Chapter 6: layer_2_delta calculation about grokking-deep-learning HOT 2 OPEN

  layer_2_delta = (walk_vs_stop[i:i+1] - layer_2)
  layer_1_delta = layer_2_delta.dot(weights_1_2.T)*relu2deriv(layer_1)

  weights_1_2 += alpha * layer_1.T.dot(layer_2_delta)
  weights_0_1 += alpha * layer_0.T.dot(layer_1_delta)     

  #### is same #######

  layer_2_delta = (layer_2 - walk_vs_stop[i:i+1])
  layer_1_delta=layer_2_delta.dot(weights_1_2.T)*relu2deriv(layer_1)

  weights_1_2 -= alpha * layer_1.T.dot(layer_2_delta)
  weights_0_1 -= alpha * layer_0.T.dot(layer_1_delta)


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the two code result is same, but  i think the last code is logic right.