diff --git a/iris.py b/iris.py index a28c833..e9c80ed 100644 --- a/iris.py +++ b/iris.py @@ -39,9 +39,27 @@ x_train, x_test, y_train, y_test = load_data() loss = 'categorical_crossentropy' -pso_iris = Optimizer(model, loss=loss, n_particles=50, c0=0.4, c1=0.8, w_min=0.7, w_max=1.0, random=0.2) +pso_iris = Optimizer( + model, + loss=loss, + n_particles=75, + c0=0.4, + c1=0.8, + w_min=0.7, + w_max=1.0, + negative_swarm=0.25 + ) -weight, score = pso_iris.fit( - x_train, y_train, epochs=500, save=True, save_path="./result/iris", renewal="acc", empirical_balance=False, Dispersion=False, check_point=25) +best_score = pso_iris.fit( + x_train, + y_train, + epochs=200, + save=True, + save_path="./result/iris", + renewal="acc", + empirical_balance=False, + Dispersion=False, + check_point=25 + ) gc.collect() diff --git a/iris_relu_acc_200.png b/iris_relu_acc_200.png new file mode 100644 index 0000000..7d5c171 Binary files /dev/null and b/iris_relu_acc_200.png differ diff --git a/mnist.py b/mnist.py index 34fd717..8602989 100644 --- a/mnist.py +++ b/mnist.py @@ -61,9 +61,7 @@ def make_model(): return model - # %% - model = make_model() x_test, y_test = get_data_test() # loss = 'binary_crossentropy' @@ -73,16 +71,32 @@ x_test, y_test = get_data_test() # loss = 'poisson' # loss = 'cosine_similarity' # loss = 'log_cosh' -loss = 'huber_loss' +# loss = 'huber_loss' # loss = 'mean_absolute_error' # loss = 'mean_absolute_percentage_error' -# loss = 'mean_squared_error' +loss = 'mean_squared_error' +pso_mnist = Optimizer( + model, + loss=loss, + n_particles=50, + c0=0.35, + c1=0.8, + w_min=0.7, + w_max=1.1, + negative_swarm=0.25 + ) -pso_mnist = Optimizer(model, loss=loss, n_particles=50, c0=0.4, c1=0.8, w_min=0.4, w_max=0.95, negative_swarm=0.3) -weight, score = pso_mnist.fit( - x_test, y_test, epochs=500, save=True, save_path="./result/mnist", renewal="acc", empirical_balance=True, Dispersion=False, check_point=10) +best_score = pso_mnist.fit( + x_test, + y_test, + epochs=200, + save=True, + save_path="./result/mnist", + renewal="acc", + empirical_balance=False, + Dispersion=False, + check_point=25 + ) # pso_mnist.model_save("./result/mnist") # pso_mnist.save_info("./result/mnist") - -gc.collect() \ No newline at end of file diff --git a/plt.ipynb b/plt.ipynb index d7f568b..5a3e82b 100644 --- a/plt.ipynb +++ b/plt.ipynb @@ -19,7 +19,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 64, "metadata": { "tags": [] }, @@ -28,34 +28,23 @@ "name": "stdout", "output_type": "stream", "text": [ - "(316, 150)\n" + "(44, 100)\n" ] }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "16646" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ - "iris = pd.read_csv(\"./result/mnist/05-31-19-54_75_500_0.4_0.8_0.6_acc.csv\", header=None)\n", + "iris = pd.read_csv(\"./result/mnist/06-03-17-09_50_200_0.35_0.8_0.7_acc.csv\", header=None)\n", "print(iris.shape)\n", - "# print(iris.head)\n", "loss = []\n", "acc = []\n", "\n", @@ -70,8 +59,11 @@ "plt.ylabel(\"loss\")\n", "plt.title(f\"loss and acc\")\n", "for i in range(len(loss)):\n", - " plt.plot(loss[i], label=i)\n", - "\n", + " try:\n", + " plt.plot(loss[i], label=i)\n", + " except Exception as e:\n", + " print(e)\n", + " \n", "plt.subplot(2,1,2)\n", "plt.grid()\n", "plt.xlabel(\"epoch\")\n", @@ -83,10 +75,7 @@ "plt.clf()\n", "plt.clf()\n", "\n", - "plt.close()\n", - "\n", - "gc.collect()\n", - "# sleep(1) " + "plt.close()" ] }, { @@ -101,7 +90,7 @@ "kernelspec": { "display_name": "pso", "language": "python", - "name": "pso" + "name": "python3" }, "language_info": { "codemirror_mode": { diff --git a/pso/optimizer.py b/pso/optimizer.py index 75dac86..9274692 100644 --- a/pso/optimizer.py +++ b/pso/optimizer.py @@ -16,6 +16,13 @@ from copy import copy, deepcopy from pso.particle import Particle +gpus = tf.config.experimental.list_physical_devices("GPU") +if gpus: + try: + # tf.config.experimental.set_visible_devices(gpus[0], "GPU") + tf.config.experimental.set_memory_growth(gpus[0], True) + except RuntimeError as e: + print(e) class Optimizer: """ @@ -90,7 +97,6 @@ class Optimizer: w_gpu = np.append(w_gpu, w_) del weights - gc.collect() return w_gpu, shape, lenght """ @@ -116,7 +122,6 @@ class Optimizer: del weight del shape del lenght - gc.collect() return weights @@ -124,8 +129,6 @@ class Optimizer: self.model.set_weights(weights) self.model.compile(loss=self.loss, optimizer="sgd", metrics=["accuracy"]) score = self.model.evaluate(x, y, verbose=0)[1] - - gc.collect() if score > 0: return 1 / (1 + score) else: @@ -163,6 +166,7 @@ class Optimizer: self.g_best_score = 0 elif renewal == "loss": self.g_best_score = np.inf + try: if save: if save_path is None: @@ -175,9 +179,9 @@ class Optimizer: except ValueError as e: print(e) sys.exit(1) - # for i, p in enumerate(self.particles): + for i in tqdm(range(self.n_particles), desc="Initializing Particles"): - p = copy(self.particles[i]) + p = self.particles[i] local_score = p.get_score(x, y, renewal=renewal) if renewal == "acc": @@ -190,8 +194,24 @@ class Optimizer: self.g_best_score = local_score[0] self.g_best = p.get_best_weights() self.g_best_ = p.get_best_weights() + + if local_score[0] == None: + local_score[0] = np.inf + + if local_score[1] == None: + local_score[1] = 0 + + if save: + with open( + f"./{save_path}/{self.day}_{self.n_particles}_{epochs}_{self.c0}_{self.c1}_{self.w_min}_{renewal}.csv", + "a", + ) as f: + f.write(f"{local_score[0]}, {local_score[1]}") + if i != self.n_particles - 1: + f.write(", ") + else: + f.write("\n") del local_score - del p gc.collect() print(f"initial g_best_score : {self.g_best_score}") @@ -266,6 +286,10 @@ class Optimizer: self.g_best_score = score[0] self.g_best = self.particles[i].get_best_weights() + if score[0] == None: + score[0] = np.inf + if score[1] == None: + score[1] = 0 loss = loss + score[0] acc = acc + score[1] if score[0] < min_loss: @@ -295,7 +319,6 @@ class Optimizer: f"loss min : {round(min_loss, 4)} | acc max : {round(max_score, 4)} | Best {renewal} : {self.g_best_score}" ) - gc.collect() if check_point is not None: if _ % check_point == 0: @@ -303,6 +326,8 @@ class Optimizer: self._check_point_save(f"./{save_path}/{self.day}/ckpt-{_}") self.avg_score = acc / self.n_particles + gc.collect() + except KeyboardInterrupt: print("Ctrl + C : Stop Training") except MemoryError: @@ -315,7 +340,7 @@ class Optimizer: self.save_info(save_path) print("save info") - return self.g_best, self.g_best_score + return self.g_best_score def get_best_model(self): model = keras.models.model_from_json(self.model.to_json()) diff --git a/pso/particle.py b/pso/particle.py index 27e12f1..7556544 100644 --- a/pso/particle.py +++ b/pso/particle.py @@ -12,7 +12,7 @@ class Particle: self.loss = loss init_weights = self.model.get_weights() i_w_, s_, l_ = self._encode(init_weights) - i_w_ = np.random.rand(len(i_w_)) / 5 - 0.10 + i_w_ = np.random.rand(len(i_w_)) / 2 - 0.25 self.velocities = self._decode(i_w_, s_, l_) self.negative = negative self.best_score = 0 @@ -40,7 +40,7 @@ class Particle: lenght.append(len(w_)) # w_gpu = cp.append(w_gpu, w_) w_gpu = np.append(w_gpu, w_) - gc.collect() + return w_gpu, shape, lenght """ @@ -62,7 +62,7 @@ class Particle: del start, end, w_ del shape, lenght del weight - gc.collect() + return weights def get_score(self, x, y, renewal: str = "acc"): @@ -77,7 +77,7 @@ class Particle: if score[0] < self.best_score: self.best_score = score[0] self.best_weights = self.model.get_weights() - gc.collect() + return score def _update_velocity(self, local_rate, global_rate, w, g_best): @@ -105,7 +105,6 @@ class Particle: del encode_p, p_sh, p_len del encode_g, g_sh, g_len del r0, r1 - gc.collect() def _update_velocity_w(self, local_rate, global_rate, w, w_p, w_g, g_best): encode_w, w_sh, w_len = self._encode(weights=self.model.get_weights()) @@ -132,7 +131,6 @@ class Particle: del encode_p, p_sh, p_len del encode_g, g_sh, g_len del r0, r1 - gc.collect() def _update_weights(self): encode_w, w_sh, w_len = self._encode(weights=self.model.get_weights()) @@ -141,12 +139,10 @@ class Particle: self.model.set_weights(self._decode(new_w, w_sh, w_len)) del encode_w, w_sh, w_len del encode_v, v_sh, v_len - gc.collect() def f(self, x, y, weights): self.model.set_weights(weights) score = self.model.evaluate(x, y, verbose=0)[1] - gc.collect() if score > 0: return 1 / (1 + score) else: @@ -155,7 +151,6 @@ class Particle: def step(self, x, y, local_rate, global_rate, w, g_best, renewal: str = "acc"): self._update_velocity(local_rate, global_rate, w, g_best) self._update_weights() - gc.collect() return self.get_score(x, y, renewal) def step_w( @@ -163,7 +158,6 @@ class Particle: ): self._update_velocity_w(local_rate, global_rate, w, w_p, w_g, g_best) self._update_weights() - gc.collect() return self.get_score(x, y, renewal) def get_best_score(self): diff --git a/readme.md b/readme.md index 7008668..ee6f18b 100644 --- a/readme.md +++ b/readme.md @@ -74,6 +74,96 @@ pso 알고리즘을 이용하여 오차역전파 함수를 최적화 하는 방 위의 아이디어는 원래의 목표와 다른 방향으로 가고 있습니다. 따라서 다른 방법을 모색해야할 것 같습니다
+## 3. PSO 알고리즘을 이용하여 풀이한 문제들의 정확도 + +### 1. xor 문제 +``` python + loss = 'mean_squared_error' + + pso_xor = Optimizer( + model, + loss=loss, + n_particles=75, + c0=0.35, + c1=0.8, + w_min=0.6, + w_max=1.2, + negative_swarm=0.25 + ) + + best_score = pso_xor.fit( + x_test, + y_test, + epochs=200, + save=True, + save_path="./result/xor", + renewal="acc", + empirical_balance=False, + Dispersion=False, + check_point=25 + ) +``` +위의 파라미터 기준 40 세대 이후부터 정확도가 100%가 나오는 것을 확인하였습니다 +![xor](./xor_sigmoid_2_acc_40.png) + +2. iris 문제 +``` python +loss = 'categorical_crossentropy' + +pso_iris = Optimizer( + model, + loss=loss, + n_particles=50, + c0=0.4, + c1=0.8, + w_min=0.7, + w_max=1.0, + negative_swarm=0.2 + ) + +best_score = pso_iris.fit( + x_train, + y_train, + epochs=200, + save=True, + save_path="./result/iris", + renewal="acc", + empirical_balance=False, + Dispersion=False, + check_point=25 + ) +``` +위의 파라미터 기준 2 세대에 94%의 정확도를, 7 세대에 96%, 106 세대에 99.16%의 정확도를 보였습니다 +![iris](./iris_relu_acc_200.png) + +3. mnist 문제 +``` python +loss = 'mean_squared_error' + +pso_mnist = Optimizer( + model, + loss=loss, + n_particles=50, + c0=0.35, + c1=0.8, + w_min=0.7, + w_max=1.0, + negative_swarm=0.2 + ) + +best_score = pso_mnist.fit( + x_test, + y_test, + epochs=200, + save=True, + save_path="./result/mnist", + renewal="acc", + empirical_balance=False, + Dispersion=False, + check_point=25 + ) +``` + ### Trouble Shooting > 1. 딥러닝 알고리즘 특성상 weights는 처음 컴파일시 무작위하게 생성된다. weights의 각 지점의 중요도는 매번 무작위로 정해지기에 전역 최적값으로 찾아갈 때 값이 높은 loss를 향해서 상승하는 현상이 나타난다.
