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23-07-07

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dev container 설정 - tqdm + tensorflow 자동 설치 env name = pso 로 자동 생성
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jung-geun
2023-07-07 18:30:08 +09:00
parent c163de6cb6
commit 7410ed9e04
12 changed files with 124 additions and 135 deletions
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18
.devcontainer/devcontainer.json
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@@ -2,14 +2,20 @@
// README at: https://github.com/devcontainers/templates/tree/main/src/miniconda
{
"name": "Miniconda (Python 3)",
// Configure tool-specific properties.
"customizations": {
"vscode": {
"extensions": [
"ms-python.python",
"ms-toolsai.jupyter"
"ms-toolsai.jupyter",
"donjayamanne.python-extension-pack",
"tht13.python",
"esbenp.prettier-vscode",
"ms-python.black-formatter"
]
}
},
// Features to add to the dev container. More info: https://containers.dev/features.
"features": {
"ghcr.io/devcontainers/features/nvidia-cuda:1": {
"installCudnn": true
@@ -22,15 +28,13 @@
"version": "3.9"
}
},
"postCreateCommand": "conda env create --file environment.yaml --name pso"
// Features to add to the dev container. More info: https://containers.dev/features.
// "features": {},
// Use 'forwardPorts' to make a list of ports inside the container available locally.
// "forwardPorts": [],
// Use 'postCreateCommand' to run commands after the container is created.
// "postCreateCommand": "python --version",
// Configure tool-specific properties.
// "customizations": {},
"postCreateCommand": [
"conda env create --file environment.yaml --name pso",
"conda activate pso"
]
// Uncomment to connect as root instead. More info: https://aka.ms/dev-containers-non-root.
// "remoteUser": "root"
}

24
.github/workflows/python-package-conda.yml vendored
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@@ -1,24 +0,0 @@
name: Python Package using Conda
on: [push]
jobs:
build-linux:
runs-on: ubuntu-latest
strategy:
max-parallel: 5
steps:
- uses: actions/checkout@v3
- name: Set up Python 3.9
uses: actions/setup-python@v3
with:
python-version: "3.9"
- name: Add conda to system path
run: |
# $CONDA is an environment variable pointing to the root of the miniconda directory
echo $CONDA/bin >> $GITHUB_PATH
- name: Install dependencies
run: |
conda env create --file environment.yaml --name pso
conda activate pso

84
auto_tunning.py
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@@ -1,30 +1,25 @@
# %%
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
import tensorflow as tf
tf.random.set_seed(777) # for reproducibility
from tensorflow import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K
# from pso_tf import PSO
from pso import Optimizer
import gc
from datetime import date
import numpy as np
from datetime import date
from keras import backend as K
from keras.datasets import mnist
from keras.layers import Conv2D, Dense, Dropout, Flatten, MaxPooling2D
from keras.models import Sequential
from tensorflow import keras
from tqdm import tqdm
import gc
from pso import Optimizer
# print(tf.__version__)
# print(tf.config.list_physical_devices())
# print(f"Num GPUs Available: {len(tf.config.list_physical_devices('GPU'))}")
def get_data():
(x_train, y_train), (x_test, y_test) = mnist.load_data()
@@ -37,26 +32,30 @@ def get_data():
print(f"x_test : {x_test[0].shape} | y_test : {y_test[0].shape}")
return x_train, y_train, x_test, y_test
def get_data_test():
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_test = x_test.reshape((10000, 28, 28, 1))
return x_test, y_test
def make_model():
model = Sequential()
model.add(Conv2D(32, kernel_size=(5, 5),
activation='relu', input_shape=(28, 28, 1)))
model.add(
Conv2D(32, kernel_size=(5, 5), activation="relu", input_shape=(28, 28, 1))
)
model.add(MaxPooling2D(pool_size=(3, 3)))
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
model.add(Conv2D(64, kernel_size=(3, 3), activation="relu"))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dense(10, activation='softmax'))
model.add(Dense(128, activation="relu"))
model.add(Dense(10, activation="softmax"))
return model
# %%
model = make_model()
x_test, y_test = get_data_test()
@@ -67,12 +66,23 @@ 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 = ['mse', 'categorical_crossentropy', 'binary_crossentropy', 'kullback_leibler_divergence', 'poisson', 'cosine_similarity', 'log_cosh', 'huber_loss', 'mean_absolute_error', 'mean_absolute_percentage_error']
loss = [
"mse",
"categorical_crossentropy",
"binary_crossentropy",
"kullback_leibler_divergence",
"poisson",
"cosine_similarity",
"log_cosh",
"huber_loss",
"mean_absolute_error",
"mean_absolute_percentage_error",
]
n_particles = [50, 75, 100]
c0 = [0.25, 0.35, 0.45, 0.55]
c1 = [0.5, 0.6, 0.7, 0.8, 0.9]
@@ -93,31 +103,31 @@ if __name__ == "__main__":
for n_s in negative_swarm:
pso_mnist = Optimizer(
model,
loss=loss_,
loss=loss_,
n_particles=n,
c0=c_0,
c1=c_1,
c0=c_0,
c1=c_1,
w_min=w_m,
w_max=w_M,
negative_swarm=n_s
)
negative_swarm=n_s,
)
best_score = pso_mnist.fit(
x_test,
y_test,
epochs=200,
save=True,
save_path="./result/mnist",
renewal="acc",
save_path="./result/mnist",
renewal="acc",
empirical_balance=False,
Dispersion=False,
check_point=25
)
Dispersion=False,
check_point=25,
)
del pso_mnist
gc.collect()
gc.collect()
tf.keras.backend.clear_session()
except KeyboardInterrupt:
print("KeyboardInterrupt")
finally:

