{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "ae46c64f", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 2, "id": "9e8036a2", "metadata": {}, "outputs": [], "source": [ "delim = ';'\n", "\n", "base_path = '/opt/iui-datarelease1-sose2021/'\n", "\n", "Xpickle_file = './X.pickle'\n", "\n", "ypickle_file = './y.pickle'" ] }, { "cell_type": "code", "execution_count": 3, "id": "9be46372", "metadata": {}, "outputs": [], "source": [ "THRESH = [70]\n", "LEEWAY = [0]\n", "EPOCH = [20, 30, 50]\n", "\n", "DENSE_COUNT = range(1,4)\n", "DENSE_NEURONS = range(600, 2401, 600)\n", "\n", "DENSE2_COUNT = range(1,4)\n", "DENSE2_NEURONS = range(600, 2401, 600)\n", "\n", "AVG_FROM = 30\n", "\n", "threshold_p = 0.99" ] }, { "cell_type": "code", "execution_count": 4, "id": "880c0975", "metadata": {}, "outputs": [], "source": [ "import pickle\n", "\n", "def load_pickles():\n", " _p = open(Xpickle_file, 'rb')\n", " X = pickle.load(_p)\n", " _p.close()\n", " \n", " _p = open(ypickle_file, 'rb')\n", " y = pickle.load(_p)\n", " _p.close()\n", " \n", " return (np.asarray(X, dtype=pd.DataFrame), np.asarray(y, dtype=str))" ] }, { "cell_type": "code", "execution_count": 5, "id": "c550e984", "metadata": {}, "outputs": [], "source": [ "import os\n", "\n", "def load_data():\n", " if os.path.isfile(Xpickle_file) and os.path.isfile(ypickle_file):\n", " return load_pickles()\n", " data = []\n", " label = []\n", " for user in range(0, user_count):\n", " user_path = base_path + str(user) + '/split_letters_csv/'\n", " for file in os.listdir(user_path):\n", " file_name = user_path + file\n", " letter = ''.join(filter(lambda x: x.isalpha(), file))[0]\n", " data.append(pd.read_csv(file_name, delim))\n", " label.append(letter)\n", " return (np.asarray(data, dtype=pd.DataFrame), np.asarray(label, dtype=str), np.asarray(file_name))" ] }, { "cell_type": "code", "execution_count": 6, "id": "6856de47", "metadata": {}, "outputs": [], "source": [ "def shorten(npList, thresh, leeway):\n", " temp = npList['Force']\n", " \n", " temps_over_T = np.where(temp > thresh)[0]\n", " return npList[max(temps_over_T[0]-leeway,0):temps_over_T[-1]+leeway]" ] }, { "cell_type": "code", "execution_count": 7, "id": "78eb6f3f", "metadata": {}, "outputs": [], "source": [ "from tensorflow.keras.preprocessing.sequence import pad_sequences\n", "\n", "def preproc(data, label, thresh, leeway):\n", " #shorten\n", " XX = np.array(list(map(shorten, data, [thresh for _ in range(len(data))], [leeway for _ in range(len(data))])),dtype=object)\n", "\n", " #filter\n", " len_mask = np.where(np.asarray(list(map(len, XX))) <= int(pd.Series(np.asarray(list(map(len, XX)))).quantile(threshold_p)))\n", " X_filter = XX[len_mask] \n", " y_filter = label[len_mask]\n", " \n", " #drop millis\n", " [x.drop(labels='Millis', axis=1) for x in X_filter]\n", "\n", " #pad\n", " X_filter = pad_sequences(X_filter, dtype=float, padding='post')\n", " \n", " return (X_filter, y_filter)" ] }, { "cell_type": "code", "execution_count": 8, "id": "e4b4925a", "metadata": {}, "outputs": [], "source": [ "import tensorflow as tf\n", "from tensorflow.keras.models import Sequential\n", "from tensorflow.keras.layers import Dense, Flatten, BatchNormalization\n", "\n", "def build_model(dcount, dnons, dcount2, dnons2, X_shape):\n", " model = Sequential()\n", "\n", " model.add(BatchNormalization(input_shape=X_shape))\n", " \n", " model.add(Flatten())\n", "\n", " for i in range(dcount):\n", " model.add(Dense(dnons, activation='relu'))\n", " \n", " for i in range(dcount2):\n", " model.add(Dense(dnons2, activation='relu'))\n", " \n", " model.add(Dense(26, activation='softmax'))\n", "\n", " model.compile(\n", " optimizer=tf.keras.optimizers.Adam(0.001),\n", " loss=\"categorical_crossentropy\", \n", " metrics=[\"acc\"],\n", " )\n", "\n", " return model\n", "\n" ] }, { "cell_type": "code", "execution_count": 9, "id": "fc8caabb", "metadata": {}, "outputs": [], "source": [ "def get_avg_acc(X_train, y_train, X_test, y_test, epoch, dcount, dnons, dcount2, dnons2):\n", " accs = []\n", " for i in range(AVG_FROM):\n", " model = build_model(dcount, dnons, dcount2, dnons2, X_train[0].shape)\n", " model.fit(X_train, y_train, \n", " epochs=epoch,\n", " batch_size=128,\n", " shuffle=True,\n", " validation_data=(X_test, y_test),\n", " verbose=0,\n", " )\n", " results = model.evaluate(X_test, y_test, batch_size=128, verbose=0)\n", " accs.append((model,results[1]))\n", " return np.mean(np.delete(accs,0,1).astype('float64'))\n" ] }, { "cell_type": "code", "execution_count": 10, "id": "6894af8f", "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import LabelEncoder, LabelBinarizer\n", "import tensorflow as tf\n", "X, y = load_data()\n", "result = pd.DataFrame({'Threshold': pd.Series([], dtype='int'),\n", " 'Leeway': pd.Series([], dtype='int'),\n", " 'Epoch': pd.Series([], dtype='int'),\n", " 'DENSE_COUNT1': pd.Series([], dtype='int'),\n", " 'DENSE_NEURON1': pd.Series([], dtype='int'),\n", " 'DENSE_COUNT2': pd.Series([], dtype='int'),\n", " 'DENSE_NEURON2': pd.Series([], dtype='int'),\n", " 'Accuracy': pd.Series([], dtype='float')})" ] }, { "cell_type": "code", "execution_count": 11, "id": "a2e2840b", "metadata": {}, "outputs": [], "source": [ "# FIRST CELL: set these variables to limit GPU usage.\n", "os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true' # this is required\n", "os.environ['CUDA_VISIBLE_DEVICES'] = '1' # set to '0' for GPU0, '1' for GPU1 or '2' for GPU2. Check \"gpustat\" in a terminal." ] }, { "cell_type": "code", "execution_count": 12, "id": "bfa164f1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 1\n", " Dense Neurons 1: 600\n", " Dense Count 2: 1\n", " Dense Neurons 2: 600\n", "Accuracy: 76.83\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 1\n", " Dense Neurons 1: 600\n", " Dense Count 2: 1\n", " Dense Neurons 2: 1200\n", "Accuracy: 77.67\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 1\n", " Dense Neurons 1: 600\n", " Dense Count 2: 1\n", " Dense Neurons 2: 1800\n", "Accuracy: 77.85\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 1\n", " Dense Neurons 1: 600\n", " Dense Count 2: 1\n", " Dense Neurons 2: 2400\n", "Accuracy: 77.80\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 1\n", " Dense Neurons 1: 600\n", " Dense Count 2: 2\n", " Dense Neurons 2: 600\n", "Accuracy: 78.19\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 1\n", " Dense Neurons 1: 600\n", " Dense Count 2: 2\n", " Dense Neurons 2: 1200\n", "Accuracy: 77.89\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 1\n", " Dense Neurons 1: 600\n", " Dense Count 2: 2\n", " Dense Neurons 2: 1800\n", "Accuracy: 77.72\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 1\n", " Dense Neurons 1: 600\n", " Dense Count 2: 2\n", " Dense Neurons 2: 2400\n", "Accuracy: 78.04\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 1\n", " Dense Neurons 1: 600\n", " Dense Count 2: 3\n", " Dense Neurons 2: 600\n", "Accuracy: 78.24\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 1\n", " Dense Neurons 1: 600\n", " Dense Count 2: 3\n", " Dense Neurons 2: 1200\n", "Accuracy: 78.49\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 1\n", " Dense Neurons 1: 600\n", " Dense Count 2: 3\n", " Dense Neurons 2: 1800\n", "Accuracy: 78.35\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 1\n", " Dense Neurons 1: 600\n", " Dense Count 2: 3\n", " Dense Neurons 2: 2400\n", "Accuracy: 77.62\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 1\n", " Dense Neurons 1: 1200\n", " Dense Count 2: 1\n", " Dense Neurons 2: 600\n", "Accuracy: 77.26\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 1\n", " Dense Neurons 1: 1200\n", " Dense Count 2: 1\n", " Dense Neurons 2: 1200\n", "Accuracy: 78.00\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 1\n", " Dense Neurons 1: 1200\n", " Dense Count 2: 1\n", " Dense Neurons 2: 1800\n", "Accuracy: 77.84\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 1\n", " Dense Neurons 1: 1200\n", " Dense Count 2: 1\n", " Dense Neurons 2: 2400\n", "Accuracy: 77.82\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 1\n", " Dense Neurons 1: 1200\n", " Dense Count 2: 2\n", " Dense Neurons 2: 600\n", "Accuracy: 77.73\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 1\n", " Dense Neurons 