def __init__(self): CNN.__init__( self, layers=[ ('input', layers.InputLayer), ('conv1', layers.Conv2DLayer), ('pool1', layers.MaxPool2DLayer), ('dropout1', layers.DropoutLayer), ('conv2', layers.Conv2DLayer), ('pool2', layers.MaxPool2DLayer), ('dropout2', layers.DropoutLayer), ('conv3', layers.Conv2DLayer), ('pool3', layers.MaxPool2DLayer), ('dropout3', layers.DropoutLayer), ('conv4', layers.Conv2DLayer), ('pool4', layers.MaxPool2DLayer), ('dropout4', layers.DropoutLayer), ('hidden5', layers.DenseLayer), ('dropout5', layers.DropoutLayer), ('output', layers.DenseLayer), ], # input input_shape=(None, 3, 75, 75), # conv2d + pool + dropout conv1_filter_size=(3, 3), conv1_num_filters=64, pool1_pool_size=(2, 2), dropout1_p=0.2, # conv2d + pool + dropout conv2_filter_size=(3, 3), conv2_num_filters=48, pool2_pool_size=(2, 2), dropout2_p=0.2, # conv2d + pool + dropout conv3_filter_size=(3, 3), conv3_num_filters=48, pool3_pool_size=(2, 2), dropout3_p=0.2, # conv2d + pool + dropout conv4_filter_size=(3, 3), conv4_num_filters=48, pool4_pool_size=(2, 2), dropout4_p=0.2, # dense layer 1 hidden5_num_units=512, hidden5_nonlinearity=nonlinearities.rectify, dropout5_p=0.5, # dense layer 2 output_num_units=2, output_nonlinearity=nonlinearities.softmax)
def __init__(self, num_label): self.layers = [ ['conv', 512, 64, 1, 0], ['norm'], ['relu'], ] self._make_layers(BlockType.BASIC, 64, 64, 2) self._make_layers(BlockType.BASIC, 64, 128, 2) self._make_layers(BlockType.BASIC, 128, 256, 2) self._make_layers(BlockType.BASIC, 256, 512, 2) self.layers.extend([['avgPool', 4, 1], ['flatten'], ['fc', 512, num_label]]) CNN.__init__(self)
def __init__(self, num_label): self.layers = [ ['conv', 3, 64, 3, 1], ] out_dim = self.add_res_layers() self.layers.extend([ #['norm'], ['relu'], ['avgPool', 4, 1], #['conv', 512, 256, 1, 0], ['norm'], ['relu'], #['dropout', 0.5], #['conv', 256, 256, 1, 0], ['norm'], ['relu'], #['dropout', 0.5], ['flatten'], ['fc', out_dim, num_label] ]) CNN.__init__(self)
def __init__(self, dataset: AbstractDataset): H, W, C = dataset.num_dims() out_dim = H // 8 self.layers = [['conv', C, 64, 3, 1], ['norm'], ['relu'], ['conv', 64, 64, 3, 1], ['norm'], ['relu'], ['maxPool', 2, 2], ['conv', 64, 128, 3, 1], ['norm'], ['relu'], ['conv', 128, 128, 3, 1], ['norm'], ['relu'], ['conv', 128, 128, 3, 1], ['norm'], ['relu'], ['maxPool', 2, 2], ['conv', 128, 256, 3, 1], ['norm'], ['relu'], ['conv', 256, 256, 3, 1], ['norm'], ['relu'], ['conv', 256, 256, 3, 1], ['norm'], ['relu'], ['maxPool', 2, 2], ['conv', 256, 512, 3, 1], ['norm'], ['relu'], ['conv', 512, 512, 3, 1], ['norm'], ['relu'], ['conv', 512, 512, 3, 1], ['norm'], ['relu'], ['avgPool', out_dim, 1], ['flatten'], ['fc', 512, dataset.num_labels()]] CNN.__init__(self)
def __init__(self, num_label): self.layers = [['conv', 3, 64, 3, 1], ['norm'], ['relu'], ['conv', 64, 64, 3, 1], ['norm'], ['relu'], ['nonlocal', 64], ['maxPool', 2, 2], ['conv', 64, 128, 3, 1], ['norm'], ['relu'], ['conv', 128, 128, 3, 1], ['norm'], ['relu'], ['conv', 128, 128, 3, 1], ['norm'], ['relu'], ['maxPool', 2, 2], ['conv', 128, 256, 3, 1], ['norm'], ['relu'], ['conv', 256, 256, 3, 1], ['norm'], ['relu'], ['conv', 256, 256, 3, 1], ['norm'], ['relu'], ['maxPool', 2, 2], ['conv', 256, 512, 3, 1], ['norm'], ['relu'], ['conv', 512, 512, 3, 1], ['norm'], ['relu'], ['conv', 512, 512, 3, 1], ['norm'], ['relu'], ['avgPool', 4, 1], ['conv', 512, 256, 1, 0], ['norm'], ['relu'], ['dropout', 0.5], ['conv', 256, 256, 1, 0], ['norm'], ['relu'], ['dropout', 0.5], ['flatten'], ['fc', 256, num_label]] CNN.__init__(self)
