我正在试着训练CNN的视频节目。我的输入_数据具有表示(n_采样、通道、帧、高度、宽度)的形状(5874、1、10、128、128)。误差为给定4个维度,但预期为5个维度或给定6个维度。管理Conv3D的正确方法是什么
将Input((1,10,128,128))
结果设置为:ValueError: Error when checking input: expected input_1 to have 5 dimensions, but got array with shape (1, 128, 128, 10)
。但拟合后会产生误差
执行模型后(拟合前),将Input((1,1,10,128,128))
结果设置为:ValueError: Input 0 of layer conv3d_6 is incompatible with the layer: expected ndim=5, found ndim=6. Full shape received: [None, 1, 1, 128, 128, 10]
我已经浏览了所有可能的文档和论坛,但什么也没找到。任何提示都会有帮助
dataset = tf.data.Dataset.from_tensor_slices((data, labels)))
dataset = dataset.shuffle(10000)
train_dataset, valid_dataset = split_dataset(dataset, 0.02)
model = tf.keras.Sequential()
model.add(Input((1,10,128,128)))
model.add(Conv3D(filters = 8, kernel_size=(10,5,5), padding="same", activation="relu", data_format="channels_first"))
model.add(BatchNormalization())
model.add(Conv3D(filters = 8, kernel_size=(10,3,3), padding="same", activation="relu"))
model.add(BatchNormalization())
model.add(MaxPool3D(pool_size=(1,2,2), strides=(1,1,1)))
model.add(Conv3D(filters = 16, kernel_size=(5,5,5), padding="same", activation="relu"))
model.add(BatchNormalization())
model.add(Conv3D(filters = 16, kernel_size=(5,3,3), padding="same", activation="relu"))
model.add(BatchNormalization())
model.add(MaxPool3D(pool_size=(2,2,2), strides=(1,1,1)))
model.add(Conv3D(filters = 32, kernel_size=(5,5,5), padding="same", activation="relu"))
model.add(BatchNormalization())
model.add(Conv3D(filters = 32, kernel_size=(3,3,3), padding="same", activation="relu"))
model.add(BatchNormalization())
model.add(MaxPool3D(pool_size=(2,2,2), strides=(1,1,1)))
model.add(Flatten())
model.add(Dropout(0.2))
model.add(Dense(512, activation="relu"))
model.add(Dropout(0.2))
model.add(Dense(5, activation="softmax"))
model.compile(optimizer=tf.keras.optimizers.Adam(lr=0.01) , loss="sparse_categorical_crossentropy", metrics=["accuracy"])
r = model.fit(train_dataset, verbose=1, validation_data=valid_dataset, epochs=50)
在模型中,Tensorflow在迭代数据的开头添加一个维度。所以输入应该只得到最后四个维度。但是
fit
需要5个。在使用Dataset.from_tensor_slices
之后,必须使用dataset.batch
,否则拟合时会出错相关问题 更多 >
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