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@@ 71-84 (lines=14) @@
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| 68 |
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# for anything by this brick. |
| 69 |
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get_output_shape = staticmethod(get_conv_output_shape) |
| 70 |
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| 71 |
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@lazy(allocation=['filter_size', 'num_filters', 'num_channels']) |
| 72 |
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def __init__(self, filter_size, num_filters, num_channels, batch_size=None, |
| 73 |
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image_size=(None, None), step=(1, 1), border_mode='valid', |
| 74 |
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tied_biases=True, **kwargs): |
| 75 |
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super(Convolutional, self).__init__(**kwargs) |
| 76 |
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| 77 |
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self.filter_size = filter_size |
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self.num_filters = num_filters |
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self.batch_size = batch_size |
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self.num_channels = num_channels |
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self.image_size = image_size |
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self.step = step |
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self.border_mode = border_mode |
| 84 |
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self.tied_biases = tied_biases |
| 85 |
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| 86 |
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def _allocate(self): |
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W = shared_floatx_nans((self.num_filters, self.num_channels) + |
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@@ 493-505 (lines=13) @@
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| 490 |
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an explicitly specified border mode will be pushed down the hierarchy. |
| 491 |
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| 492 |
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""" |
| 493 |
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@lazy(allocation=['num_channels']) |
| 494 |
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def __init__(self, layers, num_channels, batch_size=None, |
| 495 |
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image_size=(None, None), border_mode=None, tied_biases=None, |
| 496 |
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**kwargs): |
| 497 |
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self.layers = [a if isinstance(a, Brick) else a.brick for a in layers] |
| 498 |
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self.image_size = image_size |
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self.num_channels = num_channels |
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self.batch_size = batch_size |
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self.border_mode = border_mode |
| 502 |
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self.tied_biases = tied_biases |
| 503 |
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| 504 |
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super(ConvolutionalSequence, self).__init__( |
| 505 |
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application_methods=layers, **kwargs) |
| 506 |
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| 507 |
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def get_dim(self, name): |
| 508 |
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if name == 'input_': |
mila-udem /
blocks
