Source code for jax._src.nn.initializers

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"""
Common neural network layer initializers, consistent with definitions
used in Keras and Sonnet.
"""


from functools import partial

import numpy as np

import jax.numpy as jnp
from jax import lax
from jax import ops
from jax import random
from jax import core
from jax._src.util import prod

[docs]def zeros(key, shape, dtype=jnp.float32): return jnp.zeros(shape, dtype)
[docs]def ones(key, shape, dtype=jnp.float32): return jnp.ones(shape, dtype)
[docs]def uniform(scale=1e-2, dtype=jnp.float32): def init(key, shape, dtype=dtype): return random.uniform(key, shape, dtype) * scale return init
[docs]def normal(stddev=1e-2, dtype=jnp.float32): def init(key, shape, dtype=dtype): return random.normal(key, shape, dtype) * stddev return init
def _compute_fans(shape: core.NamedShape, in_axis=-2, out_axis=-1): receptive_field_size = shape.total / shape[in_axis] / shape[out_axis] fan_in = shape[in_axis] * receptive_field_size fan_out = shape[out_axis] * receptive_field_size return fan_in, fan_out
[docs]def variance_scaling(scale, mode, distribution, in_axis=-2, out_axis=-1, dtype=jnp.float32): def init(key, shape, dtype=dtype): shape = core.as_named_shape(shape) fan_in, fan_out = _compute_fans(shape, in_axis, out_axis) if mode == "fan_in": denominator = fan_in elif mode == "fan_out": denominator = fan_out elif mode == "fan_avg": denominator = (fan_in + fan_out) / 2 else: raise ValueError( "invalid mode for variance scaling initializer: {}".format(mode)) variance = jnp.array(scale / denominator, dtype=dtype) if distribution == "truncated_normal": # constant is stddev of standard normal truncated to (-2, 2) stddev = jnp.sqrt(variance) / jnp.array(.87962566103423978, dtype) return random.truncated_normal(key, -2, 2, shape, dtype) * stddev elif distribution == "normal": return random.normal(key, shape, dtype) * jnp.sqrt(variance) elif distribution == "uniform": return random.uniform(key, shape, dtype, -1) * jnp.sqrt(3 * variance) else: raise ValueError("invalid distribution for variance scaling initializer") return init
xavier_uniform = glorot_uniform = partial(variance_scaling, 1.0, "fan_avg", "uniform") xavier_normal = glorot_normal = partial(variance_scaling, 1.0, "fan_avg", "truncated_normal") lecun_uniform = partial(variance_scaling, 1.0, "fan_in", "uniform") lecun_normal = partial(variance_scaling, 1.0, "fan_in", "truncated_normal") kaiming_uniform = he_uniform = partial(variance_scaling, 2.0, "fan_in", "uniform") kaiming_normal = he_normal = partial(variance_scaling, 2.0, "fan_in", "truncated_normal") def orthogonal(scale=1.0, column_axis=-1, dtype=jnp.float32): """ Construct an initializer for uniformly distributed orthogonal matrices. If the shape is not square, the matrices will have orthonormal rows or columns depending on which side is smaller. """ def init(key, shape, dtype=dtype): if len(shape) < 2: raise ValueError("orthogonal initializer requires at least a 2D shape") n_rows, n_cols = prod(shape) // shape[column_axis], shape[column_axis] matrix_shape = (n_cols, n_rows) if n_rows < n_cols else (n_rows, n_cols) A = random.normal(key, matrix_shape, dtype) Q, R = jnp.linalg.qr(A) diag_sign = lax.broadcast_to_rank(jnp.sign(jnp.diag(R)), rank=Q.ndim) Q *= diag_sign # needed for a uniform distribution if n_rows < n_cols: Q = Q.T Q = jnp.reshape(Q, tuple(np.delete(shape, column_axis)) + (shape[column_axis],)) Q = jnp.moveaxis(Q, -1, column_axis) return scale * Q return init def delta_orthogonal(scale=1.0, column_axis=-1, dtype=jnp.float32): """ Construct an initializer for delta orthogonal kernels; see arXiv:1806.05393. The shape must be 3D, 4D or 5D. """ def init(key, shape, dtype=dtype): if len(shape) not in [3, 4, 5]: raise ValueError("Delta orthogonal initializer requires a 3D, 4D or 5D " "shape.") if shape[-1] < shape[-2]: raise ValueError("`fan_in` must be less or equal than `fan_out`. ") ortho_init = orthogonal(scale=scale, column_axis=column_axis, dtype=dtype) ortho_matrix = ortho_init(key, shape[-2:]) W = jnp.zeros(shape, dtype=dtype) if len(shape) == 3: k = shape[0] return ops.index_update(W, ops.index[(k-1)//2, ...], ortho_matrix) elif len(shape) == 4: k1, k2 = shape[:2] return ops.index_update(W, ops.index[(k1-1)//2, (k2-1)//2, ...], ortho_matrix) else: k1, k2, k3 = shape[:3] return ops.index_update(W, ops.index[(k1-1)//2, (k2-1)//2, (k3-1)//2, ...], ortho_matrix) return init