:00:00 2 02:00:00 3 03:00:00 My expected dataframe df1 : A B 0 02:00:00 2 1 01:00:00 1 2 02:00:00 2 3 03:00:00 3 What I am trying: df1['B'] = df1['A'].astype(int) This fails because: TypeError: cannot astype a timedelta from [timedelta64[ns]] to [int32] What is the best way to do this? EDIT If I try df['B'] = df['A'].dt.hour , then I get: AttributeError:

问题 Here's a sample of some data Tag.ID TimeStep.coa Latitude.coa Longitude.coa <chr> <dttm> <dbl> <dbl> 1 1657 2017-08-17 12:00:00 72.4 -81.1 2 1657 2017-08-17 18:00:00 72.3 -81.1 3 1658 2017-08-14 18:00:00 72.3 -81.2 4 1658 2017-08-15 00:00:00 72.3 -81.3 5 1659 2017-08-14 18:00:00 72.3 -81.1 6 1659 2017-08-15 00:00:00 72.3 -81.2 7 1660 2017-08-20 18:00:00 72.3 -81.1 8 1660 2017-08-21 00:00:00 72.3 -81.2 9 1660 2017-08-21 06:00:00 72.3 -81.2 10 1660 2017-08-21 12:00:00 72.3 -81.3 11 1661 2017-08