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"\n",
"\n",
"# <!-- TITLE --> Logistic regression, in pure Tensorflow\n",
"<!-- DESC --> Logistic Regression with Mini-Batch Gradient Descent using pure TensorFlow. \n",
"<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
" - A logistic regression has the objective of providing a probability of belonging to a class. \n",
" - Découvrir une implémentation 100% Tensorflow ..et apprendre à aimer Keras\n",
"\n",
"## What we're going to do :\n",
"\n",
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"X contains characteristics \n",
"y contains the probability of membership (1 or 0) \n",
"\n",
"We'll look for a value of $\\theta$ such that the linear regression $\\theta^{T}X$ can be used to calculate our probability: \n",
"\n",
"$\\hat{p} = h_\\theta(X) = \\sigma(\\theta^T{X})$ \n",
"\n",
"Where $\\sigma$ is the logit function, typically a sigmoid (S) function: \n",
"\n",
"$\n",
"\\sigma(t) = \\dfrac{1}{1 + \\exp(-t)}\n",
"$ \n",
"\n",
"The predicted value $\\hat{y}$ will then be calculated as follows:\n",
"\n",
"$\n",
"\\hat{y} =\n",
"\\begin{cases}\n",
" 0 & \\text{if } \\hat{p} < 0.5 \\\\\n",
" 1 & \\text{if } \\hat{p} \\geq 0.5\n",
"\\end{cases}\n",
"$\n",
"\n",
"**Calculation of the cost of the regression:** \n",
"For a training observation x, the cost can be calculated as follows: \n",
"\n",
"$\n",
"c(\\theta) =\n",
"\\begin{cases}\n",
" -\\log(\\hat{p}) & \\text{if } y = 1 \\\\\n",
" -\\log(1 - \\hat{p}) & \\text{if } y = 0\n",
"\\end{cases}\n",
"$\n",
"\n",
"The regression cost function (log loss) over the whole training set can be written as follows: \n",
"\n",
"$\n",
"J(\\theta) = -\\dfrac{1}{m} \\sum_{i=1}^{m}{\\left[ y^{(i)} log\\left(\\hat{p}^{(i)}\\right) + (1 - y^{(i)}) log\\left(1 - \\hat{p}^{(i)}\\right)\\right]}\n",
"$\n",
"## Step 1 - Import and init"
]
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"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
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"name": "stdout",
"output_type": "stream",
"text": [
"WARNING:tensorflow:From /home/pjluc/anaconda3/envs/fidle/lib/python3.7/site-packages/tensorflow_core/python/compat/v2_compat.py:65: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"non-resource variables are not supported in the long term\n"
]
},
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"text": [
"\n",
"FIDLE 2020 - Practical Work Module\n",
"Version : 0.2.9\n",
"Run time : Tuesday 18 February 2020, 21:34:05\n",
"TensorFlow version : 2.0.0\n",
"Keras version : 2.2.4-tf\n"
]
}
],
"source": [
"import numpy as np\n",
"import sklearn as sl\n",
"from sklearn import metrics\n",
"\n",
"import tensorflow.compat.v1 as tf\n",
"tf.disable_v2_behavior()\n",
"\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"import math\n",
"import random\n",
"import os\n",
"import sys\n",
"\n",
"sys.path.append('..')\n",
"import fidle.pwk as ooo\n",
"\n",
"ooo.init()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.1 - Usefull stuff"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def vector_infos(name,V):\n",
" '''Displaying some information about a vector'''\n",
" with np.printoptions(precision=4, suppress=True):\n",
" print(\"{:16} : ndim={} shape={:10} Mean = {} Std = {}\".format( name,V.ndim, str(V.shape), V.mean(axis=0), V.std(axis=0)))\n",
