深度学习拟合错误（传递给模型的Numpy数组列表不是模型预期的大小。）

Question

我是深度学习的新手。我试图跟随fast.ai系列讲座，并试图在Kaggle内核中手动重现工作。

我正在努力通过Kaggle中的猫对狗的Redux。我不关心准确性，我只是想让一些东西发挥作用。

我使用的是Keras和VGG16模型，如fast中所述。当然。我还依靠本文中概述的代码来帮助我起步。

这是我的卡格尔笔记本。

我在尝试拟合我的模型时遇到了一个错误，我不知道如何解释：

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-26-596f25281fc2> in <module>()
     12 #model.fit(input[0].transpose(), output[0].transpose())
     13 
---> 14 model.fit(X, Y, epochs=100, batch_size=6000, verbose=1)

/opt/conda/lib/python3.6/site-packages/Keras-2.1.2-py3.6.egg/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)
   1591             class_weight=class_weight,
   1592             check_batch_axis=False,
-> 1593             batch_size=batch_size)
   1594         # Prepare validation data.
   1595         do_validation = False

/opt/conda/lib/python3.6/site-packages/Keras-2.1.2-py3.6.egg/keras/engine/training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, check_batch_axis, batch_size)
   1428                                     output_shapes,
   1429                                     check_batch_axis=False,
-> 1430                                     exception_prefix='target')
   1431         sample_weights = _standardize_sample_weights(sample_weight,
   1432                                                      self._feed_output_names)

/opt/conda/lib/python3.6/site-packages/Keras-2.1.2-py3.6.egg/keras/engine/training.py in _standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
     81                 'Expected to see ' + str(len(names)) + ' array(s), '
     82                 'but instead got the following list of ' +
---> 83                 str(len(data)) + ' arrays: ' + str(data)[:200] + '...')
     84         elif len(names) > 1:
     85             raise ValueError(

ValueError: Error when checking model target: 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 24500 arrays: [array([[1],
       [0]]), array([[1],
       [0]]), array([[0],
       [1]]), array([[1],
       [0]]), array([[1],
       [0]]), array([[1],
       [0]]), array([[1],
       [0]]), array([[0],
     ...

以下是更多信息：

X = np.array([i[0] for i in train]).reshape(-1,IMG_SIZE,IMG_SIZE,3)
Y = [i[1] for i in train]

> type(X)
numpy.ndarray

> X.shape
(24500, 50, 50, 3)

> type(Y)
list

> len(Y)
24500

> Y[0]
[1 0]

> model.summary()
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_7 (InputLayer)         (None, 50, 50, 3)         0         
_________________________________________________________________
block1_conv1 (Conv2D)        (None, 50, 50, 64)        1792      
_________________________________________________________________
block1_conv2 (Conv2D)        (None, 50, 50, 64)        36928     
_________________________________________________________________
block1_pool (MaxPooling2D)   (None, 25, 25, 64)        0         
_________________________________________________________________
block2_conv1 (Conv2D)        (None, 25, 25, 128)       73856     
_________________________________________________________________
block2_conv2 (Conv2D)        (None, 25, 25, 128)       147584    
_________________________________________________________________
block2_pool (MaxPooling2D)   (None, 12, 12, 128)       0         
_________________________________________________________________
block3_conv1 (Conv2D)        (None, 12, 12, 256)       295168    
_________________________________________________________________
block3_conv2 (Conv2D)        (None, 12, 12, 256)       590080    
_________________________________________________________________
block3_conv3 (Conv2D)        (None, 12, 12, 256)       590080    
_________________________________________________________________
block3_pool (MaxPooling2D)   (None, 6, 6, 256)         0         
_________________________________________________________________
block4_conv1 (Conv2D)        (None, 6, 6, 512)         1180160   
_________________________________________________________________
block4_conv2 (Conv2D)        (None, 6, 6, 512)         2359808   
_________________________________________________________________
block4_conv3 (Conv2D)        (None, 6, 6, 512)         2359808   
_________________________________________________________________
block4_pool (MaxPooling2D)   (None, 3, 3, 512)         0         
_________________________________________________________________
block5_conv1 (Conv2D)        (None, 3, 3, 512)         2359808   
_________________________________________________________________
block5_conv2 (Conv2D)        (None, 3, 3, 512)         2359808   
_________________________________________________________________
block5_conv3 (Conv2D)        (None, 3, 3, 512)         2359808   
_________________________________________________________________
block5_pool (MaxPooling2D)   (None, 1, 1, 512)         0         
=================================================================
Total params: 14,714,688
Trainable params: 14,714,688
Non-trainable params: 0
_________________________________________________________________

和模型：

model = VGG16(weights='imagenet', include_top=False, input_shape=(img_rows, img_cols, img_channel))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X, Y, epochs=100, batch_size=6000, verbose=1)

我在谷歌上搜索了一下，但我不知道如何解释这个问题。这个SO问题看起来很相似，似乎表明输出是问题所在，但我不确定这将如何适用于我。

Answer 1

只需将Y变换为具有形状（24500,2）的numpy数组：

Y = np.ndarray(Y)