1: 1200\n", " Dense Count 2: 2\n", " Dense Neurons 2: 1200\n", "Accuracy: 78.04\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 1\n", " Dense Neurons 1: 1200\n", " Dense Count 2: 2\n", " Dense Neurons 2: 1800\n", "Accuracy: 78.02\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 1\n", " Dense Neurons 1: 1200\n", " Dense Count 2: 2\n", " Dense Neurons 2: 2400\n", "Accuracy: 78.30\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 1\n", " Dense Neurons 1: 1200\n", " Dense Count 2: 3\n", " Dense Neurons 2: 600\n", "Accuracy: 78.06\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 1\n", " Dense Neurons 1: 1200\n", " Dense Count 2: 3\n", " Dense Neurons 2: 1200\n", "Accuracy: 78.31\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 1\n", " Dense Neurons 1: 1200\n", " Dense Count 2: 3\n", " Dense Neurons 2: 1800\n", "Accuracy: 78.44\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 1\n", " Dense Neurons 1: 1200\n", " Dense Count 2: 3\n", " Dense Neurons 2: 2400\n", "Accuracy: 78.24\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 1\n", " Dense Neurons 1: 1800\n", " Dense Count 2: 1\n", " Dense Neurons 2: 600\n", "Accuracy: 77.21\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 1\n", " Dense Neurons 1: 1800\n", " Dense Count 2: 1\n", " Dense Neurons 2: 1200\n", "Accuracy: 77.88\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 1\n", " Dense Neurons 1: 1800\n", " Dense Count 2: 1\n", " Dense Neurons 2: 1800\n", "Accuracy: 77.46\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 1\n", " Dense Neurons 1: 1800\n", " Dense Count 2: 1\n", " Dense Neurons 2: 2400\n", "Accuracy: 77.99\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 1\n", " Dense Neurons 1: 1800\n", " Dense Count 2: 2\n", " Dense Neurons 2: 600\n", "Accuracy: 78.13\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 1\n", " Dense Neurons 1: 1800\n", " Dense Count 2: 2\n", " Dense Neurons 2: 1200\n", "Accuracy: 78.58\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 1\n", " Dense Neurons 1: 1800\n", " Dense Count 2: 2\n", " Dense Neurons 2: 1800\n", "Accuracy: 78.19\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 1\n", " Dense Neurons 1: 1800\n", " Dense Count 2: 2\n", " Dense Neurons 2: 2400\n", "Accuracy: 78.35\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 1\n", " Dense Neurons 1: 1800\n", " Dense Count 2: 3\n", " Dense Neurons 2: 600\n", "Accuracy: 78.37\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 1\n", " Dense Neurons 1: 1800\n", " Dense Count 2: 3\n", " Dense Neurons 2: 1200\n", "Accuracy: 78.53\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 1\n", " Dense Neurons 1: 1800\n", " Dense Count 2: 3\n", " Dense Neurons 2: 1800\n", "Accuracy: 78.48\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 1\n", " Dense Neurons 1: 1800\n", " Dense Count 2: 3\n", " Dense Neurons 2: 2400\n", "Accuracy: 78.07\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 1\n", " Dense Neurons 1: 2400\n", " Dense Count 2: 1\n", " Dense Neurons 2: 600\n", "Accuracy: 76.76\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 1\n", " Dense Neurons 1: 2400\n", " Dense Count 2: 1\n", " Dense Neurons 2: 1200\n", "Accuracy: 77.73\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 1\n", " Dense Neurons 1: 2400\n", " Dense Count 2: 1\n", " Dense Neurons 2: 1800\n", "Accuracy: 77.93\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 1\n", " Dense Neurons 1: 2400\n", " Dense Count 2: 1\n", " Dense Neurons 2: 2400\n", "Accuracy: 77.75\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 1\n", " Dense Neurons 1: 2400\n", " Dense Count 2: 2\n", " Dense Neurons 2: 600\n", "Accuracy: 78.17\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 1\n", " Dense Neurons 1: 2400\n", " Dense Count 2: 2\n", " Dense Neurons 2: 1200\n", "Accuracy: 78.36\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 1\n", " Dense Neurons 1: 2400\n", " Dense Count 2: 2\n", " Dense Neurons 2: 1800\n", "Accuracy: 78.51\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 1\n", " Dense Neurons 1: 2400\n", " Dense Count 2: 2\n", " Dense Neurons 2: 2400\n", "Accuracy: 