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[WIP] Usando diferentes funções de ativação #7

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efc5fa3
Using different activation functions on NN
MarcoRLK Apr 6, 2019
554d16d
Merge branch 'master' of https://github.com/deeplearningunb/building-…
MarcoRLK Apr 13, 2019
fff7ec4
Evaluating, improving and tuning ANN
MarcoRLK Apr 27, 2019
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160 changes: 141 additions & 19 deletions ArtificialNeuralNetwork.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,7 @@
"cells": [
{
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"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -22,16 +22,62 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 2,
"metadata": {},
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: tensorflow==1.13.1 in /home/marco/dl_env/lib/python3.6/site-packages (from -r requirements.txt (line 1)) (1.13.1)\n",
"Requirement already satisfied: numpy==1.16.2 in /home/marco/dl_env/lib/python3.6/site-packages (from -r requirements.txt (line 2)) (1.16.2)\n",
"Requirement already satisfied: pandas==0.24.2 in /home/marco/dl_env/lib/python3.6/site-packages (from -r requirements.txt (line 3)) (0.24.2)\n",
"Requirement already satisfied: matplotlib==3.0.3 in /home/marco/dl_env/lib/python3.6/site-packages (from -r requirements.txt (line 4)) (3.0.3)\n",
"Requirement already satisfied: Keras==2.2.4 in /home/marco/dl_env/lib/python3.6/site-packages (from -r requirements.txt (line 5)) (2.2.4)\n",
"Collecting sklearn==0.0 (from -r requirements.txt (line 6))\n",
" Downloading https://files.pythonhosted.org/packages/1e/7a/dbb3be0ce9bd5c8b7e3d87328e79063f8b263b2b1bfa4774cb1147bfcd3f/sklearn-0.0.tar.gz\n",
"Requirement already satisfied: keras-applications>=1.0.6 in /home/marco/dl_env/lib/python3.6/site-packages (from tensorflow==1.13.1->-r requirements.txt (line 1)) (1.0.7)\n",
"Requirement already satisfied: tensorboard<1.14.0,>=1.13.0 in /home/marco/dl_env/lib/python3.6/site-packages (from tensorflow==1.13.1->-r requirements.txt (line 1)) (1.13.1)\n",
"Requirement already satisfied: keras-preprocessing>=1.0.5 in /home/marco/dl_env/lib/python3.6/site-packages (from tensorflow==1.13.1->-r requirements.txt (line 1)) (1.0.9)\n",
"Requirement already satisfied: protobuf>=3.6.1 in /home/marco/dl_env/lib/python3.6/site-packages (from tensorflow==1.13.1->-r requirements.txt (line 1)) (3.7.1)\n",
"Requirement already satisfied: gast>=0.2.0 in /home/marco/dl_env/lib/python3.6/site-packages (from tensorflow==1.13.1->-r requirements.txt (line 1)) (0.2.2)\n",
"Requirement already satisfied: absl-py>=0.1.6 in /home/marco/dl_env/lib/python3.6/site-packages (from tensorflow==1.13.1->-r requirements.txt (line 1)) (0.7.1)\n",
"Requirement already satisfied: grpcio>=1.8.6 in /home/marco/dl_env/lib/python3.6/site-packages (from tensorflow==1.13.1->-r requirements.txt (line 1)) (1.19.0)\n",
"Requirement already satisfied: astor>=0.6.0 in /home/marco/dl_env/lib/python3.6/site-packages (from tensorflow==1.13.1->-r requirements.txt (line 1)) (0.7.1)\n",
"Requirement already satisfied: termcolor>=1.1.0 in /home/marco/dl_env/lib/python3.6/site-packages (from tensorflow==1.13.1->-r requirements.txt (line 1)) (1.1.0)\n",
"Requirement already satisfied: tensorflow-estimator<1.14.0rc0,>=1.13.0 in /home/marco/dl_env/lib/python3.6/site-packages (from tensorflow==1.13.1->-r requirements.txt (line 1)) (1.13.0)\n",
"Requirement already satisfied: six>=1.10.0 in /home/marco/dl_env/lib/python3.6/site-packages (from tensorflow==1.13.1->-r requirements.txt (line 1)) (1.12.0)\n",
