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{
"cells": [
{
"cell_type": "code",
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"execution_count": 1,
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"metadata": {
"executionInfo": {
"elapsed": 2450,
"status": "ok",
"timestamp": 1615991459232,
"user": {
"displayName": "Andy Pack",
"photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjA4K4ZhdArHXAFbAGr4n0aCv2HmyUpx4cy6zcUq34=s64",
"userId": "16615063155528027547"
},
"user_tz": 0
},
"id": "TGIxH9Tmt5zp"
},
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"outputs": [],
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"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import tensorflow as tf\n",
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"import tensorflow.keras.optimizers as tf_optim\n",
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"tf.get_logger().setLevel('ERROR')\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib as mpl\n",
"import seaborn as sns\n",
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"import random\n",
"import pickle\n",
"import json\n",
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"import math\n",
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"import datetime\n",
"import os\n",
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"\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
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"fig_dpi = 70"
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]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fksHv5rXACEX"
},
"source": [
"# Neural Network Training\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "l4zqVWyRAM0Z"
},
"source": [
"## Load Dataset\n",
"\n",
"Read CSVs dumped from MatLab and parse into Pandas DataFrames"
]
},
{
"cell_type": "code",
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"execution_count": 2,
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"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 331
},
"executionInfo": {
"elapsed": 2441,
"status": "ok",
"timestamp": 1615991459234,
"user": {
"displayName": "Andy Pack",
"photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjA4K4ZhdArHXAFbAGr4n0aCv2HmyUpx4cy6zcUq34=s64",
"userId": "16615063155528027547"
},
"user_tz": 0
},
"id": "Hj5l_tdZuYh7",
"outputId": "fbfa9406-f662-4ebc-8ba2-67950714627c"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Clump thickness</th>\n",
" <th>Uniformity of cell size</th>\n",
" <th>Uniformity of cell shape</th>\n",
" <th>Marginal adhesion</th>\n",
" <th>Single epithelial cell size</th>\n",
" <th>Bare nuclei</th>\n",
" <th>Bland chomatin</th>\n",
" <th>Normal nucleoli</th>\n",
" <th>Mitoses</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>699.000000</td>\n",
" <td>699.000000</td>\n",
" <td>699.000000</td>\n",
" <td>699.000000</td>\n",
" <td>699.000000</td>\n",
" <td>699.000000</td>\n",
" <td>699.000000</td>\n",
" <td>699.000000</td>\n",
" <td>699.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.441774</td>\n",
" <td>0.313448</td>\n",
" <td>0.320744</td>\n",
" <td>0.280687</td>\n",
" <td>0.321602</td>\n",
" <td>0.354363</td>\n",
" <td>0.343777</td>\n",
" <td>0.286695</td>\n",
" <td>0.158941</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.281574</td>\n",
" <td>0.305146</td>\n",
" <td>0.297191</td>\n",
" <td>0.285538</td>\n",
" <td>0.221430</td>\n",
" <td>0.360186</td>\n",
" <td>0.243836</td>\n",
" <td>0.305363</td>\n",
" <td>0.171508</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.100000</td>\n",
" <td>0.100000</td>\n",
" <td>0.100000</td>\n",
" <td>0.100000</td>\n",
" <td>0.100000</td>\n",
" <td>0.100000</td>\n",
" <td>0.100000</td>\n",
" <td>0.100000</td>\n",
" <td>0.100000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>0.200000</td>\n",
" <td>0.100000</td>\n",
" <td>0.100000</td>\n",
" <td>0.100000</td>\n",
" <td>0.200000</td>\n",
" <td>0.100000</td>\n",
" <td>0.200000</td>\n",
" <td>0.100000</td>\n",
" <td>0.100000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>0.400000</td>\n",
" <td>0.100000</td>\n",
" <td>0.100000</td>\n",
" <td>0.100000</td>\n",
" <td>0.200000</td>\n",
" <td>0.100000</td>\n",
" <td>0.300000</td>\n",
" <td>0.100000</td>\n",
" <td>0.100000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>0.600000</td>\n",
" <td>0.500000</td>\n",
" <td>0.500000</td>\n",
" <td>0.400000</td>\n",
" <td>0.400000</td>\n",
" <td>0.500000</td>\n",
" <td>0.500000</td>\n",
" <td>0.400000</td>\n",
" <td>0.100000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Clump thickness Uniformity of cell size Uniformity of cell shape \\\n",
"count 699.000000 699.000000 699.000000 \n",
"mean 0.441774 0.313448 0.320744 \n",
"std 0.281574 0.305146 0.297191 \n",
"min 0.100000 0.100000 0.100000 \n",
"25% 0.200000 0.100000 0.100000 \n",
"50% 0.400000 0.100000 0.100000 \n",
"75% 0.600000 0.500000 0.500000 \n",
"max 1.000000 1.000000 1.000000 \n",
"\n",
" Marginal adhesion Single epithelial cell size Bare nuclei \\\n",
"count 699.000000 699.000000 699.000000 \n",
"mean 0.280687 0.321602 0.354363 \n",
"std 0.285538 0.221430 0.360186 \n",
"min 0.100000 0.100000 0.100000 \n",
"25% 0.100000 0.200000 0.100000 \n",
"50% 0.100000 0.200000 0.100000 \n",
"75% 0.400000 0.400000 0.500000 \n",
"max 1.000000 1.000000 1.000000 \n",
"\n",
" Bland chomatin Normal nucleoli Mitoses \n",
"count 699.000000 699.000000 699.000000 \n",
"mean 0.343777 0.286695 0.158941 \n",
"std 0.243836 0.305363 0.171508 \n",
"min 0.100000 0.100000 0.100000 \n",
"25% 0.200000 0.100000 0.100000 \n",
"50% 0.300000 0.100000 0.100000 \n",
"75% 0.500000 0.400000 0.100000 \n",
"max 1.000000 1.000000 1.000000 "
]
},
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"execution_count": 2,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = pd.read_csv('features.csv', header=None).T\n",
"data.columns = ['Clump thickness', 'Uniformity of cell size', 'Uniformity of cell shape', 'Marginal adhesion', 'Single epithelial cell size', 'Bare nuclei', 'Bland chomatin', 'Normal nucleoli', 'Mitoses']\n",
"labels = pd.read_csv('targets.csv', header=None).T\n",
"labels.columns = ['Benign', 'Malignant']\n",
"data.describe()"
]
},
{
"cell_type": "code",
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"execution_count": 31,
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"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
},
"executionInfo": {
"elapsed": 2436,
"status": "ok",
"timestamp": 1615991459236,
"user": {
"displayName": "Andy Pack",
"photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjA4K4ZhdArHXAFbAGr4n0aCv2HmyUpx4cy6zcUq34=s64",
"userId": "16615063155528027547"
},
"user_tz": 0
},
"id": "qc1Mku6h041u",
"outputId": "94e38c34-0419-4a02-ac8c-17bbc83f777b"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Benign</th>\n",
" <th>Malignant</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Benign Malignant\n",
"0 1 0\n",
"1 1 0\n",
"2 1 0\n",
"3 0 1\n",
"4 1 0"
]
},
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"execution_count": 31,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"labels.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "h9QsJjWEMbLu"
},
"source": [
"### Explore Dataset\n",
"\n",
"The classes are uneven in their occurences, stratify when splitting later on"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"executionInfo": {
"elapsed": 2430,
"status": "ok",
"timestamp": 1615991459237,
"user": {
"displayName": "Andy Pack",
"photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjA4K4ZhdArHXAFbAGr4n0aCv2HmyUpx4cy6zcUq34=s64",
"userId": "16615063155528027547"
},
"user_tz": 0
},
"id": "rjjiSYAZMa4k",
"outputId": "ae0c3772-00be-4f2b-80d2-9cd62a6b6e08"
},
"outputs": [
{
"data": {
"text/plain": [
"Benign 458\n",
"Malignant 241\n",
"dtype: int64"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"labels.astype(bool).sum(axis=0)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "E9lVYI14AUMf"
},
"source": [
"## Split Dataset\n",
"\n",
"Using a 50/50 split"
]
},
{
"cell_type": "code",
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"execution_count": 3,
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"metadata": {
"executionInfo": {
"elapsed": 2604,
"status": "ok",
"timestamp": 1615991459418,
"user": {
"displayName": "Andy Pack",
"photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjA4K4ZhdArHXAFbAGr4n0aCv2HmyUpx4cy6zcUq34=s64",
"userId": "16615063155528027547"
},
"user_tz": 0
},
"id": "L83Ae5l9wM35"
},
"outputs": [],
"source": [
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"data_train, data_test, labels_train, labels_test = train_test_split(data, labels, test_size=0.5, stratify=labels)"
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]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Qf2U199fNjmJ"
},
"source": [
"## Generate & Retrieve Model\n",
"\n",
"Get a shallow model with a single hidden layer of varying nodes"
]
},
{
"cell_type": "code",
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"execution_count": 4,
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"metadata": {
"executionInfo": {
"elapsed": 2598,
"status": "ok",
"timestamp": 1615991459419,
"user": {
"displayName": "Andy Pack",
"photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjA4K4ZhdArHXAFbAGr4n0aCv2HmyUpx4cy6zcUq34=s64",
"userId": "16615063155528027547"
},
"user_tz": 0
},
"id": "SgoQ-NjWB0T5"
},
"outputs": [],
"source": [
"def get_model(hidden_nodes=9, activation=lambda: 'sigmoid', weight_init=lambda: 'glorot_uniform'):\n",
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" layers = [tf.keras.layers.InputLayer(input_shape=(9,), name='Input'), \n",
" tf.keras.layers.Dense(hidden_nodes, activation=activation(), kernel_initializer=weight_init(), name='Hidden'), \n",
" tf.keras.layers.Dense(2, activation='softmax', kernel_initializer=weight_init(), name='Output')]\n",
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"\n",
" model = tf.keras.models.Sequential(layers)\n",
" return model"
]
},
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"Get a Keras Tensorboard callback for dumping data for later analysis"
]
},
{
"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
"outputs": [],
"source": [
"def tensorboard_callback(path='tensorboard-logs', prefix=''):\n",
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" return tf.keras.callbacks.TensorBoard(\n",
" log_dir=os.path.normpath(os.path.join(path, prefix + datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))), histogram_freq=1\n",
" ) "
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]
},
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{
"cell_type": "markdown",
"metadata": {
"id": "QT5B9PTUN3pj"
},
"source": [
"# Example Training"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "mQGAUtIPAd6e"
},
"source": [
"## Define Model\n",
"\n",
"Variable number of hidden nodes. All using 9D outputs except the last layer which is 2D for binary classification"
]
},
{
"cell_type": "code",
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"execution_count": 60,
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"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"executionInfo": {
"elapsed": 7889,
"status": "ok",
"timestamp": 1615991464716,
"user": {
"displayName": "Andy Pack",
"photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjA4K4ZhdArHXAFbAGr4n0aCv2HmyUpx4cy6zcUq34=s64",
"userId": "16615063155528027547"
},
"user_tz": 0
},
"id": "fYA34P0Vu_pX",
"outputId": "aded18b8-aa7f-4362-a614-837c8a0f526f"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"Model: \"sequential_1\"\n",
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"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
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"dense_2 (Dense) (None, 9) 90 \n",
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"_________________________________________________________________\n",
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"dense_3 (Dense) (None, 2) 20 \n",
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"=================================================================\n",
"Total params: 110\n",
"Trainable params: 110\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"model = get_model(9)\n",
"model.compile('sgd', loss='categorical_crossentropy', metrics=['accuracy'])\n",
"model.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "KZSwFe-AAs1y"
},
"source": [
"## Train Model\n",
"\n",
"Example 10 epochs"
]
},
{
"cell_type": "code",
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"execution_count": 61,
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"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"executionInfo": {
"elapsed": 11304,
"status": "ok",
"timestamp": 1615991468137,
"user": {
"displayName": "Andy Pack",
"photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjA4K4ZhdArHXAFbAGr4n0aCv2HmyUpx4cy6zcUq34=s64",
"userId": "16615063155528027547"
},
"user_tz": 0
},
"id": "s8U9Atu3zelS",
"outputId": "8439e8d2-7a5d-495f-a192-a34f01e95bfa"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"Epoch 1/5\n",
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"11/11 [==============================] - 1s 2ms/step - loss: 0.6257 - accuracy: 0.6607\n",
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"Epoch 2/5\n",
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"11/11 [==============================] - 0s 3ms/step - loss: 0.6226 - accuracy: 0.6651\n",
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"Epoch 3/5\n",
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"11/11 [==============================] - 0s 2ms/step - loss: 0.6326 - accuracy: 0.6424\n",
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"Epoch 4/5\n",
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"11/11 [==============================] - 0s 3ms/step - loss: 0.6158 - accuracy: 0.6696\n",
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"Epoch 5/5\n",
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"11/11 [==============================] - 0s 2ms/step - loss: 0.6228 - accuracy: 0.6534\n"
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]
},
{
"data": {
"text/plain": [
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"<tensorflow.python.keras.callbacks.History at 0x2cd249f3400>"
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]
},
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"execution_count": 61,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
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"model.fit(data_train.to_numpy(), labels_train.to_numpy(), epochs=5)"
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]
},
{
"cell_type": "code",
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"execution_count": 62,
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"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"executionInfo": {
"elapsed": 11294,
"status": "ok",
"timestamp": 1615991468137,
"user": {
"displayName": "Andy Pack",
"photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjA4K4ZhdArHXAFbAGr4n0aCv2HmyUpx4cy6zcUq34=s64",
"userId": "16615063155528027547"
},
"user_tz": 0
},
"id": "VnUEJdXovzi-",
"outputId": "02075086-352c-4a23-fac5-ad54d11e0e35"
},
"outputs": [
{
"data": {
"text/plain": [
"['loss', 'accuracy']"
]
},
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"execution_count": 62,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.metrics_names"
]
},
{
"cell_type": "code",
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"execution_count": 63,
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"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"executionInfo": {
"elapsed": 11285,
"status": "ok",
"timestamp": 1615991468138,
"user": {
"displayName": "Andy Pack",
"photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjA4K4ZhdArHXAFbAGr4n0aCv2HmyUpx4cy6zcUq34=s64",
"userId": "16615063155528027547"
},
"user_tz": 0
},
"id": "r0vxP3Ah42ib",
"outputId": "061113ba-52db-4fbe-c7f9-b5d3d85438ed"
},
"outputs": [
{
"data": {
"text/plain": [
"<tf.Tensor: shape=(), dtype=float32, numpy=0.6561605>"
]
},
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"execution_count": 63,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.metrics[1].result()"
]
},
{
"cell_type": "markdown",
"metadata": {
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"id": "z7bn8pKTAynt",
"tags": [
"exp1"
]
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},
"source": [
"# Experiment 1\n",
"\n",
"The below function runs an iteration of layer/epoch investigations.\n",
"Returns the amount of layers/epochs used as well as the results and the model.\n",
"\n",
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"Using cancer dataset (as in E2) and 'trainscg' or an optimiser of your choice, vary nodes and epochs (that is using early stopping for epochs) over suitable range, to find optimal choice in terms of classification test error rate of node/epochs for 50/50% random train/test split (no validation set). It is suggested that you initially try epochs = [ 1 2 4 8 16 32 64], nodes = [2 8 32], so there would be 21 node/epoch combinations. \n",
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"\n",
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"(Hint1: from the 'advanced script' in E2, nodes can be changed to xx, with hiddenLayerSize = xx; and epochs changed to xx by addingnet. trainParam.epochs = xx; placed afternet = patternnet(hiddenLayerSize, trainFcn); --see 'trainscg' help documentation for changing epochs). \n",
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"\n",
"Repeat each of the 21 node/epoch combinations at least thirty times, with different 50/50 split and take average and report classification error rate and standard deviation (std). Graph classification train and test error rate and std as node-epoch changes, that is plot error rate vs epochs for different number of nodes. Report the optimal value for test error rate and associated node/epoch values. \n",
"\n",
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"(Hint2: as epochs increases you can expect the test error rate to reach a minimum and then start increasing, you may need to set the stopping criteria to achieve the desired number of epochs - Hint 3: to find classification error rates for train and test set, you need to check the code from E2, to determine how you may obtain the train and test set patterns)\n"
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]
},
{
"cell_type": "code",
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"execution_count": 16,
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"metadata": {
"executionInfo": {
"elapsed": 11274,
"status": "ok",
"timestamp": 1615991468138,
"user": {
"displayName": "Andy Pack",
"photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjA4K4ZhdArHXAFbAGr4n0aCv2HmyUpx4cy6zcUq34=s64",
"userId": "16615063155528027547"
},
"user_tz": 0
},
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"id": "mYWhCSW4A57V",
"tags": [
"exp1",
"exp-func"
]
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},
"outputs": [],
"source": [
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"# hidden_nodes = [2, 8, 16, 24, 32]\n",
"# epochs = [1, 2, 4, 8, 16, 32, 64, 100, 150, 200]\n",
"hidden_nodes = [2, 8, 16]\n",
"epochs = [1, 2, 4, 8]\n",
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"\n",
"def evaluate_parameters(hidden_nodes=hidden_nodes, \n",
" epochs=epochs, \n",
" batch_size=128,\n",
" optimizer=lambda: 'sgd',\n",
" loss=lambda: 'categorical_crossentropy',\n",
" metrics=['accuracy'],\n",
" callbacks=None,\n",
" validation_split=None,\n",
"\n",
" verbose=0,\n",
" print_params=True,\n",
" return_model=True,\n",
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" run_eagerly=False,\n",
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" tboard=True,\n",
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" \n",
" dtrain=data_train,\n",
" dtest=data_test,\n",
" ltrain=labels_train,\n",
" ltest=labels_test):\n",
" for idx1, hn in enumerate(hidden_nodes):\n",
" for idx2, e in enumerate(epochs):\n",
" if print_params:\n",
" print(f\"Nodes: {hn}, Epochs: {e}\")\n",
"\n",
" model = get_model(hn)\n",
" model.compile(\n",
" optimizer=optimizer(),\n",
" loss=loss(),\n",
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" metrics=metrics,\n",
" run_eagerly=run_eagerly\n",
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" )\n",
" \n",
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" if tboard:\n",
" if callbacks is not None:\n",
" cb = [i() for i in callbacks] + [tensorboard_callback(prefix=f'exp1-{hn}-{e}-')]\n",
" else:\n",
" cb = [tensorboard_callback(prefix=f'exp1-{hn}-{e}-')]\n",
" \n",
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" response = {\"nodes\": hn, \n",
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" \"epochs\": e,\n",
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" ##############\n",
" ## TRAIN\n",
" ##############\n",
" \"history\": model.fit(dtrain.to_numpy(), \n",
" ltrain.to_numpy(), \n",
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" epochs=e, \n",
" verbose=verbose,\n",
" \n",
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" callbacks=cb,\n",
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" validation_split=validation_split).history,\n",
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" ##############\n",
" ## TEST\n",
" ##############\n",
" \"results\": model.evaluate(dtest.to_numpy(), \n",
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" ltest.to_numpy(),\n",
" callbacks=cb,\n",
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" batch_size=batch_size, \n",
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" verbose=verbose),\n",
" \"optimizer\": model.optimizer.get_config(),\n",
" \"loss\": model.loss,\n",
" \"model_config\": json.loads(model.to_json())\n",
" }\n",
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"\n",
" if return_model:\n",
" response[\"model\"] = model\n",
"\n",
" yield response"
]
},
{
"cell_type": "markdown",
"metadata": {
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"id": "r-63V9qb-i4w",
"tags": [
"exp1"
]
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},
"source": [
"## Single Iteration\n",
"Run a single iteration of epoch/layer investigations"
]
},
{
"cell_type": "code",
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"execution_count": 17,
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"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"executionInfo": {
"elapsed": 313592,
"status": "ok",
"timestamp": 1615991770468,
"user": {
"displayName": "Andy Pack",
"photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjA4K4ZhdArHXAFbAGr4n0aCv2HmyUpx4cy6zcUq34=s64",
"userId": "16615063155528027547"
},
"user_tz": 0
},
"id": "ZmGFkE9y8E4H",
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"outputId": "243fb136-bc07-4438-afb7-f2d21758168d",
"tags": [
"exp1"
]
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},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Nodes: 2, Epochs: 1\n",
"Nodes: 2, Epochs: 2\n",
"Nodes: 2, Epochs: 4\n",
"Nodes: 2, Epochs: 8\n",
"Nodes: 8, Epochs: 1\n",
"Nodes: 8, Epochs: 2\n",
"Nodes: 8, Epochs: 4\n",
"Nodes: 8, Epochs: 8\n",
"Nodes: 16, Epochs: 1\n",
"Nodes: 16, Epochs: 2\n",
