diff --git a/Andreydemianchuk_code/tasks/task_1/Titanic_s.ipynb b/Andreydemianchuk_code/tasks/task_1/Titanic_s.ipynb new file mode 100644 index 0000000..fbbc473 --- /dev/null +++ b/Andreydemianchuk_code/tasks/task_1/Titanic_s.ipynb @@ -0,0 +1,232 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Titanic_simple.ipynb", + "provenance": [], + "collapsed_sections": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "XtClpWT-RGU4" + }, + "source": [ + "**Importing all libraries**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "QLMcjiNeO-uz" + }, + "source": [ + "import pandas as pd\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.linear_model import LogisticRegression\n", + "from lightgbm import LGBMClassifier\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.ensemble import VotingClassifier" + ], + "execution_count": 23, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jL3ggg9IRFnT" + }, + "source": [ + "**Loading the data**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "FhTUIFLbQ-SS" + }, + "source": [ + "train_data = pd.read_csv(\"/content/sample_data/train.csv\", index_col=\"PassengerId\")\n", + "test_data = pd.read_csv(\"/content/sample_data/test.csv\", index_col=\"PassengerId\")" + ], + "execution_count": 24, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "fW5Kzh3JRVbb" + }, + "source": [ + "**Feature selection**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "tt5LFPViRaiR" + }, + "source": [ + "features = [\"Pclass\", \"Sex\", \"SibSp\", \"Parch\"]\n", + "X = pd.get_dummies(train_data[features])\n", + "X_test = pd.get_dummies(test_data[features])\n", + "y = train_data[\"Survived\"]" + ], + "execution_count": 25, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6Bb17U40SG2y" + }, + "source": [ + "**Data Normalization**" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "K1ZvNjfRR_IB", + "outputId": "e7397408-010b-4822-8df8-e96f7a142a9b" + }, + "source": [ + "ss = StandardScaler()\n", + "X_scaled = ss.fit_transform(X)\n", + "X_test_scaled = ss.transform(X_test)\n", + "print(X_scaled)\n", + "print(X_test_scaled)" + ], + "execution_count": 26, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[[ 0.82737724 0.43279337 -0.47367361 -0.73769513 0.73769513]\n", + " [-1.56610693 0.43279337 -0.47367361 1.35557354 -1.35557354]\n", + " [ 0.82737724 -0.4745452 -0.47367361 1.35557354 -1.35557354]\n", + " ...\n", + " [ 0.82737724 0.43279337 2.00893337 1.35557354 -1.35557354]\n", + " [-1.56610693 -0.4745452 -0.47367361 -0.73769513 0.73769513]\n", + " [ 0.82737724 -0.4745452 -0.47367361 -0.73769513 0.73769513]]\n", + "[[ 0.82737724 -0.4745452 -0.47367361 -0.73769513 0.73769513]\n", + " [ 0.82737724 0.43279337 -0.47367361 1.35557354 -1.35557354]\n", + " [-0.36936484 -0.4745452 -0.47367361 -0.73769513 0.73769513]\n", + " ...\n", + " [ 0.82737724 -0.4745452 -0.47367361 -0.73769513 0.73769513]\n", + " [ 0.82737724 -0.4745452 -0.47367361 -0.73769513 0.73769513]\n", + " [ 0.82737724 0.43279337 0.76762988 -0.73769513 0.73769513]]\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "j4Sdo0r_SipM" + }, + "source": [ + "**Modeling**" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "LG70wCttSfdL", + "outputId": "e65c3bd8-7452-451e-ae77-5c8b9bf5095c" + }, + "source": [ + "rfc = RandomForestClassifier(n_estimators=100, max_depth=5, random_state=1)\n", + "rfc.fit(X_scaled, y)\n", + "lg = LogisticRegression(random_state=10, max_iter=1000, C=20, solver='lbfgs')\n", + "lg.fit(X_scaled, y)\n", + "lgb = LGBMClassifier()\n", + "lgb.fit(X_scaled, y)" + ], + "execution_count": 27, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "LGBMClassifier(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\n", + " importance_type='split', learning_rate=0.1, max_depth=-1,\n", + " min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,\n", + " n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,\n", + " random_state=None, reg_alpha=0.0, reg_lambda=0.0, silent=True,\n", + " subsample=1.0, subsample_for_bin=200000, subsample_freq=0)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 27 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZcKY8aNXTpjv" + }, + "source": [ + "**Ensembling**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "N_w1IJ-STus2" + }, + "source": [ + "ensemble_model = VotingClassifier(estimators=[\n", + " (\"logit\", lg),\n", + " (\"rf\", rfc),\n", + " (\"lgb\", lgb),\n", + "], voting=\"hard\")\n", + "\n", + "ensemble_model.fit(X_scaled, y)\n", + "preds = ensemble_model.predict(X_test_scaled)" + ], + "execution_count": 28, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Ti0tSHUDUAUa" + }, + "source": [ + "**Output**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "bjeO0_OGUCPb" + }, + "source": [ + "output = pd.DataFrame({'PassengerId': test_data.index,\n", + " 'Survived': preds})\n", + "\n", + "output.to_csv('submission.csv', index=False)" + ], + "execution_count": 29, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/Andreydemianchuk_code/tasks/task_1/titanic_s.py b/Andreydemianchuk_code/tasks/task_1/titanic_s.py new file mode 100644 index 0000000..d1de539 --- /dev/null +++ b/Andreydemianchuk_code/tasks/task_1/titanic_s.py @@ -0,0 +1,64 @@ +# -*- coding: utf-8 -*- +"""Titanic_simple.ipynb + +Automatically generated by Colaboratory. + +Original file is located at + https://colab.research.google.com/drive/1xML6OS-AeM6VQ1qkooNVCbykEKUXWAqV + +**Importing all libraries** +""" + +import pandas as pd +from sklearn.ensemble import RandomForestClassifier +from sklearn.linear_model import LogisticRegression +from lightgbm import LGBMClassifier +from sklearn.preprocessing import StandardScaler +from sklearn.ensemble import VotingClassifier + +"""**Loading the data**""" + +train_data = pd.read_csv("/content/sample_data/train.csv", index_col="PassengerId") +test_data = pd.read_csv("/content/sample_data/test.csv", index_col="PassengerId") + +"""**Feature selection**""" + +features = ["Pclass", "Sex", "SibSp", "Parch"] +X = pd.get_dummies(train_data[features]) +X_test = pd.get_dummies(test_data[features]) +y = train_data["Survived"] + +"""**Data Normalization**""" + +ss = StandardScaler() +X_scaled = ss.fit_transform(X) +X_test_scaled = ss.transform(X_test) +print(X_scaled) +print(X_test_scaled) + +"""**Modeling**""" + +rfc = RandomForestClassifier(n_estimators=100, max_depth=5, random_state=1) +rfc.fit(X_scaled, y) +lg = LogisticRegression(random_state=10, max_iter=1000, C=20, solver='lbfgs') +lg.fit(X_scaled, y) +lgb = LGBMClassifier() +lgb.fit(X_scaled, y) + +"""**Ensembling**""" + +ensemble_model = VotingClassifier(estimators=[ + ("logit", lg), + ("rf", rfc), + ("lgb", lgb), +], voting="hard") + +ensemble_model.fit(X_scaled, y) +preds = ensemble_model.predict(X_test_scaled) + +"""**Output**""" + +output = pd.DataFrame({'PassengerId': test_data.index, + 'Survived': preds}) + +output.to_csv('submission.csv', index=False) \ No newline at end of file diff --git a/Andreydemianchuk_code/tasks/task_2/house_prices_simple.ipynb b/Andreydemianchuk_code/tasks/task_2/house_prices_simple.ipynb new file mode 100644 index 0000000..626c7c2 --- /dev/null +++ b/Andreydemianchuk_code/tasks/task_2/house_prices_simple.ipynb @@ -0,0 +1,265 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "house_prices_simple.ipynb", + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "code", + "metadata": { + "id": "A_PIZ6w8fI1X" + }, + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn.ensemble import GradientBoostingRegressor\n", + "from sklearn.linear_model import LinearRegression\n", + "from mlxtend.regressor import StackingRegressor\n", + "from sklearn.tree import DecisionTreeRegressor\n", + "from google.colab import files" + ], + "execution_count": 61, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qaMpu_s02BNw" + }, + "source": [ + "**Loading the data**" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "resources": { + "http://localhost:8080/nbextensions/google.colab/files.js": { + "data": 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+ "ok": true, + "headers": [ + [ + "content-type", + "application/javascript" + ] + ], + "status": 200, + "status_text": "" + } + }, + "base_uri": "https://localhost:8080/", + "height": 55 + }, + "id": "fHBdDpgzm9wH", + "outputId": "bf926515-6e51-469e-d07d-2485d37fd1d4" + }, + "source": [ + "files.upload()" + ], + "execution_count": 62, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " Upload widget is only available when the cell has been executed in the\n", + " current browser session. Please rerun this cell to enable.\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "{}" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 62 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "tmdHVxPDnV-n" + }, + "source": [ + "train_data = pd.read_csv('train.csv')\n", + "test_data = pd.read_csv('test.csv')" + ], + "execution_count": 63, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "X5c2v2Du2G0o" + }, + "source": [ + "**Preprocessing**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "-eo2pA8Lz2IC" + }, + "source": [ + "y = np.log(train_data.SalePrice)\n", + "test_id = test_data['Id']\n", + "train_test = pd.concat((train_data.loc[:, 'MSSubClass':'SaleCondition'], test_data.loc[:, 'MSSubClass':'SaleCondition']))\n", + "train_test = train_test.drop(['Utilities'], axis=1)\n", + "dropped_columns = ['BsmtFinSF2', 'LowQualFinSF', 'BsmtHalfBath', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'MiscVal', 'PoolQC', 'Street', 'SaleCondition']\n", + "train_test.drop(dropped_columns, axis=1)\n", + "train_test['street'] = pd.get_dummies(train_test.Street, drop_first=True)\n", + "train_test['condition'] = train_test.SaleCondition.apply(lambda x: 1 if x == 'Normal' else 0)\n", + "train_data= all_data[:train_data.shape[0]].select_dtypes(include=[np.number]).interpolate().dropna()\n", + "test_data = all_data[train_data.shape[0]:].select_dtypes(include=[np.number]).interpolate().dropna()" + ], + "execution_count": 64, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SsAGXK4ft2hb" + }, + "source": [ + "**Modeling**" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "0vKvw1GRttmC", + "outputId": "104d93d9-493b-4f19-ffb3-f2f2d8ab4b56" + }, + "source": [ + "lr = LinearRegression(n_jobs=-1)\n", + "gb = GradientBoostingRegressor(n_estimators=40, max_depth=2)\n", + "dtr = DecisionTreeRegressor()\n", + "model = StackingRegressor(regressors=[dtr, gb], meta_regressor=lr)\n", + "\n", + "model.fit(train_data, y)" + ], + "execution_count": 65, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "StackingRegressor(meta_regressor=LinearRegression(copy_X=True,\n", + " fit_intercept=True, n_jobs=-1,\n", + " normalize=False),\n", + " refit=True,\n", + " regressors=[DecisionTreeRegressor(ccp_alpha=0.0,\n", + " criterion='mse',\n", + " max_depth=None,\n", + " max_features=None,\n", + " max_leaf_nodes=None,\n", + " min_impurity_decrease=0.0,\n", + " min_impurity_split=None,\n", + " min_samples_leaf=1,\n", + " min_samples_split=2,\n", + " min_weight_fraction_leaf=...