diff --git a/test.ipynb b/test.ipynb index a82f8bc..611b2cb 100644 --- a/test.ipynb +++ b/test.ipynb @@ -275,10 +275,122 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1/1 [==============================] - 0s 452ms/step\n", + "[[0.0000000e+00 1.0000000e+00 8.5117706e-28]\n", + " [0.0000000e+00 1.0000000e+00 0.0000000e+00]\n", + " [1.0000000e+00 3.3700031e-35 0.0000000e+00]\n", + " [1.0000000e+00 1.3158974e-19 0.0000000e+00]\n", + " [0.0000000e+00 1.0000000e+00 0.0000000e+00]\n", + " [0.0000000e+00 0.0000000e+00 1.0000000e+00]\n", + " [1.0000000e+00 1.4602315e-27 0.0000000e+00]\n", + " [0.0000000e+00 0.0000000e+00 1.0000000e+00]\n", + " [1.0000000e+00 2.4845295e-16 0.0000000e+00]\n", + " [0.0000000e+00 1.0000000e+00 1.6942224e-33]\n", + " [1.0000000e+00 0.0000000e+00 0.0000000e+00]\n", + " [0.0000000e+00 1.0000000e+00 0.0000000e+00]\n", + " [1.0000000e+00 9.0455008e-36 0.0000000e+00]\n", + " [1.0000000e+00 0.0000000e+00 0.0000000e+00]\n", + " [0.0000000e+00 1.8117375e-33 1.0000000e+00]\n", + " [0.0000000e+00 1.0000000e+00 6.7984806e-36]\n", + " [0.0000000e+00 1.7472901e-25 1.0000000e+00]\n", + " [0.0000000e+00 6.2991115e-37 1.0000000e+00]\n", + " [0.0000000e+00 0.0000000e+00 1.0000000e+00]\n", + " [0.0000000e+00 1.0598510e-30 1.0000000e+00]\n", + " [1.0000000e+00 1.7519910e-30 0.0000000e+00]\n", + " [0.0000000e+00 1.0000000e+00 0.0000000e+00]\n", + " [0.0000000e+00 1.0000000e+00 0.0000000e+00]\n", + " [1.0000000e+00 7.4562871e-27 0.0000000e+00]\n", + " [0.0000000e+00 0.0000000e+00 1.0000000e+00]\n", + " [0.0000000e+00 0.0000000e+00 1.0000000e+00]\n", + " [0.0000000e+00 0.0000000e+00 1.0000000e+00]\n", + " [0.0000000e+00 1.0000000e+00 0.0000000e+00]\n", + " [0.0000000e+00 1.0000000e+00 0.0000000e+00]\n", + " [1.0000000e+00 0.0000000e+00 0.0000000e+00]]\n", + "[[0. 1. 0.]\n", + " [0. 1. 0.]\n", + " [1. 0. 0.]\n", + " [1. 0. 0.]\n", + " [0. 1. 0.]\n", + " [0. 0. 1.]\n", + " [1. 0. 0.]\n", + " [0. 0. 1.]\n", + " [1. 0. 0.]\n", + " [0. 1. 0.]\n", + " [1. 0. 0.]\n", + " [0. 1. 0.]\n", + " [1. 0. 0.]\n", + " [1. 0. 0.]\n", + " [0. 0. 1.]\n", + " [0. 1. 0.]\n", + " [0. 0. 1.]\n", + " [0. 0. 1.]\n", + " [0. 0. 1.]\n", + " [0. 0. 1.]\n", + " [1. 0. 0.]\n", + " [0. 1. 0.]\n", + " [0. 1. 0.]\n", + " [1. 0. 0.]\n", + " [0. 0. 1.]\n", + " [0. 0. 1.]\n", + " [0. 0. 1.]\n", + " [0. 1. 0.]\n", + " [0. 1. 0.]\n", + " [1. 0. 0.]]\n", + "1/1 [==============================] - 0s 88ms/step - loss: 0.0000e+00 - accuracy: 1.0000\n", + "[0.0, 1.0]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-06-02 14:34:49.851147: I tensorflow/stream_executor/cuda/cuda_blas.cc:1614] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import tensorflow as tf\n", + "from tensorflow import keras\n", + "from tensorflow.keras import layers\n", + "from tensorflow.keras.models import Sequential\n", + "\n", + "from sklearn.datasets import load_iris\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "def get_xor():\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 get_iris():\n", + " iris = load_iris()\n", + " x = iris.data\n", + " y = iris.target\n", + "\n", + " y = keras.utils.to_categorical(y, 3)\n", + "\n", + " x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, shuffle=True, stratify=y)\n", + "\n", + " return x_train, x_test, y_train, y_test\n", + "\n", + "# model = keras.models.load_model(\"./result/xor/06-02-13-31/75_0.35_0.8_0.6.h5\")\n", + "model = keras.models.load_model(\"./result/iris/06-02-13-48/50_0.4_0.8_0.7.h5\")\n", + "# x,y = get_xor()\n", + "x_train, x_test, y_train, y_test = get_iris()\n", + "\n", + "print(model.predict(x_test))\n", + "print(y_test)\n", + "print(model.evaluate(x_test,y_test))" + ] } ], "metadata": { diff --git a/xor.ipynb b/xor.ipynb deleted file mode 100644 index a6d9756..0000000 --- a/xor.ipynb +++ /dev/null @@ -1,1650 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-05-26 10:26:11.173286: 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", - "/home/pieroot/miniconda3/envs/pso/lib/python3.8/site-packages/cupy/_environment.py:445: UserWarning: \n", - "--------------------------------------------------------------------------------\n", - "\n", - " CuPy may not function correctly because multiple CuPy packages are installed\n", - " in your environment:\n", - "\n", - " cupy, cupy-cuda11x\n", - "\n", - " Follow these steps to resolve this issue:\n", - "\n", - " 1. For all packages listed above, run the following command to remove all\n", - " existing CuPy installations:\n", - "\n", - " $ pip uninstall \n", - "\n", - " If you previously installed CuPy via conda, also run the following:\n", - "\n", - " $ conda uninstall cupy\n", - "\n", - " 2. Install the appropriate CuPy package.\n", - " Refer to the Installation Guide for detailed instructions.