[BUG] ValueError (converting boolean variables to 0 and 1)

[ShayanDodge]

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Describe the bug
In the code '15_processing_sequences_using_rnns_and_cnns', at the section 'Multivariate Time Series', the one-hot boolean variables should be converted to 0 and 1; otherwise, it will cause an error.

'ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type float).'

To Reproduce
Please copy the code that fails here, using code blocks like this:

df_mulvar = df[["bus", "rail"]] / 1e6  # use both bus & rail series as input
df_mulvar["next_day_type"] = df["day_type"].shift(-1)  # we know tomorrow's type
df_mulvar = pd.get_dummies(df_mulvar)  # one-hot encode the day type

And if you got an exception, please copy the full stacktrace here:

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "<stdin>", line 2, in inverse
ZeroDivisionError: division by zero

Expected behavior
We should add some lines of code to avoid of error.

df_mulvar = df[["bus", "rail"]] / 1e6  # use both bus & rail series as input
df_mulvar["next_day_type"] = df["day_type"].shift(-1)  # we know tomorrow's type
df_mulvar = pd.get_dummies(df_mulvar)  # one-hot encode the day type

 # Explicitly convert the last three columns to integers
 for col in df_mulvar.columns[-3:]:
     df_mulvar[col] = df_mulvar[col].astype(int)  # Convert each column to 0s and 1s

Screenshots

Versions (please complete the following information):

• OS: Win 11
• Python: 3.10.8
• TensorFlow: 2.10.0
• Scikit-Learn: [e.g., 0.24.1]
• Other libraries that may be connected with the issue: [e.g., gym 0.18.0]

Additional context
Add any other context about the problem here.