23-07-07

dev container 설정 - tqdm + tensorflow 자동 설치 env name = pso 로 자동 생성

iris_tf.py

@@ -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)