根据Keras文档，来自目录（）的图像数据集返回：
A tf.data.Dataset object.
- If label_mode is None, it yields float32 tensors of shape (batch_size, image_size[0], image_size[1], num_channels), encoding images (see below for rules regarding num_channels).
- Otherwise, it yields a tuple (images, labels), where images has shape (batch_size, image_size[0], image_size[1], num_channels), and labels follows the format described below.
Rules regarding labels format:
- if label_mode is int, the labels are an int32 tensor of shape (batch_size,).
- if label_mode is binary, the labels are a float32 tensor of 1s and 0s of shape (batch_size, 1).
- if label_mode is categorial, the labels are a float32 tensor of shape (batch_size, num_classes), representing a one-hot encoding of the class index
然而，当我使用它时：
train_dataset = image_dataset_from_directory(
directory=TRAIN_DIR,
labels="inferred",
label_mode="categorical",
class_names=["0", "10", "5"],
image_size=SIZE,
seed=SEED,
subset=None,
interpolation="bilinear",
follow_links=False,
)
我得到（无，224,3）的图像和（无，3）的标签，即使我设置标签_模式为“分类”。即使我显式地将batch_size设置为32（默认值为32，但我尝试了一下，看看它是否有区别），批大小也不会添加到形状中。由于这个原因，我在训练我的模型时遇到了一些问题，因为批量大小需要包含在时间分布层中。
#train_dataset.element_spec
(TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name=None),
TensorSpec(shape=(None, 3), dtype=tf.float32, name=None))
编辑：我试图找出为什么在使用MobileNetV2的迁移学习和LSTM对视频分类模型进行训练时会出现以下错误，并指出数据集中不存在批量大小是问题所在。
ValueError: Input 0 of layer sequential_16 is incompatible with the layer: expected ndim=5, found ndim=4. Full shape received: [None, 224, 224, 3]
模型的代码：
MobileNet V2功能：
def build_mobilenet(shape=INPUT_SHAPE, nbout=CLASSES):
# INPUT_SHAPE = (224,224,3)
# CLASSES = 3
model = MobileNetV2(
include_top=False,
input_shape=shape,
weights='imagenet')
base_model.trainable = True
output = GlobalMaxPool2D()
return Sequential([model, output])
LSTM功能：
def action_model(shape=INSHAPE, nbout=3):
# INSHAPE = (5, 224, 224, 3)
convnet = build_mobilenet(shape[1:])
model = Sequential()
model.add(TimeDistributed(convnet, input_shape=shape))
model.add(LSTM(64))
model.add(Dense(1024, activation='relu'))
model.add(Dropout(.5))
model.add(Dense(512, activation='relu'))
model.add(Dropout(.5))
model.add(Dense(128, activation='relu'))
model.add(Dropout(.5))
model.add(Dense(64, activation='relu'))
model.add(Dense(nbout, activation='softmax'))
return model
这不是批量大小的问题。但是你的输入数据格式。代码：
from tensorflow import keras
from tensorflow.keras.layers import *
def build_mobilenet(shape=(224,224,3), nbout=3):
model = tf.keras.applications.MobileNetV2(
include_top=False,
input_shape=shape,
weights='imagenet')
model.trainable = True
output = tf.keras.layers.GlobalMaxPool2D()
return tf.keras.Sequential([model, output])
def action_model(shape=(5, 224, 224, 3), nbout=3):
convnet = build_mobilenet()
model = tf.keras.Sequential()
model.add(TimeDistributed(convnet, input_shape=shape))
model.add(LSTM(64))
model.add(Dense(1024, activation='relu'))
model.add(Dropout(.5))
model.add(Dense(512, activation='relu'))
model.add(Dropout(.5))
model.add(Dense(128, activation='relu'))
model.add(Dropout(.5))
model.add(Dense(64, activation='relu'))
model.add(Dense(nbout, activation='softmax'))
return model
model = action_model()
tf.keras.utils.plot_model(model, 'my_first_model.png', show_shapes=True)
这将产生以下输出：
正如你所看到的，模型需要一个5d张量作为输入，但你提供的是4d张量。
该模型适用于5d张量：
代码：
x = tf.constant(np.random.randint(50, size =(32,5,224,224,3)), dtype = tf.float32)
model(x)
输出：
<tf.Tensor: shape=(32, 3), dtype=float32, numpy=
array([[0.30153075, 0.3630225 , 0.33544672],
[0.3018494 , 0.36799458, 0.33015603],
[0.2965148 , 0.36714798, 0.3363372 ],
[0.30032247, 0.36478844, 0.33488905],
[0.30106384, 0.36145815, 0.33747798],
[0.29292756, 0.3652076 , 0.34186485],
[0.29766476, 0.35945407, 0.34288123],
[0.29290855, 0.36984667, 0.33724475],
[0.30804047, 0.35799438, 0.33396518],
[0.30497718, 0.35853127, 0.33649153],
[0.29357925, 0.36751047, 0.33891028],
[0.29514724, 0.36558747, 0.33926526],
[0.29731706, 0.3684161 , 0.33426687],
[0.30811843, 0.3656716 , 0.32621 ],
[0.29937437, 0.36403805, 0.33658758],
[0.2967953 , 0.36977535, 0.3334294 ],
[0.30307695, 0.36372742, 0.33319563],
[0.30148408, 0.36562964, 0.33288625],
[0.29590267, 0.36651734, 0.33758003],
[0.29640752, 0.36192682, 0.3416656 ],
[0.30003947, 0.36704347, 0.332917 ],
[0.29541495, 0.3681183 , 0.33646676],
[0.29900452, 0.36397702, 0.33701843],
[0.3028345 , 0.36404026, 0.33312523],
[0.30092967, 0.36406764, 0.33500263],
[0.29969287, 0.36108258, 0.33922455],
[0.29743004, 0.36917207, 0.3333979 ],
[0.29056188, 0.3742272 , 0.33521092],
[0.30297956, 0.36698693, 0.3300335 ],
[0.29843566, 0.3594078 , 0.3421565 ],
[0.29280537, 0.36777246, 0.33942217],
[0.29983717, 0.3691762 , 0.33098662]], dtype=float32)>
您正在使用的来自目录函数的图像数据集不能生成5d张量。您必须使用自定义数据生成器从数据生成5d张量。
