Computer Vision News - January 2019

22 Computer Vision News Focus on #create an experiment with your api key experiment = Experiment(api_key="YOUR_API_KEY", project_name='mnist', auto_param_logging=False) batch_size = 128 num_classes = 10 epochs = 20 num_nodes = 64 optimizer = 'adam' activation = 'relu' #these will all get logged params={'batch_size':batch_size, 'epochs':epochs, 'layer1_type':'Dense', 'layer1_num_nodes':num_nodes, 'layer1_activation':activation, 'optimizer':optimizer } model = Sequential() model.add(Dense(num_nodes, activation='relu', input_shape=(784,))) model.add(Dense(num_classes, activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy']) #will log metrics with the prefix 'train_' with experiment.train(): history = model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, validation_data=(x_test, y_test), callbacks=[EarlyStopping(monitor='val_loss', min_delta=1e-4,patience=3, verbose=1, mode='auto')]) #will log metrics with the prefix 'test_' with experiment.test(): loss, accuracy = model.evaluate(x_test, y_test) metrics = { 'loss':loss, 'accuracy':accuracy } experiment.log_metrics(metrics) experiment.log_parameters(params) experiment.log_dataset_hash(x_train) #creates and logs a hash of your data Tip - Train Your Network