jax.experimental.stax module¶
Stax is a small but flexible neural net specification library from scratch.
For an example of its use, see examples/resnet50.py.
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jax.experimental.stax.AvgPool(window_shape, strides=None, padding='VALID')¶ Layer construction function for a pooling layer.
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jax.experimental.stax.BatchNorm(axis=(0, 1, 2), epsilon=1e-05, center=True, scale=True, beta_init=<function zeros>, gamma_init=<function ones>)[source]¶ Layer construction function for a batch normalization layer.
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jax.experimental.stax.Conv(out_chan, filter_shape, strides=None, padding='VALID', W_init=None, b_init=<function normal.<locals>.init>)¶ Layer construction function for a general convolution layer.
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jax.experimental.stax.Conv1DTranspose(out_chan, filter_shape, strides=None, padding='VALID', W_init=None, b_init=<function normal.<locals>.init>)¶ Layer construction function for a general transposed-convolution layer.
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jax.experimental.stax.ConvTranspose(out_chan, filter_shape, strides=None, padding='VALID', W_init=None, b_init=<function normal.<locals>.init>)¶ Layer construction function for a general transposed-convolution layer.
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jax.experimental.stax.Dense(out_dim, W_init=<function variance_scaling.<locals>.init>, b_init=<function normal.<locals>.init>)[source]¶ Layer constructor function for a dense (fully-connected) layer.
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jax.experimental.stax.Dropout(rate, mode='train')[source]¶ Layer construction function for a dropout layer with given rate.
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jax.experimental.stax.FanInConcat(axis=-1)[source]¶ Layer construction function for a fan-in concatenation layer.
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jax.experimental.stax.GeneralConv(dimension_numbers, out_chan, filter_shape, strides=None, padding='VALID', W_init=None, b_init=<function normal.<locals>.init>)[source]¶ Layer construction function for a general convolution layer.
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jax.experimental.stax.GeneralConvTranspose(dimension_numbers, out_chan, filter_shape, strides=None, padding='VALID', W_init=None, b_init=<function normal.<locals>.init>)[source]¶ Layer construction function for a general transposed-convolution layer.
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jax.experimental.stax.MaxPool(window_shape, strides=None, padding='VALID')¶ Layer construction function for a pooling layer.
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jax.experimental.stax.SumPool(window_shape, strides=None, padding='VALID')¶ Layer construction function for a pooling layer.
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jax.experimental.stax.elementwise(fun, **fun_kwargs)[source]¶ Layer that applies a scalar function elementwise on its inputs.
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jax.experimental.stax.glorot(in_axis=-2, out_axis=-1)¶
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jax.experimental.stax.glorot_normal(in_axis=-2, out_axis=-1)¶
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jax.experimental.stax.parallel(*layers)[source]¶ Combinator for composing layers in parallel.
The layer resulting from this combinator is often used with the FanOut and FanInSum layers.
Parameters: *layers – a sequence of layers, each an (init_fun, apply_fun) pair. Returns: A new layer, meaning an (init_fun, apply_fun) pair, representing the parallel composition of the given sequence of layers. In particular, the returned layer takes a sequence of inputs and returns a sequence of outputs with the same length as the argument layers.
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jax.experimental.stax.serial(*layers)[source]¶ Combinator for composing layers in serial.
Parameters: *layers – a sequence of layers, each an (init_fun, apply_fun) pair. Returns: A new layer, meaning an (init_fun, apply_fun) pair, representing the serial composition of the given sequence of layers.
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jax.experimental.stax.shape_dependent(make_layer)[source]¶ Combinator to delay layer constructor pair until input shapes are known.
Parameters: make_layer – a one-argument function that takes an input shape as an argument (a tuple of positive integers) and returns an (init_fun, apply_fun) pair. Returns: A new layer, meaning an (init_fun, apply_fun) pair, representing the same layer as returned by make_layer but with its construction delayed until input shapes are known.