Add new file

预测-图片-主程序
运行这个文件，会调用cnn卷积神经网络，加载上train_main.py训练出的权重文件mnist.pdparams,预测需要检测的图片中的数字

predict_images_main.py

+# 构建预测动态图过程
+import numpy as np
+import  paddle.fluid as fluid
+from cnn_net import ConvolutionalNeuralNetwork
+from images_chuli_function import load_image
+from PIL import  Image
+import matplotlib.pyplot as plt
+
+with fluid.dygraph.guard():
+    #新建模型
+    model=ConvolutionalNeuralNetwork('mnist')
+    # 读取模型
+    # 参数为保存模型参数的文件地址
+    model_dict, _ = fluid.load_dygraph('mnist')
+    # 加载模型参数
+    model.load_dict(model_dict)
+    #评估模式
+    model.eval()
+    infer_path_3='infer_3.png'
+    infer_path_4='A4.jpg'
+
+    ii=Image.open(infer_path_3)
+    plt.imshow(ii)
+    ii.show()
+
+    img = load_image(infer_path_3)
+
+    img=img/255
+
+    print(img.shape)
+    # 将np数组转换为dygraph动态图的variable
+    img = fluid.dygraph.to_variable(img)
+    result = model(img)
+    print('预测的结果是:{}'.format(np.argmax(result.numpy())))