data: dataset: {name: train, samples: 12744, type: private} datasetLoadOption: full kfold: 1 mapping: Filename: options: {Augmentation: true, Height: '224', Normalization: false, Resize: true, Scaling: 1, Width: '224', height_shift_range: '0.2', horizontal_flip: false, pretrained: ResNet50, rotation_range: '30', shear_range: '0.2', vertical_flip: true, width_shift_range: '0.2'} port: InputPort0 shape: '' type: Image Label: options: {} port: OutputPort0 shape: '' type: Categorical numPorts: 1 samples: {split: 2, test: 2548, training: 7646, validation: 2548} shuffle: true model: connections: - {source: ResNet50_1, target: Flatten_3} - {source: Input_1, target: ResNet50_1} - {source: Dense_7, target: Output_3} - {source: Flatten_3, target: Dense_7} layers: - args: {} class: Input name: Input_1 x: 127 y: 22 - args: {include_top: false, trainable: '40'} class: ResNet50 name: ResNet50_1 x: 115 y: 137 - args: {} class: Flatten name: Flatten_3 x: 623 y: 175 - args: {activation: softmax, output_dim: '3'} class: Dense name: Dense_7 x: 623 y: 291 - args: {} class: Output name: Output_3 x: 620 y: 428 params: advance_params: true batch_size: 32 is_custom_loss: false loss_func: categorical_crossentropy num_epoch: 10 optimizer: {name: Adam} project: MNIST Handwritten Digits Classifier