FLAX-LINEN . READTHEDOCS . IO {}

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Keywords {🔍}

flax, params, parameters, model, loss, state, step, bias, array, kernel, dtypefloat, layers, input, function, jax, dense, key, variables, parameter, output, shape, models, return, layer, def, optstate, data, initialization, call, method, import, linear, regression, structure, updatedstate, batchstats, updated, module, training, set, features, class, setup, install, gradient, grads, update, optax, optimizer, nnmodule,

Topics {✒️}

xa5tz\xbee\xd3\xcf\xbf\x0c\xa0n\xbf\x8a\x1f\x19\xc0\xb8-\xca x807w\xbfe\xf7=\xbf\xe4k\x96\xbf\x04\xd3a\xbf\xad\xbe\x15 xf9\xae\xbe\xfa\xdfe\xbf\xa6kernel\xc7\xd6\x01\x93\x92 x1e\xa1\xbf\xeb\xe8\x13\xbf\xdd x05\xa7float32\xc4\xc8\xc3\x19\x7f x0ew\xbec\xcb\x9c\xbe\x0f\xbd= xbf\xc2\xdfw\xbf\x86\x8ce x98\x9a\xed\xbe\xc0\xd5] x972\xfa\xbe\x8f\xc8l x9a\x92\xd6 io/warnings/venv warning custom multi-layer perceptron xc0\xda\x0b xf8\xd5\x87 xd7\xb3 flax linen api latest jaxlib version flax import linen manually written training managing state linear regression formulation install packages reliably row-based systems encoder/decoder methods 'initialized parameter shapes initialized parameter shapes �valid jax types” jnp import flax dummy input data changing optimizer hyperparameters linear activation function 𝑦 𝑖 jax import random dense layer instance linear regression pytree feed-forward function full batchnorm implementation type sequence[int] previously generated params dense layers interleaved build simple models default values provided jit def update_params environment linear regression model structure defines dense layer provided taking 'features' parameter batch normalization layers 'bytes output' 𝑥 𝑖

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