77.98\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 1\n", " Dense Neurons 1: 2400\n", " Dense Count 2: 3\n", " Dense Neurons 2: 600\n", "Accuracy: 78.27\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 1\n", " Dense Neurons 1: 2400\n", " Dense Count 2: 3\n", " Dense Neurons 2: 1200\n", "Accuracy: 78.48\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 1\n", " Dense Neurons 1: 2400\n", " Dense Count 2: 3\n", " Dense Neurons 2: 1800\n", "Accuracy: 78.62\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 1\n", " Dense Neurons 1: 2400\n", " Dense Count 2: 3\n", " Dense Neurons 2: 2400\n", "Accuracy: 78.13\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 2\n", " Dense Neurons 1: 600\n", " Dense Count 2: 1\n", " Dense Neurons 2: 600\n", "Accuracy: 77.97\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 2\n", " Dense Neurons 1: 600\n", " Dense Count 2: 1\n", " Dense Neurons 2: 1200\n", "Accuracy: 77.89\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 2\n", " Dense Neurons 1: 600\n", " Dense Count 2: 1\n", " Dense Neurons 2: 1800\n", "Accuracy: 77.77\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 2\n", " Dense Neurons 1: 600\n", " Dense Count 2: 1\n", " Dense Neurons 2: 2400\n", "Accuracy: 77.82\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 2\n", " Dense Neurons 1: 600\n", " Dense Count 2: 2\n", " Dense Neurons 2: 600\n", "Accuracy: 77.92\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 2\n", " Dense Neurons 1: 600\n", " Dense Count 2: 2\n", " Dense Neurons 2: 1200\n", "Accuracy: 78.13\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 2\n", " Dense Neurons 1: 600\n", " Dense Count 2: 2\n", " Dense Neurons 2: 1800\n", "Accuracy: 77.94\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 2\n", " Dense Neurons 1: 600\n", " Dense Count 2: 2\n", " Dense Neurons 2: 2400\n", "Accuracy: 78.08\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 2\n", " Dense Neurons 1: 600\n", " Dense Count 2: 3\n", " Dense Neurons 2: 600\n", "Accuracy: 77.92\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 2\n", " Dense Neurons 1: 600\n", " Dense Count 2: 3\n", " Dense Neurons 2: 1200\n", "Accuracy: 78.00\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 2\n", " Dense Neurons 1: 600\n", " Dense Count 2: 3\n", " Dense Neurons 2: 1800\n", "Accuracy: 77.61\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 2\n", " Dense Neurons 1: 600\n", " Dense Count 2: 3\n", " Dense Neurons 2: 2400\n", "Accuracy: 77.48\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 2\n", " Dense Neurons 1: 1200\n", " Dense Count 2: 1\n", " Dense Neurons 2: 600\n", "Accuracy: 78.17\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 2\n", " Dense Neurons 1: 1200\n", " Dense Count 2: 1\n", " Dense Neurons 2: 1200\n", "Accuracy: 78.35\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 2\n", " Dense Neurons 1: 1200\n", " Dense Count 2: 1\n", " Dense Neurons 2: 1800\n", "Accuracy: 78.19\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 2\n", " Dense Neurons 1: 1200\n", " Dense Count 2: 1\n", " Dense Neurons 2: 2400\n", "Accuracy: 78.38\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 2\n", " Dense Neurons 1: 1200\n", " Dense Count 2: 2\n", " Dense Neurons 2: 600\n", "Accuracy: 77.83\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 2\n", " Dense Neurons 1: 1200\n", " Dense Count 2: 2\n", " Dense Neurons 2: 1200\n", "Accuracy: 78.21\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 2\n", " Dense Neurons 1: 1200\n", " Dense Count 2: 2\n", " Dense Neurons 2: 1800\n", "Accuracy: 78.31\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 2\n", " Dense Neurons 1: 1200\n", " Dense Count 2: 2\n", " Dense Neurons 2: 2400\n", "Accuracy: 78.23\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 2\n", " Dense Neurons 1: 1200\n", " Dense Count 2: 3\n", " Dense Neurons 2: 600\n", "Accuracy: 78.54\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 2\n", " Dense Neurons 1: 1200\n", " Dense Count 2: 3\n", " Dense Neurons 2: 1200\n", "Accuracy: 78.53\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 2\n", " Dense Neurons 1: 1200\n", " Dense