코드 변경 내용을 요약한 커밋 메시지입니다.

test/mnist_tf.py

@@ -0,0 +1,132 @@
+from keras.models import Sequential
+from keras.layers import Conv2D, Dense, Dropout, Flatten, MaxPooling2D
+from keras.datasets import mnist
+from keras.utils import to_categorical
+from sklearn.model_selection import train_test_split
+
+# from tensorflow.data.Dataset import from_tensor_slices
+import tensorflow as tf
+import os
+
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
+
+
+gpus = tf.config.experimental.list_physical_devices("GPU")
+if gpus:
+    try:
+        tf.config.experimental.set_memory_growth(gpus[0], True)
+    except Exception as e:
+        print(e)
+    finally:
+        del gpus
+
+
+def get_data():
+    (x_train, y_train), (x_test, y_test) = mnist.load_data()
+
+    x_train, x_test = x_train / 255.0, x_test / 255.0
+    x_train = x_train.reshape((60000, 28, 28, 1))
+    x_test = x_test.reshape((10000, 28, 28, 1))
+
+    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
+
+class _batch_generator_:
+    def __init__(self, x, y, batch_size: int = None):
+        self.index = 0
+        self.x = x
+        self.y = y
+        self.setBatchSize(batch_size)
+
+    def next(self):
+        self.index += 1
+        if self.index >= self.max_index:
+            self.index = 0
+            self.__getBatchSlice(self.batch_size)
+        return self.dataset[self.index][0], self.dataset[self.index][1]
+
+    def getMaxIndex(self):
+        return self.max_index
+
+    def getIndex(self):
+        return self.index
+
+    def setIndex(self, index):
+        self.index = index
+
+    def getBatchSize(self):
+        return self.batch_size
+
+    def setBatchSize(self, batch_size: int = None):
+        if batch_size is None:
+            batch_size = len(self.x) // 10
+        elif batch_size > len(self.x):
+            batch_size = len(self.x)
+        self.batch_size = batch_size
+        print(f"batch size : {self.batch_size}")
+        self.dataset = self.__getBatchSlice(self.batch_size)
+        self.max_index = len(self.dataset)
+
+    def __getBatchSlice(self, batch_size):
+        return list(
+            tf.data.Dataset.from_tensor_slices((self.x, self.y))
+            .shuffle(len(self.x))
+            .batch(batch_size)
+        )
+
+    def getDataset(self):
+        return self.dataset
+
+
+def make_model():
+    model = Sequential()
+    model.add(
+        Conv2D(64, kernel_size=(5, 5), activation="relu", input_shape=(28, 28, 1))
+    )
+    model.add(MaxPooling2D(pool_size=(2, 2)))
+    model.add(Dropout(0.5))
+    model.add(Conv2D(128, kernel_size=(3, 3), activation="relu"))
+    model.add(MaxPooling2D(pool_size=(2, 2)))
+    model.add(Flatten())
+    model.add(Dropout(0.5))
+    model.add(Dense(2048, activation="relu"))
+    model.add(Dropout(0.8))
+    model.add(Dense(1024, activation="relu"))
+    model.add(Dropout(0.8))
+    model.add(Dense(10, activation="softmax"))
+
+    return model
+
+
+model = make_model()
+x_train, y_train, x_test, y_test = get_data()
+y_train = tf.one_hot(y_train, 10)
+y_test = tf.one_hot(y_test, 10)
+
+batch = 64
+dataset = _batch_generator_(x_train, y_train, batch)
+
+model.compile(
+    optimizer="adam",
+    loss="categorical_crossentropy",
+    metrics=["accuracy", "mse"],
+)
+
+count = 0
+print(f"batch size : {batch}")
+print("iter " + str(dataset.getMaxIndex()))
+print("Training model...")
+# while count < dataset.getMaxIndex():
+# x_batch, y_batch = dataset.next()
+# count += 1
+# print(f"iter {count}/{dataset.getMaxIndex()}")
+model.fit(x_train, y_train, epochs=1000, batch_size=batch, verbose=1)
+
+print(count)
+
+print("Evaluating model...")
+model.evaluate(x_test, y_test, verbose=1)
+
+weights = model.get_weights()