{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"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"
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import tensorflow as tf\n",
"tf.get_logger().setLevel('ERROR')\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib as mpl\n",
"import seaborn as sns\n",
"import random\n",
"import pickle\n",
"import json\n",
"\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"fig_dpi = 200\n",
"\n",
"%load_ext tensorboard"
]
},
{
"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",
"execution_count": 7,
"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": [
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Clump thickness | \n",
" Uniformity of cell size | \n",
" Uniformity of cell shape | \n",
" Marginal adhesion | \n",
" Single epithelial cell size | \n",
" Bare nuclei | \n",
" Bland chomatin | \n",
" Normal nucleoli | \n",
" Mitoses | \n",
"
\n",
" \n",
" \n",
" \n",
" count | \n",
" 699.000000 | \n",
" 699.000000 | \n",
" 699.000000 | \n",
" 699.000000 | \n",
" 699.000000 | \n",
" 699.000000 | \n",
" 699.000000 | \n",
" 699.000000 | \n",
" 699.000000 | \n",
"
\n",
" \n",
" mean | \n",
" 0.441774 | \n",
" 0.313448 | \n",
" 0.320744 | \n",
" 0.280687 | \n",
" 0.321602 | \n",
" 0.354363 | \n",
" 0.343777 | \n",
" 0.286695 | \n",
" 0.158941 | \n",
"
\n",
" \n",
" std | \n",
" 0.281574 | \n",
" 0.305146 | \n",
" 0.297191 | \n",
" 0.285538 | \n",
" 0.221430 | \n",
" 0.360186 | \n",
" 0.243836 | \n",
" 0.305363 | \n",
" 0.171508 | \n",
"
\n",
" \n",
" min | \n",
" 0.100000 | \n",
" 0.100000 | \n",
" 0.100000 | \n",
" 0.100000 | \n",
" 0.100000 | \n",
" 0.100000 | \n",
" 0.100000 | \n",
" 0.100000 | \n",
" 0.100000 | \n",
"
\n",
" \n",
" 25% | \n",
" 0.200000 | \n",
" 0.100000 | \n",
" 0.100000 | \n",
" 0.100000 | \n",
" 0.200000 | \n",
" 0.100000 | \n",
" 0.200000 | \n",
" 0.100000 | \n",
" 0.100000 | \n",
"
\n",
" \n",
" 50% | \n",
" 0.400000 | \n",
" 0.100000 | \n",
" 0.100000 | \n",
" 0.100000 | \n",
" 0.200000 | \n",
" 0.100000 | \n",
" 0.300000 | \n",
" 0.100000 | \n",
" 0.100000 | \n",
"
\n",
" \n",
" 75% | \n",
" 0.600000 | \n",
" 0.500000 | \n",
" 0.500000 | \n",
" 0.400000 | \n",
" 0.400000 | \n",
" 0.500000 | \n",
" 0.500000 | \n",
" 0.400000 | \n",
" 0.100000 | \n",
"
\n",
" \n",
" max | \n",
" 1.000000 | \n",
" 1.000000 | \n",
" 1.000000 | \n",
" 1.000000 | \n",
" 1.000000 | \n",
" 1.000000 | \n",
" 1.000000 | \n",
" 1.000000 | \n",
" 1.000000 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"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 "
]
},
"execution_count": 7,
"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",
"execution_count": 3,
"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": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Benign | \n",
" Malignant | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 1 | \n",
" 0 | \n",
"
\n",
" \n",
" 1 | \n",
" 1 | \n",
" 0 | \n",
"
\n",
" \n",
" 2 | \n",
" 1 | \n",
" 0 | \n",
"
\n",
" \n",
" 3 | \n",
" 0 | \n",
" 1 | \n",
"
\n",
" \n",
" 4 | \n",
" 1 | \n",
" 0 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Benign Malignant\n",
"0 1 0\n",
"1 1 0\n",
"2 1 0\n",
"3 0 1\n",
"4 1 0"
]
},
"execution_count": 3,
"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",
"execution_count": 8,
"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": [
"data_train, data_test, labels_train, labels_test = train_test_split(data, labels, test_size=0.5, stratify=labels)"
]
},
{
"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",
"execution_count": 9,
"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",
" layers = [tf.keras.layers.InputLayer(input_shape=(9,)), \n",
" tf.keras.layers.Dense(hidden_nodes, activation=activation(), kernel_initializer=weight_init()), \n",