Count 2: 3\n", " Dense Neurons 2: 1800\n", "Accuracy: 78.14\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 2\n", " Dense Neurons 1: 1200\n", " Dense Count 2: 3\n", " Dense Neurons 2: 2400\n", "Accuracy: 77.81\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 2\n", " Dense Neurons 1: 1800\n", " Dense Count 2: 1\n", " Dense Neurons 2: 600\n", "Accuracy: 78.81\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 2\n", " Dense Neurons 1: 1800\n", " Dense Count 2: 1\n", " Dense Neurons 2: 1200\n", "Accuracy: 78.33\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 2\n", " Dense Neurons 1: 1800\n", " Dense Count 2: 1\n", " Dense Neurons 2: 1800\n", "Accuracy: 78.43\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 2\n", " Dense Neurons 1: 1800\n", " Dense Count 2: 1\n", " Dense Neurons 2: 2400\n", "Accuracy: 78.58\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 2\n", " Dense Neurons 1: 1800\n", " Dense Count 2: 2\n", " Dense Neurons 2: 600\n", "Accuracy: 78.42\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 2\n", " Dense Neurons 1: 1800\n", " Dense Count 2: 2\n", " Dense Neurons 2: 1200\n", "Accuracy: 78.58\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 2\n", " Dense Neurons 1: 1800\n", " Dense Count 2: 2\n", " Dense Neurons 2: 1800\n", "Accuracy: 78.44\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 2\n", " Dense Neurons 1: 1800\n", " Dense Count 2: 2\n", " Dense Neurons 2: 2400\n", "Accuracy: 78.18\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 2\n", " Dense Neurons 1: 1800\n", " Dense Count 2: 3\n", " Dense Neurons 2: 600\n", "Accuracy: 78.42\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 2\n", " Dense Neurons 1: 1800\n", " Dense Count 2: 3\n", " Dense Neurons 2: 1200\n", "Accuracy: 78.40\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 2\n", " Dense Neurons 1: 1800\n", " Dense Count 2: 3\n", " Dense Neurons 2: 1800\n", "Accuracy: 78.09\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 2\n", " Dense Neurons 1: 1800\n", " Dense Count 2: 3\n", " Dense Neurons 2: 2400\n", "Accuracy: 77.56\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 2\n", " Dense Neurons 1: 2400\n", " Dense Count 2: 1\n", " Dense Neurons 2: 600\n", "Accuracy: 78.10\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 2\n", " Dense Neurons 1: 2400\n", " Dense Count 2: 1\n", " Dense Neurons 2: 1200\n", "Accuracy: 78.49\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 2\n", " Dense Neurons 1: 2400\n", " Dense Count 2: 1\n", " Dense Neurons 2: 1800\n", "Accuracy: 78.24\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 2\n", " Dense Neurons 1: 2400\n", " Dense Count 2: 1\n", " Dense Neurons 2: 2400\n", "Accuracy: 78.00\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 2\n", " Dense Neurons 1: 2400\n", " Dense Count 2: 2\n", " Dense Neurons 2: 600\n", "Accuracy: 78.61\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 2\n", " Dense Neurons 1: 2400\n", " Dense Count 2: 2\n", " Dense Neurons 2: 1200\n", "Accuracy: 78.68\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 2\n", " Dense Neurons 1: 2400\n", " Dense Count 2: 2\n", " Dense Neurons 2: 1800\n", "Accuracy: 78.00\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 2\n", " Dense Neurons 1: 2400\n", " Dense Count 2: 2\n", " Dense Neurons 2: 2400\n", "Accuracy: 78.21\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 2\n", " Dense Neurons 1: 2400\n", " Dense Count 2: 3\n", " Dense Neurons 2: 600\n", "Accuracy: 78.99\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 2\n", " Dense Neurons 1: 2400\n", " Dense Count 2: 3\n", " Dense Neurons 2: 1200\n", "Accuracy: 78.20\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 2\n", " Dense Neurons 1: 2400\n", " Dense Count 2: 3\n", " Dense Neurons 2: 1800\n", "Accuracy: 77.88\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 2\n", " Dense Neurons 1: 2400\n", " Dense Count 2: 3\n", " Dense Neurons 2: 2400\n", "Accuracy: 77.83\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 3\n", " Dense Neurons 1: 600\n", " Dense Count 2: 1\n", " Dense Neurons 2: 600\n", "Accuracy: 