Python keras型号不接受Numpy阵列

给出输出

print(x_train.shape)
model.fit(x_train, y_train, epochs=15, batch_size=10)

一个示例输入是包含5个图像的numpy数组。 我应该如何更改/预处理我的输入以使其工作

编辑：

我的Keras模型目前是一个单一的3d Conv网络

(1020, 224, 224, 3, 5)
Error when checking target:expected conv3d_11 to have 5 dimensions, but got array with shape (1020, 1)

仍然得到相同的错误。 此外，尺寸说明： 1020：样本数量， 224x224x3：单个图像尺寸，

---

5：示例中的图像数，

请参考下面的示例代码以实现Conv3D

input_shape = (224, 224, 3, 5)
model = Sequential([
Conv3D(64, (3, 3, 1), input_shape=input_shape, padding='same', activation='relu', data_format='channels_last')
])

输出：

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten
from tensorflow.keras.layers import Conv3D, MaxPooling3D, BatchNormalization
import numpy as np

input_shape=(224, 224, 3, 5)   

model = Sequential()
#C1
model.add(Conv3D(16, (3, 3, 1), strides=(1, 2, 2), padding='same',activation='relu',data_format= "channels_first", input_shape=input_shape))
model.add(MaxPooling3D(pool_size=(1, 2, 2),data_format= "channels_first", padding='same'))
model.add(BatchNormalization())
#C2
model.add(Conv3D(32, (3, 3, 1), strides=(1, 1, 1), padding='same',data_format= "channels_first",  activation='relu'))
model.add(MaxPooling3D(pool_size=(1, 2, 2),data_format= "channels_first", padding='same'))
model.add(BatchNormalization())
#C3
model.add(Conv3D(64, (3, 3, 1), strides=(1, 1, 1), padding='same',data_format= "channels_first",  activation='relu'))
model.add(MaxPooling3D(pool_size=(1, 2, 2), data_format= "channels_first", padding='same'))
model.add(BatchNormalization())

model.add(Flatten())
model.add(Dropout(0.5))
model.add(Dense(256, activation='sigmoid'))
model.add(Dropout(0.5))
model.add(Dense(2, activation='softmax'))

opt_adam = tf.keras.optimizers.Adam(lr=0.00001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)
model.compile(loss='categorical_crossentropy', optimizer=opt_adam, metrics=['accuracy'])

print(model.summary())

---

似乎

conv3d_11

不是您的第一层。在正向传播过程中，张量的形状可能发生了变化。也许您可以向我们展示您的整个Keras模型x_train是什么样的数据？我猜你是在尝试用图像数据进行训练，那么每个维度应该意味着什么呢？224、224为3个通道的宽度和高度。5和1020是什么意思？我建议将您的数据重塑为（510202242243）向我们展示模型..从Model=Sequential（）开始。。或者类似的东西用所有询问的信息更新了问题快速问题：您是在阅读图像还是只是将字符串（可能是图像名称）传递到第一层？

Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv3d (Conv3D)              (None, 16, 224, 2, 3)     32272     
_________________________________________________________________
max_pooling3d (MaxPooling3D) (None, 16, 224, 1, 2)     0         
_________________________________________________________________
batch_normalization (BatchNo (None, 16, 224, 1, 2)     8         
_________________________________________________________________
conv3d_1 (Conv3D)            (None, 32, 224, 1, 2)     4640      
_________________________________________________________________
max_pooling3d_1 (MaxPooling3 (None, 32, 224, 1, 1)     0         
_________________________________________________________________
batch_normalization_1 (Batch (None, 32, 224, 1, 1)     4         
_________________________________________________________________
conv3d_2 (Conv3D)            (None, 64, 224, 1, 1)     18496     
_________________________________________________________________
max_pooling3d_2 (MaxPooling3 (None, 64, 224, 1, 1)     0         
_________________________________________________________________
batch_normalization_2 (Batch (None, 64, 224, 1, 1)     4         
_________________________________________________________________
flatten (Flatten)            (None, 14336)             0         
_________________________________________________________________
dropout (Dropout)            (None, 14336)             0         
_________________________________________________________________
dense (Dense)                (None, 256)               3670272   
_________________________________________________________________
dropout_1 (Dropout)          (None, 256)               0         
_________________________________________________________________
dense_1 (Dense)              (None, 2)                 514       
=================================================================
Total params: 3,726,210
Trainable params: 3,726,202
Non-trainable params: 8