VGG-16 CNN 和用于视频分类的 LSTM

对于此示例，假设输入的维度为 （帧，通道，行，列） ，输出的维度为 （类） 。

from keras.applications.vgg16 import VGG16
from keras.models import Model
from keras.layers import Dense, Input
from keras.layers.pooling import GlobalAveragePooling2D
from keras.layers.recurrent import LSTM
from keras.layers.wrappers import TimeDistributed
from keras.optimizers import Nadam

video = Input(shape=(frames,
                     channels,
                     rows,
                     columns))
cnn_base = VGG16(input_shape=(channels,
                              rows,
                              columns),
                 weights="imagenet",
                 include_top=False)
cnn_out = GlobalAveragePooling2D()(cnn_base.output)
cnn = Model(input=cnn_base.input, output=cnn_out)
cnn.trainable = False
encoded_frames = TimeDistributed(cnn)(video)
encoded_sequence = LSTM(256)(encoded_frames)
hidden_layer = Dense(output_dim=1024, activation="relu")(encoded_sequence)
outputs = Dense(output_dim=classes, activation="softmax")(hidden_layer)
model = Model([video], outputs)
optimizer = Nadam(lr=0.002,
                  beta_1=0.9,
                  beta_2=0.999,
                  epsilon=1e-08,
                  schedule_decay=0.004)
model.compile(loss="categorical_crossentropy",
              optimizer=optimizer,
              metrics=["categorical_accuracy"])