77.78\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 3\n", " Dense Neurons 1: 600\n", " Dense Count 2: 1\n", " Dense Neurons 2: 1200\n", "Accuracy: 77.93\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 3\n", " Dense Neurons 1: 600\n", " Dense Count 2: 1\n", " Dense Neurons 2: 1800\n", "Accuracy: 77.98\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 3\n", " Dense Neurons 1: 600\n", " Dense Count 2: 1\n", " Dense Neurons 2: 2400\n", "Accuracy: 78.02\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 3\n", " Dense Neurons 1: 600\n", " Dense Count 2: 2\n", " Dense Neurons 2: 600\n", "Accuracy: 78.04\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 3\n", " Dense Neurons 1: 600\n", " Dense Count 2: 2\n", " Dense Neurons 2: 1200\n", "Accuracy: 77.59\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 3\n", " Dense Neurons 1: 600\n", " Dense Count 2: 2\n", " Dense Neurons 2: 1800\n", "Accuracy: 77.97\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 3\n", " Dense Neurons 1: 600\n", " Dense Count 2: 2\n", " Dense Neurons 2: 2400\n", "Accuracy: 77.55\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 3\n", " Dense Neurons 1: 600\n", " Dense Count 2: 3\n", " Dense Neurons 2: 600\n", "Accuracy: 77.38\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 3\n", " Dense Neurons 1: 600\n", " Dense Count 2: 3\n", " Dense Neurons 2: 1200\n", "Accuracy: 77.40\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 3\n", " Dense Neurons 1: 600\n", " Dense Count 2: 3\n", " Dense Neurons 2: 1800\n", "Accuracy: 77.10\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 3\n", " Dense Neurons 1: 600\n", " Dense Count 2: 3\n", " Dense Neurons 2: 2400\n", "Accuracy: 76.91\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 3\n", " Dense Neurons 1: 1200\n", " Dense Count 2: 1\n", " Dense Neurons 2: 600\n", "Accuracy: 78.61\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 3\n", " Dense Neurons 1: 1200\n", " Dense Count 2: 1\n", " Dense Neurons 2: 1200\n", "Accuracy: 78.61\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 3\n", " Dense Neurons 1: 1200\n", " Dense Count 2: 1\n", " Dense Neurons 2: 1800\n", "Accuracy: 78.44\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 3\n", " Dense Neurons 1: 1200\n", " Dense Count 2: 1\n", " Dense Neurons 2: 2400\n", "Accuracy: 78.40\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 3\n", " Dense Neurons 1: 1200\n", " Dense Count 2: 2\n", " Dense Neurons 2: 600\n", "Accuracy: 78.46\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 3\n", " Dense Neurons 1: 1200\n", " Dense Count 2: 2\n", " Dense Neurons 2: 1200\n", "Accuracy: 78.59\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 3\n", " Dense Neurons 1: 1200\n", " Dense Count 2: 2\n", " Dense Neurons 2: 1800\n", "Accuracy: 78.17\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 3\n", " Dense Neurons 1: 1200\n", " Dense Count 2: 2\n", " Dense Neurons 2: 2400\n", "Accuracy: 78.02\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 3\n", " Dense Neurons 1: 1200\n", " Dense Count 2: 3\n", " Dense Neurons 2: 600\n", "Accuracy: 78.15\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 3\n", " Dense Neurons 1: 1200\n", " Dense Count 2: 3\n", " Dense Neurons 2: 1200\n", "Accuracy: 77.58\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 3\n", " Dense Neurons 1: 1200\n", " Dense Count 2: 3\n", " Dense Neurons 2: 2400\n", "Accuracy: 77.02\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 3\n", " Dense Neurons 1: 1800\n", " Dense Count 2: 1\n", " Dense Neurons 2: 600\n", "Accuracy: 78.60\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 3\n", " Dense Neurons 1: 1800\n", " Dense Count 2: 1\n", " Dense Neurons 2: 1200\n", "Accuracy: 78.39\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 3\n", " Dense Neurons 1: 1800\n", " Dense Count 2: 1\n", " Dense Neurons 2: 1800\n", "Accuracy: 78.58\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 3\n", " Dense Neurons 1: 1800\n", " Dense Count 2: 1\n", " Dense Neurons 2: 2400\n", "Accuracy: 78.49\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 3\n", " Dense Neurons 1: 1800\n", " Dense Count 2: 