A = np.array([[0.94366988, 0.86095311, 0.88896715, 0.93630641, 0.74075403, 0.52849619
, 0.03094677, 0.85707681, 0.88457925, 0.67279696, 0.26601085, 0.4823794
, 0.74741157, 0.78575729, 0.00978911, 0.9203284, 0.02453695, 0.84884703
, 0.2050248, 0.03703224, 0.92931392, 0.11930532, 0.01411064, 0.7832698
, 0.58188015, 0.66897565, 0.75119007, 0.01323558, 0.03402649, 0.99735115
, 0.21031727, 0.78123225, 0.6815842, 0.46647604, 0.66323375, 0.03424828
, 0.08031627, 0.76570656, 0.34760863, 0.06177743, 0.6987531, 0.4106426
, 0.6648871, 0.02776868, 0.93053125, 0.46395717, 0.23971605, 0.9771735
, 0.66202407, 0.10482388]])
Convert the entries of a into 0 (if activation <= 0.5) or 1 (if activation > 0.5)
for i in range(A.shape[1]):
if A[i]>0.5:
Y_prediction[i] = 1
else:
Y_prediction[i] = 0
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
And how to use vectorize this
thx
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