我有这个错误“输入 0 与层 lstm 不兼容预期 ndim=3 发现 ndim=5”答案

【问题标题】：I have this error "input 0 is incompatible with layer lstm expected ndim=3 found ndim=5"我有这个错误“输入 0 与层 lstm 不兼容预期 ndim=3 发现 ndim=5”
【发布时间】：2023-03-12 23:30:02
【问题描述】：

我是这个领域的新手。我在互联网上搜索，但找不到解决方案。我正在等待对此领域感兴趣的人的帮助。

我的模型

def load_VGG16_model():
    base_model = VGG16(weights='imagenet', include_top=False, input_shape=(256,256,3))
    print("Model loaded..!")
    return base_model

模型总结

load_VGG16_model().summary()

添加图层

def action_model(shape=(30, 256, 256, 3), nbout=len(classes)):
    convnet = load_VGG16_model()

    model = Sequential()
    model.add(TimeDistributed(convnet, input_shape=shape))
    model.add(LSTM(30,return_sequences=True,input_shape=(30,512))) # the error shows this line.
    top_model.add(Dense(4096, activation='relu', W_regularizer=l2(0.1)))
    top_model.add(Dropout(0.5))
    top_model.add(Dense(4096, activation='relu', W_regularizer=l2(0.1)))
    top_model.add(Dropout(0.5))
    model.add(Dense(nbout, activation='softmax'))
    return model

model.add(LSTM(30,return_sequences=True,input_shape=(30,512))) ==>错误显示这一行。

【问题讨论】：

标签： machine-learning deep-learning artificial-intelligence lstm conv-neural-network

【解决方案1】：

你的问题和这个Building CNN + LSTM in Keras for a regression problem. What are proper shapes?类似

在 LSTM 之前使用重塑层应该可以正常工作

def action_model(shape=(256, 256, 3), nbout=len(classes)):
    convnet = load_VGG16_model()

    model = Sequential()
    model.add(convnet)
    model.add(tf.keras.layers.Reshape((8*8, 512))) # Shape comes from the last output of covnet
    model.add(LSTM(30,return_sequences=True,input_shape=(8*8,512))) # the error shows this line.
    top_model.add(Dense(4096, activation='relu', W_regularizer=l2(0.1)))
    top_model.add(Dropout(0.5))
    top_model.add(Dense(4096, activation='relu', W_regularizer=l2(0.1)))
    top_model.add(Dropout(0.5))
    model.add(Dense(nbout, activation='softmax'))
    return model

【讨论】：