\n", + " max_leaf_nodes=None,\n", + " min_impurity_decrease=0.0,\n", + " min_impurity_split=None,\n", + " min_samples_leaf=1,\n", + " min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0,\n", + " n_estimators=40,\n", + " n_iter_no_change=None,\n", + " presort='deprecated',\n", + " random_state=None,\n", + " subsample=1.0,\n", + " tol=0.0001,\n", + " validation_fraction=0.1,\n", + " verbose=0,\n", + " warm_start=False)],\n", + " store_train_meta_features=False,\n", + " use_features_in_secondary=False, verbose=0)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 65 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "E5UFjX3gt62n" + }, + "source": [ + "**Predicting**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "BJ11nTM5txDJ" + }, + "source": [ + "y_pred = model.predict(train_data)\n", + "\n", + "Y_pred = model.predict(test_data)\n", + "\n", + "pred = np.exp(Y_pred)" + ], + "execution_count": 66, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xJ2keEoKt-ax" + }, + "source": [ + "**Submission**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Ztz7OtpssrBQ" + }, + "source": [ + "output = pd.DataFrame(test_id, columns=['Id'])\n", + "output['SalePrice'] = pred\n", + "output.to_csv(\"submission.csv\", index=False, header=True)" + ], + "execution_count": 67, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/Andreydemianchuk_code/tasks/task_2/house_prices_simple.py b/Andreydemianchuk_code/tasks/task_2/house_prices_simple.py new file mode 100644 index 0000000..3bed5d9 --- /dev/null +++ b/Andreydemianchuk_code/tasks/task_2/house_prices_simple.py @@ -0,0 +1,59 @@ +# -*- coding: utf-8 -*- +"""house_prices_simple.ipynb + +Automatically generated by Colaboratory. + +Original file is located at + https://colab.research.google.com/drive/1uVh12mRItMzm-i8bP-l-K1EIjoX3GrtE +""" + +import numpy as np +import pandas as pd +from sklearn.ensemble import GradientBoostingRegressor +from sklearn.linear_model import LinearRegression +from mlxtend.regressor import StackingRegressor +from sklearn.tree import DecisionTreeRegressor +from google.colab import files + +"""**Loading the data**""" + +files.upload() + +train_data = pd.read_csv('train.csv') +test_data = pd.read_csv('test.csv') + +"""**Preprocessing**""" + +y = np.log(train_data.SalePrice) +test_id = test_data['Id'] +train_test = pd.concat((train_data.loc[:, 'MSSubClass':'SaleCondition'], test_data.loc[:, 'MSSubClass':'SaleCondition'])) +train_test = train_test.drop(['Utilities'], axis=1) +dropped_columns = ['BsmtFinSF2', 'LowQualFinSF', 'BsmtHalfBath', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'MiscVal', 'PoolQC', 'Street', 'SaleCondition'] +train_test.drop(dropped_columns, axis=1) +train_test['street'] = pd.get_dummies(train_test.Street, drop_first=True) +train_test['condition'] = train_test.SaleCondition.apply(lambda x: 1 if x == 'Normal' else 0) +train_data = all_data[:train_data.shape[0]].select_dtypes(include=[np.number]).interpolate().dropna() +test_data = all_data[train_data.shape[0]:].select_dtypes(include=[np.number]).interpolate().dropna() + +"""**Modeling**""" + +lr = LinearRegression(n_jobs=-1) +gb = GradientBoostingRegressor(n_estimators=40, max_depth=2) +dtr = DecisionTreeRegressor() +model = StackingRegressor(regressors=[dtr, gb], meta_regressor=lr) + +model.fit(train_data, y) + +"""**Predicting**""" + +y_pred = model.predict(train_data) + +Y_pred = model.predict(test_data) + +pred = np.exp(Y_pred) + +"""**Submission**""" + +output = pd.DataFrame(test_id, columns=['Id']) +output['SalePrice'] = pred +output.to_csv("submission.csv", index=False, header=True) diff --git a/Andreydemianchuk_code/tasks/task_3/face_mask_detection.ipynb b/Andreydemianchuk_code/tasks/task_3/face_mask_detection.ipynb new file mode 100644 index 0000000..a6ac8ce --- /dev/null +++ b/Andreydemianchuk_code/tasks/task_3/face_mask_detection.ipynb @@ -0,0 +1,232 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "face_mask_detection_simple.ipynb", + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "code", + "metadata": { + "id": "iOhlGIDieFkB" + }, + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import tensorflow as tf\n", + "from tensorflow.keras import layers\n", + "from google.colab import files\n", + "from zipfile import ZipFile" + ], + "execution_count": 8, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "36vfmQ_eev3e" + }, + "source": [ + "file_name = 'face-mask-dataset.zip'\n", + "\n", + "with ZipFile(file_name, 'r') as zip:\n", + " zip.extractall()" + ], + "execution_count": 24, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "Hpwj9zepe1F_" + }, + "source": [ + "batch_size = 40\n", + "img_height = 200\n", + "img_width = 200" + ], + "execution_count": 14, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "mAgIn4e8e1kW", + "outputId": "415354ae-dd3a-4163-d761-72a55f01f36d" + }, + "source": [ + "train_data = tf.keras.preprocessing.image_dataset_from_directory(\n", + " 'data',\n", + " validation_split=0.2,\n", + " subset=\"training\",\n", + " seed=42,\n", + " image_size=(img_height, img_width),\n", + " batch_size=batch_size\n", + ")" + ], + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Found 7553 files belonging to 2 classes.\n", + "Using 6043 files for training.\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "0wMgzq6Oe4SW", + "outputId": "8420be3d-7a24-424b-ee6f-5480d2359618" + }, + "source": [ + "test_data = tf.keras.preprocessing.image_dataset_from_directory(\n", + " 'data',\n", + " validation_split=0.2,\n", + " subset=\"validation\",\n", + " seed=42,\n", + " image_size=(img_height, img_width),\n", + " batch_size=batch_size\n", + ")" + ], + "execution_count": 16, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Found 7553 files belonging to 2 classes.\n", + "Using 1510 files for validation.\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "jv8oxX78fAtg" + }, + "source": [ + "model = tf.keras.models.Sequential([\n", + " layers.experimental.preprocessing.Rescaling(1. / 255),\n", + " layers.Conv2D(32, 3, activation='relu'),\n", + " layers.MaxPooling2D(),\n", + " layers.Conv2D(64, 3, activation='relu'),\n", + " layers.MaxPooling2D(),\n", + " layers.Conv2D(128, 3, activation='relu'),\n", + " layers.MaxPooling2D(),\n", + " layers.Conv2D(256, 3, activation='relu'),\n", + " layers.MaxPooling2D(),\n", + " layers.GlobalAveragePooling2D(),\n", + " layers.Dense(256, activation='relu'),\n", + " layers.Dense(2, activation='softmax')\n", + "])" + ], + "execution_count": 20, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "j2AUA2pJfGO4" + }, + "source": [ + "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])" + ], + "execution_count": 21, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ijUMIeZxfJwN", + "outputId": "65ebd10a-1c28-4fd1-a8a4-301169087a29" + }, + "source": [ + "model.fit(train_data, validation_data=test_data, epochs=15)" + ], + "execution_count": 22, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Epoch 1/15\n", + "152/152 [==============================] - 32s 202ms/step - loss: 0.6215 - accuracy: 0.6306 - val_loss: 0.5105 - val_accuracy: 0.7517\n", + "Epoch 2/15\n", + "152/152 [==============================] - 31s 200ms/step - loss: 0.4170 - accuracy: 0.8225 - val_loss: 0.2928 - val_accuracy: 0.8795\n", + "Epoch 3/15\n", + "152/152 [==============================] - 31s 198ms/step - loss: 0.2998 - accuracy: 0.8798 - val_loss: 0.2463 - val_accuracy: 0.9093\n", + "Epoch 4/15\n", + "152/152 [==============================] - 30s 196ms/step - loss: 0.2599 - accuracy: 0.8994 - val_loss: 0.2390 - val_accuracy: 0.9007\n", + "Epoch 5/15\n", + "152/152 [==============================] - 31s 200ms/step - loss: 0.2482 - accuracy: 0.9016 - val_loss: 0.2612 - val_accuracy: 0.9046\n", + "Epoch 6/15\n", + "152/152 [==============================] - 31s 199ms/step - loss: 0.2426 - accuracy: 0.9038 - val_loss: 0.1685 - val_accuracy: 0.9404\n", + "Epoch 7/15\n", + "152/152 [==============================] - 31s 198ms/step - loss: 0.1825 - accuracy: 0.9356 - val_loss: 0.2153 - val_accuracy: 0.9192\n", + "Epoch 8/15\n", + "152/152 [==============================] - 30s 192ms/step - loss: 0.2282 - accuracy: 0.9122 - val_loss: 0.1495 - val_accuracy: 0.9457\n", + "Epoch 9/15\n", + "152/152 [==============================] - 31s 200ms/step - loss: 0.1478 - accuracy: 0.9440 - val_loss: 0.1352 - val_accuracy: 0.9490\n", + "Epoch 10/15\n", + "152/152 [==============================] - 31s 200ms/step - loss: 0.1292 - accuracy: 0.9493 - val_loss: 0.1429 - val_accuracy: 0.9543\n", + "Epoch 11/15\n", + "152/152 [==============================] - 30s 194ms/step - loss: 0.1537 - accuracy: 0.9433 - val_loss: 0.2374 - val_accuracy: 0.9119\n", + "Epoch 12/15\n", + "152/152 [==============================] - 31s 198ms/step - loss: 0.1665 - accuracy: 0.9370 - val_loss: 0.1149 - val_accuracy: 0.9530\n", + "Epoch 13/15\n", + "152/152 [==============================] - 31s 200ms/step - loss: 0.1096 - accuracy: 0.9569 - val_loss: 0.1432 - val_accuracy: 0.9470\n", + "Epoch 14/15\n", + "152/152 [==============================] - 31s 199ms/step - loss: 0.1258 - accuracy: 0.9565 - val_loss: 0.0986 - val_accuracy: 0.9649\n", + "Epoch 15/15\n", + "152/152 [==============================] - 31s 201ms/step - loss: 0.0885 - accuracy: 0.9679 - val_loss: 0.1110 - val_accuracy: 0.9576\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 22 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "nzfz2jycfjkA" + }, + "source": [ + "model.save('maskDetector.h5')" + ], + "execution_count": 23, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/Andreydemianchuk_code/tasks/task_3/face_mask_detection.py b/Andreydemianchuk_code/tasks/task_3/face_mask_detection.py new file mode 100644 index 0000000..89c8591 --- /dev/null +++ b/Andreydemianchuk_code/tasks/task_3/face_mask_detection.py @@ -0,0 +1,60 @@ +# -*- coding: utf-8 -*- +"""face_mask_detection_simple.ipynb + +Automatically generated by Colaboratory. + +Original file is located at + https://colab.research.google.com/drive/1v_rhcXflaGq1dTBdZBGLUT_3Blvcst8n +""" + +import tensorflow as tf +from tensorflow.keras import layers +from zipfile import ZipFile + +file_name = 'face-mask-dataset.zip' + +with ZipFile(file_name, 'r') as zip: + zip.extractall() + +batch_size = 40 +img_height = 200 +img_width = 200 + +train_data = tf.keras.preprocessing.image_dataset_from_directory( + 'data', + validation_split=0.2, + subset="training", + seed=42, + image_size=(img_height, img_width), + batch_size=batch_size +) + +test_data = tf.keras.preprocessing.image_dataset_from_directory( + 'data', + validation_split=0.2, + subset="validation", + seed=42, + image_size=(img_height, img_width), + batch_size=batch_size +) + +model = tf.keras.models.Sequential([ + layers.experimental.preprocessing.Rescaling(1. / 255), + layers.Conv2D(32, 3, activation='relu'), + layers.MaxPooling2D(), + layers.Conv2D(64, 3, activation='relu'), + layers.MaxPooling2D(), + layers.Conv2D(128, 3, activation='relu'), + layers.MaxPooling2D(), + layers.Conv2D(256, 3, activation='relu'), + layers.MaxPooling2D(), + layers.GlobalAveragePooling2D(), + layers.Dense(256, activation='relu'), + layers.Dense(2, activation='softmax') +]) + +model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) + +model.fit(train_data, validation_data=test_data, epochs=15) + +model.save('maskDetector.h5') diff --git a/Andreydemianchuk_code/tasks/task_4/transfer_learning.ipynb b/Andreydemianchuk_code/tasks/task_4/transfer_learning.ipynb new file mode 100644 index 0000000..e3abdc8 --- /dev/null +++ b/Andreydemianchuk_code/tasks/task_4/transfer_learning.ipynb @@ -0,0 +1,1632 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "transfer_learning.ipynb", + "provenance": [], + "collapsed_sections": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "code", + "metadata": { + "id": "Fr61RR1Khvhh" + }, + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import tensorflow as tf\n", + "from tensorflow.keras import layers\n", + "from google.colab import files\n", + "from zipfile import ZipFile\n", + "from tensorflow.keras.optimizers import Adam\n", + "from tensorflow.keras.applications import DenseNet169" + ], + "execution_count": 18, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "36vfmQ_eev3e" + }, + "source": [ + "file_name = 'face-mask-dataset.zip'\n", + "\n", + "with ZipFile(file_name, 'r') as zip:\n", + " zip.extractall()" + ], + "execution_count": 23, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "Hpwj9zepe1F_" + }, + "source": [ + "batch_size = 40\n", + "img_height = 200\n", + "img_width = 200" + ], + "execution_count": 24, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "mAgIn4e8e1kW", + "outputId": "16587bfb-4019-4352-b21f-898fc7870b70" + }, + "source": [ + "train_data = tf.keras.preprocessing.image_dataset_from_directory(\n", + " 'data',\n", + " validation_split=0.2,\n", + " subset=\"training\",\n", + " seed=42,\n", + " image_size=(img_height, img_width),\n", + " batch_size=batch_size\n", + ")" + ], + "execution_count": 25, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Found 7553 files belonging to 2 classes.\n", + "Using 6043 files for training.\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "0wMgzq6Oe4SW", + "outputId": "b290ce22-0054-454f-ea84-e936a798c444" + }, + "source": [ + "test_data = tf.keras.preprocessing.image_dataset_from_directory(\n", + " 'data',\n", + " validation_split=0.2,\n", + " subset=\"validation\",\n", + " seed=42,\n", + " image_size=(img_height, img_width),\n", + " batch_size=batch_size\n", + ")" + ], + "execution_count": 26, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Found 7553 files belonging to 2 classes.\n", + "Using 1510 files for validation.