"Nodes: 16, Epochs: 4\n",
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"Nodes: 16, Epochs: 8\n"
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]
}
],
"source": [
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"# es = tf.keras.callbacks.EarlyStopping(monitor='val_loss', mode='min', patience = 5)\n",
"single_results = list(evaluate_parameters(return_model=False, validation_split=0.2\n",
" , optimizer = lambda: tf.keras.optimizers.SGD(learning_rate=0.5, momentum=0.5)\n",
"# , callbacks=[es]\n",
" ))"
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]
},
{
"cell_type": "markdown",
"metadata": {
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"id": "mdWK3-M6SK8_",
"tags": [
"exp1"
]
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},
"source": [
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"### Train/Test Curves\n",
"\n",
"For a single test from the set"
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]
},
{
"cell_type": "code",
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"execution_count": 68,
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"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 517
},
"executionInfo": {
"elapsed": 314527,
"status": "ok",
"timestamp": 1615991771417,
"user": {
"displayName": "Andy Pack",
"photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjA4K4ZhdArHXAFbAGr4n0aCv2HmyUpx4cy6zcUq34=s64",
"userId": "16615063155528027547"
},
"user_tz": 0
},
"id": "F9Xre1o6SesD",
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"outputId": "d6b817aa-58cd-4510-807f-e5e6bcf62f18",
"tags": [
"exp1"
]
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},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"Nodes: 2, Epochs: 4\n"
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]
},
{
"data": {
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"image/png": "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"text/plain": [
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"<Figure size 1050x490 with 2 Axes>"
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]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
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"single_result = random.choice([i for i in single_results if i[\"epochs\"] > 1])\n",
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"single_history = single_result[\"history\"]\n",
"\n",
"fig, axes = plt.subplots(1, 2, figsize=(15,7))\n",
"fig.set_dpi(fig_dpi)\n",
"\n",
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"################\n",
"## LOSS\n",
"################\n",
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"ax = axes[0]\n",
"ax.set_title(\"Training vs Validation Loss\")\n",
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"ax.plot(single_history['loss'], label=\"train\", lw=2)\n",
"ax.plot(single_history['val_loss'], label=\"validation\", lw=2, c=(1,0,0))\n",
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"ax.set_xlabel(\"Epochs\")\n",
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"ax.grid()\n",
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"ax.legend()\n",
"\n",
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"################\n",
"## ACCURACY\n",
"################\n",
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"ax = axes[1]\n",
"ax.set_title(\"Training vs Validation Accuracy\")\n",
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"ax.plot(single_history['accuracy'], label=\"train\", lw=2)\n",
"ax.plot(single_history['val_accuracy'], label=\"validation\", lw=2, c=(1,0,0))\n",
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"ax.set_xlabel(\"Epochs\")\n",
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"# ax.set_ylim(0, 1)\n",
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"ax.grid()\n",
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"ax.legend()\n",
"\n",
"print(f\"Nodes: {single_result['nodes']}, Epochs: {single_result['epochs']}\")\n",
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"# plt.tight_layout()\n",
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"# plt.savefig('fig.png')\n",
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"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
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"id": "0IQ7HfJCSDud",
"tags": [
"exp1"
]
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},
"source": [
"### Accuracy Surface"
]
},
{
"cell_type": "code",
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"execution_count": 69,
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"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 705
},
"executionInfo": {
"elapsed": 315450,
"status": "ok",
"timestamp": 1615991772345,
"user": {
"displayName": "Andy Pack",
"photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjA4K4ZhdArHXAFbAGr4n0aCv2HmyUpx4cy6zcUq34=s64",
"userId": "16615063155528027547"
},
"user_tz": 0
},
"id": "X3MWHLxJElbc",
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"outputId": "134671d0-bfd3-4ee6-aa02-1a2a5b23f3ca",
"tags": [
"exp1"
]
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},
"outputs": [
{
"data": {
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"image/png": "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
2021-03-19 17:21:00 +00:00
"text/plain": [
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"<Figure size 560x350 with 2 Axes>"
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]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"X, Y = np.meshgrid(epochs, hidden_nodes)\n",
"\n",
"shaped_result = np.reshape([r[\"results\"][1] for r in single_results], \n",
" (len(hidden_nodes), len(epochs)))\n",
"\n",
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"fig = plt.figure(figsize=(8, 5))\n",
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"fig.set_dpi(fig_dpi)\n",
"ax = plt.axes(projection='3d')\n",
"\n",
"surf = ax.plot_surface(X, Y, shaped_result, cmap='viridis')\n",
"ax.set_title('Model test accuracy over different training periods with different numbers of nodes')\n",
"ax.set_xlabel('Epochs')\n",
"ax.set_ylabel('Hidden Nodes')\n",
"ax.set_zlabel('Accuracy')\n",
"ax.view_init(30, -110)\n",
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"# ax.set_zlim([0, 1])\n",
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"fig.colorbar(surf, shrink=0.3, aspect=6)\n",
"\n",
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"plt.tight_layout()\n",
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"# plt.savefig('fig.png')\n",
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"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
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"id": "C793_RHvSGai",
"tags": [
"exp1"
]
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},
"source": [
"### Error Rate Curves"
]
},
{
"cell_type": "code",
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"execution_count": 70,
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"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 668
},
"executionInfo": {
"elapsed": 316211,
"status": "ok",
"timestamp": 1615991773109,
"user": {
"displayName": "Andy Pack",
"photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjA4K4ZhdArHXAFbAGr4n0aCv2HmyUpx4cy6zcUq34=s64",
"userId": "16615063155528027547"
},
"user_tz": 0
},
"id": "tpClZMptrq-q",
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"outputId": "f9fe93f9-7b67-4772-83e4-9e3567fd9318",
"tags": [
"exp1"
]
2021-03-19 17:21:00 +00:00
},
"outputs": [
{
"data": {
2021-03-26 20:01:05 +00:00
"image/png": "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
2021-03-19 17:21:00 +00:00
"text/plain": [
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"<Figure size 560x350 with 1 Axes>"
2021-03-19 17:21:00 +00:00
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
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"fig = plt.figure(figsize=(8, 5))\n",
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"fig.set_dpi(fig_dpi)\n",
"\n",
"for layer in hidden_nodes:\n",
" plt.plot(epochs, \n",
" 1 - np.array([i[\"results\"][1] \n",
" for i in single_results \n",
" if i[\"nodes\"] == layer]), \n",
" label=f'{layer} Nodes')\n",
"\n",
"plt.legend()\n",
"plt.grid()\n",
"plt.title(\"Test error rates for a single iteration of different epochs and hidden node training\")\n",
"plt.xlabel(\"Epochs\")\n",
"plt.ylabel(\"Error Rate\")\n",
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"plt.ylim(0)\n",
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"\n",
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"# plt.savefig('fig.png')\n",
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"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
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"id": "7mJaKjlCxEkt",
"tags": [
"exp1"
]
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},
"source": [
"## Multiple Iterations\n",
"\n",
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"Run multiple iterations of the epoch/layer investigations and average\n",
"\n",
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"### CSV Results\n",
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"\n",
"| test | learning rate | momentum | batch size | hidden nodes | epochs |\n",
"| --- | --- | --- | --- | --- | --- |\n",
"|1|0.01|0|128|2, 8, 12, 16, 24, 32, 64, 128, 256|1, 2, 4, 8, 16, 32, 64, 100, 150, 200|\n",
"|2|0.5|0.1|128|2, 8, 12, 16, 24, 32, 64, 128|1, 2, 4, 8, 16, 32, 64, 100|\n",
"|3|0.2|0.05|128|2, 8, 12, 16, 24, 32, 64, 128|1, 2, 4, 8, 16, 32, 64, 100|\n",
"|4|0.08|0.04|128|2, 8, 12, 16, 24, 32, 64, 128|1, 2, 4, 8, 16, 32, 64, 100|\n",
"|5|0.08|0|128|2, 8, 12, 16, 24, 32, 64, 128|1, 2, 4, 8, 16, 32, 64, 100|\n",
"|6|0.06|0|128|1, 2, 3, 4, 5, 6, 7, 8|1, 2, 4, 8, 16, 32, 64, 100|\n",
"|7|0.06|0|35|2, 8, 12, 16, 24, 32, 64, 128|1, 2, 4, 8, 16, 32, 64, 100|\n",
"\n",
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"### Pickle Results\n",
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"\n",
"| test | learning rate | momentum | batch size | hidden nodes | epochs |\n",
"| --- | --- | --- | --- | --- | --- |\n",
"|1|0.01|0|128|2, 8, 12, 16, 24, 32, 64, 128, 256|1, 2, 4, 8, 16, 32, 64, 100, 150, 200|\n",
"|2|0.5|0.1|128|2, 8, 12, 16, 24, 32, 64, 128|1, 2, 4, 8, 16, 32, 64, 100|\n",
"|3|1|0.3|20|2, 8, 12, 16, 24, 32, 64, 128|1, 2, 4, 8, 16, 32, 64, 100|\n",
"|4|0.6|0.1|20|2, 8, 16, 24, 32|1, 2, 4, 8, 16, 32, 64, 100, 150, 200|\n",
"|5|0.05|0.01|20|2, 8, 16, 24, 32|1, 2, 4, 8, 16, 32, 64, 100, 150, 200|\n",
"|6|1.5|0.5|20|2, 8, 16, 24, 32|1, 2, 4, 8, 16, 32, 64, 100, 150, 200|"
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]
},
{
"cell_type": "code",
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"execution_count": 30,
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"metadata": {
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"id": "-lsKo4BCP3yw",
"tags": [
"exp1"
]
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},
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Iteration 1/30\n",
"Iteration 2/30\n",
"Iteration 3/30\n",
"Iteration 4/30\n",
"Iteration 5/30\n",
"Iteration 6/30\n",
"Iteration 7/30\n",
"Iteration 8/30\n",
"Iteration 9/30\n",
"Iteration 10/30\n",
"Iteration 11/30\n",
"Iteration 12/30\n",
"Iteration 13/30\n",
"Iteration 14/30\n",
"Iteration 15/30\n",
"Iteration 16/30\n",
"Iteration 17/30\n",
"Iteration 18/30\n",
"Iteration 19/30\n",
"Iteration 20/30\n",
"Iteration 21/30\n",
"Iteration 22/30\n",
"Iteration 23/30\n",
"Iteration 24/30\n",
"Iteration 25/30\n",
"Iteration 26/30\n",
"Iteration 27/30\n",
"Iteration 28/30\n",
"Iteration 29/30\n",
"Iteration 30/30\n"
]
}