"\n",
"def random_batch(X_train, y_train, batch_size):\n",
" '''Returning a data set for a batch'''\n",
" indices = np.random.randint(0, len(X_train), batch_size)\n",
" X_batch = X_train[indices]\n",
" y_batch = y_train[indices]\n",
" return X_batch, y_batch"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.2 - Parameters"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"data_size = 1000 # Number of observations\n",
"data_cols = 2 # observation size\n",
"data_noise = 0.2\n",
"test_ratio = 0.2 # Ratio of data reserved for validation\n",
"random_seed = 123\n",
"\n",
"learning_rate = 0.01\n",
"n_epochs = 1000\n",
"batch_size = 50\n",
"\n",
"epsilon = 1e-7 # To avoid overflows on some calculations (log())\n",
"\n",
"learning_rate2 = 0.01 # Pour la version 2\n",
"n_epochs2 = 6000\n",
"batch_size2 = 50\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 2 - Data preparation\n",
"### 2.1 - Get some data\n",
"The data here are totally fabricated and represent the **examination results** (passed or failed) based on the students' **working** and **sleeping hours** . \n",
"X=(working hours, sleeping hours) y={result} where result=0 (failed) or 1 (passed)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def do_i_have_it(hours_of_work, hours_of_sleep):\n",
" '''Returns the exam result based on work and sleep hours'''\n",
" hours_of_sleep_min = 5\n",
" hours_of_work_min = 4\n",
" hours_of_game_max = 3\n",
" # ---- Have to sleep and work\n",
" if hours_of_sleep < hours_of_sleep_min: return 0\n",
" if hours_of_work < hours_of_work_min: return 0\n",
" # ---- Gameboy is not good for you\n",
" hours_of_game = 24 - 10 - hours_of_sleep - hours_of_work + random.gauss(0,0.4)\n",
" if hours_of_game > hours_of_game_max: return 0\n",
" # ---- Fine, you got it\n",
" return 1\n",
"\n",
"def make_students_dataset(size, noise):\n",
" '''Fabrique un dataset pour <size> étudiants'''\n",
" x = []\n",
" y = []\n",
" for i in range(size):\n",
" w = random.gauss(5,1)\n",
" s = random.gauss(7,1.5)\n",
" r = do_i_have_it(w,s)\n",
" x.append([w,s])\n",
" y.append(r)\n",
" return (np.array(x), np.array(y))\n",
"\n",
"X_data,y_data=make_students_dataset(data_size,data_noise)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.2 - Show it"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dataset X : ndim=2 shape=(1000, 2) Mean = [5.0192 6.9813] Std = [1.0307 1.5238]\n",
"Dataset y : ndim=1 shape=(1000,) Mean = 0.64 Std = 0.48\n"
]
}
],
"source": [
"fig, ax = plt.subplots(1, 1)\n",
"fig.set_size_inches(8,6)\n",
"ax.plot(X_data[y_data == 1, 0], X_data[y_data == 1, 1], 'go', markersize=4, label=\"y=1 (positive)\")\n",
"ax.plot(X_data[y_data == 0, 0], X_data[y_data == 0, 1], 'ro', markersize=4, label=\"y=0 (negative)\")\n",
"plt.tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)\n",
"plt.xlabel('Hours of work')\n",
"plt.ylabel('Hours of sleep')\n",
"plt.show()\n",
"\n",
"vector_infos('Dataset X',X_data)\n",
"vector_infos('Dataset y',y_data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.3 - Preparation of data\n",
"\n",
"We're going to:\n",
"- normalize the data\n",
"- add a column of 1 for bias\n",
"- Transform y_moons into a vector\n",
"- split the data to have : :\n",
" - a training set\n",
" - a test set"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"X_scaled : ndim=2 shape=(1000, 2) Mean = [-0. 0.] Std = [1. 1.]\n",