\n", - "\n", - " https://docs.cupy.dev/en/stable/install.html\n", - "\n", - "--------------------------------------------------------------------------------\n", - "\n", - " warnings.warn(f'''\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2.10.0\n", - "[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'), PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\n" - ] - } - ], - "source": [ - "import os\n", - "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n", - "\n", - "import tensorflow as tf\n", - "tf.random.set_seed(777) # for reproducibility\n", - "\n", - "# from pso_tf import PSO\n", - "from pso import Optimizer\n", - "from tensorflow import keras\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from tqdm import tqdm\n", - "\n", - "from tensorflow import keras\n", - "from tensorflow.keras.models import Sequential\n", - "from tensorflow.keras import layers\n", - "\n", - "from datetime import datetime\n", - "\n", - "import json\n", - "\n", - "print(tf.__version__)\n", - "print(tf.config.list_physical_devices())\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", - " 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", - " return model" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 0/99: 4%|4 | 4/100 [00:00<00:18, 5.11it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 5 calls to .test_function at 0x7f8a2a586e50> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 0/99: 5%|5 | 5/100 [00:01<00:15, 6.06it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:6 out of the last 6 calls to .test_function at 0x7f8a2a509280> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 0/99: 100%|##########| 100/100 [00:12<00:00, 8.33it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.707333293557167 | acc avg : 0.5 | Best score : 0.6307240724563599\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 1/99: 100%|##########| 100/100 [00:03<00:00, 30.91it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.7328412693738937 | acc avg : 0.505 | Best score : 0.6244710087776184\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 2/99: 100%|##########| 100/100 [00:03<00:00, 30.94it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.7291719603538513 | acc avg : 0.5075 | Best score : 0.621765673160553\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 3/99: 100%|##########| 100/100 [00:03<00:00, 30.73it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5099389508366585 | acc avg : 0.7175 | Best score : 0.3762193024158478\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 4/99: 100%|##########| 100/100 [00:03<00:00, 31.01it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.4717184057831764 | acc avg : 0.7525 | Best score : 0.37326303124427795\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 5/99: 100%|##########| 100/100 [00:03<00:00, 31.35it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5643444469571114 | acc avg : 0.675 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 6/99: 100%|##########| 100/100 [00:03<00:00, 31.47it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.6967811548709869 | acc avg : 0.605 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 7/99: 100%|##########| 100/100 [00:03<00:00, 31.09it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.7016823583841324 | acc avg : 0.6225 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 8/99: 100%|##########| 100/100 [00:03<00:00, 31.32it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5891013583540916 | acc avg : 0.7 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 9/99: 100%|##########| 100/100 [00:03<00:00, 26.91it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5186755350232124 | acc avg : 0.7375 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 10/99: 100%|##########| 100/100 [00:03<00:00, 30.86it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5601680752635002 | acc avg : 0.7075 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 11/99: 100%|##########| 100/100 [00:03<00:00, 31.87it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.6089285349845887 | acc avg : 0.69 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 12/99: 100%|##########| 100/100 [00:03<00:00, 31.42it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.621009130179882 | acc avg : 0.685 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 13/99: 100%|##########| 100/100 [00:03<00:00, 31.64it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.601193311214447 | acc avg : 0.7 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 14/99: 100%|##########| 100/100 [00:03<00:00, 31.60it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.582365850508213 | acc avg : 0.7125 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 15/99: 100%|##########| 100/100 [00:03<00:00, 31.50it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5699197337031364 | acc avg : 0.72 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 16/99: 100%|##########| 100/100 [00:03<00:00, 31.53it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5882085087895393 | acc avg : 0.695 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 