Python 如何为卷积神经网络模型重塑我的数据？

我需要在卷积神经网络模型中输入一个重塑数据， 但我的问题是代码行：

model = Sequential()
input_traces = Input(shape=(3253,))
model.add(Convolution1D(nb_filter=32, filter_length=3, 
activation='relu',input_shape = input_traces))      

这一行给出了以下错误：

   CNN_Based_Attack.py:139: UserWarning: Update your `Conv1D` call to the Keras 2 API: `Conv1D(activation="relu", input_shape=(None, /in..., padding="same", filters=32, kernel_size=3)`
  model.add(Convolution1D(nb_filter=32, filter_length=3, border_mode='same', activation='relu',input_dim=input_traces))
Traceback (most recent call last):
  File "CNN_Based_Attack.py", line 139, in <module>
    model.add(Convolution1D(nb_filter=32, filter_length=3, border_mode='same', activation='relu',input_dim=input_traces))
  File "/home/.local/lib/python2.7/site-packages/keras/models.py", line 430, in add layer(x)
  File "/home/.local/lib/python2.7/site-packages/keras/engine/topology.py", line 557, in __call_self.build(input_shapes[0])
  File "/home/.local/lib/python2.7/site-packages/keras/layers/convolutional.py", line 134, in build
    constraint=self.kernel_constraint)
  File "/home/.local/lib/python2.7/site-packages/keras/legacy/interfaces.py", line 88, in wrapper return func(*args, **kwargs)
  File "/home/.local/lib/python2.7/site-packages/keras/engine/topology.py", line 390, in add_weight
    weight = K.variable(initializer(shape), dtype=dtype, name=name)
  File "/home/.local/lib/python2.7/site-packages/keras/initializers.py", line 200, in __call__
    scale /= max(1., float(fan_in + fan_out) / 2)
TypeError: float() argument must be a string or a number

/home/.local/lib/python2.7/site-packages/keras/models.py:834: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.
  warnings.warn('The `nb_epoch` argument in `fit` '
Traceback (most recent call last):
  File "CNN_Based_Attack.py", line 113, in <module>
    model.fit(x_train, y_train, batch_size=15, nb_epoch=30)
  File "/home/.local/lib/python2.7/site-packages/keras/models.py", line 853, in fit
    initial_epoch=initial_epoch)
  File "/home/.local/lib/python2.7/site-packages/keras/engine/training.py", line 1424, in fit
    batch_size=batch_size)
  File "/home/.local/lib/python2.7/site-packages/keras/engine/training.py", line 1300, in _standardize_user_data
    exception_prefix='input')
  File "/home/.local/lib/python2.7/site-packages/keras/engine/training.py", line 127, in _standardize_input_data
    str(array.shape))
ValueError: Error when checking input: expected conv1d_1_input to have 3 dimensions, but got array with shape (500000, 3253)

它给出了以下错误：

   CNN_Based_Attack.py:139: UserWarning: Update your `Conv1D` call to the Keras 2 API: `Conv1D(activation="relu", input_shape=(None, /in..., padding="same", filters=32, kernel_size=3)`
  model.add(Convolution1D(nb_filter=32, filter_length=3, border_mode='same', activation='relu',input_dim=input_traces))
Traceback (most recent call last):
  File "CNN_Based_Attack.py", line 139, in <module>
    model.add(Convolution1D(nb_filter=32, filter_length=3, border_mode='same', activation='relu',input_dim=input_traces))
  File "/home/.local/lib/python2.7/site-packages/keras/models.py", line 430, in add layer(x)
  File "/home/.local/lib/python2.7/site-packages/keras/engine/topology.py", line 557, in __call_self.build(input_shapes[0])
  File "/home/.local/lib/python2.7/site-packages/keras/layers/convolutional.py", line 134, in build
    constraint=self.kernel_constraint)
  File "/home/.local/lib/python2.7/site-packages/keras/legacy/interfaces.py", line 88, in wrapper return func(*args, **kwargs)
  File "/home/.local/lib/python2.7/site-packages/keras/engine/topology.py", line 390, in add_weight
    weight = K.variable(initializer(shape), dtype=dtype, name=name)
  File "/home/.local/lib/python2.7/site-packages/keras/initializers.py", line 200, in __call__
    scale /= max(1., float(fan_in + fan_out) / 2)
TypeError: float() argument must be a string or a number

