Tensorflow-TypeError:'int'对象不可迭代 [英] Tensorflow - TypeError: 'int' object is not iterable

问题描述

我遇到了一个错误，但是它被埋在TensorFlow库中，所以我正在努力弄清楚我的模型出了什么问题.

I'm getting an error but it's buried down in the TensorFlow library so I'm struggling to figure out what's wrong with my model.

我正在尝试将RNN与LSTM结合使用.我的模型如下:

I'm trying to use an RNN with LSTM. My model looks like this:

model = Sequential()

    model.add(LSTM(128, activation='relu',
                   input_shape=1000, return_sequences=True))
    model.add(Dropout(0.2))
    model.add(LSTM(128, activation='relu'))
    model.add(Dropout(0.2))
    model.add(Dense(32, activation='relu'))
    model.add(Dropout(0.2))
    model.add(Dense(2, activation='softmax'))

    opt = tf.keras.optimizers.Adam(lr=1e-3, decay=1e-5)

    model.compile(optimizer='rmsprop',
                  loss='binary_crossentropy',
                  metrics=['accuracy'])

    model.fit(x_train, y_train, epochs=3, validation_data=(x_test, y_test))

我的训练数据是一个列表列表，每个列表包含1000个浮点数.例如，x_train[0] =

My training data is a list of lists each comprised of 1000 floats. For example, x_train[0] =

[0.0, 0.0, 0.1, 0.25, 0.5, ...]

我收到此错误:

   File "C:\Users\bencu\Desktop\ProjectFiles\Code\Program.py", line 74, in FitModel
    input_shape=1000, return_sequences=True))
  File "C:\Users\bencu\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\keras\layers\recurrent_v2.py", line 881, in __init__
    **kwargs)
  File "C:\Users\bencu\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\keras\layers\recurrent.py", line 1007, in __init__
    super(DropoutRNNCellMixin, self).__init__(*args, **kwargs)
  File "C:\Users\bencu\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\keras\layers\recurrent.py", line 2541, in __init__
    **kwargs)
  File "C:\Users\bencu\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\keras\layers\recurrent.py", line 395, in __init__
    super(RNN, self).__init__(**kwargs)
  File "C:\Users\bencu\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\training\tracking\base.py", line 457, in _method_wrapper
    result = method(self, *args, **kwargs)
  File "C:\Users\bencu\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\keras\engine\base_layer.py", line 356, in __init__
    batch_input_shape = (batch_size,) + tuple(kwargs['input_shape'])
TypeError: 'int' object is not iterable

我对ML很陌生，所以如果有人可以弄清楚我要去哪里错了，那将不胜感激.谢谢.

I'm pretty new to ML so if someone could figure out where I'm going wrong that would be much appreciated. Thank you.

推荐答案

Keras期望input_shape始终是 tuple ；对于单个值，它看起来像(1000,).

Keras expects input_shape to always be a tuple; for a single value, it'd look like (1000,).

但是，对于LSTM，预期的完整形状(batch_shape)为:(num_samples, timesteps, num_channels)-或等效地为(batch_size, timesteps, features). input_shape就是batch_shape，没有维度0-即(timesteps, num_channels).如果您输入的数据是单变量(例如1D序列)，则num_channels=1-因此:

For LSTM, however, the expected full shape (batch_shape) is: (num_samples, timesteps, num_channels) - or equivalently, (batch_size, timesteps, features). input_shape is simply batch_shape without dimension 0 - i.e., (timesteps, num_channels). If your input data is univariate (e.g. 1D sequence), then num_channels=1 - thus:

model.add(LSTM(128, activation='relu', input_shape=(1000, 1), return_sequences=True))

最后，对于'binary_crossentropy'，更好的输出层将是Dense(1, activation='sigmoid').有关更多信息，请参见此答案.

Lastly, for 'binary_crossentropy', a better output layer would be Dense(1, activation='sigmoid'). For more info, see this answer.

提示:为确保正常，请运行print(x_train.shape)，并确保除first(第0维)以外的所有值都与您的input_shape相匹配.但是，我建议始终使用batch_shape而不是input_shape，除非应用程序涉及可变的批处理大小-这样会使调试更加容易.

Tip: to be sure, run print(x_train.shape), and make sure all the values except first (dim 0) match your input_shape. I'd recommend, however, to always use batch_shape over input_shape, unless the application involves variable batch sizes - it makes debugging much easier.

对于您的一维示例，如果它返回类似(32, 1000)的内容，则需要添加尺寸以使其成为3D:x_train = np.expand_dims(x_train, -1)(-1 =最后一个轴)

For your 1D example, if it returns something like (32, 1000), you'll need to add a dimension to make it 3D: x_train = np.expand_dims(x_train, -1) (-1 = last axis)

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