17/99: 100%|##########| 100/100 [00:03<00:00, 30.72it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.588711973130703 | acc avg : 0.71 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 18/99: 100%|##########| 100/100 [00:03<00:00, 31.07it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.6058803015947342 | acc avg : 0.715 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 19/99: 100%|##########| 100/100 [00:03<00:00, 31.21it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5897892582416534 | acc avg : 0.715 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 20/99: 100%|##########| 100/100 [00:03<00:00, 25.47it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5821597665548325 | acc avg : 0.73 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 21/99: 100%|##########| 100/100 [00:03<00:00, 29.90it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5819245061278343 | acc avg : 0.7425 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 22/99: 100%|##########| 100/100 [00:03<00:00, 31.59it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5784307581186294 | acc avg : 0.7425 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 23/99: 100%|##########| 100/100 [00:03<00:00, 32.03it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5862669295072556 | acc avg : 0.7275 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 24/99: 100%|##########| 100/100 [00:03<00:00, 31.06it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5841098502278328 | acc avg : 0.73 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 25/99: 100%|##########| 100/100 [00:03<00:00, 31.17it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.589279696047306 | acc avg : 0.7325 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 26/99: 100%|##########| 100/100 [00:03<00:00, 31.84it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5820297047495842 | acc avg : 0.7325 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 27/99: 100%|##########| 100/100 [00:03<00:00, 31.29it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5813658729195594 | acc avg : 0.7325 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 28/99: 100%|##########| 100/100 [00:03<00:00, 31.12it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5836457046866417 | acc avg : 0.7425 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 29/99: 100%|##########| 100/100 [00:03<00:00, 31.67it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5902055448293686 | acc avg : 0.7375 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 30/99: 100%|##########| 100/100 [00:03<00:00, 29.97it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.6035681679844856 | acc avg : 0.715 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 31/99: 100%|##########| 100/100 [00:04<00:00, 24.32it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.6004572728276253 | acc avg : 0.715 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 32/99: 100%|##########| 100/100 [00:03<00:00, 30.03it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5800464290380478 | acc avg : 0.74 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 33/99: 100%|##########| 100/100 [00:03<00:00, 31.34it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5788086211681366 | acc avg : 0.74 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 34/99: 100%|##########| 100/100 [00:03<00:00, 30.63it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5828433361649513 | acc avg : 0.735 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 35/99: 100%|##########| 100/100 [00:03<00:00, 31.56it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5914300563931465 | acc avg : 0.72 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 36/99: 100%|##########| 100/100 [00:03<00:00, 31.02it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5877807715535164 | acc avg : 0.7375 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 37/99: 100%|##########| 100/100 [00:03<00:00, 31.83it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5808902844786644 | acc avg : 0.7275 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 38/99: 100%|##########| 100/100 [00:03<00:00, 31.48it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5850541380047798 | acc avg : 0.725 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 39/99: 100%|##########| 100/100 [00:03<00:00, 31.54it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5824474242329597 | acc avg : 0.7375 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 40/99: 100%|##########| 100/100 [00:03<00:00, 31.48it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5857477381825447 | acc avg : 0.7325 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 41/99: 100%|##########| 100/100 [00:03<00:00, 31.43it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5872031468153 | acc avg : 0.73 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 42/99: 100%|##########| 100/100 [00:03<00:00, 25.95it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5911645150184631 | acc avg : 0.7275 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 43/99: 100%|##########| 