我是深度学习的新手。我正在尝试跟随fast.ai讲座系列,并尝试在Kaggle内核中手动重现工作。
我正在尝试通过Kaggle中的猫与狗Redux一起工作。我并不关心准确性,我只是希望得到一些有用的东西。
我正在使用Keras和VGG16模型,如fast.ai课程中所述。 I'm also leaning on code outlined in this article to get me off the ground
我在尝试使用我不知道如何解释的模型时遇到错误:
---------------------------------------------------------------------------
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
_________________________________________________________________
模特:
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)
我已经四处搜索了,但我对如何理解这一点感到茫然。这个SO question似乎相似,似乎表明输出是问题所在,但我不确定这对我有什么影响。
答案 0 :(得分:1)
您应该简单地将Y转换为具有形状(24500,2)的numpy数组:
Y = np.ndarray(Y)