2\n", " Dense Neurons 2: 600\n", "Accuracy: 78.37\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 3\n", " Dense Neurons 1: 1800\n", " Dense Count 2: 2\n", " Dense Neurons 2: 1200\n", "Accuracy: 78.45\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 3\n", " Dense Neurons 1: 1800\n", " Dense Count 2: 2\n", " Dense Neurons 2: 1800\n", "Accuracy: 78.02\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 3\n", " Dense Neurons 1: 1800\n", " Dense Count 2: 2\n", " Dense Neurons 2: 2400\n", "Accuracy: 77.69\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 3\n", " Dense Neurons 1: 1800\n", " Dense Count 2: 3\n", " Dense Neurons 2: 600\n", "Accuracy: 77.95\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 3\n", " Dense Neurons 1: 1800\n", " Dense Count 2: 3\n", " Dense Neurons 2: 1200\n", "Accuracy: 77.59\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 3\n", " Dense Neurons 1: 1800\n", " Dense Count 2: 3\n", " Dense Neurons 2: 1800\n", "Accuracy: 77.53\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 3\n", " Dense Neurons 1: 1800\n", " Dense Count 2: 3\n", " Dense Neurons 2: 2400\n", "Accuracy: 77.26\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 3\n", " Dense Neurons 1: 2400\n", " Dense Count 2: 1\n", " Dense Neurons 2: 600\n", "Accuracy: 78.33\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 3\n", " Dense Neurons 1: 2400\n", " Dense Count 2: 1\n", " Dense Neurons 2: 1200\n", "Accuracy: 78.39\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 3\n", " Dense Neurons 1: 2400\n", " Dense Count 2: 1\n", " Dense Neurons 2: 1800\n", "Accuracy: 78.46\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 3\n", " Dense Neurons 1: 2400\n", " Dense Count 2: 1\n", " Dense Neurons 2: 2400\n", "Accuracy: 78.38\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 3\n", " Dense Neurons 1: 2400\n", " Dense Count 2: 2\n", " Dense Neurons 2: 600\n", "Accuracy: 78.62\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 3\n", " Dense Neurons 1: 2400\n", " Dense Count 2: 2\n", " Dense Neurons 2: 1200\n", "Accuracy: 78.02\n", "Testing with: Threshold: 70\n", " Leeway: 0\n", " Epoch: 20\n", " Dense Count 1: 3\n", " Dense Neurons 1: 2400\n", " Dense Count 2: 2\n", " Dense Neurons 2: 1800\n" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n", "\u001b[0;32m\u001b[0m in \u001b[0;36mget_avg_acc\u001b[0;34m(X_train, y_train, X_test, y_test, epoch, dcount, dnons, dcount2, dnons2)\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mAVG_FROM\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbuild_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdcount\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdnons\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdcount2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdnons2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m model.fit(X_train, y_train, \n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mepoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m128\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/opt/jupyterhub/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m 1129\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1130\u001b[0m steps_per_execution=self._steps_per_execution)\n\u001b[0;32m-> 1131\u001b[0;31m val_logs = self.evaluate(\n\u001b[0m\u001b[1;32m 1132\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_x\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1133\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/opt/jupyterhub/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, x, y, batch_size, verbose, sample_weight, steps, callbacks, max_queue_size, workers, use_multiprocessing, return_dict)\u001b[0m\n\u001b[1;32m 1387\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtrace\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTrace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'test'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstep_num\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_r\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1388\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_test_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1389\u001b[0;31m \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m 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So we can\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 861\u001b[0m \u001b[0;31m# run the first trace but we should fail if variables are created.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 862\u001b[0;31m \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 863\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_created_variables\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 864\u001b[0m raise ValueError(\"Creating variables on a non-first call to a function\"\n", "\u001b[0;32m/opt/jupyterhub/lib/python3.8/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 2940\u001b[0m (graph_function,\n\u001b[1;32m 2941\u001b[0m filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[0;32m-> 2942\u001b[0;31m return graph_function._call_flat(\n\u001b[0m\u001b[1;32m 2943\u001b[0m filtered_flat_args, captured_inputs=graph_function.captured_inputs) # pylint: disable=protected-access\n\u001b[1;32m 2944\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/opt/jupyterhub/lib/python3.8/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m 1916\u001b[0m and executing_eagerly):\n\u001b[1;32m 1917\u001b[0m \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1918\u001b[0;31m return self._build_call_outputs(self._inference_function.call(\n\u001b[0m\u001b[1;32m 1919\u001b[0m ctx, args, cancellation_manager=cancellation_manager))\n\u001b[1;32m 1920\u001b[0m forward_backward = self._select_forward_and_backward_functions(\n", "\u001b[0;32m/opt/jupyterhub/lib/python3.8/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m 553\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0m_InterpolateFunctionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 554\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcancellation_manager\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 555\u001b[0;31m outputs = execute.execute(\n\u001b[0m\u001b[1;32m 556\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msignature\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 557\u001b[0m \u001b[0mnum_outputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_outputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/opt/jupyterhub/lib/python3.8/site-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m 57\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 58\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 59\u001b[0;31m tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0m\u001b[1;32m 60\u001b[0m inputs, attrs, num_outputs)\n\u001b[1;32m 61\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "%%time\n", "\n", "for t in THRESH:\n", " for l in LEEWAY:\n", " for e in EPOCH:\n", " for dc in DENSE_COUNT:\n", " for dn in DENSE_NEURONS:\n", " for dc2 in DENSE2_COUNT:\n", " for dn2 in DENSE2_NEURONS:\n", " print(f\"Testing with: Threshold: {t}\")\n", " print(f\" Leeway: {l}\")\n", " print(f\" Epoch: {e}\")\n", " print(f\" Dense Count 1: {dc}\")\n", " print(f\" Dense Neurons 1: {dn}\")\n", " print(f\" Dense Count 2: {dc2}\")\n", " print(f\" Dense Neurons 2: {dn2}\")\n", " Xp, yp = preproc(X, y, t, l)\n", " lb = LabelBinarizer()\n", "\n", " ypt = lb.fit_transform(yp)\n", " X_train, X_test, y_train, y_test = train_test_split(Xp, ypt, test_size=0.2, random_state=177013)\n", " acc = get_avg_acc(X_train,y_train,X_test, y_test, e, dc,dn,dc2,dn2)\n", " result = result.append({'Threshold': t,\n", " 'Leeway': l,\n", " 'Epoch': e,\n", " 'DENSE_COUNT1': dc,\n", " 'DENSE_NEURON1': dn,\n", " 'DENSE_COUNT2': dc2,\n", " 'DENSE_NEURON2': dn2,\n", " 'Accuracy': acc}, ignore_index=True)\n", " print(f\"Accuracy: {acc*100:.2f}\\n\\n\")\n", " result.to_csv('results.csv', header=False)" ] }, { "cell_type": "code", "execution_count": 13, "id": "aa37500d", "metadata": {}, "outputs": [], "source": [ "result.to_csv('./results.csv')" ] }, { "cell_type": "code", "execution_count": 14, "id": "c6216ab8", "metadata": {}, "outputs": [], "source": [ "exit()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 5 }