/home/.local/lib/python2.7/site-packages/keras/models.py:834: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.
  warnings.warn('The `nb_epoch` argument in `fit` '
Traceback (most recent call last):
  File "CNN_Based_Attack.py", line 113, in <module>
    model.fit(x_train, y_train, batch_size=15, nb_epoch=30)
  File "/home/.local/lib/python2.7/site-packages/keras/models.py", line 853, in fit
    initial_epoch=initial_epoch)
  File "/home/.local/lib/python2.7/site-packages/keras/engine/training.py", line 1424, in fit
    batch_size=batch_size)
  File "/home/.local/lib/python2.7/site-packages/keras/engine/training.py", line 1300, in _standardize_user_data
    exception_prefix='input')
  File "/home/.local/lib/python2.7/site-packages/keras/engine/training.py", line 127, in _standardize_input_data
    str(array.shape))
ValueError: Error when checking input: expected conv1d_1_input to have 3 dimensions, but got array with shape (500000, 3253)

/home/.local/lib/python2.7/site packages/keras/models.py:834:UserWarning:fit中的'nb_epoch'参数已重命名为'epochs'。
warnings.warn（'fit'中的'nb_epoch'参数）
回溯（最近一次呼叫最后一次）：
文件“CNN_-Based_-Attack.py”，第113行，在
模型拟合（x系列、y系列、批量尺寸=15、nb系列=30）
文件“/home/.local/lib/python2.7/site packages/keras/models.py”，第853行
初始_历元=初始_历元）
文件“/home/.local/lib/python2.7/site packages/keras/engine/training.py”，第1424行
批次大小=批次大小）
文件“/home/.local/lib/python2.7/site packages/keras/engine/training.py”，第1300行，在用户数据中
异常（前缀为“输入”）
文件“/home/.local/lib/python2.7/site packages/keras/engine/training.py”，第127行，输入数据
str（array.shape））
ValueError：检查输入时出错：预期conv1d_1_输入有3个维度，但得到了具有形状的数组（500003253）

---

我真的不知道怎么解决它

我假设您使用的是旧版本的Keras（因为，

nb_过滤器

已更改为

过滤器

，因此，您应该遵循旧文档（例如）

在第一个代码片段中，我假设问题出在这一部分：

input\u shape=input\u traces

。

Convolution1D

构造函数需要一个

元组，例如
（32100,3）
，但是
input\u traces
被初始化为Keras层

在第二个片段中，您传递了一个
tuple
，这是正确的。错误表明它期望
input\u形状
具有3维而不是2维。首先，我想指出
nb\u filter
表示“每批数据的过滤器数量”。因此，
input\u形状
还必须包括
bach\u size
（如果您不熟悉这个概念，那么有一个包含了您需要了解的有关批次的所有信息的示例）。因此，只需通过

Convolution1D(..., input_shape = (batch_size, data_length, numof_channels), ...)

而且一切都应该正常（如果你想知道什么是
numof\u通道，它类似于图像有3个通道：红色、绿色和蓝色）。如果你想拥有任意
bach\u大小
，你可以传递
input\u shape=（无，数据长度，numof\u通道）
非常感谢您的帮助，就我而言，我不分析图像，而是分析浮点数据（一些芯片消耗采集）因此，我没有任何通道，我必须把它们放在numod_通道=None@tierrytestu如果你只有浮点值，那么在你的例子中，
numof_channels=1
。我对Keras 1不是很熟悉，但我希望我的答案能起作用。