from beagles.backend.darknet.layer import Layer
from deprecated.sphinx import deprecated
import numpy as np
DEPRECATION = """
select and extract classes should no longer be used. Use the tf.summary API and
Tensorboard to inspect network weights and biases. Use tf.saved_model API to extract
graph definitions."""
[docs]@deprecated(reason=DEPRECATION, version="1.0.0a1")
class select_layer(Layer):
[docs] def setup(self, inp, old, activation, inp_idx, out, keep, train):
self.old = old
self.keep = keep
self.train = train
self.inp_idx = inp_idx
self.activation = activation
inp_dim = inp
if inp_idx is not None:
inp_dim = len(inp_idx)
self.inp = inp_dim
self.out = out
self.wshape = {
'biases': [out],
'weights': [inp_dim, out]
}
@property
def signature(self):
sig = ['connected']
sig += self._signature[1:-4]
return sig
[docs] def present(self):
args = self.signature
self.presenter = connected_layer(*args)
[docs] def recollect(self, val):
w = val['weights']
b = val['biases']
if w is None: self.w = val; return
if self.inp_idx is not None:
w = np.take(w, self.inp_idx, 0)
keep_b = np.take(b, self.keep)
keep_w = np.take(w, self.keep, 1)
train_b = b[self.train:]
train_w = w[:, self.train:]
self.w['biases'] = np.concatenate((keep_b, train_b), axis=0)
self.w['weights'] = np.concatenate((keep_w, train_w), axis=1)
[docs] def finalize(self, *args):
"""Not Implemented"""
pass
[docs]class connected_layer(Layer):
[docs] def setup(self, input_size, output_size, activation):
self.activation = activation
self.inp = input_size
self.out = output_size
self.wshape = {
'biases': [self.out],
'weights': [self.inp, self.out]
}
[docs] def finalize(self, transpose):
weights = self.w['weights']
if weights is None:
return
shp = self.wshape['weights']
if not transpose:
weights = weights.reshape(shp[::-1])
weights = weights.transpose([1,0])
else:
weights = weights.reshape(shp)
self.w['weights'] = weights