],
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"source": [
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"multi_param_results = list()\n",
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"multi_iterations = 30\n",
"for i in range(multi_iterations):\n",
" print(f\"Iteration {i+1}/{multi_iterations}\")\n",
" data_train, data_test, labels_train, labels_test = train_test_split(data, labels, test_size=0.5, stratify=labels)\n",
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" multi_param_results.append(list(evaluate_parameters(dtrain=data_train, \n",
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" dtest=data_test, \n",
" ltrain=labels_train, \n",
" ltest=labels_test,\n",
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" optimizer=lambda: tf.keras.optimizers.SGD(learning_rate=1.5, momentum=0.5),\n",
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" return_model=False,\n",
" print_params=False,\n",
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" batch_size=20)))"
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]
},
{
"cell_type": "markdown",
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"metadata": {
"tags": [
"exp1"
]
},
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"source": [
"### Accuracy Tensor\n",
"\n",
"Create a tensor for holding the accuracy results\n",
"\n",
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"(Iterations x [Test/Train] x Number of nodes x Number of epochs)"
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]
},
{
"cell_type": "code",
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"execution_count": 52,
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"metadata": {
"tags": [
"exp1"
]
},
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"30 Tests\n",
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"Nodes: [2, 8, 12, 16, 24, 32, 64, 128, 256]\n",
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"Epochs: [1, 2, 4, 8, 16, 32, 64, 100, 150, 200]\n",
"\n",
"Loss: categorical_crossentropy\n",
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"LR: 0.01\n",
"Momentum: 0.0\n"
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]
}
],
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"source": [
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"multi_param_epochs = sorted(list({i[\"epochs\"] for i in multi_param_results[0]}))\n",
"multi_param_nodes = sorted(list({i[\"nodes\"] for i in multi_param_results[0]}))\n",
"multi_param_iter = len(multi_param_results)\n",
"\n",
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"accuracy_tensor = np.zeros((multi_param_iter, 2, len(multi_param_nodes), len(multi_param_epochs)))\n",
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"for iter_idx, iteration in enumerate(multi_param_results):\n",
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" for single_test in iteration:\n",
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" accuracy_tensor[iter_idx, :,\n",
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" multi_param_nodes.index(single_test['nodes']), \n",
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" multi_param_epochs.index(single_test['epochs'])] = [single_test[\"results\"][1], \n",
" single_test[\"history\"][\"accuracy\"][-1]]\n",
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" \n",
"mean_param_accuracy = np.mean(accuracy_tensor, axis=0)\n",
"std_param_accuracy = np.std(accuracy_tensor, axis=0)\n",
"\n",
"print(f'{multi_param_iter} Tests')\n",
"print(f'Nodes: {multi_param_nodes}')\n",
"print(f'Epochs: {multi_param_epochs}')\n",
"print()\n",
"print(f'Loss: {multi_param_results[0][0][\"loss\"]}')\n",
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"print(f'LR: {multi_param_results[0][0][\"optimizer\"][\"learning_rate\"]:.3}')\n",
"print(f'Momentum: {multi_param_results[0][0][\"optimizer\"][\"momentum\"]:.3}')"
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]
},
{
"cell_type": "markdown",
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"metadata": {
"tags": [
"exp1"
]
},
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"source": [
"#### Export/Import Test Sets\n",
"\n",
"Export mean and standard deviations for retrieval and visualisation "
]
},
{
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"cell_type": "raw",
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"metadata": {
"tags": [
"exp1"
]
},
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"source": [
"pickle.dump(multi_param_results, open(\"result.p\", \"wb\"))"
]
},
{
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"cell_type": "code",
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"execution_count": 51,
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"metadata": {
"tags": [
"exp1"
]
},
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"outputs": [],
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"source": [
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"exp1_testname = 'exp1-test1'\n",
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"multi_param_results = pickle.load(open(f\"results/{exp1_testname}.p\", \"rb\"))"
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]
},
{
"cell_type": "raw",
"metadata": {},
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"source": [
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"np.savetxt(\"exp1-mean.csv\", mean_param_accuracy, delimiter=',')\n",
"np.savetxt(\"exp1-std.csv\", std_param_accuracy, delimiter=',')"
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]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
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"mean_param_accuracy = np.loadtxt(\"results/test1-exp1-mean.csv\", delimiter=',')\n",
"std_param_accuracy = np.loadtxt(\"results/test1-exp1-std.csv\", delimiter=',')\n",
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"# multi_iterations = 30"
]
},
{
"cell_type": "markdown",
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"metadata": {
"tags": [
"exp1"
]
},
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"source": [
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"### Best Results"
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]
},
{
"cell_type": "code",
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"execution_count": 53,
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"metadata": {
"tags": [
"exp1"
]
},
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"Nodes: 256, Epochs: 200, 1e+02% Accurate\n"
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]
}
],
"source": [
"best_param_accuracy_idx = np.unravel_index(np.argmax(mean_param_accuracy[0, :, :]), mean_param_accuracy.shape)\n",
"best_param_accuracy = mean_param_accuracy[best_param_accuracy_idx]\n",
"best_param_accuracy_nodes = multi_param_nodes[best_param_accuracy_idx[1]]\n",
"best_param_accuracy_epochs = multi_param_epochs[best_param_accuracy_idx[2]]\n",
"\n",
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"print(f'Nodes: {best_param_accuracy_nodes}, Epochs: {best_param_accuracy_epochs}, {best_param_accuracy * 100:.1}% Accurate')"
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]
},
{
"cell_type": "markdown",
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"metadata": {
"tags": [
"exp1"
]
},
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"source": [
"### Test Accuracy Surface"
]
},
{
"cell_type": "code",
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"execution_count": 54,
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"metadata": {
"executionInfo": {
"elapsed": 2653358,
"status": "aborted",
"timestamp": 1615994110345,
"user": {
"displayName": "Andy Pack",
"photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjA4K4ZhdArHXAFbAGr4n0aCv2HmyUpx4cy6zcUq34=s64",
"userId": "16615063155528027547"
},
"user_tz": 0
},
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"id": "ZGJVhz6iJU-7",
"tags": [
"exp1"
]
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},
"outputs": [
{
"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAUUAAAEaCAYAAACGrEV/AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Z1A+gAAAACXBIWXMAAArEAAAKxAFmbYLUAACXk0lEQVR4nOy9d5wkVb33/z5VndPknGfDbJjdnd2dXUAQVkAQCaJIEAT1mq6KV0UMV8Trc39XMaFXH4xgQEUwXyMPoFdUBGR3ZnOOsxN2dvJM93SuOr8/erq3Z6bT5Ab6/Xr1a2e7q6tOd1d96nvONwkpJTly5MiRI4Ky1APIkSNHjmwiJ4o5cuTIEUdOFHPkyJEjjpwo5siRI0ccOVHMkSNHjjhyopgjR44cceREMUeOHDniyIniEiCEOCyEaF/qccwHQohbhRBSCHHdUo8lR475ICeKi4wQYitQCTQLIVbP876FEGKxf9M3A8MT/y4KS/Q5c7xMyJ1Yi8+bgd8ATwG3AwghaoQQbiGELbqREOItQog/TfxtFUI8IIToEUJ0CSE+HrfdDyZe+1/ACywTQvyLEOLIxD73CCG2xW1fJoR4QggxJoT4ixDi60KIh+Jef6MQYr8QYkgI8VshRGmyDzLx2quB9wHXCiFcca/ZhRDfmBjzsBDiR3Gv3SyE2Dcxvr1CiKaJ56UQojpuu6eFEG+e5edMePwJKz1+uwYhxIgQwpLqR8vx8iEniouIEMIA3Az8FHgMuFUIIaSUncAe4Oq4zaPbAXwJKARWAluB24UQ18RtewvwEcAJnAJ6gcuAfOD/Ao8JIcwT234D6AJKgX8Hbosb31bgvyf2VwYcmtg+GbcAe6SUjwLdwBvjXvtvoBZYP3Gsb08c40Lg68C7gTzgRmAsxTGmHi/Tz5nw+MAjwJvi9vkm4H+klP4Mx5DjpY6UMvdYpAfwWiJTTRORC9sHXDzx2r8BP5/4uwAYB4oAQcQyqozbz53ADyb+/gHw7TTHPQM0AwYgDNTGvfYj4KGJv78FfCLuNScQAgxJ9vsC8NGJvz8D/O/E3wrgB1YkeM+DwGeS7E8C1XH/fxp48yw+Z6rjNwIDgHHi/3uBK5b63Mg9sueRsxQXlzcTsUqCUko38EfOrcX9HHiNEMIOvAH4m5RyECgBrMCBiWneCPBZIpZclK74gwghrhdCtMdtX0pEYIsBlYhVl+i9tcA9ce/rJCKi5VM/iBBiJdAK/GziqceASyamvyWAGTiZ4DuoTvJ8JmT6OZMeX0p5AjgCXCGEaJ7Y9s+zHE+OlyCGpR7AywUhhAN4HaAJIa6aeNoOhIQQ75dSnhFC7ACuBW4CHp3YZgAIAI1SyqEku4+VOpqYPj5KRFiflFJqQogzRCzOAUAj4ujpnHhL9cT+ISKW90opv5zBR4qK+fNCiOhzCnArkel+AKgHjk15X+fE84nwErkBRCmb8nqmn7M/xfEBfkxk2twB/FRKqSUZT46XITlLcfF4A5GpcxPQMvFYRcQSi64lPga8F7gI+B8AKaUOPAzcL4TIF0IoQojVE+t/iTATmZ73AQghPkDEGkJKGQZ+C3xKCGGe2Me1ce/9PnCnEGLDxHsLhRCvS3Kc24C74z5LC3APkemuDvwQ+LIQokgIYZxYS2Tis7xbCHHBhBe5SQhRMfHabuAWIYQqhLgDWJ7k2Ok+Z6rjQ2St9rUTn+EnKY6R42VIThQXjzcTWbs7I6XsnXh0A9/jnNX1S+ACImtzI3Hv/RAwSmT9a4jIBV+Q6CBSyjEizogniDgiiphsLb0XqCNiNX6eyPQ3MPHeZ4kI3Q+FEGNAOxAvJgAIIV5BZKr6YNxn6QW+CSwXQqwH7gJ6gP3AWeBdE8f4B/CBic89RmTZIOq1/hARoRoCNgPPJvkuM/mcCY8/8d5B4BkgLKX8Z7Jj5Hh5IqTMFZl9OSOEeBTYK6X87FKPZTERQjwADEsp713qseTILnKi+DJDCLEW0IHDwKVEptNbpZT7lnRgi4gQopxICNT5E46XHDli5KbPLz/ygT8AHiLT3fe+zATxTiLT7G/mBDFHInKWYo4cOXLEkbMUc+TIkSOOnCjmyJEjRxwLFbydm5PnyPHSQKTf5KVFzlLMkSNHjjhyopgjR44cceREMUeOHDniyIlijhw5csSRE8UcOXLkiCMnijly5MgRR04Uc+TIkSOOnCjmyJEjRxw5UcyRI0eOOHKimCNHjhxx5EQxR44cOeLIiWKOHDlyxJETxRw5cuSIIyeKOXLkyBFHThRz5MiRI46cKObIkSNHHDlRzJEjR444cqKYI0eOHHHkRDFHjhw54siJYo6XFVJKdF0nFAqh6/pSDydHFrJQjaty5FhSouKn6zqapsUeUSHUdR2LxYLVakWIl11vphwpyIlijhc1icRveHgYRVGw2Wyx7YQQCCFQFAUhRGz7UCiE0WjMCWOOGDlRzJH1SCmTWn5STu6mK4RgZGQEq9WK0+lMuV9FUdA0DSEEBoMhJ4w5gJwo5sgi4sVv6pQ3kfhFLb9E+8lU4IQQhMNhAIxG49w/RI4XPTlRzLHopFvvi24TFb5k4pdq/+lEMSqy0e3C4XDMYszx8iZ3BuRYMDIRPzgnTNH1vvk4bib7iW4T/ffgwYOsXr06J4wvc3K/fo45MXW9T9d1wuEwo6Oj6LqO0+lMaPUt5PrdTKbPUYQQDA4OEgqFEEKgquoCjS5HtpMTxRwZMdP1vvHxcTRNIz8/f0nGKoQgNDKGZ+cBUBQKLtma0XuFEASDQcxm84ym7DleOuREMcckplp98QI4ldms9y0UmseLe88hPO0HGPrbPxk42kGwowcAYVBZ+9hXKbz0/LT7iVqYUWHMeaRffuRE8WXKTJ0ds5nyLpSg6P4Anr1HcO86gHvnATw7D+I9egqSZKjIsMbBt36MDX/4Do51TWn3H41jDAQCOWF8GZITxZc48VZfdL0v+nc8i7XeN1NkOMz4gWO4dx7EvfMA7p378R46gQyFZ7QfzTPOvls+SMsT38dSXZ52e0VR0HWdYDCIyWTKqu8kx8KSE8WXAImCm8PhML29vRQXF0+6oLNV/ACkruM9cipi/e06gLv9AOP7j6L7A/Oy/+CZfvbd+G+0/L/vgjW9BRi1GHPC+PIiJ4ovIqLiF53mpnN29PT0UFJSsiSe1KnjSYTvROeE9XcAz66DePYcQvN4F3Rc3sMn2H/73TT95MtwLgsw4XijIhi1sHNZLy8PcqKYhcxXcLMQIiNxWgwCPX242/dPEsHwyNiSjGX0mTba3nI3rT/571gqoJQy6XcI5LJeXkbkRHEJmUlw82ymvEtl1chwmOCR4/j3HeLoidN4du5FsVpx7+5A9wWXZExT0f6ynbYP/iev+OZnMJlM6Lqe9PvKZb28vMj9ugvMTIsZzOd632JZiuExN55d+3C378Gzay+evQfRvb5p2+VfuJahP+1c8PFkSvinT/BCeTEX/OfdSS3FKNHfIxQKAeSE8SVM7pedJxIFNw8PD2OxWKat6S1mfN9CiKL/VCfuXXsjIrhzL77jpyCD44zv3U/hFVsYenL7vI9ptvgfeJQdFSVseMetGTte9u7dy/r163NZLy9RcqI4Q2ay3tfd3U11dXXaElYLxXxYinowyPi+Q7h37sWzcw/uXfsIDw7Pen/je/aQ94r1jD67Z07jmjc0Hc+nv8mB4kLEytq0m0spGR8fz2W9vITJiWIS5qOYgaqqS+romM0UPDQ4NCGAe3Hv3Mv4/sPI4DyuA0pJoPskhmWVhI/3zN9+54D0Bxj+yJcwfeVu2LQp5ba6rsd+61zWy0uTl7UoJitmEL/eN5diBtHp1lKRzlKUUuI7egLPrr242yMiGDjdteDjkv4A5mIHojif0MDIgh8vE+TwGIF7HqBzzWpq1iTPeokXxVzWy0uTl4UoThW/qPClKl4afcyFaFbEUhL/+TSvD8/eA3ja90Sswd370cbcSzKu8MAg9qZGRsfGkcHQkoxhGt19HH3bx7H86huUVFUk3CTeS53Lenlp8pISxalW3/j4OEajccmKGSiKsuTTZykl+riHkZ98n7N/2sH
