def train_fine_tuning(net, learning_rate, batch_size=128, num_epochs=5):
train_iter = gdata.DataLoader(
train_imgs.trainform_first(train_augs), batch_size, shuffle = True)
test_iter = gdata.DataLoader(
test_imgs.transform_first(test_augs), batch_size)
ctx = d2l.try_all_gpus()
net.collect_params().resnet_ctx(ctx)
net.hybridize()
loss = gloss.SoftmaxCrossEntropyLoss()
trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': learning_rate,
'wd': 0.001})
d2l.train(train_iter, test_iter, net, loss, trainer, ctx, num_epochs)
self.constant_embedding(inputs),
dim=2)
# 根据Conv1D要求的输入格式，将词向量维，即一维卷积层的通道维，变换到前一维
embeddings = embeddings.transpose((0, 2, 1))
# 对于每个一维卷积层，在时序最大池化后会得到一个形状为(批量大小, 通道大小, 1)的
# NDArray。使用flatten函数去掉最后一维，然后在通道维上连结
encoding = nd.concat(
*[nd.flatten(self.pool(conv(embeddings))) for conv in self.convs],
dim=1)
# 应用丢弃法后使用全连接层得到输出
outputs = self.decoder(self.dropout(encoding))
return outputs
embed_size, kernel_sizes, nums_channels = 100, [3, 4, 5], [100, 100, 100]
ctx = d2l.try_all_gpus()
net = TextCNN(vocab, embed_size, kernel_sizes, nums_channels)
net.initialize(init.Xavier(), ctx=ctx)
glove_embedding = text.embedding.create(
'glove', pretrained_file_name='glove.6B.100d.txt', vocabulary=vocab)
net.embedding.weight.set_data(glove_embedding.idx_to_vec)
net.constant_embedding.weight.set_data(glove_embedding.idx_to_vec)
net.constant_embedding.collect_params().setattr('grad_req', 'null')
lr, num_epochs = 0.001, 5
trainer = gluon.Trainer(net.collect_params(), 'adam', {'learning_rate': lr})
loss = gloss.SoftmaxCrossEntropyLoss()
d2l.train(train_iter, test_iter, net, loss, trainer, ctx, num_epochs)
d2l.predict_sentiment(net, vocab, ['this', 'movie', 'is', 'so', 'great'])
d2l.predict_sentiment(net, vocab, ['this', 'movie', 'is', 'so', 'bad'])
x = F.flatten(self.feature_extractor(x)) # x变为NF的二维
x = x.expand_dims(axis=0) # x新增了一维，
xs.append(x)
X_ = nd.concat(*xs, dim=0) # X_变为(T,N,F)
output, state = self.rnn_layer(X_, state) # 输出也成为TNF
output = output.sum(
axis=0, keepdims=False) / output.shape[0] # 在时间步求平均，转换为NF
Y = self.dense(output) # Y输出(N,num_classes)
return Y
return LRCN_action_recognition_model(ctx=ctx)
net = get_action_recognition_model(ctx=ctx)
print(net)
loss = gloss.SoftmaxCrossEntropyLoss()
optimizer_params = {'learning_rate': 0.01, 'momentum': 0.9, 'wd': 5e-4}
# trainer = gluon.Trainer(net.collect_params('LRCN'), 'sgd', optimizer_params)
trainer = gluon.Trainer(net.collect_params(), 'sgd', optimizer_params)
net.rnn_layer.collect_params().setattr('lr_mult', 10)
net.dense.collect_params().setattr('lr_mult', 10)
d2l.train(train_iter=train_data,
test_iter=val_data,
net=net,
loss=loss,
trainer=trainer,
ctx=ctx,
num_epochs=num_epochs)
net.save_parameters(filename='./LRCN_AR_vgg16.params')