\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "LIdn9m7_LBfu" + }, + "source": [ + "densenet_model = DenseNet169(weights='imagenet', include_top=False, input_shape=(200, 200, 3))" + ], + "execution_count": 27, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "ifNOm2XYO2P4" + }, + "source": [ + "densenet_model.trainable = False" + ], + "execution_count": 28, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "5qtaly9CQqj0", + "outputId": "6ee5c35e-9c85-4dc5-a79a-2c1b4be45e90" + }, + "source": [ + "densenet_model.summary()" + ], + "execution_count": 29, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Model: \"densenet169\"\n", + "__________________________________________________________________________________________________\n", + "Layer (type) Output Shape Param # Connected to \n", + "==================================================================================================\n", + "input_2 (InputLayer) [(None, 200, 200, 3) 0 \n", + "__________________________________________________________________________________________________\n", + "zero_padding2d_2 (ZeroPadding2D (None, 206, 206, 3) 0 input_2[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1/conv (Conv2D) (None, 100, 100, 64) 9408 zero_padding2d_2[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1/bn (BatchNormalization) (None, 100, 100, 64) 256 conv1/conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv1/relu (Activation) (None, 100, 100, 64) 0 conv1/bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "zero_padding2d_3 (ZeroPadding2D (None, 102, 102, 64) 0 conv1/relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "pool1 (MaxPooling2D) (None, 50, 50, 64) 0 zero_padding2d_3[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2_block1_0_bn (BatchNormali (None, 50, 50, 64) 256 pool1[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2_block1_0_relu (Activation (None, 50, 50, 64) 0 conv2_block1_0_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2_block1_1_conv (Conv2D) (None, 50, 50, 128) 8192 conv2_block1_0_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2_block1_1_bn (BatchNormali (None, 50, 50, 128) 512 conv2_block1_1_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2_block1_1_relu (Activation (None, 50, 50, 128) 0 conv2_block1_1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2_block1_2_conv (Conv2D) (None, 50, 50, 32) 36864 conv2_block1_1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2_block1_concat (Concatenat (None, 50, 50, 96) 0 pool1[0][0] \n", + " conv2_block1_2_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2_block2_0_bn (BatchNormali (None, 50, 50, 96) 384 conv2_block1_concat[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2_block2_0_relu (Activation (None, 50, 50, 96) 0 conv2_block2_0_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2_block2_1_conv (Conv2D) (None, 50, 50, 128) 12288 conv2_block2_0_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2_block2_1_bn (BatchNormali (None, 50, 50, 128) 512 conv2_block2_1_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2_block2_1_relu (Activation (None, 50, 50, 128) 0 conv2_block2_1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2_block2_2_conv (Conv2D) (None, 50, 50, 32) 36864 conv2_block2_1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2_block2_concat (Concatenat (None, 50, 50, 128) 0 conv2_block1_concat[0][0] \n", + " conv2_block2_2_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2_block3_0_bn (BatchNormali (None, 50, 50, 128) 512 conv2_block2_concat[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2_block3_0_relu (Activation (None, 50, 50, 128) 0 conv2_block3_0_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2_block3_1_conv (Conv2D) (None, 50, 50, 128) 16384 conv2_block3_0_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2_block3_1_bn (BatchNormali (None, 50, 50, 128) 512 conv2_block3_1_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2_block3_1_relu (Activation (None, 50, 50, 128) 0 conv2_block3_1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2_block3_2_conv (Conv2D) (None, 50, 50, 32) 36864 conv2_block3_1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2_block3_concat (Concatenat (None, 50, 50, 160) 0 conv2_block2_concat[0][0] \n", + " conv2_block3_2_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2_block4_0_bn (BatchNormali (None, 50, 50, 160) 640 conv2_block3_concat[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2_block4_0_relu (Activation (None, 50, 50, 160) 0 conv2_block4_0_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2_block4_1_conv (Conv2D) (None, 50, 50, 128) 20480 conv2_block4_0_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2_block4_1_bn (BatchNormali (None, 50, 50, 128) 512 conv2_block4_1_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2_block4_1_relu (Activation (None, 50, 50, 128) 0 conv2_block4_1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2_block4_2_conv (Conv2D) (None, 50, 50, 32) 36864 conv2_block4_1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2_block4_concat (Concatenat (None, 50, 50, 192) 0 conv2_block3_concat[0][0] \n", + " conv2_block4_2_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2_block5_0_bn (BatchNormali (None, 50, 50, 192) 768 conv2_block4_concat[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2_block5_0_relu (Activation (None, 50, 50, 192) 0 conv2_block5_0_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2_block5_1_conv (Conv2D) (None, 50, 50, 128) 24576 conv2_block5_0_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2_block5_1_bn (BatchNormali (None, 50, 50, 128) 512 conv2_block5_1_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2_block5_1_relu (Activation (None, 50, 50, 128) 0 conv2_block5_1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2_block5_2_conv (Conv2D) (None, 50, 50, 32) 36864 conv2_block5_1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2_block5_concat (Concatenat (None, 50, 50, 224) 0 conv2_block4_concat[0][0] \n", + " conv2_block5_2_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2_block6_0_bn (BatchNormali (None, 50, 50, 224) 896 conv2_block5_concat[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2_block6_0_relu (Activation (None, 50, 50, 224) 0 conv2_block6_0_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2_block6_1_conv (Conv2D) (None, 50, 50, 128) 28672 conv2_block6_0_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2_block6_1_bn (BatchNormali (None, 50, 50, 128) 512 conv2_block6_1_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2_block6_1_relu (Activation (None, 50, 50, 128) 0 conv2_block6_1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2_block6_2_conv (Conv2D) (None, 50, 50, 32) 36864 conv2_block6_1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2_block6_concat (Concatenat (None, 50, 50, 256) 0 conv2_block5_concat[0][0] \n", + " conv2_block6_2_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "pool2_bn (BatchNormalization) (None, 50, 50, 256) 1024 conv2_block6_concat[0][0] \n", + "__________________________________________________________________________________________________\n", + "pool2_relu (Activation) (None, 50, 50, 256) 0 pool2_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "pool2_conv (Conv2D) (None, 50, 50, 128) 32768 pool2_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "pool2_pool (AveragePooling2D) (None, 25, 25, 128) 0 pool2_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block1_0_bn (BatchNormali (None, 25, 25, 128) 512 pool2_pool[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block1_0_relu (Activation (None, 25, 25, 128) 0 conv3_block1_0_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block1_1_conv (Conv2D) (None, 25, 25, 128) 16384 conv3_block1_0_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block1_1_bn (BatchNormali (None, 25, 25, 128) 512 conv3_block1_1_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block1_1_relu (Activation (None, 25, 25, 128) 0 conv3_block1_1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block1_2_conv (Conv2D) (None, 25, 25, 32) 36864 conv3_block1_1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block1_concat (Concatenat (None, 25, 25, 160) 0 pool2_pool[0][0] \n", + " conv3_block1_2_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block2_0_bn (BatchNormali (None, 25, 25, 160) 640 conv3_block1_concat[0][0] \n", + 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"__________________________________________________________________________________________________\n", + "conv3_block2_concat (Concatenat (None, 25, 25, 192) 0 conv3_block1_concat[0][0] \n", + " conv3_block2_2_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block3_0_bn (BatchNormali (None, 25, 25, 192) 768 conv3_block2_concat[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block3_0_relu (Activation (None, 25, 25, 192) 0 conv3_block3_0_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block3_1_conv (Conv2D) (None, 25, 25, 128) 24576 conv3_block3_0_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block3_1_bn (BatchNormali (None, 25, 25, 128) 512 conv3_block3_1_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block3_1_relu (Activation (None, 25, 25, 128) 0 conv3_block3_1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block3_2_conv (Conv2D) (None, 25, 25, 32) 36864 conv3_block3_1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block3_concat (Concatenat (None, 25, 25, 224) 0 conv3_block2_concat[0][0] \n", + " conv3_block3_2_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block4_0_bn (BatchNormali (None, 25, 25, 224) 896 conv3_block3_concat[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block4_0_relu (Activation (None, 25, 25, 224) 0 conv3_block4_0_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block4_1_conv (Conv2D) (None, 25, 25, 128) 28672 conv3_block4_0_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block4_1_bn (BatchNormali (None, 25, 25, 128) 512 conv3_block4_1_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block4_1_relu (Activation (None, 25, 25, 128) 0 conv3_block4_1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block4_2_conv (Conv2D) (None, 25, 25, 32) 36864 conv3_block4_1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block4_concat (Concatenat (None, 25, 25, 256) 0 conv3_block3_concat[0][0] \n", + " conv3_block4_2_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block5_0_bn (BatchNormali (None, 25, 25, 256) 1024 conv3_block4_concat[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block5_0_relu (Activation (None, 25, 25, 256) 0 conv3_block5_0_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block5_1_conv (Conv2D) (None, 25, 25, 128) 32768 conv3_block5_0_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block5_1_bn (BatchNormali (None, 25, 25, 128) 512 conv3_block5_1_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block5_1_relu (Activation (None, 25, 25, 128) 0 conv3_block5_1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block5_2_conv (Conv2D) (None, 25, 25, 32) 36864 conv3_block5_1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block5_concat (Concatenat (None, 25, 25, 288) 0 conv3_block4_concat[0][0] \n", + " conv3_block5_2_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block6_0_bn (BatchNormali (None, 25, 25, 288) 1152 conv3_block5_concat[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block6_0_relu (Activation (None, 25, 25, 288) 0 conv3_block6_0_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block6_1_conv (Conv2D) (None, 25, 25, 128) 36864 conv3_block6_0_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block6_1_bn (BatchNormali (None, 25, 25, 128) 512 conv3_block6_1_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block6_1_relu (Activation (None, 25, 25, 128) 0 conv3_block6_1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block6_2_conv (Conv2D) (None, 25, 25, 32) 36864 conv3_block6_1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block6_concat (Concatenat (None, 25, 25, 320) 0 conv3_block5_concat[0][0] \n", + " conv3_block6_2_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block7_0_bn (BatchNormali (None, 25, 25, 320) 1280 conv3_block6_concat[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block7_0_relu (Activation (None, 25, 25, 320) 0 conv3_block7_0_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block7_1_conv (Conv2D) (None, 25, 25, 128) 40960 conv3_block7_0_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block7_1_bn (BatchNormali (None, 25, 25, 128) 512 conv3_block7_1_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block7_1_relu (Activation (None, 25, 25, 128) 0 conv3_block7_1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block7_2_conv (Conv2D) (None, 25, 25, 32) 36864 conv3_block7_1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block7_concat (Concatenat (None, 25, 25, 352) 0 conv3_block6_concat[0][0] \n", + " conv3_block7_2_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block8_0_bn (BatchNormali (None, 25, 25, 352) 1408 conv3_block7_concat[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block8_0_relu (Activation (None, 25, 25, 352) 0 conv3_block8_0_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block8_1_conv (Conv2D) (None, 25, 25, 128) 45056 conv3_block8_0_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block8_1_bn (BatchNormali (None, 25, 25, 128) 512 conv3_block8_1_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block8_1_relu (Activation (None, 25, 25, 128) 0 conv3_block8_1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block8_2_conv (Conv2D) (None, 25, 25, 32) 36864 conv3_block8_1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block8_concat (Concatenat (None, 25, 25, 384) 0 conv3_block7_concat[0][0] \n", + " conv3_block8_2_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block9_0_bn (BatchNormali (None, 25, 25, 384) 1536 conv3_block8_concat[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block9_0_relu (Activation (None, 25, 25, 384) 0 conv3_block9_0_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block9_1_conv (Conv2D) (None, 25, 25, 128) 49152 conv3_block9_0_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block9_1_bn (BatchNormali (None, 25, 25, 128) 512 conv3_block9_1_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block9_1_relu (Activation (None, 25, 25, 128) 0 conv3_block9_1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block9_2_conv (Conv2D) (None, 25, 25, 32) 36864 conv3_block9_1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block9_concat (Concatenat (None, 25, 25, 416) 0 conv3_block8_concat[0][0] \n", + " conv3_block9_2_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block10_0_bn (BatchNormal (None, 25, 25, 416) 1664 conv3_block9_concat[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block10_0_relu (Activatio (None, 25, 25, 416) 0 conv3_block10_0_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block10_1_conv (Conv2D) (None, 25, 25, 128) 53248 conv3_block10_0_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block10_1_bn (BatchNormal (None, 25, 25, 128) 512 conv3_block10_1_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block10_1_relu (Activatio (None, 25, 25, 128) 0 conv3_block10_1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block10_2_conv (Conv2D) (None, 25, 25, 32) 36864 conv3_block10_1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block10_concat (Concatena (None, 25, 25, 448) 0 conv3_block9_concat[0][0] \n", + " conv3_block10_2_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block11_0_bn (BatchNormal (None, 25, 25, 448) 1792 conv3_block10_concat[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block11_0_relu (Activatio (None, 25, 25, 448) 0 conv3_block11_0_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block11_1_conv (Conv2D) (None, 25, 25, 128) 57344 conv3_block11_0_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block11_1_bn (BatchNormal (None, 25, 25, 128) 512 conv3_block11_1_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block11_1_relu (Activatio (None, 25, 25, 128) 0 conv3_block11_1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block11_2_conv (Conv2D) (None, 25, 25, 32) 36864 conv3_block11_1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block11_concat (Concatena (None, 25, 25, 480) 0 conv3_block10_concat[0][0] \n", + " conv3_block11_2_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block12_0_bn (BatchNormal (None, 25, 25, 480) 1920 conv3_block11_concat[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block12_0_relu (Activatio (None, 25, 25, 480) 0 conv3_block12_0_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block12_1_conv (Conv2D) (None, 25, 25, 128) 61440 conv3_block12_0_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block12_1_bn (BatchNormal (None, 25, 25, 128) 512 conv3_block12_1_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block12_1_relu (Activatio (None, 25, 25, 128) 0 conv3_block12_1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block12_2_conv (Conv2D) (None, 25, 25, 32) 36864 conv3_block12_1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv3_block12_concat (Concatena (None, 25, 25, 512) 0 conv3_block11_concat[0][0] \n", + " conv3_block12_2_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "pool3_bn (BatchNormalization) (None, 25, 25, 512) 2048 conv3_block12_concat[0][0] \n", + "__________________________________________________________________________________________________\n", + "pool3_relu (Activation) (None, 25, 25, 512) 0 pool3_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "pool3_conv (Conv2D) (None, 25, 25, 256) 131072 pool3_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "pool3_pool (AveragePooling2D) (None, 12, 12, 256) 0 pool3_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block1_0_bn (BatchNormali (None, 12, 12, 256) 1024 pool3_pool[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block1_0_relu (Activation (None, 12, 12, 256) 0 conv4_block1_0_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block1_1_conv (Conv2D) (None, 12, 12, 128) 32768 conv4_block1_0_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block1_1_bn (BatchNormali (None, 12, 12, 128) 512 conv4_block1_1_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block1_1_relu (Activation (None, 12, 12, 128) 0 conv4_block1_1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block1_2_conv (Conv2D) (None, 12, 12, 32) 36864 conv4_block1_1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block1_concat (Concatenat (None, 12, 12, 288) 0 pool3_pool[0][0] \n", + " conv4_block1_2_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block2_0_bn (BatchNormali (None, 12, 12, 288) 1152 conv4_block1_concat[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block2_0_relu (Activation (None, 12, 12, 288) 0 conv4_block2_0_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block2_1_conv (Conv2D) (None, 12, 12, 128) 36864 conv4_block2_0_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block2_1_bn (BatchNormali (None, 12, 12, 128) 512 conv4_block2_1_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block2_1_relu (Activation (None, 12, 12, 128) 0 conv4_block2_1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block2_2_conv (Conv2D) (None, 12, 12, 32) 36864 conv4_block2_1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block2_concat (Concatenat (None, 12, 12, 320) 0 conv4_block1_concat[0][0] \n", + " conv4_block2_2_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block3_0_bn (BatchNormali (None, 12, 12, 320) 1280 conv4_block2_concat[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block3_0_relu (Activation (None, 12, 12, 320) 0 conv4_block3_0_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block3_1_conv (Conv2D) (None, 12, 12, 128) 40960 conv4_block3_0_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block3_1_bn (BatchNormali (None, 12, 12, 128) 512 conv4_block3_1_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block3_1_relu (Activation (None, 12, 12, 128) 0 conv4_block3_1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block3_2_conv (Conv2D) (None, 12, 12, 32) 36864 conv4_block3_1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block3_concat (Concatenat (None, 12, 12, 352) 0 conv4_block2_concat[0][0] \n", + " conv4_block3_2_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block4_0_bn (BatchNormali (None, 12, 12, 352) 1408 conv4_block3_concat[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block4_0_relu (Activation (None, 12, 12, 352) 0 conv4_block4_0_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block4_1_conv (Conv2D) (None, 12, 12, 128) 45056 conv4_block4_0_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block4_1_bn (BatchNormali (None, 12, 12, 128) 512 conv4_block4_1_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block4_1_relu (Activation (None, 12, 12, 128) 0 conv4_block4_1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block4_2_conv (Conv2D) (None, 12, 12, 32) 36864 conv4_block4_1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block4_concat (Concatenat (None, 12, 12, 384) 0 conv4_block3_concat[0][0] \n", + " conv4_block4_2_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block5_0_bn (BatchNormali (None, 12, 12, 384) 1536 conv4_block4_concat[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block5_0_relu (Activation (None, 12, 12, 384) 0 conv4_block5_0_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block5_1_conv (Conv2D) (None, 12, 12, 128) 49152 conv4_block5_0_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block5_1_bn (BatchNormali (None, 12, 12, 128) 512 conv4_block5_1_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block5_1_relu (Activation (None, 12, 12, 128) 0 conv4_block5_1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block5_2_conv (Conv2D) (None, 12, 12, 32) 36864 conv4_block5_1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block5_concat (Concatenat (None, 12, 12, 416) 0 conv4_block4_concat[0][0] \n", + " conv4_block5_2_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block6_0_bn (BatchNormali (None, 12, 12, 416) 1664 conv4_block5_concat[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block6_0_relu (Activation (None, 12, 12, 416) 0 conv4_block6_0_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block6_1_conv (Conv2D) (None, 12, 12, 128) 53248 conv4_block6_0_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block6_1_bn (BatchNormali (None, 12, 12, 128) 512 conv4_block6_1_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block6_1_relu (Activation (None, 12, 12, 128) 0 conv4_block6_1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block6_2_conv (Conv2D) (None, 12, 12, 32) 36864 conv4_block6_1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block6_concat (Concatenat (None, 12, 12, 448) 0 conv4_block5_concat[0][0] \n", + " conv4_block6_2_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block7_0_bn (BatchNormali (None, 12, 12, 448) 1792 conv4_block6_concat[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block7_0_relu (Activation (None, 12, 12, 448) 0 conv4_block7_0_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block7_1_conv (Conv2D) (None, 12, 12, 128) 57344 conv4_block7_0_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block7_1_bn (BatchNormali (None, 12, 12, 128) 512 conv4_block7_1_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block7_1_relu (Activation (None, 12, 12, 128) 0 conv4_block7_1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block7_2_conv (Conv2D) (None, 12, 12, 32) 36864 conv4_block7_1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block7_concat (Concatenat (None, 12, 12, 480) 0 conv4_block6_concat[0][0] \n", + " conv4_block7_2_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block8_0_bn (BatchNormali 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conv4_block8_1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block8_concat (Concatenat (None, 12, 12, 512) 0 conv4_block7_concat[0][0] \n", + " conv4_block8_2_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block9_0_bn (BatchNormali (None, 12, 12, 512) 2048 conv4_block8_concat[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block9_0_relu (Activation (None, 12, 12, 512) 0 conv4_block9_0_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block9_1_conv (Conv2D) (None, 12, 12, 128) 65536 conv4_block9_0_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block9_1_bn (BatchNormali (None, 12, 12, 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conv4_block16_1_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block16_1_relu (Activatio (None, 12, 12, 128) 0 conv4_block16_1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block16_2_conv (Conv2D) (None, 12, 12, 32) 36864 conv4_block16_1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block16_concat (Concatena (None, 12, 12, 768) 0 conv4_block15_concat[0][0] \n", + " conv4_block16_2_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block17_0_bn (BatchNormal (None, 12, 12, 768) 3072 conv4_block16_concat[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block17_0_relu (Activatio (None, 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conv4_block16_concat[0][0] \n", + " conv4_block17_2_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block18_0_bn (BatchNormal (None, 12, 12, 800) 3200 conv4_block17_concat[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block18_0_relu (Activatio (None, 12, 12, 800) 0 conv4_block18_0_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block18_1_conv (Conv2D) (None, 12, 12, 128) 102400 conv4_block18_0_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block18_1_bn (BatchNormal (None, 12, 12, 128) 512 conv4_block18_1_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block18_1_relu (Activatio 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conv4_block23_1_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block23_1_relu (Activatio (None, 12, 12, 128) 0 conv4_block23_1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block23_2_conv (Conv2D) (None, 12, 12, 32) 36864 