" tf.keras.layers.Dense(2, activation='softmax', kernel_initializer=weight_init())]\n",
"\n",
" model = tf.keras.models.Sequential(layers)\n",
" return model"
]
},
{
"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",
"execution_count": 6,
"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": [
"Model: \"sequential\"\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"dense (Dense) (None, 9) 90 \n",
"_________________________________________________________________\n",
"dense_1 (Dense) (None, 2) 20 \n",
"=================================================================\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",
"execution_count": 7,
"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": [
"Epoch 1/5\n",
"11/11 [==============================] - 1s 2ms/step - loss: 0.7427 - accuracy: 0.6265\n",
"Epoch 2/5\n",
"11/11 [==============================] - 0s 2ms/step - loss: 0.6953 - accuracy: 0.6543\n",
"Epoch 3/5\n",
"11/11 [==============================] - 0s 2ms/step - loss: 0.7317 - accuracy: 0.6061\n",
"Epoch 4/5\n",
"11/11 [==============================] - 0s 3ms/step - loss: 0.6081 - accuracy: 0.7133\n",
"Epoch 5/5\n",
"11/11 [==============================] - 0s 2ms/step - loss: 0.6659 - accuracy: 0.6554\n"
]
},
{
"data": {
"text/plain": [
""
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.fit(data_train, labels_train, epochs=5)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"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']"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.metrics_names"
]
},
{
"cell_type": "code",
"execution_count": 11,
"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": [
""
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.metrics[1].result()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "z7bn8pKTAynt"
},
"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",
"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",
"\n",
"(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",
"\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",
"(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",
"\n",
"### test1 params\n",
"learning_rate=0.01, momentum=0.0, batch_size=128\n",
"hidden_nodes = [2, 8, 12, 16, 24, 32, 64, 128, 256]\n",
"epochs = [1, 2, 4, 8, 16, 32, 64, 100, 150, 200]\n",
"\n",
"### test2 params\n",
"learning_rate=0.5, momentum=0.1, batch_size=128\n",
"hidden_nodes = [2, 8, 12, 16, 24, 32, 64, 128]\n",
"epochs = [1, 2, 4, 8, 16, 32, 64, 100]\n",
"\n",
"### test3 params\n",
"learning_rate=0.2, momentum=0.05, batch_size=128\n",
"hidden_nodes = [2, 8, 12, 16, 24, 32, 64, 128]\n",
"epochs = [1, 2, 4, 8, 16, 32, 64, 100]\n",
"\n",
"### test4 params\n",
"learning_rate=0.08, momentum=0.04, batch_size=128\n",
"hidden_nodes = [2, 8, 12, 16, 24, 32, 64, 128]\n",
"epochs = [1, 2, 4, 8, 16, 32, 64, 100]\n",
"\n",
"### test5 params\n",
"learning_rate=0.08, momentum=0, batch_size=128\n",
"hidden_nodes = [2, 8, 12, 16, 24, 32, 64, 128]\n",
"epochs = [1, 2, 4, 8, 16, 32, 64, 100]\n",
"\n",
"### test6 params\n",
"learning_rate=0.06, momentum=0, batch_size=128\n",
"hidden_nodes = [1, 2, 3, 4, 5, 6, 7, 8]\n",
"epochs = [1, 2, 4, 8, 16, 32, 64, 100]\n",
"\n",
"### test7 params\n",
"learning_rate=0.06, momentum=0, batch_size=35\n",
"hidden_nodes = [2, 8, 12, 16, 24, 32, 64, 128]\n",
"epochs = [1, 2, 4, 8, 16, 32, 64, 100]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"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
},
"id": "mYWhCSW4A57V"
},
"outputs": [],
"source": [
"hidden_nodes = [2, 8, 12, 16, 24, 32, 64, 128]\n",
"epochs = [1, 2, 4, 8, 16, 32, 64, 100]\n",
"\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",
" \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",
" metrics=metrics\n",
" )\n",
" \n",
" response = {\"nodes\": hn, \n",
" \"epochs\": e,\n",
" \"history\": model.fit(dtrain, \n",
" ltrain, \n",
" epochs=e, \n",
" verbose=verbose,\n",
" \n",
" callbacks=callbacks,\n",
" validation_split=validation_split).history,\n",
" \"results\": model.evaluate(dtest, \n",
" ltest, \n",
" batch_size=batch_size, \n",
" verbose=verbose),\n",
" \"optimizer\": model.optimizer.get_config(),\n",
" \"loss\": model.loss,\n",
" \"model_config\": json.loads(model.to_json())\n",
" }\n",
"\n",
" if return_model:\n",
" response[\"model\"] = model\n",
"\n",
" yield response"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "r-63V9qb-i4w"
},
"source": [
"## Single Iteration\n",
"Run a single iteration of epoch/layer investigations"
]
},
{
"cell_type": "code",
"execution_count": 24,
"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",
"outputId": "243fb136-bc07-4438-afb7-f2d21758168d"
},
"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: 2, Epochs: 16\n",
"Nodes: 2, Epochs: 32\n",
"Nodes: 2, Epochs: 64\n",
"Nodes: 2, Epochs: 100\n",
"Nodes: 2, Epochs: 150\n",
"Nodes: 2, Epochs: 200\n",
"Nodes: 8, Epochs: 1\n",
"Nodes: 8, Epochs: 2\n",
"Nodes: 8, Epochs: 4\n",
"Nodes: 8, Epochs: 8\n",
"Nodes: 8, Epochs: 16\n",
"Nodes: 8, Epochs: 32\n",
"Nodes: 8, Epochs: 64\n",
"Nodes: 8, Epochs: 100\n",
"Nodes: 8, Epochs: 150\n",
"Nodes: 8, Epochs: 200\n",
"Nodes: 12, Epochs: 1\n",
"Nodes: 12, Epochs: 2\n",
"Nodes: 12, Epochs: 4\n",
"Nodes: 12, Epochs: 8\n",
"Nodes: 12, Epochs: 16\n",
"Nodes: 12, Epochs: 32\n",
"Nodes: 12, Epochs: 64\n",
"Nodes: 12, Epochs: 100\n",
"Nodes: 12, Epochs: 150\n",
"Nodes: 12, Epochs: 200\n",
"Nodes: 16, Epochs: 1\n",
"Nodes: 16, Epochs: 2\n",
"Nodes: 16, Epochs: 4\n",
"Nodes: 16, Epochs: 8\n",
"Nodes: 16, Epochs: 16\n",
"Nodes: 16, Epochs: 32\n",
"Nodes: 16, Epochs: 64\n",
"Nodes: 16, Epochs: 100\n",
"Nodes: 16, Epochs: 150\n",
"Nodes: 16, Epochs: 200\n",
"Nodes: 24, Epochs: 1\n",
"Nodes: 24, Epochs: 2\n",
"Nodes: 24, Epochs: 4\n",
"Nodes: 24, Epochs: 8\n",
"Nodes: 24, Epochs: 16\n",
"Nodes: 24, Epochs: 32\n",
"Nodes: 24, Epochs: 64\n",
"Nodes: 24, Epochs: 100\n",
"Nodes: 24, Epochs: 150\n",
"Nodes: 24, Epochs: 200\n",
"Nodes: 32, Epochs: 1\n",
"Nodes: 32, Epochs: 2\n",
"Nodes: 32, Epochs: 4\n",
"Nodes: 32, Epochs: 8\n",
"Nodes: 32, Epochs: 16\n",
"Nodes: 32, Epochs: 32\n",
"Nodes: 32, Epochs: 64\n",
"Nodes: 32, Epochs: 100\n",
"Nodes: 32, Epochs: 150\n",
"Nodes: 32, Epochs: 200\n",
"Nodes: 64, Epochs: 1\n",
"Nodes: 64, Epochs: 2\n",
"Nodes: 64, Epochs: 4\n",
"Nodes: 64, Epochs: 8\n",
"Nodes: 64, Epochs: 16\n",
"Nodes: 64, Epochs: 32\n",
"Nodes: 64, Epochs: 64\n",
"Nodes: 64, Epochs: 100\n",
"Nodes: 64, Epochs: 150\n",
"Nodes: 64, Epochs: 200\n",
"Nodes: 128, Epochs: 1\n",
"Nodes: 128, Epochs: 2\n",
"Nodes: 128, Epochs: 4\n",
"Nodes: 128, Epochs: 8\n",
"Nodes: 128, Epochs: 16\n",
"Nodes: 128, Epochs: 32\n",
"Nodes: 128, Epochs: 64\n",
"Nodes: 128, Epochs: 100\n",
"Nodes: 128, Epochs: 150\n",
"Nodes: 128, Epochs: 200\n",
"Nodes: 256, Epochs: 1\n",
"Nodes: 256, Epochs: 2\n",
"Nodes: 256, Epochs: 4\n",
"Nodes: 256, Epochs: 8\n",
"Nodes: 256, Epochs: 16\n",
"Nodes: 256, Epochs: 32\n",
"Nodes: 256, Epochs: 64\n",
"Nodes: 256, Epochs: 100\n",
"Nodes: 256, Epochs: 150\n",
"Nodes: 256, Epochs: 200\n"
]
}
],
"source": [
"es = tf.keras.callbacks.EarlyStopping(monitor='val_loss', mode='min', patience = 5)\n",
"single_results = list(\n",
" 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",
" )\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "mdWK3-M6SK8_"
},
"source": [
"### Train/Test Curves\n",
"\n",
"For a single test from the set"
]
},
{
"cell_type": "code",
"execution_count": 25,
"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",
"outputId": "d6b817aa-58cd-4510-807f-e5e6bcf62f18"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Nodes: 2, Epochs: 100\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"