老师您好，我在运行 cats_vs_dogs 图像分类时，显示 UnicodeDecodeError: ‘utf-8’ codec can’t decode byte 0xd5 in position 150: invalid continuation byte，是为什么？我的代码如下：
num_epochs = 10
batch_size = 32
learning_rate = 0.001
data_dir = 'E:\datasets\cats_vs_dogs'
train_cats_dir = data_dir + '/train/cats'
train_dogs_dir = data_dir + '/train/dogs'
test_cats_dir = data_dir + '/valid/cats'
test_dogs_dir = data_dir + '/valid/dogs'
def _decode_and_resize (filename,label):
image_string = tf.io.read_file (filename)
image_decoded = tf.image.decode_jpeg (image_string)
image_resized = tf.image.resize (image_decoded,[256,256]) / 255.0
return image_resized,label
if __name__ == '__main__':
train_cat_filenames = tf.constant ([train_cats_dir + filename for filename in os.listdir (train_cats_dir)])
train_dog_filenames = tf.constant ([train_dogs_dir + filename for filename in os.listdir (train_dogs_dir)])
train_filenames = tf.concat ([train_cat_filenames,train_dog_filenames],axis=-1)
train_labels = tf.concat ([
tf.zeros (train_cat_filenames.shape,dtype=tf.int32),
tf.ones (train_dog_filenames.shape,dtype=tf.int32)],
axis=-1)
train_datasets = tf.data.Dataset.from_tensor_slices ((train_filenames,train_labels))
train_datasets = train_datasets.map (
map_func=_decode_and_resize,
num_parallel_calls=tf.data.experimental.AUTOTUNE)
train_datasets = train_datasets.shuffle (buffer_size=23000)
train_datasets = train_datasets.batch (batch_size)
train_datasets = train_datasets.prefetch (tf.data.experimental.AUTOTUNE)
model = tf.keras.Sequential ([
tf.keras.layers.Conv2D (32, 3, activation='relu', input_shape=(256, 256, 3)),
tf.keras.layers.MaxPooling2D (),
tf.keras.layers.Conv2D (32, 5, activation='relu'),
tf.keras.layers.MaxPooling2D (),
tf.keras.layers.Flatten (),
tf.keras.layers.Dense (64, activation='relu'),
tf.keras.layers.Dense (2, activation='softmax')
])
model.compile (
optimizer=tf.keras.optimizers.Adam (learning_rate=learning_rate),
loss=tf.keras.losses.sparse_categorical_crossentropy,
metrics=[tf.keras.metrics.sparse_categorical_accuracy]
)
model.fit (train_datasets, epochs=num_epochs)