Python Array Reshaping Issue to Array with Shape (None, 192)

Question

我有这个错误，我不知道如何重塑有None 的维度。

Exception: Error when checking : expected input_1 to have shape (None, 192) but got array with shape (192, 1)

如何将数组重塑为 (None, 192)？

我有数组accuracy，形状为(12, 16)，我做了accuracy.reshape(-1)，得到(192,)。然而这不是(None, 192)。

Answer 1

在keras/keras/engine/training.py

def standardize_input_data(data, names, shapes=None,
                           check_batch_dim=True,
                           exception_prefix=''):
     ...

    # check shapes compatibility
    if shapes:
        for i in range(len(names)):
        ...
        for j, (dim, ref_dim) in enumerate(zip(array.shape, shapes[i])):
            if not j and not check_batch_dim:
                # skip the first axis
                continue
            if ref_dim:
                if ref_dim != dim:
                    raise Exception('Error when checking ' + exception_prefix +
                                    ': expected ' + names[i] +
                                    ' to have shape ' + str(shapes[i]) +
                                    ' but got array with shape ' +
                                    str(array.shape))

与错误比较

Error when checking : expected input_1 to have shape (None, 192) but got array with shape (192, 1)

所以它正在比较(None, 192) 和(192, 1)，并跳过第一个轴；这是比较192 和1。如果array 的形状为(n, 192)，它可能会通过。

所以基本上，是什么生成了(192,1) 形状，而不是(1,192) 或可广播的(192,) 导致错误。

我将keras 添加到标签中，猜测这是问题模块。

搜索其他keras 标记的 SO 问题：

Exception: Error when checking model target: expected dense_3 to have shape (None, 1000) but got array with shape (32, 2)

Error: Error when checking model input: expected dense_input_6 to have shape (None, 784) but got array with shape (784L, 1L)

Dimensions not matching in keras LSTM model

Getting shape dimension errors with a simple regression using Keras

Deep autoencoder in Keras converting one dimension to another i

我对@9​​87654340@ 的了解不够多，无法理解答案，但不仅仅是简单地重塑您的输入数组。