Spaces:
Sleeping
Sleeping
File size: 3,911 Bytes
c92f2e7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 |
{
"cells": [
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"from mygrad import Layer\n",
"from mygrad import Value\n",
"import pickle\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"def predict(x):\n",
" x1 = hiddenLayer1(x) \n",
" final = outputLayer([x1] + x)\n",
" return final"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.metrics import accuracy_score, precision_score, f1_score, recall_score\n",
"def getAccuracy(X, Y):\n",
" predicted = [1 if predict(x).data > 0.5 else 0 for x in X ]\n",
" return accuracy_score(Y, predicted)\n",
"def getPrecision(X, Y):\n",
" predicted = [1 if predict(x).data > 0.5 else 0 for x in X ]\n",
" return precision_score(Y, predicted)\n",
"def getf1(X, Y):\n",
" predicted = [1 if predict(x).data > 0.5 else 0 for x in X ]\n",
" return f1_score(Y, predicted)\n",
"def getRecall(X, Y):\n",
" predicted = [1 if predict(x).data > 0.5 else 0 for x in X ]\n",
" return recall_score(Y, predicted)\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"# Load model\n",
"\n",
"def loadModel():\n",
" neuron1weightsbias, outputneuronweightsbias = [], []\n",
" with open(f'parameters/neuron1weightsbias_fn_reLu.pckl', 'rb') as file:\n",
" neuron1weightsbias = pickle.load(file)\n",
" with open('parameters/outputneuronweightsbias_fn_reLu.pckl', 'rb') as file:\n",
" outputneuronweightsbias = pickle.load(file)\n",
" hiddenLayer1_ = Layer(10, 1, 'reLu')\n",
" outputLayer_ = Layer(11, 1, 'sigmoid')\n",
"\n",
" hiddenLayer1_.neurons[0].w = [Value(i) for i in neuron1weightsbias[:-1]]\n",
" hiddenLayer1_.neurons[0].b = Value(neuron1weightsbias[-1])\n",
"\n",
" outputLayer_.neurons[0].w = [Value(i) for i in outputneuronweightsbias[:-1]]\n",
" outputLayer_.neurons[0].b = Value(outputneuronweightsbias[-1])\n",
" return hiddenLayer1_, outputLayer_, neuron1weightsbias, outputneuronweightsbias"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"hiddenLayer1, outputLayer, neuron1weightsbias, outputneuronweightsbias = loadModel()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"import pickle\n",
"with open('data.pckl', 'rb') as file:\n",
" data = pickle.load(file)\n",
"from sklearn.utils import shuffle\n",
"data = shuffle(data)\n",
"X = [list(number) for number in data['number']]\n",
"Y = [label for label in data['label']]\n",
"for ix, row in enumerate(X):\n",
" X[ix] = [Value(float(item)) for item in row]\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.9609375"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"getAccuracy(X, Y)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"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": "3.11.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|