100/100 [00:03<00:00, 30.14it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5885934352874755 | acc avg : 0.735 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 44/99: 100%|##########| 100/100 [00:03<00:00, 31.60it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5766231667995453 | acc avg : 0.7375 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 45/99: 100%|##########| 100/100 [00:03<00:00, 31.48it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5796105325222015 | acc avg : 0.7275 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 46/99: 100%|##########| 100/100 [00:03<00:00, 31.67it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5890544068813324 | acc avg : 0.7325 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 47/99: 100%|##########| 100/100 [00:03<00:00, 31.54it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5860040760040284 | acc avg : 0.7425 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 48/99: 100%|##########| 100/100 [00:03<00:00, 31.59it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5852086874842644 | acc avg : 0.7325 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 49/99: 100%|##########| 100/100 [00:03<00:00, 31.71it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5891327604651451 | acc avg : 0.73 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 50/99: 100%|##########| 100/100 [00:03<00:00, 31.47it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5878473871946335 | acc avg : 0.735 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 51/99: 100%|##########| 100/100 [00:03<00:00, 31.59it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5877139037847519 | acc avg : 0.7425 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 52/99: 100%|##########| 100/100 [00:03<00:00, 31.53it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5883922311663627 | acc avg : 0.7375 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 53/99: 100%|##########| 100/100 [00:03<00:00, 25.49it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5879773423075676 | acc avg : 0.7375 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 54/99: 100%|##########| 100/100 [00:03<00:00, 30.00it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5927090826630592 | acc avg : 0.7325 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 55/99: 100%|##########| 100/100 [00:03<00:00, 31.61it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.590822865664959 | acc avg : 0.74 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 56/99: 100%|##########| 100/100 [00:03<00:00, 31.29it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5883511942625046 | acc avg : 0.7325 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 57/99: 100%|##########| 100/100 [00:03<00:00, 31.66it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.584270852804184 | acc avg : 0.7375 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 58/99: 100%|##########| 100/100 [00:03<00:00, 31.81it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.584944163262844 | acc avg : 0.73 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 59/99: 100%|##########| 100/100 [00:03<00:00, 31.22it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5890933072566986 | acc avg : 0.72 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 60/99: 100%|##########| 100/100 [00:03<00:00, 31.70it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.59233806848526 | acc avg : 0.73 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 61/99: 100%|##########| 100/100 [00:03<00:00, 31.13it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5855201661586762 | acc avg : 0.735 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 62/99: 100%|##########| 100/100 [00:03<00:00, 31.50it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5888289919495583 | acc avg : 0.7375 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 63/99: 100%|##########| 100/100 [00:03<00:00, 31.55it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5848286512494087 | acc avg : 0.735 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 64/99: 100%|##########| 100/100 [00:03<00:00, 25.72it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5835971942543984 | acc avg : 0.74 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 65/99: 100%|##########| 100/100 [00:03<00:00, 30.24it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.58549885481596 | acc avg : 0.7275 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 66/99: 100%|##########| 100/100 [00:03<00:00, 31.78it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5821312037110329 | acc avg : 0.7375 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 67/99: 100%|##########| 100/100 [00:03<00:00, 31.46it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5888780787587166 | acc avg : 0.735 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 68/99: 100%|##########| 100/100 [00:03<00:00, 31.63it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5965503272414208 | acc avg : 0.7275 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 69/99: 100%|##########| 100/100 [00:03<00:00, 