TinyML Gesture TensorFlow Training Tutorial Error

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I'm trying to work through the first tutorial with my Nano BLE 33 Sense and have gotten to the Jupyter notebook portion where you're supposed to be training the model - but when I try to run the code in the 'Run with Test Data' section I keep getting the error:

ValueError                                Traceback (most recent call last)
<ipython-input-25-797f8421ff49> in <cell line: 11>()
      9 plt.clf()
     10 plt.title('Training data predicted vs actual values')
---> 11 plt.plot(inputs_test, outputs_test, 'b.', label='Actual')
     12 plt.plot(inputs_test, predictions, 'r.', label='Predicted')
     13 plt.show()

3 frames
/usr/local/lib/python3.9/dist-packages/matplotlib/axes/_base.py in _plot_args(self, tup, kwargs, return_kwargs, ambiguous_fmt_datakey)
    520         ncx, ncy = x.shape[1], y.shape[1]
    521         if ncx > 1 and ncy > 1 and ncx != ncy:
--> 522             raise ValueError(f"x has {ncx} columns but y has {ncy} columns")
    523         if ncx == 0 or ncy == 0:
    524             return []

ValueError: x has 714 columns but y has 2 columns

I've tried with 2 different data sets but can't work out where to change the array size and how. Would appreciate your help!

This is the code it's trying to run:

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import tensorflow as tf

print(f"TensorFlow version = {tf.__version__}\n")

# Set a fixed random seed value, for reproducibility, this will allow us to get
# the same random numbers each time the notebook is run
SEED = 1337
np.random.seed(SEED)
tf.random.set_seed(SEED)

# the list of gestures that data is available for
GESTURES = [
    "punch",
    "flex",
]

SAMPLES_PER_GESTURE = 119

NUM_GESTURES = len(GESTURES)

# create a one-hot encoded matrix that is used in the output
ONE_HOT_ENCODED_GESTURES = np.eye(NUM_GESTURES)

inputs = []
outputs = []

# read each csv file and push an input and output
for gesture_index in range(NUM_GESTURES):
  gesture = GESTURES[gesture_index]
  print(f"Processing index {gesture_index} for gesture '{gesture}'.")
  
  output = ONE_HOT_ENCODED_GESTURES[gesture_index]
  
  df = pd.read_csv("/content/" + gesture + ".csv")
  
  # calculate the number of gesture recordings in the file
  num_recordings = int(df.shape[0] / SAMPLES_PER_GESTURE)
  
  print(f"\tThere are {num_recordings} recordings of the {gesture} gesture.")
  
  for i in range(num_recordings):
    tensor = []
    for j in range(SAMPLES_PER_GESTURE):
      index = i * SAMPLES_PER_GESTURE + j
      # normalize the input data, between 0 to 1:
      # - acceleration is between: -4 to +4
      # - gyroscope is between: -2000 to +2000
      tensor += [
          (df['aX'][index] + 4) / 8,
          (df['aY'][index] + 4) / 8,
          (df['aZ'][index] + 4) / 8,
          (df['gX'][index] + 2000) / 4000,
          (df['gY'][index] + 2000) / 4000,
          (df['gZ'][index] + 2000) / 4000
      ]

    inputs.append(tensor)
    outputs.append(output)

# convert the list to numpy array
inputs = np.array(inputs)
outputs = np.array(outputs)

print("Data set parsing and preparation complete.")

# build the model and train it
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(50, activation='relu')) # relu is used for performance
model.add(tf.keras.layers.Dense(15, activation='relu'))
model.add(tf.keras.layers.Dense(NUM_GESTURES, activation='softmax')) # softmax is used, because we only expect one gesture to occur per input
model.compile(optimizer='rmsprop', loss='mse', metrics=['mae'])
history = model.fit(inputs_train, outputs_train, epochs=600, batch_size=1, validation_data=(inputs_validate, outputs_validate))

# use the model to predict the test inputs
predictions = model.predict(inputs_test)

# print the predictions and the expected ouputs
print("predictions =\n", np.round(predictions, decimals=3))
print("actual =\n", outputs_test)

# Plot the predictions along with to the test data
plt.clf()
plt.title('Training data predicted vs actual values')
plt.plot(inputs_test, outputs_test, 'b.', label='Actual')
plt.plot(inputs_test, predictions, 'r.', label='Predicted')
plt.show()