Torch.save(model, "./model_architecture.pt") Nn.Linear(in_features= 50, out_features= 10, bias= True), Nn.Linear(in_features= 100, out_features= 50, bias= True), Nn.Linear(in_features= 256, out_features= 100, bias= True), Nn.Linear(in_features= 256, out_features= 256, bias= True), Nn.Linear(in_features= 784, out_features= 256, bias= True), zip file that contains your model in pickle format by running the command zip mymodel.zip model_architecture.pickle. SKLearn Kmeans # SKLearn Kmeans from sklearn.cluster import KMeans With open( "./model_architecture.pickle", 'wb') as f: Model = SGDRegressor(loss= 'huber', penalty= 'l2') SKLearn regression # Sklearn regression from sklearn.linear_model import SGDRegressor Joblib.dump(model, "./model_architecture.pickle") Class labels must be contained in a numpy array. # You must specify the class label for IBM Federated Learning using model.classes. Model = SGDClassifier(loss= 'log', penalty= 'l2') SKLearn classification # SKLearn classification from sklearn.linear_model import SGDClassifier To compress your files, run the command zip -r mymodel.zip model_architecture. A Keras model can be saved in SavedModel format by using tf.(). If you choose Tensorflow as the model framework, you need to save a Keras model as the SavedModel format. pute_output_shape(input_shape=input_shape)ĭir = "./model_architecture" if not os.path.exists( dir): Input_shape = ( None, img_rows, img_cols, 1) compile(optimizer=optimizer, loss=loss_object, metrics=) Loss_object = tf.(Īcc = tf.(name= 'accuracy') Self.d1 = Dense( 128, activation= 'relu') Save the Tensorflow model import tensorflow as tf Parties can create and save the initial model before training, following a set of examples.Ĭonsider the configuration examples that match your model type. model %>% layer_conv_2d( filters = 32, kernel_size = c( 3, 3), activation = 'relu', input_shape = c( 100, 100, 3)) %>% layer_conv_2d( filters = 32, kernel_size = c( 3, 3), activation = 'relu') %>% layer_max_pooling_2d( pool_size = c( 2, 2)) %>% layer_dropout( rate = 0.25) %>% layer_conv_2d( filters = 64, kernel_size = c( 3, 3), activation = 'relu') %>% layer_conv_2d( filters = 64, kernel_size = c( 3, 3), activation = 'relu') %>% layer_max_pooling_2d( pool_size = c( 2, 2)) %>% layer_dropout( rate = 0.25) %>% layer_flatten() %>% layer_dense( units = 256, activation = 'relu') %>% layer_dropout( rate = 0.25) %>% layer_dense( units = 10, activation = 'softmax') %>% compile( loss = 'categorical_crossentropy', optimizer = optimizer_sgd( lr = 0.01, decay = 1e-6, momentum = 0.Creating the initial model Creating the initial model Creating the initial model # this applies 32 convolution filters of size 3x3 each. Library(keras) # generate dummy data x_train % round() %>% matrix( nrow = 100, ncol = 1) %>% to_categorical( num_classes = 10) x_test % round() %>% matrix( nrow = 20, ncol = 1) %>% to_categorical( num_classes = 10) # create model model (100, 100, 3) tensors. Library(keras) # generate dummy data x_train % round() %>% matrix( nrow = 1000, ncol = 1) %>% to_categorical( num_classes = 10) x_test % round() %>% matrix( nrow = 100, ncol = 1) %>% to_categorical( num_classes = 10) # create model model % layer_dense( units = 64, activation = 'relu', input_shape = c( 20)) %>% layer_dropout( rate = 0.5) %>% layer_dense( units = 64, activation = 'relu') %>% layer_dropout( rate = 0.5) %>% layer_dense( units = 10, activation = 'softmax') %>% compile( loss = 'categorical_crossentropy', optimizer = optimizer_sgd( lr = 0.01, decay = 1e-6, momentum = 0.9, nesterov = TRUE), metrics = c( 'accuracy') ) # train model %>% fit(x_train, y_train, epochs = 20, batch_size = 128) # evaluate score % evaluate(x_test, y_test, batch_size = 128)
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