ZjTdSrSSKJr SSKJr SSKJr \"SS9\"SS \555rS /rg ) z EfficientNet model configuration)strict)PreTrainedConfig)auto_docstringzgoogle/efficientnet-b7) checkpointc^\rSrSr%SrSrSr\\S'Sr \\ \-\ \\4-\S'Sr \ \S 'S r\ \S 'S r\\S 'Sr\ \\ \S4-\S'Sr\ \\ \S4-\S'Sr\ \\ \S4-\S'Sr\ \\ \S4-\S'Sr\ \\ \S4-\S'Sr\ \\ \S4-\S'Sr\ \\ \S4-\S'Sr\ \S'Sr\\S 'S!r\\S"'S#r\\S$'S%r\ \S&'S'r\ \S('S)r\ \S*'S+r\ \-\S,'S-r \ \-\S.'U4S/jr!S0r"U=r#$)1EfficientNetConfigaq width_coefficient (`float`, *optional*, defaults to 2.0): Scaling coefficient for network width at each stage. depth_coefficient (`float`, *optional*, defaults to 3.1): Scaling coefficient for network depth at each stage. depth_divisor (`int`, *optional*, defaults to 8): A unit of network width. kernel_sizes (`list[int]`, *optional*, defaults to `[3, 3, 5, 3, 5, 5, 3]`): List of kernel sizes to be used in each block. out_channels (`list[int]`, *optional*, defaults to `[16, 24, 40, 80, 112, 192, 320]`): List of output channel sizes to be used in each block for convolutional layers. depthwise_padding (`list[int]`, *optional*, defaults to `[]`): List of block indices with square padding. num_block_repeats (`list[int]`, *optional*, defaults to `[1, 2, 2, 3, 3, 4, 1]`): List of the number of times each block is to repeated. expand_ratios (`list[int]`, *optional*, defaults to `[1, 6, 6, 6, 6, 6, 6]`): List of scaling coefficient of each block. squeeze_expansion_ratio (`float`, *optional*, defaults to 0.25): Squeeze expansion ratio. pooling_type (`str` or `function`, *optional*, defaults to `"mean"`): Type of final pooling to be applied before the dense classification head. Available options are [`"mean"`, `"max"`] batch_norm_momentum (`float`, *optional*, defaults to 0.99): The momentum used by the batch normalization layers. drop_connect_rate (`float`, *optional*, defaults to 0.2): The drop rate for skip connections. Example: ```python >>> from transformers import EfficientNetConfig, EfficientNetModel >>> # Initializing a EfficientNet efficientnet-b7 style configuration >>> configuration = EfficientNetConfig() >>> # Initializing a model (with random weights) from the efficientnet-b7 style configuration >>> model = EfficientNetModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` efficientnetr num_channelsiX image_sizeg@width_coefficientg@depth_coefficient depth_divisor)rrrrrr. kernel_sizes) (Pp in_channels)rrrrrri@ out_channelsdepthwise_padding)r r rr rstrides)rr r rrrnum_block_repeats)rr$r$r$r$r$ expand_ratiosg?squeeze_expansion_ratioswish hidden_acti hidden_dimmean pooling_typeg{Gz?initializer_rangegMbP?batch_norm_epsgGz?batch_norm_momentumg? dropout_rateg?drop_connect_ratec `>[TU]"S0UD6 [UR5S-Ulg)Nr"r)super __post_init__sumr#num_hidden_layers)selfkwargs __class__s ڌ/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/models/efficientnet/configuration_efficientnet.pyr3 EfficientNetConfig.__post_init__Zs+ ''!$T%;%;!rNsN'.3#34DA)DA5DAN r;