31.32it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5966133666038513 | acc avg : 0.725 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 70/99: 100%|##########| 100/100 [00:03<00:00, 31.51it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5885627806186676 | acc avg : 0.735 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 71/99: 100%|##########| 100/100 [00:03<00:00, 31.42it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5855536741018296 | acc avg : 0.7325 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 72/99: 100%|##########| 100/100 [00:03<00:00, 31.97it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5875397002696991 | acc avg : 0.7375 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 73/99: 100%|##########| 100/100 [00:03<00:00, 31.62it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5828473618626595 | acc avg : 0.7425 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 74/99: 100%|##########| 100/100 [00:03<00:00, 31.45it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5906080171465874 | acc avg : 0.74 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 75/99: 100%|##########| 100/100 [00:03<00:00, 25.97it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.584847458600998 | acc avg : 0.7325 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 76/99: 100%|##########| 100/100 [00:03<00:00, 30.39it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.591842500269413 | acc avg : 0.7375 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 77/99: 100%|##########| 100/100 [00:03<00:00, 31.56it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5940096437931061 | acc avg : 0.7325 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 78/99: 100%|##########| 100/100 [00:03<00:00, 31.84it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5915093126893044 | acc avg : 0.735 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 79/99: 100%|##########| 100/100 [00:03<00:00, 31.88it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5805989000201225 | acc avg : 0.7425 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 80/99: 100%|##########| 100/100 [00:03<00:00, 31.87it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5855838099122047 | acc avg : 0.7275 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 81/99: 100%|##########| 100/100 [00:03<00:00, 31.77it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5896048584580421 | acc avg : 0.7325 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 82/99: 100%|##########| 100/100 [00:03<00:00, 31.78it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5899882999062538 | acc avg : 0.735 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 83/99: 100%|##########| 100/100 [00:03<00:00, 31.88it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.589127992093563 | acc avg : 0.7375 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 84/99: 100%|##########| 100/100 [00:03<00:00, 31.76it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5816178262233734 | acc avg : 0.7325 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 85/99: 100%|##########| 100/100 [00:03<00:00, 31.89it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5880844354629516 | acc avg : 0.74 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 86/99: 100%|##########| 100/100 [00:03<00:00, 25.91it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5908357509970665 | acc avg : 0.73 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 87/99: 100%|##########| 100/100 [00:03<00:00, 30.36it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5950687676668167 | acc avg : 0.72 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 88/99: 100%|##########| 100/100 [00:03<00:00, 31.64it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5923378536105156 | acc avg : 0.735 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 89/99: 100%|##########| 100/100 [00:03<00:00, 31.75it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.591719012260437 | acc avg : 0.73 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 90/99: 100%|##########| 100/100 [00:03<00:00, 31.62it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5900497680902481 | acc avg : 0.7375 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 91/99: 100%|##########| 100/100 [00:03<00:00, 31.81it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5890539279580116 | acc avg : 0.7375 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 92/99: 100%|##########| 100/100 [00:03<00:00, 31.47it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5918287739157677 | acc avg : 0.73 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 93/99: 100%|##########| 100/100 [00:03<00:00, 31.68it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5798978772759438 | acc avg : 0.7375 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 94/99: 100%|##########| 100/100 [00:03<00:00, 31.78it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5842345660924911 | acc avg : 0.735 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 95/99: 100%|##########| 100/100 [00:03<00:00, 