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build_model.py

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