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52 lines
2.2 KiB
52 lines
2.2 KiB
from 手势识别.generate_data import data_reader
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import paddle.fluid as fluid
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from paddle.fluid.dygraph import Linear
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import paddle as paddle
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import numpy as np
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train_reader = paddle.batch(reader=paddle.reader.shuffle(reader=data_reader('./train_data.list'), buf_size=256), batch_size=32)
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class FullNet_Model(fluid.dygraph.Layer):
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def __init__(self):
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super(FullNet_Model,self).__init__()
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self.hidden1 = Linear(input_dim=100,output_dim=100,act='relu')
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self.hidden2 = Linear(input_dim=100,output_dim=100,act='relu')
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self.hidden3 = Linear(input_dim=100,output_dim=100,act='relu')
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self.hidden4 = Linear(input_dim=3*100*100,output_dim=10,act='softmax')
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def forward(self, input):
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x = self.hidden1(input)
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x= self.hidden2(x)
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x = self.hidden3(x)
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x= fluid.layers.reshape(x,shape=[-1,3*100*100])
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y =self.hidden4(x)
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return y
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'''
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#用动态图进行训练
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with fluid.dygraph.guard():
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model = FullNet_Model()
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model.train()
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opt =fluid.optimizer.SGDOptimizer(learning_rate=0.001,parameter_list=model.parameters())
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epochs_num = 20 #设置迭代次数
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for epoch in range(epochs_num):
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for batch_id,data in enumerate(train_reader()):
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images = np.array([x[0].reshape(3,100,100) for x in data],np.float32)
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labels = np.array([x[1] for x in data]).astype('int64')
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labels = labels[:,np.newaxis]
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image = fluid.dygraph.to_variable(images)
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label = fluid.dygraph.to_variable(labels)
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predict = model(image)
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loss= fluid.layers.cross_entropy(predict,label)
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avg_loss= fluid.layers.mean(loss)
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acc = fluid.layers.accuracy(predict,label)
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if batch_id != 0 and batch_id % 50 == 0:
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print(
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"train_pass:{},batch_id:{},train_loss:{},train_acc:{}".format(epoch, batch_id, avg_loss.numpy(),
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acc.numpy()))
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avg_loss.backward()
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opt.minimize(avg_loss)
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model.clear_gradients()
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fluid.save_dygraph(model.state_dict(), 'FullNet_Model') # 保存模型
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''' |