【发布时间】：2020-05-12 05:57:54
【问题描述】：
我正在尝试从多级数据的数据集中预测电影的类型。输入是这样的
Id Genre Action Adventure Animation Biography Comedy Crime Documentary Drama Family Fantasy History Horror Music
tt0086425 ['Comedy', 'Drama'] 0 0 0 0 1 0 0 1 0 0 0 0 0
like this 25 columns of genre are there for each movie poster
我已经完成了 EDA，现在尝试为这个多级数据制作预测模型，我的训练和测试分离看起来像这样
y = np.array(movies.drop(['Id', 'Genre'],axis=1))
from skmultilearn.model_selection import iterative_train_test_split
X_train, X_test, y_train, y_test = iterative_train_test_split(X, y, test_size=0.2)
X_train.shape, X_test.shape, y_train.shape, y_test.shape
o/p : ((5791, 224, 224, 3), (5791, 25), (1463, 224, 224, 3), (1463, 25))
y_test[0]
o/p :array([0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0], dtype=int64)
pd.DataFrame({
'train': Counter(str(combination) for row in get_combination_wise_output_matrix(X_test, order=2) for combination in row),
'test' : Counter(str(combination) for row in get_combination_wise_output_matrix(y_test, order=2) for combination in row)
}).T.fillna(0.0)
o/p:
(0, 0) (0, 1) (0, 10) (0, 11) (0, 12) (0, 13) (0, 14) (0, 18) (0, 19) (0, 2) ... (9, 13) (9, 14) (9, 18) (9, 19) (9, 20) (9, 21) (9, 22) (9, 23) (9, 24) (9, 9)
train 1074.0 323.0 11.0 56.0 2.0 4.0 41.0 40.0 148.0 33.0 ... 4.0 18.0 32.0 7.0 3.0 1.0 14.0 1.0 0.0 370.0
test 269.0 81.0 2.0 14.0 1.0 0.0 7.0 10.0 37.0 4.0 ... 2.0 4.0 20.0 2.0 0.0 0.0 4.0 0.0 1.0 97.0
2 rows × 228 columns
现在为了创建预测图像分类器，我编写了以下代码：
model = Sequential()
model.add(Conv2D(filters=16, kernel_size=(5, 5), activation="relu", input_shape=(224,224,3)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(filters=32, kernel_size=(5, 5), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(filters=64, kernel_size=(5, 5), activation="relu"))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(filters=64, kernel_size=(5, 5), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(25, activation='sigmoid'))
model.summary()
Model: "sequential_12"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_41 (Conv2D) (None, 220, 220, 16) 1216
_________________________________________________________________
max_pooling2d_41 (MaxPooling (None, 110, 110, 16) 0
_________________________________________________________________
dropout_47 (Dropout) (None, 110, 110, 16) 0
_________________________________________________________________
conv2d_42 (Conv2D) (None, 106, 106, 32) 12832
_________________________________________________________________
max_pooling2d_42 (MaxPooling (None, 53, 53, 32) 0
_________________________________________________________________
dropout_48 (Dropout) (None, 53, 53, 32) 0
_________________________________________________________________
conv2d_43 (Conv2D) (None, 49, 49, 64) 51264
_________________________________________________________________
max_pooling2d_43 (MaxPooling (None, 24, 24, 64) 0
_________________________________________________________________
dropout_49 (Dropout) (None, 24, 24, 64) 0
_________________________________________________________________
conv2d_44 (Conv2D) (None, 20, 20, 64) 102464
_________________________________________________________________
max_pooling2d_44 (MaxPooling (None, 10, 10, 64) 0
_________________________________________________________________
dropout_50 (Dropout) (None, 10, 10, 64) 0
_________________________________________________________________
flatten_12 (Flatten) (None, 6400) 0
_________________________________________________________________
dense_29 (Dense) (None, 128) 819328
_________________________________________________________________
dropout_51 (Dropout) (None, 128) 0
_________________________________________________________________
dense_30 (Dense) (None, 64) 8256
_________________________________________________________________
dropout_52 (Dropout) (None, 64) 0
_________________________________________________________________
dense_31 (Dense) (None, 25) 1625
=================================================================
Total params: 996,985
Trainable params: 996,985
Non-trainable params: 0
_________________________________________________________________
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
但是一旦我运行模型拟合，我就会得到值错误：
model.fit(X_train, y_train, epochs=10, validation_data=(X_test, y_test), batch_size=64)
~\AppData\Local\Continuum\anaconda3\lib\site-packages\keras\engine\training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, check_array_lengths, batch_size)
793 feed_output_shapes,
794 check_batch_axis=False, # Don't enforce the batch size.
--> 795 exception_prefix='target')
796
797 # Generate sample-wise weight values given the `sample_weight` and
~\AppData\Local\Continuum\anaconda3\lib\site-packages\keras\engine\training_utils.py in standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
129 ': expected ' + names[i] + ' to have ' +
130 str(len(shape)) + ' dimensions, but got array '
--> 131 'with shape ' + str(data_shape))
132 if not check_batch_axis:
133 data_shape = data_shape[1:]
ValueError: Error when checking target: expected dense_31 to have 2 dimensions, but got array with shape (1463, 224, 224, 3)
我无法弄清楚最后一步如何解决这个问题以及我在哪里犯了错误，好像已经在 conv2d 和密集层之间添加了 flatten 层，但是它没有修复。任何帮助将不胜感激
【问题讨论】：
标签： python-3.x keras scikit-learn deep-learning conv-neural-network