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"text/plain": [
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"<Figure size 420x280 with 2 Axes>"
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]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
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"X, Y = np.meshgrid(multi_param_epochs, multi_param_nodes)\n",
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"\n",
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"# fig = plt.figure(figsize=(10, 5))\n",
"fig = plt.figure()\n",
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"fig.set_dpi(fig_dpi)\n",
"ax = plt.axes(projection='3d')\n",
"\n",
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"surf = ax.plot_surface(X, Y, mean_param_accuracy[0, :, :], cmap='coolwarm')\n",
"ax.set_title(f'Average Accuracy')\n",
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"ax.set_xlabel('Epochs')\n",
"ax.set_ylabel('Hidden Nodes')\n",
"ax.set_zlabel('Accuracy')\n",
"ax.view_init(30, -110)\n",
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"# ax.set_zlim([0, 1])\n",
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"fig.colorbar(surf, shrink=0.3, aspect=6)\n",
"\n",
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"plt.tight_layout()\n",
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"# plt.savefig(f'graphs/{exp1_testname}-acc-surf.png')\n",
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"plt.show()"
]
},
{
"cell_type": "markdown",
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"metadata": {
"tags": [
"exp1"
]
},
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"source": [
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"### Test Error Rate Curves"
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]
},
{
"cell_type": "code",
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"execution_count": 55,
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"metadata": {
"executionInfo": {
"elapsed": 2653349,
"status": "aborted",
"timestamp": 1615994110347,
"user": {
"displayName": "Andy Pack",
"photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjA4K4ZhdArHXAFbAGr4n0aCv2HmyUpx4cy6zcUq34=s64",
"userId": "16615063155528027547"
},
"user_tz": 0
},
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"id": "Jrn3hKQAlGcc",
"tags": [
"exp1"
]
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},
"outputs": [
{
"data": {
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"image/png": "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
2021-03-19 17:21:00 +00:00
"text/plain": [
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"<Figure size 420x280 with 1 Axes>"
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]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
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"# fig = plt.figure(figsize=(7, 5))\n",
"fig = plt.figure()\n",
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"fig.set_dpi(fig_dpi)\n",
"\n",
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"for idx, layer in enumerate(mean_param_accuracy[0, :, :]):\n",
"# plt.errorbar(epochs, 1- layer, yerr=std_param_accuracy[idx], label=f'{hidden_nodes[idx]} Nodes')\n",
" plt.plot(multi_param_epochs, 1 - layer, '-', label=f'{multi_param_nodes[idx]} Nodes', lw=2)\n",
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"\n",
"plt.legend()\n",
"plt.grid()\n",
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"plt.title(f\"Test error rates for different epochs and hidden nodes\")\n",
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"plt.xlabel(\"Epochs\")\n",
"plt.ylabel(\"Error Rate\")\n",
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"plt.ylim(0)\n",
"\n",
"plt.tight_layout()\n",
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"# plt.savefig(f'graphs/{exp1_testname}-error-rate-curves.png')\n",
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"plt.show()"
]
},
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{
"cell_type": "markdown",
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"metadata": {
"tags": [
"exp1"
]
},
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"source": [
"### Test/Train Error Over Nodes"
]
},
{
"cell_type": "code",
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"execution_count": 56,
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"metadata": {
"tags": [
"exp1"
]
},
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"outputs": [
{
"data": {
2021-04-28 21:57:13 +01:00
"image/png": "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
2021-03-22 20:49:29 +00:00
"text/plain": [
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"<Figure size 560x933.333 with 10 Axes>"
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]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, axes = plt.subplots(math.ceil(len(multi_param_nodes) / 2), 2, figsize=(8, 8*math.ceil(len(multi_param_nodes) / 2)/3))\n",
"fig.set_dpi(fig_dpi)\n",
"\n",
"for idx, (nodes, ax) in enumerate(zip(multi_param_nodes, axes.flatten())):\n",
" ax.set_title(f'Error Rates For {nodes} Nodes')\n",
"# ax.errorbar(multi_param_epochs, 1 - mean_param_accuracy[0, idx, :], fmt='x', ls='-', yerr=std_param_accuracy[0, idx, :], markersize=4, lw=1, label='Test', capsize=4, c=(0, 0, 1), ecolor=(0, 0, 1, 0.5))\n",
"# ax.errorbar(multi_param_epochs, 1 - mean_param_accuracy[1, idx, :], fmt='x', ls='-', yerr=std_param_accuracy[1, idx, :], markersize=4, lw=1, label='Train', capsize=4, c=(1, 0, 0), ecolor=(1, 0, 0, 0.5))\n",
" ax.plot(multi_param_epochs, 1 - mean_param_accuracy[0, idx, :], 'x', ls='-', lw=1, label='Test', c=(0, 0, 1))\n",
" ax.plot(multi_param_epochs, 1 - mean_param_accuracy[1, idx, :], 'x', ls='-', lw=1, label='Train', c=(1, 0, 0))\n",
" ax.set_ylim(0, np.round(np.max(1 - mean_param_accuracy + std_param_accuracy) + 0.05, 1))\n",
" ax.legend()\n",
" ax.grid()\n",
"\n",
"fig.tight_layout()\n",
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"# fig.savefig(f'graphs/{exp1_testname}-test-train-error-rate.png')"
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]
},
{
"cell_type": "code",
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"execution_count": 126,
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"metadata": {
"tags": [
"exp1"
]
},
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"outputs": [
{
"data": {
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"image/png": "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"text/plain": [
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"<Figure size 560x560 with 6 Axes>"
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]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, axes = plt.subplots(math.ceil(len(multi_param_nodes) / 2), 2, figsize=(8, 8*math.ceil(len(multi_param_nodes) / 2)/3))\n",
"fig.set_dpi(fig_dpi)\n",
"\n",
"for idx, (nodes, ax) in enumerate(zip(multi_param_nodes, axes.flatten())):\n",
" ax.set_title(f'Error Rate Std Dev. For {nodes} Nodes')\n",
" ax.plot(multi_param_epochs, std_param_accuracy[0, idx, :], 'x', ls='-', lw=1, label='Test', c=(0, 0, 1))\n",
" ax.plot(multi_param_epochs, std_param_accuracy[1, idx, :], 'x', ls='-', lw=1, label='Train', c=(1, 0, 0))\n",
" ax.set_ylim(0, np.round(np.max(std_param_accuracy) + 0.05, 1))\n",
" ax.legend()\n",
" ax.grid()\n",
"\n",
"fig.tight_layout()\n",
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"# fig.savefig(f'graphs/{exp1_testname}-test-train-error-rate-std.png')"
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]
},
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{
"cell_type": "markdown",
"metadata": {
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"id": "eUPJuxUtVUc3",
"tags": [
"exp2"
]
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},
"source": [
"# Experiment 2\n",
"\n",
"For cancer dataset, choose an appropriate value of node and epochs, based on Exp 1) and use ensemble of individual (base) classifiers with random starting weights and Majority Vote to see if performance improves - repeat the majority vote ensemble at least thirty times with different 50/50 split and average and graph (Each classifier in the ensemble sees the same training patterns). Repeat for a different odd number (prevents tied vote) of individual classifiers between 3 and 25, and comment on the result of individualclassifier accuracy vs ensemble accuracy as number of base classifiers varies. Consider changing the number of nodes/epochs (both less complex and more complex) to see if you obtain better performance, and comment on the result with respect to why the optimal node/epoch combination may be different for an ensemble compared with the base classifier, as in Exp 1). \n",
"\n",
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"(Hint4: to implement majority vote you need to determine the predicted class labels -probably easier to implement yourself rather than use the ensemble matlab functions)\n"
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]
},
{
"cell_type": "code",
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"execution_count": 6,
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"metadata": {
"tags": [
"exp2",
"exp-func"
]
},
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"outputs": [],
"source": [
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"num_models=[1, 3, 9, 15, 25]\n",
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"\n",
"def evaluate_ensemble_vote(hidden_nodes=16, \n",
" epochs=50, \n",
" batch_size=128,\n",
" optimizer=lambda: 'sgd',\n",
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" weight_init=lambda: 'glorot_uniform',\n",
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" loss=lambda: 'categorical_crossentropy',\n",
" metrics=['accuracy'],\n",
" callbacks=None,\n",
" validation_split=None,\n",
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" round_predictions=True,\n",
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"\n",
" nmodels=num_models,\n",
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" tboard=True,\n",
" exp='2',\n",
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"\n",
" verbose=0,\n",
" print_params=True,\n",
" return_model=True,\n",
"\n",
" dtrain=data_train,\n",
" dtest=data_test,\n",
" ltrain=labels_train,\n",
" ltest=labels_test):\n",
" for m in nmodels:\n",
" if print_params:\n",
" print(f\"Models: {m}\")\n",
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" \n",
" response = {\"epochs\": list(),\n",
" \"num_models\": m}\n",
" \n",
" ###################\n",
" ## GET MODELS\n",
" ###################\n",
" if isinstance(hidden_nodes, tuple): # for range of hidden nodes, calculate value per model\n",
" if m == 1:\n",
" models = [get_model(int(np.mean(hidden_nodes)), weight_init=weight_init)]\n",
" response[\"nodes\"] = [int(np.mean(hidden_nodes))]\n",
" \n",
" else:\n",
" models = [get_model(int(i), weight_init=weight_init) \n",
" for i in np.linspace(*hidden_nodes, num=m)]\n",
" response[\"nodes\"] = [int(i) for i in np.linspace(*hidden_nodes, num=m)]\n",
" \n",
" else: # not a range of epochs, just set to given value\n",
" models = [get_model(hidden_nodes, weight_init=weight_init) for _ in range(m)]\n",
" response[\"nodes\"] = hidden_nodes\n",
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"\n",
" for model in models: \n",
" model.compile(\n",
" optimizer=optimizer(),\n",
" loss=loss(),\n",
" metrics=metrics\n",
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" ) \n",
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" \n",
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" if tboard:\n",
" if callbacks is not None:\n",
" cb = [i() for i in callbacks] + [tensorboard_callback(prefix=f'exp{exp}-{m}-')]\n",
" else:\n",
" cb = [tensorboard_callback(prefix=f'exp{exp}-{m}-')]\n",
" \n",
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" ###################\n",
" ## TRAIN MODELS\n",
" ###################\n",
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" histories = list()\n",
" for idx, model in enumerate(models):\n",
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" if isinstance(epochs, tuple): # for range of epochs, calculate value per model\n",
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" if m == 1:\n",
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" e = np.mean(epochs) # average, not lower bound if single model\n",
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" else:\n",
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" e = np.linspace(*epochs, num=m)[idx]\n",
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" e = int(e)\n",
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" else: # not a range of epochs, just set to given value\n",
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" e = epochs\n",
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" \n",
"# print(m, e) # debug\n",
" \n",
" history = model.fit(dtrain.to_numpy(), \n",
" ltrain.to_numpy(), \n",
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" epochs=e, \n",
" verbose=verbose,\n",
"\n",
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" callbacks=cb,\n",
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" validation_split=validation_split)\n",
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" histories.append(history.history)\n",
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" response[\"epochs\"].append(e)\n",
"\n",
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" ########################\n",
" ## FEEDFORWARD TEST\n",
" ########################\n",
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" # TEST DATA PREDICTIONS\n",
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" response[\"predictions\"] = [model(dtest.to_numpy()) for model in models]\n",
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" # TEST LABEL TENSOR\n",
" ltest_tensor = tf.constant(ltest.to_numpy())\n",
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"\n",
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" ########################\n",
" ## ENSEMBLE ACCURACY\n",
" ########################\n",
" ensem_sum_rounded = sum(tf.math.round(pred) for pred in response[\"predictions\"])\n",
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" ensem_sum = sum(response[\"predictions\"])\n",
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" # round predictions to onehot vectors and sum over all ensemble models\n",
" # take argmax for ensemble predicted class\n",
" \n",
" correct = 0 # number of correct ensemble predictions\n",
" correct_num_models = 0 # when correctly predicted ensembley, proportion of models correctly classifying\n",
" individual_accuracy = 0 # proportion of models correctly classifying\n",
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" \n",
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" # pc = predicted class, pcr = rounded predicted class, gt = ground truth\n",
" for pc, pcr, gt in zip(ensem_sum, ensem_sum_rounded, ltest_tensor):\n",
" gt_argmax = tf.math.argmax(gt)\n",
" \n",
" if round_predictions:\n",
" pred_val = pcr\n",
" else:\n",
" pred_val = pc\n",
" \n",
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" correct_models = pcr[gt_argmax] / m # use rounded value so will divide nicely\n",
" individual_accuracy += correct_models\n",
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" \n",
" if tf.math.argmax(pred_val) == gt_argmax: # ENSEMBLE EVALUATE HERE\n",
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" correct += 1\n",
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" correct_num_models += correct_models\n",
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" \n",
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"# print(pc.numpy(), pcr.numpy(), gt.numpy(), (pcr[gt_argmax] / m).numpy(), True) # debug\n",
"# else:\n",
"# print(pc.numpy(), pcr.numpy(), gt.numpy(), (pcr[gt_argmax] / m).numpy(), False)\n",
" \n",
" ########################\n",
" ## RESULTS\n",
" ########################\n",
" response.update({\n",
" \"history\": histories,\n",
" \"optimizer\": model.optimizer.get_config(),\n",
" \"model_config\": json.loads(model.to_json()),\n",
" \"loss\": model.loss,\n",
" \"round_predictions\": round_predictions,\n",
" \n",
" \"accuracy\": correct / len(ltest), # average number of correct ensemble predictions\n",
" \"agreement\": correct_num_models / correct, # when correctly predicted ensembley, average proportion of models correctly classifying\n",
" \"individual_accuracy\": individual_accuracy / len(ltest) # average proportion of individual models correctly classifying\n",
" })\n",
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"\n",
" if return_model:\n",
" response[\"models\"] = models\n",
"\n",
" yield response"
]
},
{
"cell_type": "markdown",
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"metadata": {
"tags": [
"exp2"
]
},
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"source": [
"## Single Iteration\n",
"Run a single iteration of ensemble model investigations"
]
},
{
"cell_type": "code",
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"execution_count": 20,
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"metadata": {
"tags": [
"exp2"
]
},
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Models: 1\n",
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"13 [50]\n",
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"Models: 3\n",
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"[1, 13, 25] [50, 50, 50]\n",
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"Models: 9\n",
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"[1, 4, 7, 10, 13, 16, 19, 22, 25] [50, 50, 50, 50, 50, 50, 50, 50, 50]\n",
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"Models: 15\n",
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"[1, 2, 4, 6, 7, 9, 11, 13, 14, 16, 18, 19, 21, 23, 25] [50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50]\n",
"Models: 25\n",
"[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25] [50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50]\n"
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]
}
],
"source": [
"single_ensem_results = list()\n",
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"# for test in evaluate_ensemble_vote(epochs=(5, 300), optimizer=lambda: tf.keras.optimizers.SGD(learning_rate=0.02)):\n",
"for test in evaluate_ensemble_vote(hidden_nodes=(1, 25), optimizer=lambda: tf.keras.optimizers.SGD(learning_rate=0.02)):\n",
" single_ensem_results.append(test)\n",
" print(test[\"nodes\"], test[\"epochs\"])"
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]
},
{
"cell_type": "code",
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"execution_count": 23,
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"metadata": {
"tags": [
"exp2"
]
},
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"outputs": [
{
"data": {
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"image/png": "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"text/plain": [
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"<Figure size 560x350 with 1 Axes>"
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]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
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"fig = plt.figure(figsize=(8, 5))\n",
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"fig.set_dpi(fig_dpi)\n",
"\n",
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"ensem_x = [i[\"num_models\"] for i in single_ensem_results]\n",
"\n",
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"plt.plot(ensem_x, 1 - np.array([i[\"accuracy\"] for i in single_ensem_results]), 'x-', label='Ensemble Test')\n",
"plt.plot(ensem_x, 1 - np.array([i[\"individual_accuracy\"] for i in single_ensem_results]), 'x-', label='Individual Test')\n",
"plt.plot(ensem_x, 1 - np.array([i[\"agreement\"] for i in single_ensem_results]), 'x-', label='Disagreement')\n",
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"\n",
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"plt.title(\"Test Error Rates for Horizontal Model Ensembles\")\n",
"plt.ylim(0)\n",
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"plt.grid()\n",
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"plt.legend()\n",
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"plt.ylabel(\"Error Rate\")\n",
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"plt.xlabel(\"Number of Models\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
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"metadata": {
"tags": [
"exp2"
]
},
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"source": [
"## Multiple Iterations\n",
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"Run multiple iterations of the ensemble model investigations and average\n",
"\n",
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"### CSV Results\n",
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"\n",
"| test | learning rate | momentum | batch size | hidden nodes | epochs | models |\n",
"| --- | --- | --- | --- | --- | --- | --- |\n",
"|1|0.06|0|128|16|50|1, 3, 9, 15, 25|\n",
"|2|0.06|0|35|16|1 - 100|1, 3, 9, 15, 25|\n",
"\n",
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"### Pickle Results\n",
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"\n",
"| test | learning rate | momentum | batch size | hidden nodes | epochs | models |\n",
"| --- | --- | --- | --- | --- | --- | --- |\n",
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"|3|0.06|0.05|35|16|1 - 300|1, 3, 9, 15, 25|\n",
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"|4|0.06|0.05|35|1 - 50|50|1, 3, 9, 15, 25|\n",
"|5|0.06|0.05|35|1 - 300|50|1, 3, 9, 15, 25|\n",
"|6|0.001|0.01|35|1 - 400|50|1, 3, 9, 15, 25|\n",
"|7|0.01|0.01|35|1 - 400|30 - 150|1, 3, 9, 15, 25|\n",
"|8|0.03|0.01|35|1 - 400|5 - 100|1, 3, 9, 15, 25|\n",
"|9|0.1|0.01|35|1 - 400|20|1, 3, 9, 15, 25|\n",
"|10|0.15|0.01|35|1 - 400|20|1, 3, 9, 15, 25, 35, 45|\n",
"|11|0.15|0.01|35|1 - 400|10|1, 3, 9, 15, 25, 35, 45|"
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]
},
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{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"batch_size=35\n",
"test_size=0.5\n",
"epochs=10\n",
"lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(0.1,\n",
" decay_steps=100000,\n",
" decay_rate=0.96)"
]
},
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{
"cell_type": "code",
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"execution_count": 36,
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"metadata": {
"tags": [
"exp2"
]
},
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"Iteration 1/30\n",
"Iteration 2/30\n",
"Iteration 3/30\n",
"Iteration 4/30\n",
"Iteration 5/30\n",
"Iteration 6/30\n",
"Iteration 7/30\n",
"Iteration 8/30\n",
"Iteration 9/30\n",
"Iteration 10/30\n",
"Iteration 11/30\n",
"Iteration 12/30\n",