"Requirement already satisfied: wheel>=0.26 in /home/marco/dl_env/lib/python3.6/site-packages (from tensorflow==1.13.1->-r requirements.txt (line 1)) (0.33.1)\n",
"Requirement already satisfied: pytz>=2011k in /home/marco/dl_env/lib/python3.6/site-packages (from pandas==0.24.2->-r requirements.txt (line 3)) (2018.9)\n",
"Requirement already satisfied: python-dateutil>=2.5.0 in /home/marco/dl_env/lib/python3.6/site-packages (from pandas==0.24.2->-r requirements.txt (line 3)) (2.8.0)\n",
"Requirement already satisfied: kiwisolver>=1.0.1 in /home/marco/dl_env/lib/python3.6/site-packages (from matplotlib==3.0.3->-r requirements.txt (line 4)) (1.0.1)\n",
"Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /home/marco/dl_env/lib/python3.6/site-packages (from matplotlib==3.0.3->-r requirements.txt (line 4)) (2.3.1)\n",
"Requirement already satisfied: cycler>=0.10 in /home/marco/dl_env/lib/python3.6/site-packages (from matplotlib==3.0.3->-r requirements.txt (line 4)) (0.10.0)\n",
"Requirement already satisfied: h5py in /home/marco/dl_env/lib/python3.6/site-packages (from Keras==2.2.4->-r requirements.txt (line 5)) (2.9.0)\n",
"Requirement already satisfied: scipy>=0.14 in /home/marco/dl_env/lib/python3.6/site-packages (from Keras==2.2.4->-r requirements.txt (line 5)) (1.2.1)\n",
"Requirement already satisfied: pyyaml in /home/marco/dl_env/lib/python3.6/site-packages (from Keras==2.2.4->-r requirements.txt (line 5)) (5.1)\n",
"Requirement already satisfied: scikit-learn in /home/marco/dl_env/lib/python3.6/site-packages (from sklearn==0.0->-r requirements.txt (line 6)) (0.20.3)\n",
"Requirement already satisfied: werkzeug>=0.11.15 in /home/marco/dl_env/lib/python3.6/site-packages (from tensorboard<1.14.0,>=1.13.0->tensorflow==1.13.1->-r requirements.txt (line 1)) (0.15.2)\n",
"Requirement already satisfied: markdown>=2.6.8 in /home/marco/dl_env/lib/python3.6/site-packages (from tensorboard<1.14.0,>=1.13.0->tensorflow==1.13.1->-r requirements.txt (line 1)) (3.1)\n",
"Requirement already satisfied: setuptools in /home/marco/dl_env/lib/python3.6/site-packages (from protobuf>=3.6.1->tensorflow==1.13.1->-r requirements.txt (line 1)) (41.0.0)\n",
"Requirement already satisfied: mock>=2.0.0 in /home/marco/dl_env/lib/python3.6/site-packages (from tensorflow-estimator<1.14.0rc0,>=1.13.0->tensorflow==1.13.1->-r requirements.txt (line 1)) (2.0.0)\n",
"Requirement already satisfied: pbr>=0.11 in /home/marco/dl_env/lib/python3.6/site-packages (from mock>=2.0.0->tensorflow-estimator<1.14.0rc0,>=1.13.0->tensorflow==1.13.1->-r requirements.txt (line 1)) (5.1.3)\n",
"Building wheels for collected packages: sklearn\n",
" Building wheel for sklearn (setup.py) ... \u001b[?25ldone\n",
"\u001b[?25h Stored in directory: /home/marco/.cache/pip/wheels/76/03/bb/589d421d27431bcd2c6da284d5f2286c8e3b2ea3cf1594c074\n",
"Successfully built sklearn\n",
"Installing collected packages: sklearn\n",
"Successfully installed sklearn-0.0\n"
]
}
],
"source": [
"!pip install -r requirements.txt"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -43,7 +89,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -55,9 +101,22 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 5,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/marco/dl_env/lib/python3.6/site-packages/sklearn/preprocessing/_encoders.py:371: FutureWarning: The handling of integer data will change in version 0.22. Currently, the categories are determined based on the range [0, max(values)], while in the future they will be determined based on the unique values.\n",
"If you want the future behaviour and silence this warning, you can specify \"categories='auto'\".\n",