conv4_block23_1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block23_concat (Concatena (None, 12, 12, 992) 0 conv4_block22_concat[0][0] \n", + " conv4_block23_2_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block24_0_bn (BatchNormal (None, 12, 12, 992) 3968 conv4_block23_concat[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block24_0_relu (Activatio (None, 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conv4_block23_concat[0][0] \n", + " conv4_block24_2_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block25_0_bn (BatchNormal (None, 12, 12, 1024) 4096 conv4_block24_concat[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block25_0_relu (Activatio (None, 12, 12, 1024) 0 conv4_block25_0_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block25_1_conv (Conv2D) (None, 12, 12, 128) 131072 conv4_block25_0_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block25_1_bn (BatchNormal (None, 12, 12, 128) 512 conv4_block25_1_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block25_1_relu (Activatio 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conv4_block28_0_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block28_1_bn (BatchNormal (None, 12, 12, 128) 512 conv4_block28_1_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block28_1_relu (Activatio (None, 12, 12, 128) 0 conv4_block28_1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block28_2_conv (Conv2D) (None, 12, 12, 32) 36864 conv4_block28_1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block28_concat (Concatena (None, 12, 12, 1152) 0 conv4_block27_concat[0][0] \n", + " conv4_block28_2_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block29_0_bn (BatchNormal (None, 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conv4_block29_1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block29_concat (Concatena (None, 12, 12, 1184) 0 conv4_block28_concat[0][0] \n", + " conv4_block29_2_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block30_0_bn (BatchNormal (None, 12, 12, 1184) 4736 conv4_block29_concat[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block30_0_relu (Activatio (None, 12, 12, 1184) 0 conv4_block30_0_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block30_1_conv (Conv2D) (None, 12, 12, 128) 151552 conv4_block30_0_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv4_block30_1_bn (BatchNormal 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conv5_block1_1_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block1_1_relu (Activation (None, 6, 6, 128) 0 conv5_block1_1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block1_2_conv (Conv2D) (None, 6, 6, 32) 36864 conv5_block1_1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block1_concat (Concatenat (None, 6, 6, 672) 0 pool4_pool[0][0] \n", + " conv5_block1_2_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block2_0_bn (BatchNormali (None, 6, 6, 672) 2688 conv5_block1_concat[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block2_0_relu (Activation (None, 6, 6, 672) 0 conv5_block2_0_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block2_1_conv (Conv2D) (None, 6, 6, 128) 86016 conv5_block2_0_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block2_1_bn (BatchNormali (None, 6, 6, 128) 512 conv5_block2_1_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block2_1_relu (Activation (None, 6, 6, 128) 0 conv5_block2_1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block2_2_conv (Conv2D) (None, 6, 6, 32) 36864 conv5_block2_1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block2_concat (Concatenat (None, 6, 6, 704) 0 conv5_block1_concat[0][0] \n", + " conv5_block2_2_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block3_0_bn (BatchNormali (None, 6, 6, 704) 2816 conv5_block2_concat[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block3_0_relu (Activation (None, 6, 6, 704) 0 conv5_block3_0_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block3_1_conv (Conv2D) (None, 6, 6, 128) 90112 conv5_block3_0_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block3_1_bn (BatchNormali (None, 6, 6, 128) 512 conv5_block3_1_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block3_1_relu (Activation (None, 6, 6, 128) 0 conv5_block3_1_bn[0][0] \n", + 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conv5_block12_2_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block13_0_bn (BatchNormal (None, 6, 6, 1024) 4096 conv5_block12_concat[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block13_0_relu (Activatio (None, 6, 6, 1024) 0 conv5_block13_0_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block13_1_conv (Conv2D) (None, 6, 6, 128) 131072 conv5_block13_0_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block13_1_bn (BatchNormal (None, 6, 6, 128) 512 conv5_block13_1_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block13_1_relu (Activatio (None, 6, 6, 128) 0 conv5_block13_1_bn[0][0] 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conv5_block14_0_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block14_1_bn (BatchNormal (None, 6, 6, 128) 512 conv5_block14_1_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block14_1_relu (Activatio (None, 6, 6, 128) 0 conv5_block14_1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block14_2_conv (Conv2D) (None, 6, 6, 32) 36864 conv5_block14_1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block14_concat (Concatena (None, 6, 6, 1088) 0 conv5_block13_concat[0][0] \n", + " conv5_block14_2_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block15_0_bn (BatchNormal (None, 6, 6, 1088) 4352 conv5_block14_concat[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block15_0_relu (Activatio (None, 6, 6, 1088) 0 conv5_block15_0_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block15_1_conv (Conv2D) (None, 6, 6, 128) 139264 conv5_block15_0_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block15_1_bn (BatchNormal (None, 6, 6, 128) 512 conv5_block15_1_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block15_1_relu (Activatio (None, 6, 6, 128) 0 conv5_block15_1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block15_2_conv (Conv2D) (None, 6, 6, 32) 36864 conv5_block15_1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block15_concat (Concatena (None, 6, 6, 1120) 0 conv5_block14_concat[0][0] \n", + " conv5_block15_2_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block16_0_bn (BatchNormal (None, 6, 6, 1120) 4480 conv5_block15_concat[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block16_0_relu (Activatio (None, 6, 6, 1120) 0 conv5_block16_0_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block16_1_conv (Conv2D) (None, 6, 6, 128) 143360 conv5_block16_0_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block16_1_bn (BatchNormal (None, 6, 6, 128) 512 conv5_block16_1_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block16_1_relu (Activatio (None, 6, 6, 128) 0 conv5_block16_1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block16_2_conv (Conv2D) (None, 6, 6, 32) 36864 conv5_block16_1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block16_concat (Concatena (None, 6, 6, 1152) 0 conv5_block15_concat[0][0] \n", + " conv5_block16_2_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block17_0_bn (BatchNormal (None, 6, 6, 1152) 4608 conv5_block16_concat[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block17_0_relu (Activatio (None, 6, 6, 1152) 0 conv5_block17_0_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block17_1_conv (Conv2D) (None, 6, 6, 128) 147456 conv5_block17_0_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block17_1_bn (BatchNormal (None, 6, 6, 128) 512 conv5_block17_1_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block17_1_relu (Activatio (None, 6, 6, 128) 0 conv5_block17_1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block17_2_conv (Conv2D) (None, 6, 6, 32) 36864 conv5_block17_1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block17_concat (Concatena (None, 6, 6, 1184) 0 conv5_block16_concat[0][0] \n", + " conv5_block17_2_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block18_0_bn (BatchNormal (None, 6, 6, 1184) 4736 conv5_block17_concat[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block18_0_relu (Activatio (None, 6, 6, 1184) 0 conv5_block18_0_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block18_1_conv (Conv2D) (None, 6, 6, 128) 151552 conv5_block18_0_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block18_1_bn (BatchNormal (None, 6, 6, 128) 512 conv5_block18_1_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block18_1_relu (Activatio (None, 6, 6, 128) 0 conv5_block18_1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block18_2_conv (Conv2D) (None, 6, 6, 32) 36864 conv5_block18_1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block18_concat (Concatena (None, 6, 6, 1216) 0 conv5_block17_concat[0][0] \n", + " conv5_block18_2_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block19_0_bn (BatchNormal (None, 6, 6, 1216) 4864 conv5_block18_concat[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block19_0_relu (Activatio (None, 6, 6, 1216) 0 conv5_block19_0_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block19_1_conv (Conv2D) (None, 6, 6, 128) 155648 conv5_block19_0_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block19_1_bn (BatchNormal (None, 6, 6, 128) 512 conv5_block19_1_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block19_1_relu (Activatio (None, 6, 6, 128) 0 conv5_block19_1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block19_2_conv (Conv2D) (None, 6, 6, 32) 36864 conv5_block19_1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block19_concat (Concatena (None, 6, 6, 1248) 0 conv5_block18_concat[0][0] \n", + " conv5_block19_2_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block20_0_bn (BatchNormal (None, 6, 6, 1248) 4992 conv5_block19_concat[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block20_0_relu (Activatio (None, 6, 6, 1248) 0 conv5_block20_0_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block20_1_conv (Conv2D) (None, 6, 6, 128) 159744 conv5_block20_0_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block20_1_bn (BatchNormal (None, 6, 6, 128) 512 conv5_block20_1_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block20_1_relu (Activatio (None, 6, 6, 128) 0 conv5_block20_1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block20_2_conv (Conv2D) (None, 6, 6, 32) 36864 conv5_block20_1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block20_concat (Concatena (None, 6, 6, 1280) 0 conv5_block19_concat[0][0] \n", + " conv5_block20_2_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block21_0_bn (BatchNormal (None, 6, 6, 1280) 5120 conv5_block20_concat[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block21_0_relu (Activatio (None, 6, 6, 1280) 0 conv5_block21_0_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block21_1_conv (Conv2D) (None, 6, 6, 128) 163840 conv5_block21_0_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block21_1_bn (BatchNormal (None, 6, 6, 128) 512 conv5_block21_1_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block21_1_relu (Activatio (None, 6, 6, 128) 0 conv5_block21_1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block21_2_conv (Conv2D) (None, 6, 6, 32) 36864 conv5_block21_1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block21_concat (Concatena (None, 6, 6, 1312) 0 conv5_block20_concat[0][0] \n", + " conv5_block21_2_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block22_0_bn (BatchNormal (None, 6, 6, 1312) 5248 conv5_block21_concat[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block22_0_relu (Activatio (None, 6, 6, 1312) 0 conv5_block22_0_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block22_1_conv (Conv2D) (None, 6, 6, 128) 167936 conv5_block22_0_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block22_1_bn (BatchNormal (None, 6, 6, 128) 512 conv5_block22_1_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block22_1_relu (Activatio (None, 6, 6, 128) 0 conv5_block22_1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block22_2_conv (Conv2D) (None, 6, 6, 32) 36864 conv5_block22_1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block22_concat (Concatena (None, 6, 6, 1344) 0 conv5_block21_concat[0][0] \n", + " conv5_block22_2_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block23_0_bn (BatchNormal (None, 6, 6, 1344) 5376 conv5_block22_concat[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block23_0_relu (Activatio (None, 6, 6, 1344) 0 conv5_block23_0_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block23_1_conv (Conv2D) (None, 6, 6, 128) 172032 conv5_block23_0_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block23_1_bn (BatchNormal (None, 6, 6, 128) 512 conv5_block23_1_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block23_1_relu (Activatio (None, 6, 6, 128) 0 conv5_block23_1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block23_2_conv (Conv2D) (None, 6, 6, 32) 36864 conv5_block23_1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block23_concat (Concatena (None, 6, 6, 1376) 0 conv5_block22_concat[0][0] \n", + " conv5_block23_2_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block24_0_bn (BatchNormal (None, 6, 6, 1376) 5504 conv5_block23_concat[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block24_0_relu (Activatio (None, 6, 6, 1376) 0 conv5_block24_0_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block24_1_conv (Conv2D) (None, 6, 6, 128) 176128 conv5_block24_0_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block24_1_bn (BatchNormal (None, 6, 6, 128) 512 conv5_block24_1_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block24_1_relu (Activatio (None, 6, 6, 128) 0 conv5_block24_1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block24_2_conv (Conv2D) (None, 6, 6, 32) 36864 conv5_block24_1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block24_concat (Concatena (None, 6, 6, 1408) 0 conv5_block23_concat[0][0] \n", + " conv5_block24_2_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block25_0_bn (BatchNormal (None, 6, 6, 1408) 5632 conv5_block24_concat[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block25_0_relu (Activatio (None, 6, 6, 1408) 0 conv5_block25_0_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block25_1_conv (Conv2D) (None, 6, 6, 128) 180224 conv5_block25_0_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block25_1_bn (BatchNormal (None, 6, 6, 128) 512 conv5_block25_1_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block25_1_relu (Activatio (None, 6, 6, 128) 0 conv5_block25_1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block25_2_conv (Conv2D) (None, 6, 6, 32) 36864 conv5_block25_1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block25_concat (Concatena (None, 6, 6, 1440) 0 conv5_block24_concat[0][0] \n", + " conv5_block25_2_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block26_0_bn (BatchNormal (None, 6, 6, 1440) 5760 conv5_block25_concat[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block26_0_relu (Activatio (None, 6, 6, 1440) 0 conv5_block26_0_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block26_1_conv (Conv2D) (None, 6, 6, 128) 184320 conv5_block26_0_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block26_1_bn (BatchNormal (None, 6, 6, 128) 512 conv5_block26_1_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block26_1_relu (Activatio (None, 6, 6, 128) 0 conv5_block26_1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block26_2_conv (Conv2D) (None, 6, 6, 32) 36864 conv5_block26_1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block26_concat (Concatena (None, 6, 6, 1472) 0 conv5_block25_concat[0][0] \n", + " conv5_block26_2_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block27_0_bn (BatchNormal (None, 6, 6, 1472) 5888 conv5_block26_concat[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block27_0_relu (Activatio (None, 6, 6, 1472) 0 conv5_block27_0_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block27_1_conv (Conv2D) (None, 6, 6, 128) 188416 conv5_block27_0_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block27_1_bn (BatchNormal (None, 6, 6, 128) 512 conv5_block27_1_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block27_1_relu (Activatio (None, 6, 6, 128) 0 conv5_block27_1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block27_2_conv (Conv2D) (None, 6, 6, 32) 36864 conv5_block27_1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block27_concat (Concatena (None, 6, 6, 1504) 0 conv5_block26_concat[0][0] \n", + " conv5_block27_2_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block28_0_bn (BatchNormal (None, 6, 6, 1504) 6016 conv5_block27_concat[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block28_0_relu (Activatio (None, 6, 6, 1504) 0 conv5_block28_0_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block28_1_conv (Conv2D) (None, 6, 6, 128) 192512 conv5_block28_0_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block28_1_bn (BatchNormal (None, 6, 6, 128) 512 conv5_block28_1_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block28_1_relu (Activatio (None, 6, 6, 128) 0 conv5_block28_1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block28_2_conv (Conv2D) (None, 6, 6, 32) 36864 conv5_block28_1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block28_concat (Concatena (None, 6, 6, 1536) 0 conv5_block27_concat[0][0] \n", + " conv5_block28_2_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block29_0_bn (BatchNormal (None, 6, 6, 1536) 6144 conv5_block28_concat[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block29_0_relu (Activatio (None, 6, 6, 1536) 0 conv5_block29_0_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block29_1_conv (Conv2D) (None, 6, 6, 128) 196608 conv5_block29_0_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block29_1_bn (BatchNormal (None, 6, 6, 128) 512 conv5_block29_1_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block29_1_relu (Activatio (None, 6, 6, 128) 0 conv5_block29_1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block29_2_conv (Conv2D) (None, 6, 6, 32) 36864 conv5_block29_1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block29_concat (Concatena (None, 6, 6, 1568) 0 conv5_block28_concat[0][0] \n", + " conv5_block29_2_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block30_0_bn (BatchNormal (None, 6, 6, 1568) 6272 conv5_block29_concat[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block30_0_relu (Activatio (None, 6, 6, 1568) 0 conv5_block30_0_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block30_1_conv (Conv2D) (None, 6, 6, 128) 200704 conv5_block30_0_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block30_1_bn (BatchNormal (None, 6, 6, 128) 512 conv5_block30_1_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block30_1_relu (Activatio (None, 6, 6, 128) 0 conv5_block30_1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block30_2_conv (Conv2D) (None, 6, 6, 32) 36864 conv5_block30_1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block30_concat (Concatena (None, 6, 6, 1600) 0 conv5_block29_concat[0][0] \n", + " conv5_block30_2_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block31_0_bn (BatchNormal (None, 6, 6, 1600) 6400 conv5_block30_concat[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block31_0_relu (Activatio (None, 6, 6, 1600) 0 conv5_block31_0_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block31_1_conv (Conv2D) (None, 6, 6, 128) 204800 conv5_block31_0_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block31_1_bn (BatchNormal (None, 6, 6, 128) 512 conv5_block31_1_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block31_1_relu (Activatio (None, 6, 6, 128) 0 conv5_block31_1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block31_2_conv (Conv2D) (None, 6, 6, 32) 36864 conv5_block31_1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block31_concat (Concatena (None, 6, 6, 1632) 0 conv5_block30_concat[0][0] \n", + " conv5_block31_2_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block32_0_bn (BatchNormal (None, 6, 6, 1632) 6528 conv5_block31_concat[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block32_0_relu (Activatio (None, 6, 6, 1632) 0 conv5_block32_0_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block32_1_conv (Conv2D) (None, 6, 6, 128) 208896 conv5_block32_0_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block32_1_bn (BatchNormal (None, 6, 6, 128) 512 conv5_block32_1_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block32_1_relu (Activatio (None, 6, 6, 128) 0 conv5_block32_1_bn[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block32_2_conv (Conv2D) (None, 6, 6, 32) 36864 conv5_block32_1_relu[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv5_block32_concat (Concatena (None, 6, 6, 1664) 0 conv5_block31_concat[0][0] \n", + " conv5_block32_2_conv[0][0] \n", + "__________________________________________________________________________________________________\n", + "bn (BatchNormalization) (None, 6, 6, 1664) 6656 conv5_block32_concat[0][0] \n", + "__________________________________________________________________________________________________\n", + "relu (Activation) (None, 6, 6, 1664) 0 bn[0][0] \n", + "==================================================================================================\n", + "Total params: 12,642,880\n", + "Trainable params: 0\n", + "Non-trainable params: 12,642,880\n", + "__________________________________________________________________________________________________\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "mBJ0yBkxRPOH" + }, + "source": [ + "model = tf.keras.models.Sequential([\n", + " densenet_model,\n", + " layers.Flatten(),\n", + " layers.Dense(256, activation='relu'),\n", + " layers.Dropout(0.5),\n", + " layers.Dense(1, activation='sigmoid') \n", + "])" + ], + "execution_count": 30, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "iwgTQ9EwR8rN", + "outputId": "479de703-d91d-44b0-bb72-76489db73d9d" + }, + "source": [ + "model.summary()" + ], + "execution_count": 31, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Model: \"sequential_1\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "densenet169 (Functional) (None, 6, 6, 1664) 12642880 \n", + "_________________________________________________________________\n", + "flatten_1 (Flatten) (None, 59904) 0 \n", + "_________________________________________________________________\n", + "dense_2 (Dense) (None, 256) 15335680 \n", + "_________________________________________________________________\n", + "dropout_1 (Dropout) (None, 256) 0 \n", + "_________________________________________________________________\n", + "dense_3 (Dense) (None, 1) 257 \n", + "=================================================================\n", + "Total params: 27,978,817\n", + "Trainable params: 15,335,937\n", + "Non-trainable params: 12,642,880\n", + "_________________________________________________________________\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "NdzikOkASnUG" + }, + "source": [ + "model.compile(optimizer=Adam(lr=1e-5), loss='binary_crossentropy', metrics=['accuracy'])" + ], + "execution_count": 32, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "9Qro3AHJUBmu", + "outputId": "d3872cc1-3535-4713-b118-c1a11c8e39e6" + }, + "source": [ + "model.fit(train_data, validation_data=test_data, epochs=5)" + ], + "execution_count": 33, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Epoch 1/5\n", + "152/152 [==============================] - 35s 182ms/step - loss: 2.2198 - accuracy: 0.6645 - val_loss: 0.4104 - val_accuracy: 0.8424\n", + "Epoch 2/5\n", + "152/152 [==============================] - 25s 163ms/step - loss: 0.5587 - accuracy: 0.7995 - val_loss: 0.3383 - val_accuracy: 0.8682\n", + "Epoch 3/5\n", + "152/152 [==============================] - 25s 159ms/step - loss: 0.3630 - accuracy: 0.8542 - val_loss: 0.2908 - val_accuracy: 0.8848\n", + "Epoch 4/5\n", + "152/152 [==============================] - 25s 162ms/step - loss: 0.2967 - accuracy: 0.8872 - val_loss: 0.2737 - val_accuracy: 0.8980\n", + "Epoch 5/5\n", + "152/152 [==============================] - 25s 160ms/step - loss: 0.2526 - accuracy: 0.9069 - val_loss: 0.2706 - val_accuracy: 0.8974\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 33 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "CxPqFix9mM5N" + }, + "source": [ + "densenet_model.trainable = True\n", + "trainable = False\n", + "for layer in densenet_model.layers:\n", + " if layer.name == 'conv1_block32':\n", + " trainable = True\n", + " layer.trainable = trainable" + ], + "execution_count": 34, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "0bV9eQasmkha" + }, + "source": [ + "model.compile(loss='binary_crossentropy', optimizer=Adam(lr=1e-5), metrics=['accuracy'])" + ], + "execution_count": 35, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "x0Jjq3Gymn6L", + "outputId": "10e9c990-1f81-4b89-e26a-af9ba95c3fb6" + }, + "source": [ + "model.fit(train_data, validation_data=test_data, epochs=5)" + ], + "execution_count": 37, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Epoch 1/5\n", + "152/152 [==============================] - 26s 167ms/step - loss: 0.1452 - accuracy: 0.9429 - val_loss: 0.2219 - val_accuracy: 0.9245\n", + "Epoch 2/5\n", + "152/152 [==============================] - 25s 164ms/step - loss: 0.1200 - accuracy: 0.9535 - val_loss: 0.2414 - val_accuracy: 