"X_train : ndim=2 shape=(800, 3) Mean = [ 1. -0.006 -0.0049] Std = [0. 0.992 0.9893]\n",
"y_train : ndim=2 shape=(800, 1) Mean = [0.6338] Std = [0.4818]\n",
"X_test : ndim=2 shape=(200, 3) Mean = [1. 0.0239 0.0197] Std = [0. 1.0312 1.0415]\n",
"y_test : ndim=2 shape=(200, 1) Mean = [0.665] Std = [0.472]\n"
]
}
],
"source": [
"# ----- Normalisation des données\n",
"scaler = sl.preprocessing.StandardScaler()\n",
"X_scaled = scaler.fit_transform(X_data)\n",
"\n",
"# ----- Ajout de la colonne de 1\n",
"X_scaled_1 = np.c_[np.ones((data_size, 1)), X_scaled]\n",
"\n",
"# ----- Verticalisation de y_moons\n",
"y_data_v = y_data.reshape(-1,1)\n",
"\n",
"# ----- Partage des données\n",
"test_size = int(data_size * test_ratio)\n",
"X_train = X_scaled_1[:-test_size]\n",
"X_test = X_scaled_1[-test_size:]\n",
"y_train = y_data_v[:-test_size]\n",
"y_test = y_data_v[-test_size:]\n",
"\n",
"vector_infos('X_scaled',X_scaled)\n",
"vector_infos('X_train',X_train)\n",
"vector_infos('y_train',y_train)\n",
"vector_infos('X_test',X_test)\n",
"vector_infos('y_test',y_test)\n",
"\n",
"y_train_h = y_train.reshape(-1,) # nécessaire pour la visu."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.4 - Have a look"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"#### Train data :"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"#### Test data :"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ooo.display_md(\"#### Train data :\")\n",
"fig, axs = plt.subplots()\n",
"fig.set_size_inches(8,6)\n",
"axs.plot(X_train[y_train_h == 1, 1], X_train[y_train_h == 1, 2], 'o', color='green', markersize=4, label=\"Train / Positifs\")\n",
"axs.plot(X_train[y_train_h == 0, 1], X_train[y_train_h == 0, 2], 'o', color='red', markersize=4, label=\"Train / Négatifs\")\n",
"plt.tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)\n",
"plt.xlabel('Hours of work')\n",
"plt.ylabel('Hours of sleep')\n",
"plt.show()\n",
"\n",
"ooo.display_md(\"#### Test data :\")\n",
"fig, axs = plt.subplots()\n",
"fig.set_size_inches(8,6)\n",
"axs.plot(X_test[:, 1], X_test[:, 2], 'o',color='gray', markersize=4, label=\"A classer !\")\n",
"plt.tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)\n",
"plt.xlabel('Hours of work')\n",
"plt.ylabel('Hours of sleep')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 3 - Logistic model #1\n",
"### 3.1 - Build model"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"tf.reset_default_graph()\n",
"\n",
"X = tf.placeholder(tf.float32, shape=(None, data_cols + 1), name=\"X\")\n",
"y = tf.placeholder(tf.float32, shape=(None, 1), name=\"y\")\n",
"\n",
"initializer = tf.random_uniform([data_cols + 1, 1], -1.0, 1.0, seed=random_seed)\n",
"theta = tf.Variable(initializer, name=\"theta\")\n",
"\n",
"logits = tf.matmul(X, theta, name=\"logits\")\n",
"\n",
"#y_proba = tf.sigmoid(logits)\n",
"y_proba = 1 / (1 + tf.exp(-logits))\n",
"\n",
"#loss = tf.losses.log_loss(y, y_proba)\n",
"loss = -tf.reduce_mean(y * tf.log(y_proba + epsilon) + (1 - y) * tf.log(1 - y_proba + epsilon))\n",
"\n",
"optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
"training_op = optimizer.minimize(loss)\n",
"\n",
"init = tf.global_variables_initializer()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.2 - Training"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch: 0 \tLoss: 1.3474569\n",
"Epoch: 100 \tLoss: 0.28945148\n",
"Epoch: 200 \tLoss: 0.2590584\n",
"Epoch: 300 \tLoss: 0.24997473\n",
"Epoch: 400 \tLoss: 0.24577877\n",
"Epoch: 500 \tLoss: 0.24390194\n",
"Epoch: 600 \tLoss: 0.24321868\n",
"Epoch: 700 \tLoss: 0.2436808\n",
"Epoch: 800 \tLoss: 0.24361208\n",
"Epoch: 900 \tLoss: 0.24339706\n",
"Epoch: 1000 \tLoss: 0.24412125\n"
]
}
],
"source": [
"nb_batches = int(np.ceil(data_size / batch_size))\n",
"\n",
"with tf.Session() as sess:\n",
" sess.run(init)\n",
"\n",
" for epoch in range(n_epochs+1):\n",
" for batch_index in range(nb_batches):\n",
" X_batch, y_batch = random_batch(X_train, y_train, batch_size)\n",
" sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\n",
" \n",
" loss_val = loss.eval({X: X_test, y: y_test})\n",
" \n",
" if epoch % 100 == 0:\n",
" print(\"Epoch:\", epoch, \"\\tLoss:\", loss_val)\n",
"\n",
" y_proba_val = y_proba.eval(feed_dict={X: X_test, y: y_test})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"### 3.3 - Evaluation\n",
"\n",
"Accuracy = Ability to avoid false positives = $\\frac{Tp}{Tp+Fp}$ \n",
"Recall = Ability to find the right positives = $\\frac{Tp}{Tp+Fn}$ \n",
"Avec : \n",
"$T_p$ (true positive) Correct positive answer \n",
"$F_p$ (false positive) False positive answer \n",
"$T_n$ (true negative) Correct negative answer \n",
"$F_n$ (false negative) Wrong negative answer "
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy = 0.924 Recall = 0.910\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1008x720 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def show_results(y_proba_val):\n",
" y_pred = (y_proba_val >= 0.5)\n",
"\n",
" precision = metrics.precision_score(y_test, y_pred)\n",
" recall = metrics.recall_score(y_test, y_pred)\n",
"\n",
" print(\"Accuracy = {:5.3f} Recall = {:5.3f}\".format(precision, recall))\n",
"\n",
" y_pred_1d = y_pred.reshape(-1)\n",
" y_test_1d = y_test.reshape(-1)\n",
"\n",
" X_pred_positives = X_test[ y_pred_1d == True] # items prédits positifs\n",
" X_real_positives = X_test[ y_test_1d == 1 ] # items réellement positifs\n",
" X_pred_negatives = X_test[ y_pred_1d == False] # items prédits négatifs\n",
" X_real_negatives = X_test[ y_test_1d == 0 ] # items réellement négatifs\n",
"\n",
" fig, axs = plt.subplots(2, 2)\n",
" fig.subplots_adjust(wspace=.1,hspace=0.2)\n",
" fig.set_size_inches(14,10)\n",
" \n",
" axs[0,0].plot(X_pred_positives[:,1], X_pred_positives[:,2], 'o',color='lightgreen', markersize=10, label=\"Prédits positifs\")\n",
" axs[0,0].plot(X_real_positives[:,1], X_real_positives[:,2], 'o',color='green', markersize=4, label=\"Réels positifs\")\n",
" axs[0,0].legend()\n",
" axs[0,0].tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)\n",
" axs[0,0].set_xlabel('$x_1$')\n",
" axs[0,0].set_ylabel('$x_2$')\n",
"\n",
"\n",
" axs[0,1].plot(X_pred_negatives[:,1], X_pred_negatives[:,2], 'o',color='lightsalmon', markersize=10, label=\"Prédits négatifs\")\n",
" axs[0,1].plot(X_real_negatives[:,1], X_real_negatives[:,2], 'o',color='red', markersize=4, label=\"Réels négatifs\")\n",
" axs[0,1].legend()\n",
" axs[0,1].tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)\n",
" axs[0,1].set_xlabel('$x_1$')\n",
" axs[0,1].set_ylabel('$x_2$')\n",
" \n",
