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{
"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
}
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