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Corrige l'affichage des prédictions et ajuste les impressions pour une meilleure clarté

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Arthur Danjou
2025-10-29 20:10:42 +01:00
parent f53ff6a2be
commit 0af6f7a5d0
1 changed files with 12 additions and 12 deletions
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24
M2/Deep Learning/TP1 - Starter.ipynb
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@@ -487,27 +487,23 @@
},
{
"cell_type": "code",
"execution_count": 51,
"execution_count": 59,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 421us/step\n",
"[5.7584018e-04 4.8742178e-05 9.9363518e-01 4.3561440e-03 2.4410772e-06\n",
" 9.6543576e-05 7.6319941e-04 1.7040280e-04 3.4009825e-04 1.1379666e-05]\n",
"2\n",
"2\n"
"\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 712us/step\n",
"[9.3210938e-06 9.0745144e-04 9.9405009e-01 1.4055278e-03 5.0518427e-08\n",
" 8.1401318e-05 3.4974131e-03 2.3491805e-07 4.7366044e-05 1.1456490e-06]\n"
]
}
],
"source": [
"predictions = model.predict(X_test)\n",
"\n",
"print(predictions[1])\n",
"print(np.argmax(predictions[1]))\n",
"print(y_test[1])"
"print(predictions[1])"
]
},
{
@@ -519,19 +515,23 @@
},
{
"cell_type": "code",
"execution_count": 52,
"execution_count": 63,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1.0000001 1. 1. ... 1.0000001 0.9999999 0.9999999]\n"
"1.0\n",
"2\n",
"2\n"
]
}
],
"source": [
"print(np.sum(predictions, axis=1))"
"print(np.sum(predictions[1]))\n",
"print(np.argmax(predictions[1]))\n",
"print(y_test[1])"
]
},
{