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functional-programming - keras 中的一维卷积序列

问题描述

我是一个试图建立一个网络来将 2 个浮点序列分类为 16450 个不同整数之一的菜鸟。我有 70408 个样本，我将每个样本填充为 1400 个值。所以 1 个样本有 2 个列向量，例如。[104.243,120.12...]，[125.25,14.556...]。我的 x_train 都是大小（70408,1400）。我正在尝试使用 keras 的功能 API，但似乎无法找出正确的输入形状。任何帮助，将不胜感激。

samples = 70408
mass_size = 1400
intensity_size = 1400
output_size = 16450

mass_input = Input(shape=(samples,mass_size), dtype='float32')
mass_net = layers.Conv1D(32,5,activation='relu')(mass_input)
mass_net = layers.AveragePooling1D(3)(mass_net)
mass_net = layers.Conv1D(16,5,activation='relu')(mass_net)
mass_net = layers.GlobalAveragePooling1D()(mass_net)

intensity_input = Input(shape=(samples,intensity_size), dtype='float32')
intensity_net = layers.Conv1D(32,5,activation='relu')(intensity_input)
intensity_net = layers.AveragePooling1D(3)(intensity_net)
intensity_net = layers.Conv1D(16,5,activation='relu')(intensity_net)
intensity_net = layers.GlobalAveragePooling1D()(intensity_net)

concatenated = layers.concatenate([mass_net,intensity_net],axis=-1)

output = layers.Dense(output_size,activation='softmax')(concatenated)


print(mass_data.shape, intensity_data.shape)
model = Model([mass_data,intensity_data],output)
model.compile(optimizer='rmsprop',loss='categorical_crossentropy',metrics=['acc'])

model.fit([mass_data,intensity_data],y_train,epochs=10,batch_size=128)

我不断收到的错误是：

TypeError                                 Traceback (most recent call last)
<ipython-input-18-aab93c439dd0> in <module>()
     28 
     29 print(mass_data.shape, intensity_data.shape)
---> 30 model = Model([mass_data,intensity_data],output)
     31 model.compile(optimizer='rmsprop',loss='categorical_crossentropy',metrics=['acc'])
     32 

~\Anaconda3\envs\deeplearning\lib\site-packages\keras\legacy\interfaces.py in wrapper(*args, **kwargs)
     89                 warnings.warn('Update your `' + object_name +
     90                               '` call to the Keras 2 API: ' + signature, stacklevel=2)
---> 91             return func(*args, **kwargs)
     92         wrapper._original_function = func
     93         return wrapper

~\Anaconda3\envs\deeplearning\lib\site-packages\keras\engine\topology.py in __init__(self, inputs, outputs, name)
   1528 
   1529         # Check for redundancy in inputs.
-> 1530         if len(set(self.inputs)) != len(self.inputs):
   1531             raise ValueError('The list of inputs passed to the model '
   1532                              'is redundant. '

TypeError: unhashable type: 'numpy.ndarray'

标签： functional-programmingkerasconvolution

解决方案

问题似乎在这里：

model = Model([mass_data,intensity_data],output)

您应该使用您创建的输入张量，而不是 numpy 数据：

model = Model([mass_input, intensity_input],output)

与我的旧评论相关的另一个问题是input_shape.

由于您现在将数据作为（样本、长度、特征），您需要input_shape=(length,features)

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