def __init__(self): CNN.__init__( self, layers=[ ('input', layers.InputLayer), ('conv1', layers.Conv2DLayer), ('pool1', layers.MaxPool2DLayer), ('conv2', layers.Conv2DLayer), ('pool2', layers.MaxPool2DLayer), ('conv3', layers.Conv2DLayer), ('pool3', layers.MaxPool2DLayer), ('hidden3', layers.DenseLayer), ('dropout3', layers.DropoutLayer), ('output', layers.DenseLayer), ], # input input_shape=(None, 4, 75, 75), # conv2d + pool + dropout conv1_filter_size=(13, 13), conv1_num_filters=16, pool1_pool_size=(2, 2), # conv2d + pool + dropout conv2_filter_size=(13, 13), conv2_num_filters=16, pool2_pool_size=(2, 2), # conv2d + pool + dropout conv3_filter_size=(13, 13), conv3_num_filters=16, pool3_pool_size=(2, 2), # dense layer 1 hidden3_num_units=256, hidden3_nonlinearity=nonlinearities.rectify, dropout3_p=0.5, # dense layer 2 output_num_units=2, output_nonlinearity=nonlinearities.softmax)
def __init__(self): CNN.__init__( self, layers=[ ('image_input', layers.InputLayer), ('image_conv1', layers.Conv2DLayer), ('image_pool1', layers.MaxPool2DLayer), ('image_conv2', layers.Conv2DLayer), ('image_pool2', layers.MaxPool2DLayer), ('prob_input', layers.InputLayer), ('prob_conv1', layers.Conv2DLayer), ('prob_pool1', layers.MaxPool2DLayer), ('prob_conv2', layers.Conv2DLayer), ('prob_pool2', layers.MaxPool2DLayer), ('binary_input', layers.InputLayer), ('binary_conv1', layers.Conv2DLayer), ('binary_pool1', layers.MaxPool2DLayer), ('binary_conv2', layers.Conv2DLayer), ('binary_pool2', layers.MaxPool2DLayer), ('merge', layers.ConcatLayer), ('hidden3', layers.DenseLayer), ('output', layers.DenseLayer), ], # input image_input_shape=(None, 1, 75, 75), # conv2d + pool + dropout image_conv1_filter_size=(13, 13), image_conv1_num_filters=16, image_conv1_nonlinearity=nonlinearities.rectify, image_pool1_pool_size=(2, 2), # conv2d + pool + dropout image_conv2_filter_size=(13, 13), image_conv2_num_filters=16, image_conv2_nonlinearity=nonlinearities.rectify, image_pool2_pool_size=(2, 2), prob_input_shape=(None, 1, 75, 75), # conv2d + pool + dropout prob_conv1_filter_size=(13, 13), prob_conv1_num_filters=16, prob_conv1_nonlinearity=nonlinearities.rectify, prob_pool1_pool_size=(2, 2), # conv2d + pool + dropout prob_conv2_filter_size=(13, 13), prob_conv2_num_filters=16, prob_conv2_nonlinearity=nonlinearities.rectify, prob_pool2_pool_size=(2, 2), binary_input_shape=(None, 1, 75, 75), # conv2d + pool + dropout binary_conv1_filter_size=(13, 13), binary_conv1_num_filters=16, binary_conv1_nonlinearity=nonlinearities.rectify, binary_pool1_pool_size=(2, 2), # conv2d + pool + dropout binary_conv2_filter_size=(13, 13), binary_conv2_num_filters=16, binary_conv2_nonlinearity=nonlinearities.rectify, binary_pool2_pool_size=(2, 2), # concat merge_incomings=['image_pool2', 'prob_pool2', 'binary_pool2'], # dense layer 1 hidden3_num_units=256, hidden3_nonlinearity=nonlinearities.rectify, # dense layer 2 output_num_units=2, output_nonlinearity=nonlinearities.softmax)
