Question

在Keras的视频问答示例（https://keras.io/getting-started/functional-api-guide/）中，卷积神经网络末端的vision_model.add(Flatten())做什么，为什么需要它？

完整来源：

from keras.layers import Conv2D, MaxPooling2D, Flatten
from keras.layers import Input, LSTM, Embedding, Dense
from keras.models import Model, Sequential

# First, let's define a vision model using a Sequential model.
# This model will encode an image into a vector.
vision_model = Sequential()
vision_model.add(Conv2D(64, (3, 3), activation='relu', padding='same', input_shape=(224, 224, 3)))
vision_model.add(Conv2D(64, (3, 3), activation='relu'))
vision_model.add(MaxPooling2D((2, 2)))
vision_model.add(Conv2D(128, (3, 3), activation='relu', padding='same'))
vision_model.add(Conv2D(128, (3, 3), activation='relu'))
vision_model.add(MaxPooling2D((2, 2)))
vision_model.add(Conv2D(256, (3, 3), activation='relu', padding='same'))
vision_model.add(Conv2D(256, (3, 3), activation='relu'))
vision_model.add(Conv2D(256, (3, 3), activation='relu'))
vision_model.add(MaxPooling2D((2, 2)))
vision_model.add(Flatten())

然后再

from keras.layers import TimeDistributed

video_input = Input(shape=(100, 224, 224, 3))
# This is our video encoded via the previously trained vision_model (weights are reused)
encoded_frame_sequence = TimeDistributed(vision_model)(video_input)  # the output will be a sequence of vectors
encoded_video = LSTM(256)(encoded_frame_sequence)  # the output will be a vector

Answer 1

运行：

select concat_ws(',',
                 (case when count(col1) = 0 then 'col1' end),
                 (case when count(col2) = 0 then 'col2' end),
                 (case when count(col3) = 0 then 'col3' end),
                 . . .
                ) as columns_with_all_null_values
from t;

我们得到：

vision_model.summary()

Layer (type) Output Shape Param # ================================================================= conv2d_1 (Conv2D) (None, 224, 224, 64) 1792 _________________________________________________________________ conv2d_2 (Conv2D) (None, 222, 222, 64) 36928 _________________________________________________________________ max_pooling2d_1 (MaxPooling2 (None, 111, 111, 64) 0 _________________________________________________________________ conv2d_3 (Conv2D) (None, 111, 111, 128) 73856 _________________________________________________________________ conv2d_4 (Conv2D) (None, 109, 109, 128) 147584 _________________________________________________________________ max_pooling2d_2 (MaxPooling2 (None, 54, 54, 128) 0 _________________________________________________________________ conv2d_5 (Conv2D) (None, 54, 54, 256) 295168 _________________________________________________________________ conv2d_6 (Conv2D) (None, 52, 52, 256) 590080 _________________________________________________________________ conv2d_7 (Conv2D) (None, 50, 50, 256) 590080 _________________________________________________________________ max_pooling2d_3 (MaxPooling2 (None, 25, 25, 256) 0 _________________________________________________________________ flatten_1 (Flatten) (None, 160000) 0 ================================================================= Total params: 1,735,488 Trainable params: 1,735,488 Non-trainable params: 0将vision_model.add(Flatten())从（None，25，25，256）展平为（None，160000）

在Keras视频答录机示例中变平

1 个答案: