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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",
"execution_count": 5,
"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": "iVBORw0KGgoAAAANSUhEUgAAA1gAAAGuCAYAAACNy6eFAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAArEAAAKxAFmbYLUAACAaUlEQVR4nOzddZxU1f/H8dfZJpalY5eU7u62SJVUsQMVMb4WtvwwsMUEJFQMREJQJERBlqW7uxtZuhd29/z+uAMsuMDCxp2ZfT8fj3kwc2s+Z2aYu585536OsdYiIiIiIiIiqRfgdgAiIiIiIiL+QgmWiIiIiIhIGlGCJSIiIiIikkaUYImIiIiIiKQRJVgiIiIiIiJpRAmWiIiIiIhIGlGCJV7BGPO1MebFFG670hhTP71jyijGmOLGmPgkjycaY+64xLbNjDEbrvF5Ghtjll5rnCIicp7OWzpviVyK0TxYkhrGmGNJHmYDTgBnP1QVrLXbMj6qjGeMaQBMAApaa08lWZ4d+Beob61ddol9iwMbrLVBKXieZsBga22pFGy7BbjHWjsjBU24ZsaYITjxv5OezyMikhZ03nJk5vNWkucbCDwAFLLW7s+I55TMQT1YkirW2uxnb0AcUDHJsm0AxuHXnzVr7SxgP9DmolXtgY2XOkmJiEjG0nnLkdnPW8aYUKATcAxItvctnZ73ikmp+D6//vIQ9xhjhhhjvjLG/IPz62BJY8xDxph1xpijxphlnl+1km7/uuf+A8aYf4wx/Y0xR4wxq4wxNZJsu8UY0yjJfl8YY6Z4jvuXMSZ3km0fMcbsMMbs8dy3xpjCycQ72Bjzfxct22SMaWSMyecZ/nDIGLPPGDPsEs0eCtx10bK7gZ+MMbmMMX969o81xgz0fLkn99pFG2Pu8dwPNMZ8bozZb4xZC9S7aNsvjTG7PLH9ZYwperY9QFHgL2PMMWPM3RcP0zDGVDTGTPfsu9AY0/Ci1/h5Y8xqz/qvLtHmSzLGhBlj+npe+23GmJ5n/2AxxtQzxiz2vL87jTHPXm65iEh603nrnMxy3moDHAfeB+65KMYSxpjxnjbsNsY87VkeZIx5yxiz1Rhz2BgT7Vn+n2GQSd83T2wvGmNWAxsu9zpc6vmNMYU9n61sSbZ70Bjz1xXaKS5QgiXp6U6gBxAObAH2ADcAOYEvgV8u9WUNNAZigFzAaODTyzzP7cCzQD4gEPgfgDGmMvAR0A4oATS4zDGGe46DZ986QDAwE3ge2AzkBaI8sSfnJ6C1MSbCc4wCwPXAzzj/1/p69q8C1AIev0w8Zz0GNAcqev7tctH6mUB5oBCwA/gCwFrbFdgG3Oz5VXZo0p2MMSHAH8BInNftQ+APY0yuJJu1w3kfKgG3G2OapyDepN7wxF0eaIRzArvPs+4z4GNrbQ7P8aOvsFxEJCPovJV5zlv3ACOAX4C6xpjrPM8TBIwH5nvaXtYTM8BLQEvPc+QGel7x1TivA9DM03a4xOtwqee31u4AFgC3JjlmF5z3SryMEixJT79aaxdaa+OttWestROstduttQnW2kE4Y95LX2LfNdbaYdbaBJwvj6qXeZ6R1tplnjHkvybZtiMw2lq7wFp7ErjcNUL/APmMMRU9j2/3HNcCZ3C+AItYa+M8wyr+w1q7DljueV5wTtQzrLU7rLX7rbV/ePbfDQzASTqupDPQx1q7x1q7i4tOktbaX6y1hz3t+yCFxwSoCwRYa7/wvDfDgbU4J46zPrPW7vN8qUdz+fcgOXcCb1prD3qG3XzC+RPtGaCUMSa3Z/3iKywXEckIOm9lgvOWMSYn0BoY7jk/zeF8L1ZdnAT7LWvtKWvtEWvtQs+6B4DXrLXbPJ+JmBTGDvC5tfZfT7sv9zpc7vl/wnMe9STDDXCSefEySrAkPe1I+sAY084Ys8jTHX4IyA/kucS+/ya5fwLIfpnnudS2BS+K4YJ4kvKcEEcBdxhjDM4JYrhn9Uc4v6pNM8asMcY8fJlYfuL8cIu7PY8xxoQbY37wDPs4AvTh0m1PqhCwPcnjpPcxxrxmjNngOea8FB4TIPLiYwFbPcvPupr34FLPkfRi8aTH74rz6+YGY8wMc7661qWWi4hkBJ23Msd563Zgl7V2nufxLzhtBygMbLXWJiazX2GcnsFrcfFn61Kvw+WefxTQ1NNr1xn401p75BrjkXSkBEvS07kSlZ4hFcOA14A81tqcwF7ApOPz78HpXj/rP2PYL3J2uEU9INFaOxfA8+vR/6y1RXF+vfry7FCCZPwCNDLGNMUZojDKs/w5nCEN1TzD354jZW3fDRRJ8vjcfc9zdMf5FS4CqHPRvpcrEbrrouOCM/Z9VwpiSqldnmP+5/jW2rXW2ttx/lj5BeezccnlIiIZROetzHHeugeINM51bnuAXkAZzzDL7UAxT9J6se1A8WSWHweynH3g6V26WNLP1uVeh0s+vyeZmoTT49gF5xo68UJKsCSjhAIhOCcnjDH/w/niTk9jgI7GmBrGmDDg1StsPx2nW/4dnHHZABhj2hhjrvN82R3G+ZJMSO4A1tq9OMM2fgDGJfllKRzn17TDxphiOF+sKTEKeNYYU8AYUwh4Msm6cJxhIPtwSg2/ftG+e0n+RAAw19O2Jz0X7XbGGQv+ZwrjuliQcYpanL0F4Zz43zDOhdJFcE7Ov3ie925jTB5rbTxwFM/reanlIiIu0HnLD89bnrY0xLk+rJrnVhHnuqd7cHqTjuKcv8KMMTmMMTU9uw8B3jHGFDFOMY8mnuXrgFzGmKaexPyNK4Rxudfhcs8PTg/js562T7iatkvGUYIlGcLzhd0D55eXPThd4dc08eBVPOdS4GWci2K3AGfHMMddYvtEnItnryfJiQooA0zF+cIbDzxjrd16maf+CedXtZ+SLPscZ9jEQZzx9mNS2IwBOBdNr8YZT/5LknV/4lwkuxVnDP3FY+w/AN73DG25oEqUtfY0zoWyXXDK9L4C3GqtPZjCuC72f8DJJLevgbdxxsevAWZ7Yv/es31rYK0x5ijwNOeLX1xquYhIhtJ5y2/PW3cD0621sz3Xie2x1u4BvuJ8ufa2ONc37cY5j50drv4RMMUT936cni+stYdxCpWMwBlCOP8KMVzydfD8wHip5weYCBQAxlhrk/1ciPs00bBkGsaYssAyIMzqgy8iIl5O5y1JjjFmBfA/a+0Ut2OR5KkHS/yaMaatp4s9AngPGKuTlIiIeCudt+RyjDE3AVlxeijFSynBEn93B05VoS04n/enXI1GRHyOMWaMMeagMWbUJdbXMcas9FQEu5p5cUSSo/OWJMsYMxxnyOXTl6gyKF5CQwRFREQuwxjTDOei9PuttZ2SWT8feBhYiXNdxSPW2uUZGaOIiHgP9WCJiIhchrU2GqdYwH8YYyKBIM+ksQk4vy63zcDwRETEywS5HUBSBQoUsCVKlEjVMY4dO0b27Fc7H6rvUPt8m9rn2/y9fZD6Ns6dO/dfa23BNAzJ20UCO5M83gk0TW5DY0xXnMm0yZo1a93ixYun6okTEhIIDAxM1TG8mdrn29Q+36b2XdmqVasueb7zqgSrRIkSzJkzJ1XHiImJoUmTJlfe0Eepfb5N7fNt/t4+SH0bjTFb0i4a/2KtHQwMBqhXr57V+e7y1D7fpvb5NrXvyi53vtMQQRERkWu3C4hK8jjKs0xERDIpJVgiIiLXyFq7C0gwxlQxxgQCd+JMEisiIpmUVw0RFBER8TbGmMlAVSCbMWYH0Bl4A+jqSbCeBIYBYcCPqiAoIuIF4uPh+HE4duw/t3zz50OJElCkSLo8tRIsEclU4uPj2bFjB6dOnbrqfcPDw1mzZk06ROU9rqaNhQoVIiIiIp0jcp+19sZkFrdOsn4OUDG1z3O1n01//zxea/vCwsIoXLgwQUH6E0fEZ5w5899E6OjRZJOjFN9Onrzk05UHqFhRCZaISFrYsWMH4eHhFCtWDGPMVe179OhRwsPD0yky75DSNp46dYodO3ZkigQro1ztZ9PfP4/X0j5rLQcOHGDHjh2ktkqjiCTDWoiLS3mSk9Ik6fTptI0zMBAiIiB7ducWHn7
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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",
"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",
2021-03-26 20:01:05 +00:00
"outputId": "134671d0-bfd3-4ee6-aa02-1a2a5b23f3ca",
"tags": [
"exp1"
]
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},
"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 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",
"ax.set_zlim([0, 1])\n",
"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": {
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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 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": 173,
2021-03-26 20:01:05 +00:00
"metadata": {
"tags": [
"exp1"
]
},
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"30 Tests\n",
"Nodes: [2, 8, 16, 24, 32]\n",
"Epochs: [1, 2, 4, 8, 16, 32, 64, 100, 150, 200]\n",
"\n",
"Loss: categorical_crossentropy\n",
"LR: 0.05000000074505806\n",
"Momentum: 0.009999999776482582\n"
]
}
],
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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": "raw",
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"metadata": {
"tags": [
"exp1"
]
},
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"source": [
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"exp1_testname = 'exp1-test5'\n",
"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": 141,
2021-03-26 20:01:05 +00:00
"metadata": {
"tags": [
"exp1"
]
},
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Nodes: 24, Epochs: 8, 96.9% Accurate\n"
]
}
],
"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",
"execution_count": 174,
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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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2021-03-19 17:21:00 +00:00
"text/plain": [
"<Figure size 3000x2000 with 2 Axes>"
]
},
"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",
"ax.set_zlim([0, 1])\n",
"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",
2021-03-22 20:49:29 +00:00
"execution_count": 175,
2021-03-19 17:21:00 +00:00
"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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"text/plain": [
"<Figure size 3000x2000 with 1 Axes>"
]
},
"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",
"execution_count": 169,
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"metadata": {
"tags": [
"exp1"
]
},
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"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 4000x4000 with 6 Axes>"
]
},
"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",
"execution_count": 170,
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"metadata": {
"tags": [
"exp1"
]
},
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"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 4000x4000 with 6 Axes>"
]
},
"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",
"\n",
" models = [get_model(hidden_nodes, weight_init=weight_init) for _ in range(m)]\n",
" for model in models: \n",
" model.compile(\n",
" optimizer=optimizer(),\n",
" loss=loss(),\n",
" metrics=metrics\n",
" )\n",
" \n",
" \n",
"\n",
" response = {\"nodes\": hidden_nodes, \n",
" \"epochs\": list(),\n",
" \"num_models\": m}\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'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",
" if isinstance(epochs, tuple):\n",
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" if m == 1:\n",
" e = (epochs[0] + epochs[1]) / 2 # average, not lower bound if single model\n",
" else:\n",
" e = np.linspace(epochs[0], epochs[1], num=m)[idx]\n",
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" e = int(e)\n",
" else:\n",
" 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": 11,
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"metadata": {
"tags": [
"exp2"
]
},
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Models: 1\n",
"Models: 3\n",
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"Models: 9\n",
"Models: 15\n",
"Models: 25\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",
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" single_ensem_results.append(test)"
]
},
{
"cell_type": "code",
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"execution_count": 16,
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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",
"plt.plot(ensem_x, [i[\"accuracy\"] for i in single_ensem_results], 'x-', label='Ensemble Accuracy')\n",
"plt.plot(ensem_x, [i[\"individual_accuracy\"] for i in single_ensem_results], 'x-', label='Individual Accuracy')\n",
"plt.plot(ensem_x, [i[\"agreement\"] for i in single_ensem_results], 'x-', label='Agreement')\n",
"\n",
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"plt.title(\"Test Accuracy for Horizontal Model Ensembles\")\n",
"plt.ylim(0, 1)\n",
"plt.grid()\n",
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"plt.legend()\n",
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"plt.ylabel(\"Accuracy\")\n",
"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",
"|3|0.06|0.05|35|16|1 - 300|1, 3, 9, 15, 25|"
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]
},
{
"cell_type": "code",
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"execution_count": 24,
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"metadata": {
"tags": [
"exp2"
]
},
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"Iteration 1/2\n",
"Iteration 2/2\n"
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]
}
],
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"source": [
"multi_ensem_results = list()\n",
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"multi_ensem_iterations = 2\n",
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"for i in range(multi_ensem_iterations):\n",
" print(f\"Iteration {i+1}/{multi_ensem_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_ensem_results.append(list(evaluate_ensemble_vote(epochs=(1, 100),\n",
" hidden_nodes=16,\n",
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" nmodels=[1, 3, 7, 11, 15],\n",
" optimizer=lambda: tf.keras.optimizers.SGD(learning_rate=0.05, momentum=0.01),\n",
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" 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",
