我从tensorflow基本图像分类指南中获得了一个名为train_images和train_labels的集合:
https://www.tensorflow.org/tutorials/keras/classification
我通过以下方式加载数据集:
fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()
这两个列表的形状分别是: (60000,28,28)(60000,)
此后,我想使用ImageDataGenerator水平翻转一些图像,但是当我将模型与训练列表拟合时,它返回一条错误消息,提示x应该是4级数组。
我已经尝试做
train_images = (np.expand_dims(train_images,0))
因此形状变为(1,60000,28,28) (我必须这样做才能让模型检查单个图像) 但不适用于模型
这是其余的代码:
aug = ImageDataGenerator(rotation_range=20, horizontal_flip=True)
model = keras.Sequential([
keras.layers.Flatten(input_shape=(28,28)),
keras.layers.Dense(128, activation='relu'),
keras.layers.Dense(10, activation='softmax')
])
model.compile(
optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy']
)
BS=32
EPOCHS=10
H = model.fit_generator(
aug.flow(train_images, train_labels, batch_size=BS),
validation_data=(test_images, test_labels),
steps_per_epoch=len(train_images) // BS,
epochs=EPOCHS)
这是生成的错误:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-65-e49da92bcb89> in <module>()
5 #train_images.shape
6 H = model.fit_generator(
----> 7 aug.flow(train_images, train_labels, batch_size=BS),
8 validation_data=(test_images, test_labels),
9 steps_per_epoch=len(train_images) // BS,
1 frames
/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/numpy_array_iterator.py in __init__(self, x, y, image_data_generator, batch_size, shuffle, sample_weight, seed, data_format, save_to_dir, save_prefix, save_format, subset, dtype)
115 raise ValueError('Input data in `NumpyArrayIterator` '
116 'should have rank 4. You passed an array '
--> 117 'with shape', self.x.shape)
118 channels_axis = 3 if data_format == 'channels_last' else 1
119 if self.x.shape[channels_axis] not in {1, 3, 4}:
ValueError: ('Input data in `NumpyArrayIterator` should have rank 4. You passed an array with shape', (60000, 28, 28))
实际上train_images是(图像的N°,宽度,高度)它正在等待的第4轴是什么? 该如何执行?
答案 0 :(得分:0)
您应该将图像转换为4D张量。现在您有了NHW格式(批量尺寸,高度,宽度)。该错误表明您应该具有NHWC格式-批处理,高度,宽度,通道。所以你需要做
train_images = (np.expand_dims(train_images, axis=3))
这将添加一个通道尺寸(大小为1),结果形状为(60000,28,28,1),它应该可以解决您的问题。
答案 1 :(得分:0)
通道应该是4D张量的最后一个维度。因此,请尝试使用train_images = (np.expand_dims(train_images,0))
而不是train_images = (np.expand_dims(train_images, -1))
。希望对您有所帮助。