我有一个X_train形状为的np.array (1433, 1)。第一维（1433）是要训练的图像数。第二维（1）是一个np.array，它本身具有形状(224, 224, 3)。我可以通过确认X_train[0][0].shape。我需要适合X_train模型：
model.fit([X_train, y_train[:,1:]], y_train[:,0], epochs=50, batch_size=32, verbose=1)
错误输出是不言自明的：
Traceback (most recent call last):
File "/home/combined/file_01.py", line 97, in <module>
img_output = Flatten()(x_1)
File "/usr/local/lib/python3.5/dist-packages/keras/engine/base_layer.py", line 414, in __call__
self.assert_input_compatibility(inputs)
File "/usr/local/lib/python3.5/dist-packages/keras/engine/base_layer.py", line 327, in assert_input_compatibility
str(K.ndim(x)))
ValueError: Input 0 is incompatible with layer flatten_1: expected min_ndim=3, found ndim=2
y_train[:,1:]看起来不错(1433, 9)。
我需要做什么与做X_train的model.fit成功可以作为输入（1433，224，224，3）？
看来您有这样的情况：
import numpy as np
x_train = np.zeros((1433, 1), dtype=object)
for i in range(x_train.shape[0]):
x_train[i, 0] = np.random.random((224, 224, 3))
x_train.shape # (1433, 1)
x_train[0, 0].shape # (224, 224, 3)
数组（如嵌套列表）在哪里x_train而object不是numeric数组。
您需要更改x_train为纯numeric数组：
x_train = np.array([x for x in x_train.flatten()], dtype=float)
x_train.shape # (1433, 224, 224, 3)
x_train[0].shape # (224, 224, 3)
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我来说两句