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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",
"execution_count": 7,
"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": 25,
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"metadata": {
"tags": [
"exp2"
]
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"2 Tests\n",
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"Models: [1, 3, 7, 11, 15]\n",
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"\n",
"Loss: categorical_crossentropy\n",
"LR: 0.05\n",
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"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": "raw",
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"metadata": {
"tags": [
"exp2"
]
},
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"source": [
"pickle.dump(multi_ensem_results, open(\"result.p\", \"wb\"))"
]
},
{
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"cell_type": "code",
"execution_count": 22,
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"metadata": {},
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"outputs": [],
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"source": [
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"multi_ensem_results = pickle.load(open(\"results/exp2-test3.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": 26,
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"metadata": {
"tags": [
"exp2"
]
},
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"Models: 3, 77.1% 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": 29,
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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.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",
"| 1 | SGD | Adam | RMSprop | 0.1 | 0.0 | 1e7 | 35 | 16 | 1 - 100 | 1, 3, 9, 15, 25 |"
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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 = 0.0\n",
"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",
" multi_optim_results.append(list(evaluate_optimisers(epochs=(1, 100),\n",
" hidden_nodes=16,\n",
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" nmodels=[1, 3, 9, 15, 25],\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": 10,
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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]\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",
"execution_count": 13,
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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-test1.p\", \"wb\"))"
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]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"multi_optim_results = pickle.load(open(\"results/exp3-test1.p\", \"rb\"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Best Results"
]
},
{
"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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"SGD: 3 Models, 96.3% Accurate\n",
"Adam: 25 Models, 96.6% Accurate\n",
"RMSprop: 9 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": 12,
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"metadata": {},
"outputs": [
{
"data": {
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2021-03-29 18:34:04 +01:00
"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",
"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",
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
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},
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},
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