"In case you used a LabelEncoder before this OneHotEncoder to convert the categories to integers, then you can now use the OneHotEncoder directly.\n",
" warnings.warn(msg, FutureWarning)\n",
"/home/marco/dl_env/lib/python3.6/site-packages/sklearn/preprocessing/_encoders.py:392: DeprecationWarning: The 'categorical_features' keyword is deprecated in version 0.20 and will be removed in 0.22. You can use the ColumnTransformer instead.\n",
" \"use the ColumnTransformer instead.\", DeprecationWarning)\n"
]
}
],
"source": [
"# Encoding categorical data\n",
"from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
Expand All @@ -72,7 +131,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -83,7 +142,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -96,9 +155,17 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 8,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
}
],
"source": [
"# Part 2 - Now let's make the ANN!\n",
"\n",
Expand All @@ -110,18 +177,18 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"# Initialising the ANN\n",
"classifier = Sequential()\n",
"\n",
"# Adding the input layer and the first hidden layer\n",
"classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu', input_dim = 11))\n",
"classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'sigmoid', input_dim = 11))\n",
"\n",
"# Adding the second hidden layer\n",
"classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu'))\n",
"classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'tanh'))\n",
"\n",
"# Adding the output layer\n",
"classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))\n",
Expand All @@ -132,17 +199,54 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 29,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/10\n",
"8000/8000 [==============================] - 1s 171us/step - loss: 0.5102 - acc: 0.7947\n",
"Epoch 2/10\n",
"8000/8000 [==============================] - 1s 116us/step - loss: 0.4465 - acc: 0.7975\n",
"Epoch 3/10\n",
"8000/8000 [==============================] - 1s 127us/step - loss: 0.4314 - acc: 0.8070\n",
"Epoch 4/10\n",
"8000/8000 [==============================] - 1s 124us/step - loss: 0.4292 - acc: 0.8047\n",
"Epoch 5/10\n",
"8000/8000 [==============================] - 1s 131us/step - loss: 0.4288 - acc: 0.8055\n",
"Epoch 6/10\n",
"8000/8000 [==============================] - 1s 137us/step - loss: 0.4282 - acc: 0.8061\n",
"Epoch 7/10\n",
"8000/8000 [==============================] - 1s 138us/step - loss: 0.4270 - acc: 0.8064\n",
"Epoch 8/10\n",
"8000/8000 [==============================] - 1s 127us/step - loss: 0.4251 - acc: 0.8086\n",
"Epoch 9/10\n",
"8000/8000 [==============================] - 1s 120us/step - loss: 0.4227 - acc: 0.8145\n",
"Epoch 10/10\n",
"8000/8000 [==============================] - 1s 125us/step - loss: 0.4195 - acc: 0.8174\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x7f41bb873cc0>"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Fitting the ANN to the Training set\n",
"classifier.fit(X_train, y_train, batch_size = 10, epochs = 100)"
"classifier.fit(X_train, y_train, batch_size = 10, epochs = 10)"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -158,6 +262,24 @@
"cm = confusion_matrix(y_test, y_pred)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[1532 63]\n",
" [ 284 121]]\n"
]
}
],
"source": [
"print(cm)"
]
},
{
"cell_type": "code",
"execution_count": null,
Expand All @@ -182,7 +304,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
"version": "3.6.5"
}
},
"nbformat": 4,
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