2
environment.yaml
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@@ -10,8 +10,8 @@ dependencies:
- pandas=1.5.3=py39h417a72b_0
- pip=23.0.1=py39h06a4308_0
- python=3.9.16=h7a1cb2a_2
- tqdm=4.65.0=py39hb070fc8_0
- pip:
- numpy==1.23.5
- nvidia-cudnn-cu11==8.6.0.163
- tensorflow==2.12.0
- tqdm==4.65.1.dev3+g5587f0d

57
example.py
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@@ -7,19 +7,19 @@ results with a number of independent runs of standard Backpropagation algorithm
@author Mike Holcomb (mjh170630@utdallas.edu)
"""
import tensorflow as tf
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Sequential
from psokeras import Optimizer
N = 50 # number of particles
STEPS = 500 # number of steps
LOSS = 'mse' # Loss function
BATCH_SIZE = 32 # Size of batches to train on
N = 50 # number of particles
STEPS = 500 # number of steps
LOSS = "mse" # Loss function
BATCH_SIZE = 32 # Size of batches to train on
def build_model(loss):
@@ -30,12 +30,11 @@ def build_model(loss):
:return: Keras dense model of predefined structure
"""
model = Sequential()
model.add(Dense(4, activation='sigmoid', input_dim=4, use_bias=True))
model.add(Dense(4, activation='sigmoid', use_bias=True))
model.add(Dense(3, activation='softmax', use_bias=True))
model.add(Dense(4, activation="sigmoid", input_dim=4, use_bias=True))
model.add(Dense(4, activation="sigmoid", use_bias=True))
model.add(Dense(3, activation="softmax", use_bias=True))
model.compile(loss=loss,
optimizer='adam')
model.compile(loss=loss, optimizer="adam")
return model
@@ -52,11 +51,10 @@ def vanilla_backpropagation(x_train, y_train):
for i in range(N):
model_s = build_model(LOSS)
model_s.fit(x_train, y_train,
epochs=STEPS,
batch_size=BATCH_SIZE,
verbose=0)
train_score = model_s.evaluate(x_train, y_train, batch_size=BATCH_SIZE, verbose=0)
model_s.fit(x_train, y_train, epochs=STEPS, batch_size=BATCH_SIZE, verbose=0)
train_score = model_s.evaluate(
x_train, y_train, batch_size=BATCH_SIZE, verbose=0
)
if train_score < best_score:
best_model = model_s
best_score = train_score
@@ -66,11 +64,13 @@ def vanilla_backpropagation(x_train, y_train):
if __name__ == "__main__":
# Section I: Build the data set
iris = load_iris()
x_train, x_test, y_train, y_test = train_test_split(iris.data,
keras.utils.to_categorical(iris.target, num_classes=None),
test_size=0.5,
random_state=0,
stratify=iris.target)
x_train, x_test, y_train, y_test = train_test_split(
iris.data,
keras.utils.to_categorical(iris.target, num_classes=None),
test_size=0.5,
random_state=0,
stratify=iris.target,
)
# Section II: First run the backpropagation simulation
model_s = vanilla_backpropagation(x_train=x_train, y_train=y_train)
@@ -84,13 +84,14 @@ if __name__ == "__main__":
model_p = build_model(LOSS)
# Instantiate optimizer with model, loss function, and hyperparameters
pso = Optimizer(model=model_p,
loss=LOSS,
n=N, # Number of particles
acceleration=1.0, # Contribution of recursive particle velocity (acceleration)
local_rate=0.6, # Contribution of locally best weights to new velocity
global_rate=0.4 # Contribution of globally best weights to new velocity
)
pso = Optimizer(
model=model_p,
loss=LOSS,
n=N, # Number of particles
acceleration=1.0, # Contribution of recursive particle velocity (acceleration)
local_rate=0.6, # Contribution of locally best weights to new velocity
global_rate=0.4, # Contribution of globally best weights to new velocity
)
# Train model on provided data
pso.fit(x_train, y_train, steps=STEPS, batch_size=BATCH_SIZE)