"Iteration 13/30\n",
"Iteration 14/30\n",
"Iteration 15/30\n",
"Iteration 16/30\n",
"Iteration 17/30\n",
"Iteration 18/30\n",
"Iteration 19/30\n",
"Iteration 20/30\n",
"Iteration 21/30\n",
"Iteration 22/30\n",
"Iteration 23/30\n",
"Iteration 24/30\n",
"Iteration 25/30\n",
"Iteration 26/30\n",
"Iteration 27/30\n",
"Iteration 28/30\n",
"Iteration 29/30\n",
"Iteration 30/30\n"
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]
}
],
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"source": [
"multi_ensem_results = list()\n",
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"multi_ensem_iterations = 30\n",
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"for i in range(multi_ensem_iterations):\n",
" print(f\"Iteration {i+1}/{multi_ensem_iterations}\")\n",
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" data_train, data_test, labels_train, labels_test = train_test_split(data, labels, test_size=test_size, stratify=labels)\n",
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" multi_ensem_results.append(list(evaluate_ensemble_vote(epochs=10,\n",
" hidden_nodes=(1, 400),\n",
" nmodels=[1, 3, 9, 15, 25, 35, 45],\n",
" optimizer=lambda: tf.keras.optimizers.SGD(learning_rate=0.15, momentum=0.01),\n",
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" weight_init=lambda: 'random_uniform',\n",
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" batch_size=batch_size,\n",
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" dtrain=data_train, \n",
" dtest=data_test, \n",
" ltrain=labels_train, \n",
" ltest=labels_test,\n",
" return_model=False,\n",
" print_params=False)))"
]
},
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{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"699"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(data)"
]
},
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{
"cell_type": "markdown",
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"metadata": {
"tags": [
"exp2"
]
},
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"source": [
"### Accuracy Tensor\n",
"\n",
"Create a tensor for holding the accuracy results\n",
"\n",
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"(Iterations x Param x Number of models)\n",
"\n",
"#### Params\n",
"0. Test Accuracy\n",
"1. Train Accuracy\n",
"2. Individual Accuracy\n",
"3. Agreement"
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]
},
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{
"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
"outputs": [],
"source": [
"def test_tensor_data(test):\n",
" return [test[\"accuracy\"], \n",
" np.mean([i[\"accuracy\"][-1] for i in test[\"history\"]]), # avg train acc\n",
" test[\"individual_accuracy\"], \n",
" test[\"agreement\"]]"
]
},
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{
"cell_type": "code",
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"execution_count": 48,
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"metadata": {
"tags": [
"exp2"
]
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"30 Tests\n",
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"Models: [1, 3, 9, 15, 25, 35, 45]\n",
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"\n",
"Loss: categorical_crossentropy\n",
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"LR: 0.15\n",
"Momentum: 0.01\n"
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]
}
],
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"source": [
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"multi_ensem_models = sorted(list({i[\"num_models\"] for i in multi_ensem_results[0]}))\n",
"multi_ensem_iter = len(multi_ensem_results)\n",
"\n",
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"accuracy_ensem_tensor = np.zeros((multi_ensem_iter, 4, len(multi_ensem_models)))\n",
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"for iter_idx, iteration in enumerate(multi_ensem_results):\n",
" for single_test in iteration:\n",
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" \n",
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" ensem_models_idx = multi_ensem_models.index(single_test['num_models'])\n",
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" accuracy_ensem_tensor[iter_idx, :, ensem_models_idx] = test_tensor_data(single_test)\n",
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" \n",
"mean_ensem_accuracy = np.mean(accuracy_ensem_tensor, axis=0)\n",
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"std_ensem_accuracy = np.std(accuracy_ensem_tensor, axis=0)\n",
"\n",
"print(f'{multi_ensem_iter} Tests')\n",
"print(f'Models: {multi_ensem_models}')\n",
"print()\n",
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"print(f'Loss: {multi_ensem_results[0][0][\"loss\"]}')\n",
"print(f'LR: {multi_ensem_results[0][0][\"optimizer\"][\"learning_rate\"]:.3}')\n",
"print(f'Momentum: {multi_ensem_results[0][0][\"optimizer\"][\"momentum\"]:.3}')"
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]
},
{
"cell_type": "markdown",
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"metadata": {
"tags": [
"exp2"
]
},
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"source": [
"#### Export/Import Test Sets\n",
"\n",
"Export mean and standard deviations for retrieval and visualisation "
]
},
{
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"cell_type": "code",
"execution_count": 37,
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"metadata": {
"tags": [
"exp2"
]
},
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"outputs": [],
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"source": [
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"pickle.dump(multi_ensem_results, open(\"results/exp2-test11.p\", \"wb\"))"
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]
},
{
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"cell_type": "code",
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"execution_count": 47,
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"metadata": {},
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"outputs": [],
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"source": [
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"exp2_testname = 'exp2-test11'\n",
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"multi_ensem_results = pickle.load(open(f\"results/{exp2_testname}.p\", \"rb\"))"
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]
},
{
"cell_type": "raw",
"metadata": {},
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"source": [
"np.savetxt(\"exp2-mean.csv\", mean_ensem_accuracy, delimiter=',')\n",
"np.savetxt(\"exp2-std.csv\", std_ensem_accuracy, delimiter=',')"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"mean_ensem_accuracy = np.loadtxt(\"results/test1-exp2-mean.csv\", delimiter=',')\n",
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"std_ensem_accuracy = np.loadtxt(\"results/test1-exp2-std.csv\", delimiter=',')"
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]
},
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{
"cell_type": "markdown",
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"metadata": {
"tags": [
"exp2"
]
},
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"source": [
"### Best Results"
]
},
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{
"cell_type": "code",
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"execution_count": 49,
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"metadata": {
"tags": [
"exp2"
]
},
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"Models: 1, 71.8% Accurate\n"
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]
}
],
"source": [
"best_ensem_accuracy_idx = np.unravel_index(np.argmax(mean_ensem_accuracy[0, :]), mean_ensem_accuracy.shape)\n",
"best_ensem_accuracy = mean_ensem_accuracy[best_ensem_accuracy_idx]\n",
"best_ensem_accuracy_models = multi_ensem_models[best_ensem_accuracy_idx[1]]\n",
"\n",
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"print(f'Models: {best_ensem_accuracy_models}, {best_ensem_accuracy * 100:.3}% Accurate')"
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]
},
{
"cell_type": "markdown",
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"metadata": {
"tags": [
"exp2"
]
},
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"source": [
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"### Test/Train Error Over Model Numbers"
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]
},
{
"cell_type": "code",
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"execution_count": 50,
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"metadata": {
"tags": [
"exp2"
]
},
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"outputs": [
{
"data": {
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"image/png": "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"text/plain": [
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"<Figure size 560x350 with 1 Axes>"
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]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
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"fig = plt.figure(figsize=(8, 5))\n",
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"fig.set_dpi(fig_dpi)\n",
"\n",
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"# plt.plot(multi_ensem_models, 1 - mean_ensem_accuracy[0, :], 'x-', label='Ensemble Test')\n",
"# plt.plot(multi_ensem_models, 1 - mean_ensem_accuracy[2, :], 'x-', label='Individual Test')\n",
"# plt.plot(multi_ensem_models, 1 - mean_ensem_accuracy[1, :], 'x-', label='Individual Train')\n",
"# plt.plot(multi_ensem_models, 1 - mean_ensem_accuracy[3, :], 'x-', label='Agreement')\n",
"\n",
"plt.errorbar(multi_ensem_models, 1 - mean_ensem_accuracy[0, :], yerr=std_ensem_accuracy[0, :], capsize=4, label='Ensemble Test')\n",
"plt.errorbar(multi_ensem_models, 1 - mean_ensem_accuracy[2, :], yerr=std_ensem_accuracy[2, :], capsize=4, label='Individual Test')\n",
"plt.errorbar(multi_ensem_models, 1 - mean_ensem_accuracy[1, :], yerr=std_ensem_accuracy[1, :], capsize=4, label='Individual Train')\n",
"plt.errorbar(multi_ensem_models, 1 - mean_ensem_accuracy[3, :], yerr=std_ensem_accuracy[3, :], capsize=4, label='Disagreement')\n",
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"\n",
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"plt.title(f\"Error Rate for Horizontal Ensemble Models\")\n",
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"# plt.ylim(0, 1)\n",
"plt.ylim(0, np.max(1 - mean_ensem_accuracy + std_ensem_accuracy) + 0.05)\n",
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"plt.grid()\n",
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"plt.legend()\n",
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"plt.xlabel(\"Number of Models\")\n",
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"plt.ylabel(\"Error Rate\")\n",
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"plt.savefig(f'graphs/{exp2_testname}-error-rate-curves.png')\n",
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"plt.show()"
]
},
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{
"cell_type": "markdown",
"metadata": {
"id": "FSZq1mNiVZq_",
"tags": [
"ex3"
]
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},
"source": [
"# Experiment 3\n",
"\n",
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"Repeat Exp 2) for cancer dataset with two different optimisers of your choice e.g. 'trainlm' and 'trainrp'. Comment and discuss the result and decide which is more appropriate training algorithm for the problem. In your discussion, include in your description a detailed account of how the training algorithms (optimisations) work."
