![]() ![]() ![]() Categorical cross entropy as loss function.ainable = False for layer in model.layers:įinally, I have trained the model with the following parameters : Then, we set all the model's layer as non trainable except for the last 20 layers. Model = Model(inputs=base_model.input,outputs=preds) Preds = Dense(120,activation= 'softmax')(x) Secondly, we add to this model a pooling and some dense layers where the last one has 120 neurons because we have 120 different classes. base_model = MobileNet(input_shape=( 224, 224, 3),weights= 'imagenet',include_top= False) 3)) # default valuesįirstly, we need to import ImageNet model without the last layer, because we have a different number of classes. Split_folders.ratio( 'Images', output= "Dataset", seed= 42, ratio=(. In order to split this dataset into training and test data, I have used the python package split-folders : the training data is the 80% and test data is 20%. !kaggle datasets download -d jessicali9530/stanford-dogs-dataset The dataset is available here and it contains 120 folders for each dog's breed with the relative images inside for a total of 20.580 images. ![]() The model is built with Keras on Colaboratory. I will use transfer learning on ImageNet. The neural network we want to build takes as input an image and outputs the probability prediction of 120 different dog's breed. The app consists of classifying the breed of a dog from an input image. Firstly, I will train on Colaboratory the neural network and secondly I will store on the Android device the model to make new prediction. This article explains how to use a pre-trained neural network to make inference on Android. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |