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23-05-21

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pso 기본 알고리즘을 이용한 tensorflow model의 pso 알고리즘화 - xor 문제, mnist 분류 성공
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jung-geun
2023-05-21 14:00:03 +09:00
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2023-05-21 01:52:28.471404: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"2.10.0\n"
]
}
],
"source": [
"import os\n",
"os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n",
"import tensorflow as tf\n",
"# tf.random.set_seed(777) # for reproducibility\n",
"\n",
"from pso_tf import PSO\n",
"from tensorflow import keras\n",
"\n",
"print(tf.__version__)\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from tensorflow import keras\n",
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras import layers\n",
"\n",
"import matplotlib.pyplot as plt\n",
"\n",
"def get_data():\n",
" x = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])\n",
" y = np.array([[0], [1], [1], [0]])\n",
" \n",
" return x, y\n",
"\n",
"def make_model():\n",
" leyer = []\n",
" leyer.append(layers.Dense(2, activation='sigmoid', input_shape=(2,)))\n",
" leyer.append(layers.Dense(1, activation='sigmoid'))\n",
"\n",
" model = Sequential(leyer)\n",
"\n",
" sgd = keras.optimizers.SGD(lr=0.1, momentum=1, decay=1e-05, nesterov=True)\n",
" adam = keras.optimizers.Adam(lr=0.1, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.)\n",
" model.compile(loss='mse', optimizer=sgd, metrics=['accuracy'])\n",
"\n",
" print(model.summary())\n",
"\n",
" return model"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: \"sequential\"\n",
"_________________________________________________________________\n",
" Layer (type) Output Shape Param # \n",
"=================================================================\n",
" dense (Dense) (None, 2) 6 \n",
" \n",
" dense_1 (Dense) (None, 1) 3 \n",
" \n",
"=================================================================\n",
"Total params: 9\n",
"Trainable params: 9\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/pieroot/miniconda3/envs/pso/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/gradient_descent.py:111: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
" super().__init__(name, **kwargs)\n",
"/home/pieroot/miniconda3/envs/pso/lib/python3.8/site-packages/keras/optimizers/optimizer_v2/adam.py:114: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
" super().__init__(name, **kwargs)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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"1/1 [==============================] - 0s 40ms/step\n",
"[[1.8555788e-26]\n",
" [1.0000000e+00]\n",
" [1.0000000e+00]\n",
" [1.8555788e-26]]\n",
"[[0]\n",
" [1]\n",
" [1]\n",
" [0]]\n",
"history > [[array([[-0.9191145, -0.7256227],\n",
" [ 1.2947526, 1.0081983]], dtype=float32), array([ 0.01203067, -0.07866445], dtype=float32), array([[-0.72274315],\n",
" [ 0.88691926]], dtype=float32), array([-0.08449478], dtype=float32)], [array([[-0.7327981, -2.120965 ],\n",
" [ 3.5870228, 2.0618958]], dtype=float32), array([-0.06788628, -2.1460009 ], dtype=float32), array([[-1.8084345],\n",
" [ 3.2274616]], dtype=float32), array([0.40823892], dtype=float32)], [array([[-6.749437, -5.01979 ],\n",
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" [ 8.538484 ]], dtype=float32), array([0.8423419], dtype=float32)], [array([[-4.4376955, -7.542317 ],\n",
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"score > [0.5, 0.5]\n"
]
}
],
"source": [
"\n",
"x, y = get_data()\n",
"x_test = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])\n",
"y_test = np.array([[0], [1], [1], [0]])\n",
"\n",
"model = make_model()\n",
"\n",
"loss = keras.losses.MeanSquaredError()\n",
"optimizer = keras.optimizers.SGD(lr=0.1, momentum=1, decay=1e-05, nesterov=True)\n",
"\n",
"\n",
"\n",
"pso_xor = PSO(model=model, loss=loss, optimizer=optimizer, n_particles=5)\n",
"\n",
"best_weights, score = pso_xor.optimize(x, y, x_test, y_test, maxiter=30)\n",
"\n",
"model.set_weights(best_weights)\n",
"\n",
"y_pred = model.predict(x_test)\n",
"print(y_pred)\n",
"print(y_test)\n",
"\n",
"history = pso_xor.global_history()\n",
"\n",
"# print(f\"history > {history}\")\n",
"# print(f\"score > {score}\")\n",
"# plt.plot(history)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def test():\n",
" model = make_model()\n",
" x, y = get_data()\n",
" x_test = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])\n",
" y_test = np.array([[0], [1], [1], [0]])\n",
" \n",
" callback = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=3)\n",
"\n",
" hist = model.fit(x, y, epochs=50000, verbose=1, callbacks=[callback] , validation_data=(x_test, y_test))\n",
" y_pred=model.predict(x_test)\n",
" print(y_pred)\n",
" print(y_test)\n",
" \n",
" return hist"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"def plot_history(history):\n",
" fig, loss_ax = plt.subplots()\n",
" acc_ax = loss_ax.twinx()\n",
"\n",
" loss_ax.plot(hist.history['loss'], 'y', label='train loss')\n",
" loss_ax.plot(hist.history['val_loss'], 'r', label='val loss')\n",
" loss_ax.set_xlabel('epoch')\n",
" loss_ax.set_ylabel('loss')\n",
" loss_ax.legend(loc='upper left')\n",
"\n",
" acc_ax.plot(hist.history['accuracy'], 'b', label='train acc')\n",
" acc_ax.plot(hist.history['val_accuracy'], 'g', label='val acc')\n",
" acc_ax.set_ylabel('accuracy')\n",
" acc_ax.legend(loc='upper right')\n",
"\n",
" plt.show()\n",
"hist = test()\n",
"plot_history(hist)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"print(model(x).numpy())\n",
"print(y)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "pso",
"language": "python",
"name": "pso"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.16"
},
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"state": {},
"version_major": 2,
"version_minor": 0
}
}
},
"nbformat": 4,
"nbformat_minor": 4
}