" axs[1,0].plot(X_pred_positives[:,1], X_pred_positives[:,2], 'o',color='lightgreen', markersize=10, label=\"Prédits positifs\")\n",
" axs[1,0].plot(X_pred_negatives[:,1], X_pred_negatives[:,2], 'o',color='lightsalmon', markersize=10, label=\"Prédits négatifs\")\n",
" axs[1,0].plot(X_real_positives[:,1], X_real_positives[:,2], 'o',color='green', markersize=4, label=\"Réels positifs\")\n",
" axs[1,0].plot(X_real_negatives[:,1], X_real_negatives[:,2], 'o',color='red', markersize=4, label=\"Réels négatifs\")\n",
" axs[1,0].tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)\n",
" axs[1,0].set_xlabel('$x_1$')\n",
" axs[1,0].set_ylabel('$x_2$')\n",
"\n",
" axs[1,1].pie([precision,1-precision], explode=[0,0.1], labels=[\"\",\"Errors\"], \n",
" autopct='%1.1f%%', shadow=False, startangle=70, colors=[\"lightsteelblue\",\"coral\"])\n",
" axs[1,1].axis('equal')\n",
"\n",
" plt.show()\n",
"\n",
"show_results(y_proba_val)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 4 - Bending the space to a model #2 ;-)\n",
"\n",
"We're going to increase the characteristics of our observations, with : ${x_1}^2$, ${x_2}^2$, ${x_1}^3$ et ${x_2}^3$ \n",
"\n",
"$\n",
"X=\n",
"\\begin{bmatrix}1 & x_{11} & x_{12} \\\\\n",
"\\vdots & \\dots\\\\\n",
"1 & x_{m1} & x_{m2} \\end{bmatrix}\n",
"\\text{et }\n",
"X_{ng}=\\begin{bmatrix}1 & x_{11} & x_{12} & x_{11}^2 & x_{12}^2& x_{11}^3 & x_{12}^3 \\\\\n",
"\\vdots & & & \\dots \\\\\n",
"1 & x_{m1} & x_{m2} & x_{m1}^2 & x_{m2}^2& x_{m1}^3 & x_{m2}^3 \\end{bmatrix}\n",
"$\n",
"\n",
"Note : `sklearn.preprocessing.PolynomialFeatures` can do that for us, but we'll do it ourselves:\n",
"### 4.1 - Extend data"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"X_train_enhanced = np.c_[X_train,\n",
" X_train[:, 1] ** 2,\n",
" X_train[:, 2] ** 2,\n",
" X_train[:, 1] ** 3,\n",
" X_train[:, 2] ** 3]\n",
"X_test_enhanced = np.c_[X_test,\n",
" X_test[:, 1] ** 2,\n",
" X_test[:, 2] ** 2,\n",
" X_test[:, 1] ** 3,\n",
" X_test[:, 2] ** 3]\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 4.2 - A more readable version of our model. Yes it is.\n",
"...and with Tensorboard tracking and checkpoint recording."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"def logistic_regression(X, y, initializer=None, seed=42, learning_rate=0.01):\n",
"\n",
" n_inputs_including_bias = int(X.get_shape()[1])\n",
" \n",
" with tf.name_scope(\"logistic_regression\"):\n",
" \n",
" # ----- Construction du modèle\n",
" with tf.name_scope(\"model\"):\n",
" if initializer is None:\n",
" initializer = tf.random_uniform([n_inputs_including_bias, 1], -1.0, 1.0, seed=seed)\n",
" theta = tf.Variable(initializer, name=\"theta\")\n",
" logits = tf.matmul(X, theta, name=\"logits\")\n",
" y_proba = tf.sigmoid(logits)\n",
" \n",
" with tf.name_scope(\"train\"):\n",
" loss = tf.losses.log_loss(y, y_proba, scope=\"loss\")\n",
" optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate2)\n",
" training_op = optimizer.minimize(loss)\n",
" loss_summary = tf.summary.scalar('log_loss', loss)\n",
" \n",
" with tf.name_scope(\"init\"):\n",
" init = tf.global_variables_initializer()\n",
" \n",
" with tf.name_scope(\"save\"):\n",
" saver = tf.train.Saver(max_to_keep=4)\n",
" \n",
" return y_proba, loss, training_op, loss_summary, init, saver\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 4.3 - Build the model"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"tf.reset_default_graph()\n",