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]
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},
{
"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
"outputs": [],
"source": [
"def evaluate_optimisers(optimizers=[(lambda: 'sgd', 'sgd'), \n",
" (lambda: 'adam', 'adam'), \n",
" (lambda: 'rmsprop', 'rmsprop')],\n",
" weight_init=lambda: 'glorot_uniform',\n",
" print_params=True,\n",
" **kwargs\n",
" ):\n",
" for o in optimizers:\n",
" \n",
" if print_params:\n",
" print(f'Optimiser: {o[1]}')\n",
" \n",
" yield list(evaluate_ensemble_vote(optimizer=o[0],\n",
" weight_init=weight_init,\n",
" exp=f'3-{o[1]}',\n",
" print_params=print_params,\n",
" **kwargs\n",
" ))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Single Iteration"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimiser: sgd\n",
"Models: 1\n",
"Models: 3\n",
"Models: 5\n",
"Optimiser: adam\n",
"Models: 1\n",
"Models: 3\n",
"Models: 5\n",
"Optimiser: rmsprop\n",
"Models: 1\n",
"Models: 3\n",
"Models: 5\n"
]
}
],
"source": [
"single_optim_results = list()\n",
"for test in evaluate_optimisers(epochs=(5, 300), nmodels=[1, 3, 5]):\n",
" single_optim_results.append(test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Multiple Iterations\n",
"\n",
"### Pickle Results\n",
"\n",
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"| test | optim1 | optim2 | optim3 | lr | momentum | epsilon | batch size | hidden nodes | epochs | models |\n",
"| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n",
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"| 1 | SGD | Adam | RMSprop | 0.1 | 0.0 | 1e7 | 35 | 16 | 1 - 100 | 1, 3, 9, 15, 25 |\n",
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"| 2 | SGD | Adam | RMSprop | 0.05 | 0.01 | 1e7 | 35 | 16 | 1 - 100 | 1, 3, 9, 15, 25 |\n",
"| 3 | SGD | Adam | RMSprop | 0.1 | 0.01 | 1e7 | 35 | 1 - 400 | 20 | 1, 3, 9, 15, 25, 35, 45 |"
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]
},
{
"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"Iteration 1/30\n",
"Iteration 2/30\n",
"Iteration 3/30\n",
"Iteration 4/30\n",
"Iteration 5/30\n",
"Iteration 6/30\n",
"Iteration 7/30\n",
"Iteration 8/30\n",
"Iteration 9/30\n",
"Iteration 10/30\n",
"Iteration 11/30\n",
"Iteration 12/30\n",
"Iteration 13/30\n",
"Iteration 14/30\n",
"Iteration 15/30\n",
"Iteration 16/30\n",
"Iteration 17/30\n",
"Iteration 18/30\n",
"Iteration 19/30\n",
"Iteration 20/30\n",
"Iteration 21/30\n",
"Iteration 22/30\n",
"Iteration 23/30\n",
"Iteration 24/30\n",
"Iteration 25/30\n",
"Iteration 26/30\n",
"Iteration 27/30\n",
"Iteration 28/30\n",
"Iteration 29/30\n",
"Iteration 30/30\n"
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]
}
],
"source": [
"multi_optim_results = list()\n",
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"multi_optim_iterations = 30\n",
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"\n",
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"multi_optim_lr = 0.1\n",
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"multi_optim_mom = 0.01\n",
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"multi_optim_eps = 1e-07\n",
"multi_optims = [(lambda: tf_optim.SGD(learning_rate=multi_optim_lr, \n",
" momentum=multi_optim_mom), 'sgd'), \n",
" (lambda: tf_optim.Adam(learning_rate=multi_optim_lr, \n",
" epsilon=multi_optim_eps), 'adam'), \n",
" (lambda: tf_optim.RMSprop(learning_rate=multi_optim_lr, \n",
" momentum=multi_optim_mom, \n",
" epsilon=multi_optim_eps), 'rmsprop')]\n",
"\n",
"for i in range(multi_optim_iterations):\n",
" print(f\"Iteration {i+1}/{multi_optim_iterations}\")\n",
" data_train, data_test, labels_train, labels_test = train_test_split(data, labels, test_size=0.5, stratify=labels)\n",
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" multi_optim_results.append(list(evaluate_optimisers(epochs=20,\n",
" hidden_nodes=(1, 400),\n",
" nmodels=[1, 3, 9, 15, 25, 35, 45],\n",
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" optimizers=multi_optims,\n",
" weight_init=lambda: 'random_uniform',\n",
" batch_size=35,\n",
" dtrain=data_train, \n",
" dtest=data_test, \n",
" ltrain=labels_train, \n",
" ltest=labels_test,\n",
" return_model=False,\n",
" print_params=False)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Accuracy Tensor\n",
"\n",
"Create a tensor for holding the accuracy results\n",
"\n",
"(Iterations x Param x Number of models)\n",
"\n",
"#### Params\n",
"0. Test Accuracy\n",
"1. Train Accuracy\n",
"2. Individual Accuracy\n",
"3. Agreement"
]
},
{
"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"30 Tests\n",
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"Optimisers: ['SGD', 'Adam', 'RMSprop']\n",
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"Models: [1, 3, 9, 15, 25, 35, 45]\n",
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"\n",
"Loss: categorical_crossentropy\n"
]
}
],
"source": [
"multi_optim_results_dict = dict() # indexed by optimiser name\n",
"multi_optim_iter = len(multi_optim_results) # number of iterations (30)\n",
"\n",
"#####################################\n",
"## INDIVIDUAL RESULTS TO DICTIONARY\n",
"#####################################\n",
"for iter_idx, iteration in enumerate(multi_optim_results): # of 30 iterations\n",
" for model_idx, model_test in enumerate(iteration): # of 3 optimisers\n",
" for single_optim_test in model_test: # single tests for each optimisers\n",
" \n",
" single_optim_name = single_optim_test[\"optimizer\"][\"name\"]\n",
" if single_optim_name not in multi_optim_results_dict:\n",
" multi_optim_results_dict[single_optim_name] = list(list() for _ in range(multi_optim_iter))\n",
"\n",
" multi_optim_results_dict[single_optim_name][iter_idx].append(single_optim_test)\n",
"\n",
"# list of numbers of models used in test\n",
"multi_optim_models = sorted(list({i[\"num_models\"] for i in multi_optim_results[0][0]}))\n",
"\n",
"##################################\n",
"## DICTIONARY TO RESULTS TENSORS\n",
"##################################\n",
"optim_tensors = dict()\n",
"for optim, optim_results in multi_optim_results_dict.items():\n",
" \n",
" accuracy_optim_tensor = np.zeros((multi_optim_iter, 4, len(multi_optim_models)))\n",
" for iter_idx, iteration in enumerate(optim_results):\n",
" for single_test in iteration:\n",
"\n",
" optim_models_idx = multi_optim_models.index(single_test['num_models'])\n",
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" accuracy_optim_tensor[iter_idx, :, optim_models_idx] = test_tensor_data(single_test)\n",
" \n",
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" optim_tensors[optim] = {\n",
" \"accuracy\": accuracy_optim_tensor,\n",
" \"mean\": np.mean(accuracy_optim_tensor, axis=0),\n",
" \"std\": np.std(accuracy_optim_tensor, axis=0)\n",
" }\n",
"\n",
"print(f'{multi_optim_iter} Tests')\n",
"print(f'Optimisers: {list(multi_optim_results_dict.keys())}')\n",
"print(f'Models: {multi_optim_models}')\n",
"print()\n",
"print(f'Loss: {multi_optim_results[0][0][0][\"loss\"]}')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Export/Import Test Sets\n",
"\n",
"Export mean and standard deviations for retrieval and visualisation "
]
},
{
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"cell_type": "code",
2021-04-28 12:10:25 +01:00
"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"pickle.dump(multi_optim_results, open(\"results/exp3-test3.p\", \"wb\"))"
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]
},
{
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"cell_type": "code",
"execution_count": 97,
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"metadata": {},
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"outputs": [],
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"source": [
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"exp3_testname = 'exp3-test3'\n",
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"multi_optim_results = pickle.load(open(f\"results/{exp3_testname}.p\", \"rb\"))"
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]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Best Results"
]
},
{
"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"SGD: 35 Models, 90.4% Accurate\n",
"Adam: 15 Models, 96.9% Accurate\n",
"RMSprop: 35 Models, 96.8% Accurate\n"
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]
}
],
"source": [
"for optim, optim_results in optim_tensors.items():\n",
" best_optim_accuracy_idx = np.unravel_index(np.argmax(optim_results[\"mean\"][0, :]), optim_results[\"mean\"].shape)\n",
" best_optim_accuracy = optim_results[\"mean\"][best_optim_accuracy_idx]\n",
" best_optim_accuracy_models = multi_optim_models[best_optim_accuracy_idx[1]]\n",
"\n",
" print(f'{optim}: {best_optim_accuracy_models} Models, {best_optim_accuracy * 100:.3}% Accurate')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Optimiser Error Rates"
]
},
{
"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 1680x350 with 3 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, axes = plt.subplots(1, 3, figsize=(24, 5))\n",
"fig.set_dpi(fig_dpi)\n",
"\n",
"for idx, ((optimiser_name, tensors_dict), ax) in enumerate(zip(optim_tensors.items(), axes.flatten())):\n",
" ax.plot(multi_optim_models, 1 - tensors_dict[\"mean\"][0, :], 'x-', label='Ensemble Test')\n",
" ax.plot(multi_optim_models, 1 - tensors_dict[\"mean\"][2, :], 'x-', label='Individual Test')\n",
" ax.plot(multi_optim_models, 1 - tensors_dict[\"mean\"][1, :], 'x-', label='Individual Train')\n",
" ax.plot(multi_optim_models, 1 - tensors_dict[\"mean\"][3, :], 'x-', label='Disagreement')\n",
"\n",
"# ax.errorbar(multi_optim_models, 1 - tensors_dict[\"mean\"][0, :], yerr=tensors_dict[\"std\"][0, :], capsize=4, label='Ensemble Test')\n",
"# ax.errorbar(multi_optim_models, 1 - tensors_dict[\"mean\"][2, :], yerr=tensors_dict[\"std\"][2, :], capsize=4, label='Individual Test')\n",
"# ax.errorbar(multi_optim_models, 1 - tensors_dict[\"mean\"][1, :], yerr=tensors_dict[\"std\"][1, :], capsize=4, label='Individual Train')\n",
"# ax.errorbar(multi_optim_models, 1 - tensors_dict[\"mean\"][3, :], yerr=tensors_dict[\"std\"][3, :], capsize=4, label='Disagreement')\n",
"\n",
" ax.set_title(f\"{optimiser_name} Error Rate for Ensemble Models\")\n",
"# ax.set_ylim(0, 1)\n",
" ax.set_ylim(0, np.max([np.max(1 - i[\"mean\"] + i[\"std\"]) for i in optim_tensors.values()]) + 0.03)\n",
" ax.grid()\n",
" ax.legend()\n",
" ax.set_xlabel(\"Number of Models\")\n",
" ax.set_ylabel(\"Error Rate\")\n",
"\n",
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"# plt.savefig(f'graphs/{exp3_testname}-error-rate-curves.png')\n",
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"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
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}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"authorship_tag": "ABX9TyNAMGLKzaoWaq1wvQ+w0w8h",
"collapsed_sections": [],
"name": "nncw.ipynb",
"provenance": [],
"toc_visible": true
},
"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",
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"version": "3.8.9"
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},
"toc-autonumbering": false,
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"toc-showmarkdowntxt": false,
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"toc-showtags": false
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},
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