def __init__(self): CNN.__init__( self, layers=[ ('image_input', layers.InputLayer), ('image_conv1', layers.Conv2DLayer), ('image_pool1', layers.MaxPool2DLayer), ('image_dropout1', layers.DropoutLayer), ('image_conv2', layers.Conv2DLayer), ('image_pool2', layers.MaxPool2DLayer), ('image_dropout2', layers.DropoutLayer), ('prob_input', layers.InputLayer), ('prob_conv1', layers.Conv2DLayer), ('prob_pool1', layers.MaxPool2DLayer), ('prob_dropout1', layers.DropoutLayer), ('prob_conv2', layers.Conv2DLayer), ('prob_pool2', layers.MaxPool2DLayer), ('prob_dropout2', layers.DropoutLayer), ('binary_input', layers.InputLayer), ('binary_conv1', layers.Conv2DLayer), ('binary_pool1', layers.MaxPool2DLayer), ('binary_dropout1', layers.DropoutLayer), ('binary_conv2', layers.Conv2DLayer), ('binary_pool2', layers.MaxPool2DLayer), ('binary_dropout2', layers.DropoutLayer), ('border_input', layers.InputLayer), ('border_conv1', layers.Conv2DLayer), ('border_pool1', layers.MaxPool2DLayer), ('border_dropout1', layers.DropoutLayer), ('border_conv2', layers.Conv2DLayer), ('border_pool2', layers.MaxPool2DLayer), ('border_dropout2', layers.DropoutLayer), ('merge', layers.ConcatLayer), ('hidden3', layers.DenseLayer), ('dropout3', layers.DropoutLayer), ('output', layers.DenseLayer), ], # input image_input_shape=(None, 1, 75, 75), # conv2d + pool + dropout image_conv1_filter_size=(3, 3), image_conv1_num_filters=64, image_pool1_pool_size=(2, 2), image_dropout1_p=0.2, # conv2d + pool + dropout image_conv2_filter_size=(3, 3), image_conv2_num_filters=64, image_pool2_pool_size=(2, 2), image_dropout2_p=0.2, prob_input_shape=(None, 1, 75, 75), # conv2d + pool + dropout prob_conv1_filter_size=(3, 3), prob_conv1_num_filters=64, prob_pool1_pool_size=(2, 2), prob_dropout1_p=0.2, # conv2d + pool + dropout prob_conv2_filter_size=(3, 3), prob_conv2_num_filters=64, prob_pool2_pool_size=(2, 2), prob_dropout2_p=0.2, binary_input_shape=(None, 1, 75, 75), # conv2d + pool + dropout binary_conv1_filter_size=(3, 3), binary_conv1_num_filters=64, binary_pool1_pool_size=(2, 2), binary_dropout1_p=0.2, # conv2d + pool + dropout binary_conv2_filter_size=(3, 3), binary_conv2_num_filters=64, binary_pool2_pool_size=(2, 2), binary_dropout2_p=0.2, border_input_shape=(None, 1, 75, 75), # conv2d + pool + dropout border_conv1_filter_size=(3, 3), border_conv1_num_filters=64, border_pool1_pool_size=(2, 2), border_dropout1_p=0.2, # conv2d + pool + dropout border_conv2_filter_size=(3, 3), border_conv2_num_filters=64, border_pool2_pool_size=(2, 2), border_dropout2_p=0.2, # concat # merge_incomings=['image_pool2','prob_pool2','binary_pool2','border_pool2'], merge_incomings=[ 'image_dropout2', 'prob_dropout2', 'binary_dropout2', 'border_dropout2' ], # dense layer 1 hidden3_num_units=512, hidden3_nonlinearity=nonlinearities.rectify, dropout3_p=0.5, # dense layer 2 output_num_units=2, output_nonlinearity=nonlinearities.softmax)
def __init__(self, size=64, gray=False, gen_depth=16, dis_depth=2): CNN.__init__(self, size=size, gray=gray, gen_depth=gen_depth) self.dis_depth = dis_depth self.D = None self.combined = None