def res_net_pyramidal_model_d6_w32_k2_dr05( features, targets, mode, optimizer_type='SGD', learning_rate=0.001): return model.res_net_pyramidal_model( features=features, targets=targets, mode=mode, num_classes=2, num_blocks=int(6/3), multi_k=2, keep_prob=0.5, optimizer_type=optimizer_type, learning_rate=learning_rate, groups=[16, 16, 32, 32], scope="rnp_d6_w32_k2_dr05")
def res_net_pyramidal_model_d21_w256_k4_dr05( features, targets, mode, optimizer_type='SGD', learning_rate=0.001): return model.res_net_pyramidal_model( features=features, targets=targets, mode=mode, num_classes=2, num_blocks=int(21/3), multi_k=4, keep_prob=0.5, optimizer_type=optimizer_type, learning_rate=learning_rate, groups=[16, 64, 128, 256], scope="rnp_d21_w128_k4_dr05")
def res_net_pyramidal_model_d110_w350( features, targets, mode, optimizer_type='SGD', learning_rate=0.001): """ Deep Pyramidal Residual Networks From https://arxiv.org/abs/1610.02915 """ return model.res_net_pyramidal_model( features=features, targets=targets, mode=mode, num_classes=2, num_blocks=36, optimizer_type=optimizer_type, learning_rate=learning_rate, groups=[16, 150, 250, 350], scope="rnp_d110_w350")
def res_net_pyramidal_model_d12_w256_k2_dr05_ds( features, targets, mode, optimizer_type='SGD', learning_rate=0.001): return model.res_net_pyramidal_model( features=features, targets=targets, mode=mode, num_classes=2, num_blocks=int(12/3), multi_k=2, keep_prob=0.5, optimizer_type=optimizer_type, learning_rate=learning_rate, groups=[16, 32, 128, 256], is_double_size=True, scope="rnp_d12_w256_k2_dr05_ds")