코드 변경 내용: digits.py, iris.py, mnist.py, bean.py

Keras 모듈을 사용하여 코드를 업데이트했습니다.

bean.py

@@ -1,24 +1,21 @@
 import os
 
-import pandas as pd
-import tensorflow as tf
+from keras.layers import Dense
+from keras.models import Sequential
+from keras.utils import to_categorical
 from sklearn.model_selection import train_test_split
 from tensorflow import keras
-from tensorflow.keras import layers
-from tensorflow.keras.layers import Dense
-from tensorflow.keras.models import Sequential
-from tensorflow.keras.utils import to_categorical
 from ucimlrepo import fetch_ucirepo
 
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
+os.environ["TF_FORCE_GPU_ALLOW_GROWTH"] = "true"
 
 
 def make_model():
     model = Sequential()
-    model.add(Dense(12, input_dim=16, activation='relu'))
-    model.add(Dense(8, activation='relu'))
-    model.add(Dense(7, activation='softmax'))
+    model.add(Dense(12, input_dim=16, activation="relu"))
+    model.add(Dense(8, activation="relu"))
+    model.add(Dense(7, activation="softmax"))
 
     return model
 
@@ -34,7 +31,7 @@ def get_data():
     x = X.to_numpy()
     # object to categorical
 
-    x = x.astype('float32')
+    x = x.astype("float32")
 
     y_class = to_categorical(y)
 
@@ -49,21 +46,27 @@ def get_data():
     # y_class = to_categorical(y)
 
     x_train, x_test, y_train, y_test = train_test_split(
-        x, y_class, test_size=0.2, random_state=42, shuffle=True)
+        x, y_class, test_size=0.2, random_state=42, shuffle=True
+    )
     return x_train, x_test, y_train, y_test
 
 
 x_train, x_test, y_train, y_test = get_data()
 model = make_model()
 early_stopping = keras.callbacks.EarlyStopping(
-    patience=10, min_delta=0.001, restore_best_weights=True)
+    patience=10, min_delta=0.001, restore_best_weights=True
+)
 
 
-model.compile(loss='sparse_categorical_crossentropy',
-              optimizer='adam', metrics=['accuracy', "mse"])
+model.compile(
+    loss="sparse_categorical_crossentropy",
+    optimizer="adam",
+    metrics=["accuracy", "mse"],
+)
 
 model.summary()
 
-history = model.fit(x_train, y_train, epochs=150,
-                    batch_size=10, callbacks=[early_stopping])
+history = model.fit(
+    x_train, y_train, epochs=150, batch_size=10, callbacks=[early_stopping]
+)
 score = model.evaluate(x_test, y_test, verbose=2)