"\n",
"log_dir = './run/logs'\n",
"chk_dir = './run/models'\n",
"os.makedirs(log_dir, mode=0o750, exist_ok=True)\n",
"os.makedirs(chk_dir, mode=0o750, exist_ok=True)\n",
"\n",
"X = tf.placeholder(tf.float32, shape=(None, data_cols + 1 + 4), name=\"X\")\n",
"y = tf.placeholder(tf.float32, shape=(None, 1), name=\"y\")\n",
"\n",
"# Build model\n",
"y_proba, loss, training_op, loss_summary, init, saver = logistic_regression(X, y)\n",
"\n",
"# Save model\n",
"file_writer = tf.summary.FileWriter(log_dir, tf.get_default_graph())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 4.4 - Train the model"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch: 0 Loss: 0.7590 checkpoint: ./run/models/model-ckpt-0\n",
"Epoch: 500 Loss: 0.1350 checkpoint: ./run/models/model-ckpt-500\n",
"Epoch: 1000 Loss: 0.1152 checkpoint: ./run/models/model-ckpt-1000\n",
"Epoch: 1500 Loss: 0.1085 checkpoint: ./run/models/model-ckpt-1500\n",
"Epoch: 2000 Loss: 0.1035 checkpoint: ./run/models/model-ckpt-2000\n",
"WARNING:tensorflow:From /home/pjluc/anaconda3/envs/fidle/lib/python3.7/site-packages/tensorflow_core/python/training/saver.py:963: remove_checkpoint (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Use standard file APIs to delete files with this prefix.\n",
"Epoch: 2500 Loss: 0.1015 checkpoint: ./run/models/model-ckpt-2500\n",
"Epoch: 3000 Loss: 0.0997 checkpoint: ./run/models/model-ckpt-3000\n",
"Epoch: 3500 Loss: 0.0986 checkpoint: ./run/models/model-ckpt-3500\n",
"Epoch: 4000 Loss: 0.0978 checkpoint: ./run/models/model-ckpt-4000\n",
"Epoch: 4500 Loss: 0.0979 checkpoint: ./run/models/model-ckpt-4500\n",
"Epoch: 5000 Loss: 0.0971 checkpoint: ./run/models/model-ckpt-5000\n",
"Epoch: 5500 Loss: 0.0970 checkpoint: ./run/models/model-ckpt-5500\n"
]
}
],
"source": [
"n_batches = int(np.ceil(data_size / batch_size2))\n",
"\n",
"model_file = chk_dir + \"/model-ckpt\"\n",
"model_final = chk_dir + \"/model-final\"\n",
"\n",
"with tf.Session() as sess:\n",
" \n",
" sess.run(init)\n",
"\n",
" for epoch in range(n_epochs2):\n",
" \n",
" for batch_index in range(n_batches):\n",
" # get a batch\n",
" X_batch, y_batch = random_batch(X_train_enhanced, y_train, batch_size)\n",
" # train\n",
" sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\n",
" \n",
" # Calculation of logistic loss and logs\n",
" loss_val, summary_str = sess.run([loss, loss_summary], feed_dict={X: X_test_enhanced, y: y_test})\n",
" # Logging\n",
" file_writer.add_summary(summary_str, epoch)\n",
" \n",
" if epoch % 500 == 0:\n",
" print('Epoch: {:6d} Loss: {:8.4f} checkpoint: {}-{}'.format(epoch,loss_val,model_file,epoch))\n",
" # Save checkpoint\n",
" saver.save(sess, model_file, global_step=epoch)\n",
"\n",
" # Save the final model\n",
" saver.save(sess, model_final)\n",
" # Evaluation with test data\n",
" y_proba_val2 = y_proba.eval(feed_dict={X: X_test_enhanced, y: y_test})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 4.5 - Evaluation"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy = 0.977 Recall = 0.962\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 1008x720 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"show_results(y_proba_val2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
""
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
}