5
iris.py
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@@ -4,15 +4,14 @@ os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
import gc
import numpy as np
import tensorflow as tf
from pso import Optimizer
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.models import Sequential
from pso import Optimizer
def make_model():
model = Sequential()

32
iris_tf.py
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@@ -1,5 +1,6 @@
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
import tensorflow as tf
@@ -10,23 +11,23 @@ if gpus:
tf.config.experimental.set_memory_growth(gpus[0], True)
except RuntimeError as e:
print(e)
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.models import Sequential
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
def make_model():
model = Sequential()
model.add(layers.Dense(10, activation='relu', input_shape=(4,)))
model.add(layers.Dense(10, activation='relu'))
model.add(layers.Dense(3, activation='softmax'))
model.add(layers.Dense(10, activation="relu", input_shape=(4,)))
model.add(layers.Dense(10, activation="relu"))
model.add(layers.Dense(3, activation="softmax"))
return model
def load_data():
iris = load_iris()
x = iris.data
@@ -34,18 +35,21 @@ def load_data():
y = keras.utils.to_categorical(y, 3)
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, shuffle=True, stratify=y)
x_train, x_test, y_train, y_test = train_test_split(
x, y, test_size=0.2, shuffle=True, stratify=y
)
return x_train, x_test, y_train, y_test
if __name__ == "__main__":
model = make_model()
x_train, x_test, y_train, y_test = load_data()
print(x_train.shape, y_train.shape)
loss = ['categorical_crossentropy', 'accuracy','mse']
metrics = ['accuracy']
model.compile(optimizer='sgd', loss=loss[0], metrics=metrics[0])
loss = ["categorical_crossentropy", "accuracy", "mse"]
metrics = ["accuracy"]
model.compile(optimizer="sgd", loss=loss[0], metrics=metrics[0])
model.fit(x_train, y_train, epochs=200, batch_size=32, validation_split=0.2)
model.evaluate(x_test, y_test, batch_size=32)
model.evaluate(x_test, y_test, batch_size=32)

7
mnist.py
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@@ -5,14 +5,11 @@ os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
import gc
import tensorflow as tf
from keras import backend as K
from keras.datasets import mnist
from keras.layers import Conv2D, Dense, Dropout, Flatten, MaxPooling2D
from keras.models import Sequential
from pso import Optimizer
from tensorflow import keras
from tqdm import tqdm
def get_data():
@@ -24,6 +21,7 @@ def get_data():
print(f"x_train : {x_train[0].shape} | y_train : {y_train[0].shape}")
print(f"x_test : {x_test[0].shape} | y_test : {y_test[0].shape}")
return x_train, y_train, x_test, y_test
@@ -92,6 +90,7 @@ if __name__ == "__main__":
Dispersion=False,
check_point=25,
)
except Exception as e:
print(e)
finally:

8
plt.ipynb
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File diff suppressed because one or more lines are too long

5
pso/optimizer.py
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@@ -73,7 +73,11 @@ class Optimizer:
self.g_best = None # 최고 점수를 받은 가중치
self.g_best_ = None # 최고 점수를 받은 가중치 - 값의 분산을 위한 변수
self.avg_score = 0 # 평균 점수
self.save_path = None # 저장 위치
self.renewal = "acc"
self.Dispersion = False
self.day = datetime.now().strftime("%m-%d-%H-%M")
negative_count = 0
@@ -225,7 +229,6 @@ class Optimizer:
self.save_path = save_path
if not os.path.exists(save_path):
os.makedirs(save_path, exist_ok=True)
self.day = datetime.now().strftime("%m-%d-%H-%M")
except ValueError as e:
print(e)
sys.exit(1)

1
pso/particle.py
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@@ -1,7 +1,6 @@
import gc
import numpy as np
import tensorflow as tf
from tensorflow import keras

16
xor.py
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@@ -5,15 +5,12 @@ os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
import numpy as np
import tensorflow as tf
# from pso_tf import PSO
from pso import Optimizer
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Sequential
print(tf.__version__)
print(tf.config.list_physical_devices())
from pso import Optimizer
def get_data():
@@ -23,12 +20,9 @@ def get_data():
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)
model = Sequential()
model.add(layers.Dense(2, activation="sigmoid", input_shape=(2,)))
model.add(layers.Dense(1, activation="sigmoid"))
return model