23-07-09

dev container 조정

mnist_tf.py

@@ -43,21 +43,14 @@ def get_data_test():
 def make_model():
     model = Sequential()
     model.add(
-        Conv2D(
-            32,
-            kernel_size=(5, 5),
-            strides=(1, 1),
-            padding="same",
-            activation="relu",
-            input_shape=(28, 28, 1),
-        )
+        Conv2D(32, kernel_size=(5, 5), activation="relu", input_shape=(28, 28, 1))
     )
-    model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
-    model.add(Conv2D(64, kernel_size=(2, 2), activation="relu", padding="same"))
+    model.add(MaxPooling2D(pool_size=(3, 3)))
+    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(1000, activation="relu"))
+    model.add(Dense(128, activation="relu"))
     model.add(Dense(10, activation="softmax"))
 
     return model
@@ -66,7 +59,9 @@ def make_model():
 model = make_model()
 x_train, y_train, x_test, y_test = get_data()
 
-model.compile(optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"])
+model.compile(
+    optimizer="sgd", loss="sparse_categorical_crossentropy", metrics=["accuracy"]
+)
 
 print("Training model...")
 model.fit(x_train, y_train, epochs=1000, batch_size=128, verbose=1)