31.81it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5843563464283943 | acc avg : 0.7425 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 96/99: 100%|##########| 100/100 [00:03<00:00, 31.63it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5905592763423919 | acc avg : 0.7425 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 97/99: 100%|##########| 100/100 [00:03<00:00, 25.60it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5898371124267578 | acc avg : 0.7375 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 98/99: 100%|##########| 100/100 [00:03<00:00, 29.91it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.5929944407939911 | acc avg : 0.73 | Best score : 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "epoch 99/99: 100%|##########| 100/100 [00:03<00:00, 31.68it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loss avg : 0.593219024837017 | acc avg : 0.7325 | Best score : 0.37197786569595337\n", - "1/1 [==============================] - 0s 42ms/step\n", - "추론 > [[0.42852464]\n", - " [0.5473223 ]\n", - " [0.580507 ]\n", - " [0.5538927 ]]\n", - "실 데이터 > [[0]\n", - " [1]\n", - " [1]\n", - " [0]]\n", - "score > 0.37197786569595337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def fit(x, y, model:keras.models = make_model(), loss_method=\"binary_crossentropy\", n_particles=100, maxiter=50, c0=0.5, c1=1.5, w=0.75,renewal=\"acc\"):\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 = loss_method\n", - " day = datetime.now().strftime(\"%m-%d-%H-%M\")\n", - " pso_xor = Optimizer(model=model, loss=loss_method, n_particles=n_particles, c0=c0, c1=c1, w=w)\n", - "\n", - " weight, score = pso_xor.fit(x, y, epochs=maxiter, save=True, save_path=\"./result/xor\", renewal=renewal)\n", - " # pso_xor = PSO(model=model, loss_method=loss, n_particles=n_particles)\n", - "\n", - " # best_weights, score = pso_xor.optimize(x, y, maxiter=maxiter, c0=c0, c1=c1, w=w)\n", - "\n", - " model.set_weights(weight)\n", - "\n", - " y_pred = model.predict(x_test)\n", - " print(f\"추론 > {y_pred}\")\n", - " print(f\"실 데이터 > {y_test}\")\n", - "\n", - " # score_ = model.evaluate(x_test, y_test, verbose=2)\n", - " print(f\"score > {score}\")\n", - " \n", - " # pso_xor.plot_history()\n", - " \n", - " # history = pso_xor.global_history()\n", - " json_data = {\n", - " \"best score\": score,\n", - " \"epoch\": maxiter,\n", - " \"n_particles\": n_particles,\n", - " \"c0\": c0,\n", - " \"c1\": c1,\n", - " \"w\": w,\n", - " \"loss_method\": loss_method\n", - " }\n", - " \n", - " with open(f\"./result/xor/{day}_{loss_method}_{n_particles}_{maxiter}.json\", \"w\") as f:\n", - " json.dump(json_data, f, indent=4)\n", - " \n", - " return pso_xor\n", - "\n", - "fit(*get_data(), make_model(), n_particles=100, maxiter=100, c0=0.5, c1=1.5, w=0.65, renewal=\"loss\")\n", - "\n", - "# pso_=fit(*get_data(), make_model(), n_particles=10, maxiter=10, c0=0.5, c1=1.5, w=0.75)\n", - "# print(f\"history > {history}\")\n", - "# print(f\"score > {score}\")\n", - "# plt.plot(history)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "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 -} diff --git a/xor.py b/xor.py new file mode 100644 index 0000000..bb3bdec --- /dev/null +++ b/xor.py @@ -0,0 +1,59 @@ +# %% +import os +os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' + +import tensorflow as tf +tf.random.set_seed(777) # for reproducibility + +# from pso_tf import PSO +from pso import Optimizer +from tensorflow import keras + +import numpy as np + +from tensorflow import keras +from tensorflow.keras.models import Sequential +from tensorflow.keras import layers + +from datetime import datetime + +print(tf.__version__) +print(tf.config.list_physical_devices()) + +def get_data(): + x = np.array([[0, 0], [0, 1], [1, 0], [1, 1]]) + y = np.array([[0], [1], [1], [0]]) + return x, y + +def make_model(): + leyer = [] + leyer.append(layers.Dense(2, activation='sigmoid', input_shape=(2,))) + # leyer.append(layers.Dense(2, activation='sigmoid')) + leyer.append(layers.Dense(1, activation='sigmoid')) + + model = Sequential(leyer) + + return model + +# %% +model = make_model() +x_test, y_test = get_data() +# loss = 'binary_crossentropy' +# loss = 'categorical_crossentropy' +# loss = 'sparse_categorical_crossentropy' +# loss = 'kullback_leibler_divergence' +# loss = 'poisson' +# loss = 'cosine_similarity' +# loss = 'log_cosh' +# loss = 'huber_loss' +# loss = 'mean_absolute_error' +# loss = 'mean_absolute_percentage_error' +loss = 'mean_squared_error' + +pso_xor = Optimizer(model, + loss=loss, n_particles=75, c0=0.35, c1=0.8, w_min=0.6, w_max=1.2, negative_swarm=0.25) +best_score = pso_xor.fit( + x_test, y_test, epochs=200, save=True, save_path="./result/xor", renewal="acc", empirical_balance=False, Dispersion=False, check_point=25) + +# %% + diff --git a/xor_sigmoid_2_acc_40.png b/xor_sigmoid_2_acc_40.png new file mode 100644 index 0000000..2262707 Binary files /dev/null and b/xor_sigmoid_2_acc_40.png differ diff --git a/xor_sigmoid_3_acc_40.png b/xor_sigmoid_3_acc_40.png new file mode 100644 index 0000000..22bf24b Binary files /dev/null and b/xor_sigmoid_3_acc_40.png differ