我没能训练CNN+；LSTM模型。我怎样才能解决这个问题？数据集中是否存在问题？还是模型？（Python 3.8x） 0我用过：
Python 3.8x我没能训练CNN+；LSTM模型。我怎样才能解决这个问题？数据集中是否存在问题？还是模型？（Python 3.8x） 0我用过：,python,tensorflow,keras,conv-neural-network,lstm,Python,Tensorflow,Keras,Conv Neural Network,Lstm,Python 3.8x JupyterLab>=3.0 张量流 凯拉斯 VGG19（预培训模型） 1.我的问题 我尝试将CNN+LSTM Python模型训练为视频分类（二进制分类） 但是。。。我未能训练我的模型。我的JupyterLab（>=3.0）只打印了Epoch 1/100，并且几乎停止或重新启动了内核（我建议可能内存不足，但我的桌面有16GB RAM！） 我做错型号了吗？还是我的数据集有问题 此外，有时我会缩小训练数据的大小（2000->100），但问题没有解决 这是我的模
Epoch 1/100- 类型：numpy数组
- 形状：（2697,30,160,160,3）
- 示例：数据\u培训\u ar[10]
- 类型：numpy数组
- 形状：（2697,30,2）
- 示例：标签\u培训\u ar[10]
尝试使用Spyder或记事本，直接在命令行上运行脚本。这是为了确保您的问题与运行Jupyter的web服务器超时无关。它还允许您查看完整的堆栈跟踪。如果可能，请尝试使用PyCharm，查看错误是否仍然存在？还要检查是否是相同的错误
我在Google Colab中运行了VGG系列模型。速度相当快。我在Google Colab中也失败了。会话被关闭，内核被重新启动。我在Google Colab中也失败了。。。此问题是否与.ipynb类型文件有关？我的数据集是否太大而不适合模型？许多程序员已经将他们的模型训练成具有大数据集的视频分类。。。我真的很想知道他们是怎么做的。好吧，要排除故障，请找出是否可以直接从命令行运行它，并可能找出原因。一旦它在命令行上工作，请尝试使用Jupyter
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base_model=keras.applications.VGG19(include_top=False, input_shape=(160, 160, 3), weights='imagenet')
image_model=keras.models.Sequential()
image_model.add(base_model)
image_model.add(keras.layers.Flatten())
image_model.add(keras.layers.Dense(4096, activation='relu', name='fc1'))
image_model.add(keras.layers.Dense(4096, activation='relu', name='fc2'))
image_model.add(keras.layers.Dense(1000, activation='softmax', name='predictions'))
chunk_size=4096
n_chunks=30
rnn_size=512
model=keras.models.Sequential()
model.add(keras.layers.TimeDistributed(image_model, input_shape=(30, 160, 160, 3)))
model.add(keras.layers.LSTM(rnn_size, input_shape=(n_chunks, chunk_size))) # (30, 4096)
model.add(keras.layers.Dense(1024))
model.add(keras.layers.Activation('relu'))
model.add(keras.layers.Dense(256))
model.add(keras.layers.Activation('sigmoid'))
model.add(keras.layers.Dense(2))
model.add(keras.layers.Activation('softmax'))
model.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy'])
epoch=100
batchS=30
history=model.fit(x=data_training_ar[0:2000], y=label_training_ar[0:2000], epochs=epoch,
validation_data=(data_training_ar[2000:], label_training_ar[2000:]),
callbacks=[checkpoint_cb], #keras.callbacks.ModelCheckpoint('210429_vc_13-02_checkpoint.h5', save_best_only=True)
batch_size=batchS, verbose=2)