Python 3.x Keras Model.fit X在列表中与ndarray错误

我正在为我的自定义图像分类（使用预训练的Imagenet权重）培训VGG16，并转移学习最后一个密集层（因为输出是二进制分类，而Imagenet为1000类）。希望使用训练集进行训练，我总是会出错

这是我的VGG16代码，带有迁移学习

来自keras.models导入模型
#请注意，我们排除了最后的密集层，并在下面添加一层，我们将自己重新训练它
base=VGG16（weights='imagenet'，include_top=False，input_shape=（3224224））
#冻结卷积层
对于base.layers中的图层：
layer.trainable=错误
x=基本输出
x=展平（）（x）#从卷积张量输出展平
预测=密集（2，激活='softmax'）（x）#应与预测的类匹配#
#这是我们将要训练的模型
模型=模型（输入=基本输入，输出=预测）
编译（优化器=SGD（lr=0.0001，动量=0.9），
损失=‘分类的’

这是我的model.fit代码

model.fit（X_-train.as_-matrix（），y_-train.as_-matrix（））

使用sklearn的训练测试分割训练集。由于X_-train和y_-train是熊猫系列，我把它们变成了Ndarray。当我执行此操作时，会出现以下错误：

ValueError                                Traceback (most recent call last)
<ipython-input-74-6a40f048f921> in <module>()
----> 1 model.fit(X_train.as_matrix(),y_train.as_matrix())

~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)
    950             sample_weight=sample_weight,
    951             class_weight=class_weight,
--> 952             batch_size=batch_size)
    953         # Prepare validation data.
    954         do_validation = False

~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/keras/engine/training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, check_array_lengths, batch_size)
    749             feed_input_shapes,
    750             check_batch_axis=False,  # Don't enforce the batch size.
--> 751             exception_prefix='input')
    752 
    753         if y is not None:

~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/keras/engine/training_utils.py in standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
    126                         ': expected ' + names[i] + ' to have ' +
    127                         str(len(shape)) + ' dimensions, but got array '
--> 128                         'with shape ' + str(data_shape))
    129                 if not check_batch_axis:
    130                     data_shape = data_shape[1:]

ValueError: Error when checking input: expected input_1 to have 4 dimensions, but got array with shape (4200, 1)

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-113-783987041ff8> in <module>()
----> 1 model.fit(list(X_train),y_train)

~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)
    950             sample_weight=sample_weight,
    951             class_weight=class_weight,
--> 952             batch_size=batch_size)
    953         # Prepare validation data.
    954         do_validation = False

~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/keras/engine/training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, check_array_lengths, batch_size)
    749             feed_input_shapes,
    750             check_batch_axis=False,  # Don't enforce the batch size.
--> 751             exception_prefix='input')
    752 
    753         if y is not None:

~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/keras/engine/training_utils.py in standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
    100                 'Expected to see ' + str(len(names)) + ' array(s), '
    101                 'but instead got the following list of ' +
--> 102                 str(len(data)) + ' arrays: ' + str(data)[:200] + '...')
    103         elif len(names) > 1:
    104             raise ValueError(

ValueError: Error when checking model input: the list of Numpy arrays that you are passing to your model is not the size the model expected. Expected to see 1 array(s), but instead got the following list of 4200 arrays: [array([[[[ 149.061    ,  151.061    ,  150.061    , ...,  151.061    ,
           150.061    ,  151.061    ],
         [ 150.061    ,  123.061    ,   90.061    , ...,   77.061    ,
           100.061...

但现在我得到了这个错误：

ValueError                                Traceback (most recent call last)
<ipython-input-74-6a40f048f921> in <module>()
----> 1 model.fit(X_train.as_matrix(),y_train.as_matrix())

~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)
    950             sample_weight=sample_weight,
    951             class_weight=class_weight,
--> 952             batch_size=batch_size)
    953         # Prepare validation data.
    954         do_validation = False

~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/keras/engine/training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, check_array_lengths, batch_size)
    749             feed_input_shapes,
    750             check_batch_axis=False,  # Don't enforce the batch size.
--> 751             exception_prefix='input')
    752 
    753         if y is not None:

~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/keras/engine/training_utils.py in standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
    126                         ': expected ' + names[i] + ' to have ' +
    127                         str(len(shape)) + ' dimensions, but got array '
--> 128                         'with shape ' + str(data_shape))
    129                 if not check_batch_axis:
    130                     data_shape = data_shape[1:]

ValueError: Error when checking input: expected input_1 to have 4 dimensions, but got array with shape (4200, 1)

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-113-783987041ff8> in <module>()
----> 1 model.fit(list(X_train),y_train)

~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)
    950             sample_weight=sample_weight,
    951             class_weight=class_weight,
--> 952             batch_size=batch_size)
    953         # Prepare validation data.
    954         do_validation = False

~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/keras/engine/training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, check_array_lengths, batch_size)
    749             feed_input_shapes,
    750             check_batch_axis=False,  # Don't enforce the batch size.
--> 751             exception_prefix='input')
    752 
    753         if y is not None:

~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/keras/engine/training_utils.py in standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
    100                 'Expected to see ' + str(len(names)) + ' array(s), '
    101                 'but instead got the following list of ' +
--> 102                 str(len(data)) + ' arrays: ' + str(data)[:200] + '...')
    103         elif len(names) > 1:
    104             raise ValueError(

ValueError: Error when checking model input: the list of Numpy arrays that you are passing to your model is not the size the model expected. Expected to see 1 array(s), but instead got the following list of 4200 arrays: [array([[[[ 149.061    ,  151.061    ,  150.061    , ...,  151.061    ,
           150.061    ,  151.061    ],
         [ 150.061    ,  123.061    ,   90.061    , ...,   77.061    ,
           100.061...

---------------------------------------------------------------------------
ValueError回溯（最近一次调用上次）
在（）
---->1个模型装配（列表（X\U系列、y\U系列）
~/anaconda3/envs/tensorflow\u p36/lib/python3.6/site-packages/keras/engine/training.py适合（self、x、y、批量大小、历元、冗余、回调、验证分割、验证数据、无序排列、类权重、样本权重、初始历元、每历元的步骤、验证步骤、**kwargs）
950样品重量=样品重量，
951类重量=类重量，
-->952批次大小=批次大小）
953#准备验证数据。
954 do_验证=错误
~/anaconda3/envs/tensorflow\u p36/lib/python3.6/site-packages/keras/engine/training.py in\u-standard\u-user\u数据（自身、x、y、样本重量、类别重量、检查数组长度、批次大小）
749馈送输入形状，
750检查批处理轴=False，#不强制执行批处理大小。
-->751异常（前缀为“输入”）
752
753如果y不是无：
标准化输入数据中的~/anaconda3/envs/tensorflow\u p36/lib/python3.6/site-packages/keras/engine/training\u utils.py（数据、名称、形状、检查批处理轴、异常前缀）
100'预计将看到'+str（len（name））+'数组，'
101'但却得到了以下列表'+
-->102 str（len（data））+'数组：'+str（data）[:200]+'…'）
103 elif len（名称）>1：
104升值误差(
ValueError：检查模型输入时出错：您传递给模型的Numpy数组列表的大小不是模型预期的大小。预期会看到1个数组，但得到以下4200个数组的列表：[array（[[[149.061，151.061，150.061，…，151.061，
150.061    ,  151.061    ],
[ 150.061    ,  123.061    ,   90.061    , ...,   77.061    ,
100.061...

如果有帮助的话，我的y_列车的标签是0或1