dl_az_breast_cancer_tuning.ipynb

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            "Epoch 1/10\n"
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            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:24: DeprecationWarning: KerasClassifier is deprecated, use Sci-Keras (https://github.com/adriangb/scikeras) instead. See https://www.adriangb.com/scikeras/stable/migration.html for help migrating.\n"
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            "\u001b[1;30;43mA saída de streaming foi truncada nas últimas 5000 linhas.\u001b[0m\n",
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            "4/4 [==============================] - 0s 3ms/step\n",
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            "4/4 [==============================] - 0s 3ms/step\n",
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            "4/4 [==============================] - 0s 4ms/step\n",
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            "4/4 [==============================] - 0s 3ms/step\n",
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            "57/57 [==============================] - 0s 3ms/step - loss: 0.4602 - binary_accuracy: 0.7733\n",
            "Epoch 9/20\n",
            "57/57 [==============================] - 0s 3ms/step - loss: 0.4584 - binary_accuracy: 0.7504\n",
            "Epoch 10/20\n",
            "57/57 [==============================] - 0s 3ms/step - loss: 0.4364 - binary_accuracy: 0.7926\n",
            "Epoch 11/20\n",
            "57/57 [==============================] - 0s 3ms/step - loss: 0.4360 - binary_accuracy: 0.7768\n",
            "Epoch 12/20\n",
            "57/57 [==============================] - 0s 3ms/step - loss: 0.4392 - binary_accuracy: 0.7873\n",
            "Epoch 13/20\n",
            "57/57 [==============================] - 0s 3ms/step - loss: 0.4198 - binary_accuracy: 0.8032\n",
            "Epoch 14/20\n",
            "57/57 [==============================] - 0s 3ms/step - loss: 0.3985 - binary_accuracy: 0.8155\n",
            "Epoch 15/20\n",
            "57/57 [==============================] - 0s 3ms/step - loss: 0.3936 - binary_accuracy: 0.8102\n",
            "Epoch 16/20\n",
            "57/57 [==============================] - 0s 3ms/step - loss: 0.3585 - binary_accuracy: 0.8366\n",
            "Epoch 17/20\n",
            "57/57 [==============================] - 0s 3ms/step - loss: 0.3908 - binary_accuracy: 0.8137\n",
            "Epoch 18/20\n",
            "57/57 [==============================] - 0s 3ms/step - loss: 0.3687 - binary_accuracy: 0.8260\n",
            "Epoch 19/20\n",
            "57/57 [==============================] - 0s 3ms/step - loss: 0.3632 - binary_accuracy: 0.8260\n",
            "Epoch 20/20\n",
            "57/57 [==============================] - 0s 3ms/step - loss: 0.3835 - binary_accuracy: 0.8190\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import keras\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Dropout\n",
        "from keras.wrappers.scikit_learn import KerasClassifier\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "previsores = pd.read_csv('entradas_breast.csv')\n",
        "classe = pd.read_csv('saidas_breast.csv')\n",
        "\n",
        "def criarRede(optimizer, loos, kernel_initializer, activation, neurons, dropout_rate):\n",
        "    classificador = Sequential()\n",
        "    classificador.add(Dense(units = neurons, activation = activation, \n",
        "                        kernel_initializer = kernel_initializer, input_dim = 30))\n",
        "    classificador.add(Dropout(dropout_rate))\n",
        "    classificador.add(Dense(units = neurons, activation = activation, \n",
        "                        kernel_initializer = kernel_initializer))\n",
        "    classificador.add(Dropout(dropout_rate))\n",
        "    classificador.add(Dense(units = 1, activation = 'sigmoid'))\n",
        "    classificador.compile(optimizer = optimizer, loss = loos,\n",
        "                      metrics = ['binary_accuracy'])\n",
        "    return classificador\n",
        "\n",
        "classificador = KerasClassifier(build_fn = criarRede)\n",
        "parametros = {'batch_size': [10, 30], #qto menos melhor\n",
        "              'epochs': [10, 20],#qto mais melhor\n",
        "              'optimizer': ['adam', 'sgd'],#adam é melhor\n",
        "              'loos': ['binary_crossentropy', 'hinge'],#b.cross é melhor\n",
        "              'kernel_initializer': ['random_uniform', 'normal'],#r_u.\n",
        "              'activation': ['relu', 'tanh'],#relu melhor\n",
        "              'neurons': [16, 8],#16\n",
        "              'dropout_rate': [0.2, 0.3]}#0.3\n",
        "grid_search = GridSearchCV(estimator = classificador,\n",
        "                           param_grid = parametros,\n",
        "                           scoring = 'accuracy',\n",
        "                           cv = 5)\n",
        "grid_search = grid_search.fit(previsores, classe)\n",
        "melhores_parametros = grid_search.best_params_\n",
        "melhor_precisao = grid_search.best_score_"
      ]
    }
  ]
}