質問編集履歴
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ソースコードを補足しました
test
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test
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@@ -60,6 +60,35 @@
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### 該当のソースコード
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```python
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#データの分割
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train_ds = tf.keras.preprocessing.image_dataset_from_directory(
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data_dir,
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validation_split=0.2,
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subset="training",
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seed=123,
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image_size=(img_height, img_width),
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batch_size=batch_size)
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val_ds = tf.keras.preprocessing.image_dataset_from_directory(
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data_dir,
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validation_split=0.2,
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subset="validation",
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seed=123,
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image_size=(img_height, img_width),
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batch_size=batch_size)
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#標準化
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AUTOTUNE = tf.data.AUTOTUNE
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train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)
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val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)
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normalization_layer = layers.experimental.preprocessing.Rescaling(1./255)
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normalized_ds = train_ds.map(lambda x, y: (normalization_layer(x), y))
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image_batch, labels_batch = next(iter(normalized_ds))
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first_image = image_batch[0]
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print(np.min(first_image), np.max(first_image))
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num_classes = 4
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def conv2d(filters, kernel_size, strides=1, bias_init=1, **kwargs):
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@@ -120,12 +149,11 @@
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model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
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return model
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# コンパイル
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model = AlexNet()
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# モデル構成の確認
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model.summary()
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#訓練
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history = model.fit(
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train_ds,
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validation_data=val_ds,
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