0.9225\n", + "Epoch 3/5\n", + "152/152 [==============================] - 26s 167ms/step - loss: 0.1123 - accuracy: 0.9553 - val_loss: 0.2127 - val_accuracy: 0.9318\n", + "Epoch 4/5\n", + "152/152 [==============================] - 25s 163ms/step - loss: 0.0886 - accuracy: 0.9654 - val_loss: 0.2031 - val_accuracy: 0.9404\n", + "Epoch 5/5\n", + "152/152 [==============================] - 26s 167ms/step - loss: 0.0834 - accuracy: 0.9707 - val_loss: 0.2139 - val_accuracy: 0.9318\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 37 + } + ] + } + ] +} \ No newline at end of file diff --git a/Andreydemianchuk_code/tasks/task_4/transfer_learning.py b/Andreydemianchuk_code/tasks/task_4/transfer_learning.py new file mode 100644 index 0000000..b1c1a89 --- /dev/null +++ b/Andreydemianchuk_code/tasks/task_4/transfer_learning.py @@ -0,0 +1,72 @@ +# -*- coding: utf-8 -*- +"""transfer_learning.ipynb + +Automatically generated by Colaboratory. + +Original file is located at + https://colab.research.google.com/drive/1D6jeElG89lWDH-1F2bHo_q6PO2lZ2Rtu +""" + +import tensorflow as tf +from tensorflow.keras import layers +from zipfile import ZipFile +from tensorflow.keras.optimizers import Adam +from tensorflow.keras.applications import DenseNet169 + +file_name = 'face-mask-dataset.zip' + +with ZipFile(file_name, 'r') as zip: + zip.extractall() + +batch_size = 40 +img_height = 200 +img_width = 200 + +train_data = tf.keras.preprocessing.image_dataset_from_directory( + 'data', + validation_split=0.2, + subset="training", + seed=42, + image_size=(img_height, img_width), + batch_size=batch_size +) + +test_data = tf.keras.preprocessing.image_dataset_from_directory( + 'data', + validation_split=0.2, + subset="validation", + seed=42, + image_size=(img_height, img_width), + batch_size=batch_size +) + +densenet_model = DenseNet169(weights='imagenet', include_top=False, input_shape=(200, 200, 3)) + +densenet_model.trainable = False + +densenet_model.summary() + +model = tf.keras.models.Sequential([ + densenet_model, + layers.Flatten(), + layers.Dense(256, activation='relu'), + layers.Dropout(0.5), + layers.Dense(1, activation='sigmoid') +]) + +model.summary() + +model.compile(optimizer=Adam(lr=1e-5), loss='binary_crossentropy', metrics=['accuracy']) + +model.fit(train_data, validation_data=test_data, epochs=5) + +densenet_model.trainable = True +trainable = False +for layer in densenet_model.layers: + if layer.name == 'conv1_block32': + trainable = True + layer.trainable = trainable + +model.compile(loss='binary_crossentropy', optimizer=Adam(lr=1e-5), metrics=['accuracy']) + +model.fit(train_data, validation_data=test_data, epochs=5) diff --git a/Andreydemianchuk_code/tasks/task_5/face_recognition_pyTorch.ipynb b/Andreydemianchuk_code/tasks/task_5/face_recognition_pyTorch.ipynb new file mode 100644 index 0000000..3443834 --- /dev/null +++ b/Andreydemianchuk_code/tasks/task_5/face_recognition_pyTorch.ipynb @@ -0,0 +1,444 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "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": "3.7.3" + }, + "colab": { + "name": "face_recognition_pyTorch.ipynb", + "provenance": [], + "toc_visible": true + }, + "accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "code", + "metadata": { + "id": "G-jnRjDKBxA1" + }, + "source": [ + "#!pip install facenet-pytorch" + ], + "execution_count": 90, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "HMe1rt4ABq90" + }, + "source": [ + "from facenet_pytorch import MTCNN, InceptionResnetV1\n", + "import torch\n", + "from torch.utils.data import DataLoader\n", + "from torchvision import datasets\n", + "import numpy as np\n", + "import pandas as pd\n", + "import os\n", + "import matplotlib.pyplot as plt\n", + "workers = 0 if os.name == 'nt' else 4" + ], + "execution_count": 91, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gtXeR0IQBq91" + }, + "source": [ + "#### Determine if an nvidia GPU is available" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "INlxITIzBq91", + "outputId": "c598ba09-47ed-4193-ea62-4abd84921d55" + }, + "source": [ + "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n", + "print('Running on device: {}'.format(device))" + ], + "execution_count": 92, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Running on device: cuda:0\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MXU5wTLeBq91" + }, + "source": [ + "#### Define MTCNN module\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "90UO2ADJBq92" + }, + "source": [ + "mtcnn = MTCNN(\n", + " image_size=160, margin=0, min_face_size=20,\n", + " thresholds=[0.6, 0.7, 0.7], factor=0.709, post_process=True,\n", + " device=device\n", + ")" + ], + "execution_count": 93, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EFwUFyaMBq92" + }, + "source": [ + "#### Define Inception Resnet V1 module\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "j4i9tEVWBq92" + }, + "source": [ + "resnet = InceptionResnetV1(pretrained='vggface2').eval().to(device)" + ], + "execution_count": 94, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6n_f12_uBq92" + }, + "source": [ + "#### Define a dataset and data loader\n", + "\n", + "We add the `idx_to_class` attribute to the dataset to enable easy recoding of label indices to identity names later on." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "qdCRnTcFBq92" + }, + "source": [ + "def collate_fn(x):\n", + " return x[0]\n", + "\n", + "dataset = datasets.ImageFolder('images')\n", + "dataset.idx_to_class = {i:c for c, i in dataset.class_to_idx.items()}\n", + "loader = DataLoader(dataset, collate_fn=collate_fn, num_workers=workers)" + ], + "execution_count": 95, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XPI8ToZBBq92" + }, + "source": [ + "#### Perfom MTCNN facial detection\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AWVFxrLyBq93" + }, + "source": [ + "#### Calculate image embeddings and check the difference\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "WRGcB4P_HEQe" + }, + "source": [ + "def is_same_person(x1, x2):\n", + " plt.subplot(1, 2, 1)\n", + " plt.imshow(x1)\n", + " plt.subplot(1, 2, 2)\n", + " plt.imshow(x2)\n", + " x_aligned_1, prob_1 = mtcnn(x1, return_prob=True)\n", + " x_aligned_2, prob_2 = mtcnn(x2, return_prob=True)\n", + " aligned = torch.stack([x_aligned_1, x_aligned_2]).to(device)\n", + " embeddings = resnet(aligned).detach().cpu()\n", + " dist = (embeddings[0] - embeddings[1]).norm().item()\n", + "\n", + " if dist < 0.75:\n", + " print(\"It's the same person\")\n", + " else:\n", + " print(\"It's not the same person\")" + ], + "execution_count": 96, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "MYiVT0vtBq93" + }, + "source": [ + "images = []\n", + "\n", + "for features, label in loader:\n", + " images.append(features)" + ], + "execution_count": 97, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 221 + }, + "id": "jQlT3IZSSOEU", + "outputId": "02872304-51cc-45c1-bab1-5178e306c74f" + }, + "source": [ + "is_same_person(images[0], images[1])" + ], + "execution_count": 98, + "outputs": [ + { + "output_type": "stream", + "text": [ + "It's the same person\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 272 + }, + "id": "GZE4EBe2SWPi", + "outputId": "5d1fa6a3-a284-4b76-bc42-cfdf78024807" + }, + "source": [ + "is_same_person(images[0], images[2])" + ], + "execution_count": 99, + "outputs": [ + { + "output_type": "stream", + "text": [ + "It's not the same person\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 272 + }, + "id": "NQU28Z-MSWnk", + "outputId": "9ef44c77-58c1-47b2-cdbd-cf0a8af23717" + }, + "source": [ + "is_same_person(images[2], images[3])" + ], + "execution_count": 100, + "outputs": [ + { + "output_type": "stream", + "text": [ + "It's the same person\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 221 + }, + "id": "HyEHl0eoSW3m", + "outputId": "d10fa7b2-80ac-4b04-932f-91dbd0e52da7" + }, + "source": [ + "is_same_person(images[0], images[4])" + ], + "execution_count": 106, + "outputs": [ + { + "output_type": "stream", + "text": [ + "It's not the same person\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 174 + }, + "id": "zME2dwBmSXNW", + "outputId": "2996b351-9225-44e4-b8b4-75f13d857814" + }, + "source": [ + "is_same_person(images[1], images[3])" + ], + "execution_count": 102, + "outputs": [ + { + "output_type": "stream", + "text": [ + "It's not the same person\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 272 + }, + "id": "A-n24qZgUZsf", + "outputId": "1faae751-8792-44bb-d3f9-b32ee761701e" + }, + "source": [ + "is_same_person(images[2], images[4])" + ], + "execution_count": 105, + "outputs": [ + { + "output_type": "stream", + "text": [ + "It's not the same person\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + } + ] +} \ No newline at end of file diff --git a/Andreydemianchuk_code/tasks/task_5/face_recognition_pytorch.py b/Andreydemianchuk_code/tasks/task_5/face_recognition_pytorch.py new file mode 100644 index 0000000..0cf384a --- /dev/null +++ b/Andreydemianchuk_code/tasks/task_5/face_recognition_pytorch.py @@ -0,0 +1,92 @@ +# -*- coding: utf-8 -*- +"""face_recognition_pyTorch.ipynb + +Automatically generated by Colaboratory. + +Original file is located at + https://colab.research.google.com/drive/1yIsT0-cWXJY2Kj7liHjUTMQLgqS3C16s +""" + + +from facenet_pytorch import MTCNN, InceptionResnetV1 +import torch +from torch.utils.data import DataLoader +from torchvision import datasets +import os +import matplotlib.pyplot as plt +workers = 0 if os.name == 'nt' else 4 + +"""#### Determine if an nvidia GPU is available""" + +device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') +print('Running on device: {}'.format(device)) + +"""#### Define MTCNN module + +""" + +mtcnn = MTCNN( + image_size=160, margin=0, min_face_size=20, + thresholds=[0.6, 0.7, 0.7], factor=0.709, post_process=True, + device=device +) + +"""#### Define Inception Resnet V1 module + +""" + +resnet = InceptionResnetV1(pretrained='vggface2').eval().to(device) + +"""#### Define a dataset and data loader + +We add the `idx_to_class` attribute to the dataset to enable easy recoding of label indices to identity names later on. +""" + + +def collate_fn(x): + return x[0] + + +dataset = datasets.ImageFolder('images') +dataset.idx_to_class = {i: c for c, i in dataset.class_to_idx.items()} +loader = DataLoader(dataset, collate_fn=collate_fn, num_workers=workers) + +"""#### Perfom MTCNN facial detection + +#### Calculate image embeddings and check the difference +""" + + +def is_same_person(x1, x2): + plt.subplot(1, 2, 1) + plt.imshow(x1) + plt.subplot(1, 2, 2) + plt.imshow(x2) + x_aligned_1, prob_1 = mtcnn(x1, return_prob=True) + x_aligned_2, prob_2 = mtcnn(x2, return_prob=True) + aligned = torch.stack([x_aligned_1, x_aligned_2]).to(device) + embeddings = resnet(aligned).detach().cpu() + dist = (embeddings[0] - embeddings[1]).norm().item() + + if dist < 0.75: + print("It's the same person") + else: + print("It's not the same person") + + +images = [] + +for features, label in loader: + images.append(features) + +is_same_person(images[0], images[1]) + +is_same_person(images[0], images[2]) + +is_same_person(images[2], images[3]) + +is_same_person(images[0], images[4]) + +is_same_person(images[1], images[3]) + +is_same_person(images[2], images[4]) diff --git a/tasks/task_1/Classification_example_with_Iris_dataset.ipynb b/tasks/task_1/Classification_example_with_Iris_dataset.ipynb deleted file mode 100644 index 7e8e1a2..0000000 --- a/tasks/task_1/Classification_example_with_Iris_dataset.ipynb +++ /dev/null @@ -1,492 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "entertaining-fishing", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "\n", - "from sklearn import datasets\n", - "from matplotlib import pyplot as plt\n", - "\n", - "from sklearn.linear_model import LogisticRegression\n", - "from sklearn.tree import DecisionTreeClassifier\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import confusion_matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "choice-location", - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext pycodestyle_magic\n", - "%flake8_on" - ] - }, - { - "cell_type": "markdown", - "id": "accepting-threshold", - "metadata": {}, - "source": [ - "# Classification example with Iris dataset" - ] - }, - { - "cell_type": "markdown", - "id": "derived-yorkshire", - "metadata": {}, - "source": [ - "This example dataset task is in classifying flower based on its features" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "increasing-johnston", - "metadata": {}, - "outputs": [], - "source": [ - "# This dataset boult in `sklearn` library so you can load it directly\n", - "iris = datasets.load_iris()\n", - "iris_features = iris['feature_names']" - ] - }, - { - "cell_type": "markdown", - "id": "japanese-button", - "metadata": {}, - "source": [ - "Print all flowers and features" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "conditional-jungle", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dataset features:\n", - "['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n", - "Dataset classes:\n", - "['setosa' 'versicolor' 'virginica']\n" - ] - } - ], - "source": [ - "print(f\"Dataset features:\\n{iris['feature_names']}\")\n", - "print(f\"Dataset classes:\\n{iris.target_names}\")" - ] - }, - { - "cell_type": "markdown", - "id": "serial-savage", - "metadata": {}, - "source": [ - "Now we should visually analyze the dataset\n", - "\n", - "As we are limited by 2D displays and cannot visualize 4d data in a single plot - let's print data 2-axis at a time" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "indirect-federal", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "for j in [1, 2, 3]:\n", - " for i, class_name in enumerate(iris.target_names):\n", - " sepal_length = iris.data[:, 0][iris.target == i]\n", - " sepal_width = iris.data[:, j][iris.target == i]\n", - " plt.plot(sepal_length, sepal_width, '.', label=class_name)\n", - "\n", - " plt.title(\"Flowers\")\n", - " plt.xlabel(iris_features[0])\n", - " plt.ylabel(iris_features[j])\n", - " plt.legend()\n", - " plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "driven-animation", - "metadata": {}, - "source": [ - "## Plotting decision boundaries\n", - "\n", - "Decision boundaries allows us to visualize how given classifier thinks data should be splitted into a different classes\n", - "\n", - "For this let's focus on first 2 features ('sepal length (cm)', 'sepal width (cm)') to have consistent 2D plot" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "accurate-central", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_decision(clf, title):\n", - " x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n", - " y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n", - " xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1),\n", - " np.arange(y_min, y_max, 0.1))\n", - "\n", - " Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n", - " Z = Z.reshape(xx.shape)\n", - "\n", - " plt.contourf(xx, yy, Z, alpha=0.4)\n", - "\n", - " for i, class_name in enumerate(iris.target_names):\n", - " sepal_length = iris.data[:, 0][iris.target == i]\n", - " sepal_width = iris.data[:, 1][iris.target == i]\n", - " plt.plot(sepal_length, sepal_width, '.', label=class_name)\n", - "\n", - " plt.title(title)\n", - " plt.xlabel(iris_features[0])\n", - " plt.ylabel(iris_features[j])\n", - " plt.legend()\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "intellectual-proxy", - "metadata": {}, - "outputs": [], - "source": [ - "# Select first 2 features\n", - "X = iris.data[:, [0, 1]]\n", - "y = iris.target" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "meaning-conversion", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "l_regression = LogisticRegression()\n", - "l_regression = l_regression.fit(X, y)\n", - "plot_decision(l_regression, title=\"Log regression\")" - ] - }, - { - "cell_type": "markdown", - "id": "suitable-greene", - "metadata": {}, - "source": [ - "We can see that `LogisticRegression`model cannot properly divide 'versicolor' and 'virginica' classes based on that 2 features\n", - "\n", - "To divide classes properly we need to introduce non-linear models such and Neural Networks or Decision Trees" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "retained-crossing", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "tree = DecisionTreeClassifier()\n", - "tree = tree.fit(X, y)\n", - "plot_decision(tree, title=\"Decision Tree\")" - ] - }, - { - "cell_type": "markdown", - "id": "olympic-peeing", - "metadata": {}, - "source": [ - "# Train-Test split" - ] - }, - { - "cell_type": "markdown", - "id": "great-ferry", - "metadata": {}, - "source": [ - "Let's now evaluate model using [test-train split](https://machinelearningmastery.com/train-test-split-for-evaluating-machine-learning-algorithms/) approach\n", - "\n", - "This is a common technique for checking model generalization. \n", - "You train model on some part of the dataset (lets say 67%, or 75%) and that you check if you model generalizes well by prediction on data that left \n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "noble-compression", - "metadata": {}, - "outputs": [], - "source": [ - "X = iris.data # Use all iris features as predictors\n", - "y = iris.target" - ] - }, - { - "cell_type": "markdown", - "id": "studied-professor", - "metadata": {}, - "source": [ - "Split data randomly using [train_test_split](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html)\n", - "\n", - "Set `test_size=0.25` to use 25% data for test and 75% for train" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "intelligent-quest", - "metadata": {}, - "outputs": [], - "source": [ - "X_train, X_test, y_train, y_test = train_test_split(\n", - " X, y, test_size=0.25, random_state=117\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "weird-hamburg", - "metadata": {}, - "outputs": [], - "source": [ - "# function for printing results\n", - "def eval_model(clf, X_test, y_test):\n", - " pred = clf.predict(X_test)\n", - " accuracy = np.mean(pred == y_test)\n", - " print(f\"Model accuracy: {accuracy*100:0.2f}%\")\n", - " df = pd.DataFrame(confusion_matrix(y_test, pred))\n", - " df.columns = [\"Classified as \" + x for x in iris.target_names]\n", - " df.index = iris.target_names\n", - " return df" - ] - }, - { - "cell_type": "markdown", - "id": "velvet-bench", - "metadata": {}, - "source": [ - "Train model on `train` data " - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "wanted-devil", - "metadata": {}, - "outputs": [], - "source": [ - "l_regression = LogisticRegression()\n", - "l_regression = l_regression.fit(X_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "id": "outdoor-firewall", - "metadata": {}, - "source": [ - "And evaluate on `test` data " - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "several-ballet", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model accuracy: 94.74%\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Classified as setosaClassified as versicolorClassified as virginica
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" - ], - "text/plain": [ - " Classified as setosa Classified as versicolor \\\n", - "setosa 14 0 \n", - "versicolor 0 8 \n", - "virginica 0 2 \n", - "\n", - " Classified as virginica \n", - "setosa 0 \n", - "versicolor 0 \n", - "virginica 14 " - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "eval_model(l_regression, X_test, y_test)" - ] - }, - { - "cell_type": "markdown", - "id": "wound-aircraft", - "metadata": {}, - "source": [ - "You should see accuracy close to 100% as Iris is quite easy dataset where data can be almost perfectly classified if all 4 features used\n", - "\n", - "Using confusion matrics printed above you may also see that all examples of 'setosa' classified as 'setosa',, while there are some errors between 'versicolor'-'virginica' classes" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "confident-admission", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "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": 5 -} diff --git a/tasks/task_1/Classification_example_with_Iris_dataset.py b/tasks/task_1/Classification_example_with_Iris_dataset.py deleted file mode 100644 index a642673..0000000 --- a/tasks/task_1/Classification_example_with_Iris_dataset.py +++ /dev/null @@ -1,193 +0,0 @@ -#!/usr/bin/env python -# coding: utf-8 - -# In[1]: - - -import pandas as pd -import numpy as np - -from sklearn import datasets -from matplotlib import pyplot as plt - -from sklearn.linear_model import LogisticRegression -from sklearn.tree import DecisionTreeClassifier -from sklearn.model_selection import train_test_split -from sklearn.metrics import confusion_matrix - - -# In[2]: - - -get_ipython().run_line_magic('load_ext', 'pycodestyle_magic') -get_ipython().run_line_magic('flake8_on', '') - - -# # Classification example with Iris dataset - -# This example dataset task is in classifying flower based on its features - -# In[3]: - - -# This dataset boult in `sklearn` library so you can load it directly -iris = datasets.load_iris() -iris_features = iris['feature_names'] - - -# Print all flowers and features - -# In[4]: - - -print(f"Dataset features:\n{iris['feature_names']}") -print(f"Dataset classes:\n{iris.target_names}") - - -# Now we should visually analyze the dataset -# -# As we are limited by 2D displays and cannot visualize 4d data in a single plot - let's print data 2-axis at a time - -# In[5]: - - -for j in [1, 2, 3]: - for i, class_name in enumerate(iris.target_names): - sepal_length = iris.data[:, 0][iris.target == i] - sepal_width = iris.data[:, j][iris.target == i] - plt.plot(sepal_length, sepal_width, '.', label=class_name) - - plt.title("Flowers") - plt.xlabel(iris_features[0]) - plt.ylabel(iris_features[j]) - plt.legend() - plt.show() - - -# ## Plotting decision boundaries -# -# Decision boundaries allows us to visualize how given classifier thinks data should be splitted into a different classes -# -# For this let's focus on first 2 features ('sepal length (cm)', 'sepal width (cm)') to have consistent 2D plot - -# In[6]: - - -def plot_decision(clf, title): - x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1 - y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1 - xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1), - np.arange(y_min, y_max, 0.1)) - - Z = clf.predict(np.c_[xx.ravel(), yy.ravel()]) - Z = Z.reshape(xx.shape) - - plt.contourf(xx, yy, Z, alpha=0.4) - - for i, class_name in enumerate(iris.target_names): - sepal_length = iris.data[:, 0][iris.target == i] - sepal_width = iris.data[:, 1][iris.target == i] - plt.plot(sepal_length, sepal_width, '.', label=class_name) - - plt.title(title) - plt.xlabel(iris_features[0]) - plt.ylabel(iris_features[j]) - plt.legend() - plt.show() - - -# In[7]: - - -# Select first 2 features -X = iris.data[:, [0, 1]] -y = iris.target - - -# In[8]: - - -l_regression = LogisticRegression() -l_regression = l_regression.fit(X, y) -plot_decision(l_regression, title="Log regression") - - -# We can see that `LogisticRegression`model cannot properly divide 'versicolor' and 'virginica' classes based on that 2 features -# -# To divide classes properly we need to introduce non-linear models such and Neural Networks or Decision Trees - -# In[9]: - - -tree = DecisionTreeClassifier() -tree = tree.fit(X, y) -plot_decision(tree, title="Decision Tree") - - -# # Train-Test split - -# Let's now evaluate model using [test-train split](https://machinelearningmastery.com/train-test-split-for-evaluating-machine-learning-algorithms/) approach -# -# This is a common technique for checking model generalization. -# You train model on some part of the dataset (lets say 67%, or 75%) and that you check if you model generalizes well by prediction on data that left -# - -# In[10]: - - -X = iris.data # Use all iris features as predictors -y = iris.target - - -# Split data randomly using [train_test_split](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) -# -# Set `test_size=0.25` to use 25% data for test and 75% for train - -# In[11]: - - -X_train, X_test, y_train, y_test = train_test_split( - X, y, test_size=0.25, random_state=117 -) - - -# In[12]: - - -# function for printing results -def eval_model(clf, X_test, y_test): - pred = clf.predict(X_test) - accuracy = np.mean(pred == y_test) - print(f"Model accuracy: {accuracy*100:0.2f}%") - df = pd.DataFrame(confusion_matrix(y_test, pred)) - df.columns = ["Classified as " + x for x in iris.target_names] - df.index = iris.target_names - return df - - -# Train model on `train` data - -# In[13]: - - -l_regression = LogisticRegression() -l_regression = l_regression.fit(X_train, y_train) - - -# And evaluate on `test` data - -# In[14]: - - -eval_model(l_regression, X_test, y_test) - - -# You should see accuracy close to 100% as Iris is quite easy dataset where data can be almost perfectly classified if all 4 features used -# -# Using confusion matrics printed above you may also see that all examples of 'setosa' classified as 'setosa',, while there are some errors between 'versicolor'-'virginica' classes - -# In[ ]: - - - - diff --git a/tasks/task_1/README.md b/tasks/task_1/README.md deleted file mode 100644 index fa5cb73..0000000 --- a/tasks/task_1/README.md +++ /dev/null @@ -1,3 +0,0 @@ -# Classification example with Iris dataset - -Basic example for visualizing data and classifiers training