keras中不兼容的致密层错误

Question

我的输入是一系列视频，数量为 8500。每个视频作为一系列 50 帧馈送到 LSTM，每个帧有 960 个像素。所以输入暗淡是 8500,50,960 有 487 种可能的输出类可能，所以输出维度是 8500,487.

但是当我运行以下代码时，我在 keras 中遇到了这些错误。

非常感谢任何帮助。谢谢！

(8500, 50, 960)

(8500, 487)

正在创建模型..

添加第一层..

添加第二层..

正在添加输出层..

追溯（最近调用最后）：

文件“/Users/temp/PycharmProjects/detect_sport_video/build_model.py”，第 68 行，在 model.add(密集(487, 激活='softmax'))

文件“/Users/temp/anaconda/lib/python2.7/site-packages/Keras-1.0.3-py2.7.egg/keras/models.py”，第 146 行，在添加 output_tensor = 图层(self.outputs[0])

文件“/Users/temp/anaconda/lib/python2.7/site-packages/Keras-1.0.3-py2.7.egg/keras/engine/topology.py”，第 441 行，在 call self.assert_input_compatibility(x)

文件“/Users/temp/anaconda/lib/python2.7/site-packages/Keras-1.0.3-py2.7.egg/keras/engine/topology.py”，第 382 行，在 assert_input_compatibility 海峡（K.ndim（x）））

异常：输入 0 与层 dense_1 不兼容：预期 ndim=2，发现 ndim=3

from keras.models import Sequential
from keras.layers import LSTM, Dense
import numpy as np
from PIL import Image
import os

def atoi(video):
    return int(video) if video.isdigit() else video

def natural_keys(video):
    return [ atoi(c) for c in os.path.splitext(video) ]


input_data =np.zeros((8500,50,960))

video_index = 0
data = 'train'
video_list = sorted(os.listdir('/Users/temp/PycharmProjects/detect_sport_video/' + data + '_frame_resize1/'))
video_list.sort(key=natural_keys)


for video in video_list:
    if video != '.DS_Store':
        frame_index = 0
        frame_list = sorted(os.listdir('/Users/temp/PycharmProjects/detect_sport_video/' + data + '_frame_resize1/' + video + '/'))
        frame_list.sort(key=natural_keys)
        for frame in frame_list:
            image = np.asarray(Image.open('/Users/temp/PycharmProjects/detect_sport_video/' + data + '_frame_resize1/' + video + '/' + frame))
            image = image.reshape(image.shape[0] * image.shape[1],3)
            image = (image[:,0] + image[:,1] + image[:,2]) / 3
            image = image.reshape(len(image),1)
            image = image[:960]
            image = image.T
            input_data[video_index][frame_index] = image
            frame_index += 1
        video_index += 1

print input_data.shape

cnt = 1
output_classes = []
with open('/Users/temp/PycharmProjects/detect_sport_video/sports-1m-dataset/' + data + '_correct_links.txt') as input_file:
 while cnt <= 8500:
        output_classes.append(int(input_file.readline().split()[2]))
        cnt += 1
output_data =np.zeros((8500,487))
output_index = 0
while(output_index < 8500):
    output_data[output_index,output_classes[output_index]] = 1
    output_index += 1

print output_data.shape

print("Creating model..")
model = Sequential()
print("Adding first layer..")
model.add(LSTM(100, return_sequences=True,
               input_shape=(50, 960)))

print("Adding second layer..")
model.add(LSTM(100, return_sequences=True))

print("Adding output layer..")
model.add(Dense(487, activation='softmax'))

print "Compiling model.."
model.compile(loss='categorical_crossentropy',
              optimizer='RMSprop',
              metrics=['accuracy'])

print "Fitting model.."
model.fit(input_data,output_data,
          batch_size=50, nb_epoch=100)

此外，如果我尝试在添加每个 LSTM 层后打印 model.output_shape，我得到的输出是 (None, 50, 200) 但它应该是 (None ,200).这就是问题所在。但我不知道为什么会得到 (None,50,200)。有什么想法吗？

Answer 1

打印("Adding second layer..") model.add(LSTM(100, return_sequences=假))

keras中不兼容的致密层错误

Incompatible dense layer error in keras

python

neural-network

python-2.7

deep-learning

keras