{
"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",
"import tensorflow.keras.optimizers as tf_optim\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",
"import math\n",
"import datetime\n",
"import os\n",
"import random\n",
"\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"fig_dpi = 200"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "fksHv5rXACEX"
},
"source": [
"# Neural Network Training\n"
]
},
{
"attachments": {},
"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": 2,
"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": 2,
"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",
"
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" \n",
" 1 | \n",
" 1 | \n",
" 0 | \n",
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" 2 | \n",
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" 1 | \n",
" 0 | \n",
"
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" \n",
"
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"
"
],
"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()"
]
},
{
"attachments": {},
"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": 4,
"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": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"labels.astype(bool).sum(axis=0)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "E9lVYI14AUMf"
},
"source": [
"## Split Dataset\n",
"\n",
"Using a 50/50 split"
]
},
{
"cell_type": "code",
"execution_count": 5,
"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\n",
"# , stratify=labels\n",
" )"
]
},
{
"attachments": {},
"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": 6,
"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,), 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",
"\n",
" model = tf.keras.models.Sequential(layers)\n",
" return model"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Get a Keras Tensorboard callback for dumping data for later analysis"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"def tensorboard_callback(path='tensorboard-logs', prefix=''):\n",
" 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",
" ) "
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "QT5B9PTUN3pj"
},
"source": [
"# Example Training"
]
},
{
"attachments": {},
"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": 8,
"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",
" Hidden (Dense) (None, 9) 90 \n",
" \n",
" Output (Dense) (None, 2) 20 \n",
" \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()"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "KZSwFe-AAs1y"
},
"source": [
"## Train Model\n",
"\n",
"Example 10 epochs"
]
},
{
"cell_type": "code",
"execution_count": 9,
"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"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2023-05-27 23:23:33.467785: W tensorflow/tsl/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"11/11 [==============================] - 0s 1ms/step - loss: 0.7494 - accuracy: 0.6390\n",
"Epoch 2/5\n",
"11/11 [==============================] - 0s 856us/step - loss: 0.7457 - accuracy: 0.6390\n",
"Epoch 3/5\n",
"11/11 [==============================] - 0s 721us/step - loss: 0.7423 - accuracy: 0.6390\n",
"Epoch 4/5\n",
"11/11 [==============================] - 0s 863us/step - loss: 0.7389 - accuracy: 0.6390\n",
"Epoch 5/5\n",
"11/11 [==============================] - 0s 716us/step - loss: 0.7357 - accuracy: 0.6390\n"
]
},
{
"data": {
"text/plain": [
""
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.fit(data_train.to_numpy(), labels_train.to_numpy(), 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()"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "z7bn8pKTAynt",
"tags": [
"exp1"
]
},
"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"
]
},
{
"cell_type": "code",
"execution_count": 18,
"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",
"tags": [
"exp1",
"exp-func"
]
},
"outputs": [],
"source": [
"# 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",
"\n",
"def evaluate_parameters(hidden_nodes=hidden_nodes, \n",
" epochs=epochs, \n",
" batch_size=128,\n",
" optimizer=lambda: 'sgd',\n",
" weight_init=lambda: 'glorot_uniform',\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",
" run_eagerly=False,\n",
" tboard=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, weight_init=weight_init)\n",
" model.compile(\n",
" optimizer=optimizer(),\n",
" loss=loss(),\n",
" metrics=metrics,\n",
" run_eagerly=run_eagerly\n",
" )\n",
" \n",
" 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",
" response = {\"nodes\": hn, \n",
" \"epochs\": e,\n",
" ##############\n",
" ## TRAIN\n",
" ##############\n",
" \"history\": model.fit(dtrain.to_numpy(), \n",
" ltrain.to_numpy(), \n",
" epochs=e, \n",
" verbose=verbose,\n",
" \n",
" callbacks=cb,\n",
" validation_split=validation_split).history,\n",
" ##############\n",
" ## TEST\n",
" ##############\n",
" \"results\": model.evaluate(dtest.to_numpy(), \n",
" ltest.to_numpy(),\n",
" callbacks=cb,\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"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "r-63V9qb-i4w",
"tags": [
"exp1"
]
},
"source": [
"## Single Iteration\n",
"Run a single iteration of epoch/layer investigations"
]
},
{
"cell_type": "code",
"execution_count": 19,
"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",
"tags": [
"exp1"
]
},
"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",
"Nodes: 16, Epochs: 8\n"
]
}
],
"source": [
"# 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.legacy.SGD(learning_rate=0.5, momentum=0.5)\n",
"# , callbacks=[es]\n",
" ))"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "mdWK3-M6SK8_",
"tags": [
"exp1"
]
},
"source": [
"### Train/Test Curves\n",
"\n",
"For a single test from the set"
]
},
{
"cell_type": "code",
"execution_count": 20,
"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",
"tags": [
"exp1"
]
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Nodes: 2, Epochs: 8\n"
]
},
{
"data": {
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