anton/mlpractical
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

adding missing conv compatibility code in fc linear transform

Browse Source
This commit is contained in:
pswietojanski 2015-11-15 16:45:41 +00:00
parent fc2eecf135
commit 689b78e4f4
1 changed files with 9 additions and 0 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

9
mlp/layers.py
View File

@ -265,6 +265,11 @@ class Linear(Layer):
:param inputs: matrix of features (x) or the output of the previous layer h^{i-1} :param inputs: matrix of features (x) or the output of the previous layer h^{i-1}
:return: h^i, matrix of transformed by layer features :return: h^i, matrix of transformed by layer features
""" """
#input comes from 4D convolutional tensor, reshape to expected shape
if inputs.ndim == 4:
inputs = inputs.reshape(inputs.shape[0], -1)
a = numpy.dot(inputs, self.W) + self.b a = numpy.dot(inputs, self.W) + self.b
# here f() is an identity function, so just return a linear transformation # here f() is an identity function, so just return a linear transformation
return a return a
@ -334,6 +339,10 @@ class Linear(Layer):
since W and b are only layer's parameters since W and b are only layer's parameters
""" """
#input comes from 4D convolutional tensor, reshape to expected shape
if inputs.ndim == 4:
inputs = inputs.reshape(inputs.shape[0], -1)
#you could basically use different scalers for biases #you could basically use different scalers for biases
#and weights, but it is not implemented here like this #and weights, but it is not implemented here like this
l2_W_penalty, l2_b_penalty = 0, 0 l2_W_penalty, l2_b_penalty = 0, 0