def Block(net, name, nb_filters, stride): split = net.add(layer.Split(name + "-split", 2)) if stride > 1: net.add(layer.Conv2D(name + "-br2-conv", nb_filters, 1, stride, pad=0), split) br2bn = net.add(layer.BatchNormalization(name + "-br2-bn")) net.add(layer.Conv2D(name + "-br1-conv1", nb_filters, 3, stride, pad=1), split) net.add(layer.BatchNormalization(name + "-br1-bn1")) net.add(layer.Activation(name + "-br1-relu")) net.add(layer.Conv2D(name + "-br1-conv2", nb_filters, 3, 1, pad=1)) br1bn2 = net.add(layer.BatchNormalization(name + "-br1-bn2")) if stride > 1: net.add(layer.Merge(name + "-merge"), [br1bn2, br2bn]) else: net.add(layer.Merge(name + "-merge"), [br1bn2, split])
def create_net(input_shape, use_cpu=False): if use_cpu: layer.engine = 'singacpp' net = ffnet.FeedForwardNet(loss.SoftmaxCrossEntropy(), metric.Accuracy()) ConvBnReLUPool(net, 'conv1', 32, input_shape) ConvBnReLUPool(net, 'conv2', 64) ConvBnReLUPool(net, 'conv3', 128) ConvBnReLUPool(net, 'conv4', 128) ConvBnReLUPool(net, 'conv5', 256) ConvBnReLUPool(net, 'conv6', 256) ConvBnReLUPool(net, 'conv7', 512) ConvBnReLUPool(net, 'conv8', 512) net.add(layer.Flatten('flat')) net.add(layer.Dense('ip1', 256)) net.add(layer.BatchNormalization('bn1')) net.add(layer.Activation('relu1')) net.add(layer.Dropout('dropout1', 0.2)) net.add(layer.Dense('ip2', 16)) net.add(layer.BatchNormalization('bn2')) net.add(layer.Activation('relu2')) net.add(layer.Dropout('dropout2', 0.2)) net.add(layer.Dense('ip3', 2)) print 'Parameter intialization............' for (p, name) in zip(net.param_values(), net.param_names()): print name, p.shape if 'mean' in name or 'beta' in name: p.set_value(0.0) elif 'var' in name: p.set_value(1.0) elif 'gamma' in name: initializer.uniform(p, 0, 1) elif len(p.shape) > 1: if 'conv' in name: initializer.gaussian(p, 0, p.size()) else: p.gaussian(0, 0.02) else: p.set_value(0) print name, p.l1() return net
def create_net(use_cpu=False): if use_cpu: layer.engine = 'singacpp' net = ffnet.FeedForwardNet(loss.SoftmaxCrossEntropy(), metric.Accuracy()) ConvBnReLU(net, 'conv1_1', 64, (3, 32, 32)) net.add(layer.Dropout('drop1', 0.3)) ConvBnReLU(net, 'conv1_2', 64) net.add(layer.MaxPooling2D('pool1', 2, 2, border_mode='valid')) ConvBnReLU(net, 'conv2_1', 128) net.add(layer.Dropout('drop2_1', 0.4)) ConvBnReLU(net, 'conv2_2', 128) net.add(layer.MaxPooling2D('pool2', 2, 2, border_mode='valid')) ConvBnReLU(net, 'conv3_1', 256) net.add(layer.Dropout('drop3_1', 0.4)) ConvBnReLU(net, 'conv3_2', 256) net.add(layer.Dropout('drop3_2', 0.4)) ConvBnReLU(net, 'conv3_3', 256) net.add(layer.MaxPooling2D('pool3', 2, 2, border_mode='valid')) ConvBnReLU(net, 'conv4_1', 512) net.add(layer.Dropout('drop4_1', 0.4)) ConvBnReLU(net, 'conv4_2', 512) net.add(layer.Dropout('drop4_2', 0.4)) ConvBnReLU(net, 'conv4_3', 512) net.add(layer.MaxPooling2D('pool4', 2, 2, border_mode='valid')) ConvBnReLU(net, 'conv5_1', 512) net.add(layer.Dropout('drop5_1', 0.4)) ConvBnReLU(net, 'conv5_2', 512) net.add(layer.Dropout('drop5_2', 0.4)) ConvBnReLU(net, 'conv5_3', 512) net.add(layer.MaxPooling2D('pool5', 2, 2, border_mode='valid')) net.add(layer.Flatten('flat')) net.add(layer.Dropout('drop_flat', 0.5)) net.add(layer.Dense('ip1', 512)) net.add(layer.BatchNormalization('batchnorm_ip1')) net.add(layer.Activation('relu_ip1')) net.add(layer.Dropout('drop_ip2', 0.5)) net.add(layer.Dense('ip2', 10)) print 'Start intialization............' for (p, name) in zip(net.param_values(), net.param_names()): print name, p.shape if 'mean' in name or 'beta' in name: p.set_value(0.0) elif 'var' in name: p.set_value(1.0) elif 'gamma' in name: initializer.uniform(p, 0, 1) elif len(p.shape) > 1: if 'conv' in name: initializer.gaussian(p, 0, 3 * 3 * p.shape[0]) else: p.gaussian(0, 0.02) else: p.set_value(0) print name, p.l1() return net
def ConvBnReLU(net, name, nb_filers, sample_shape=None): net.add( layer.Conv2D(name + '_1', nb_filers, 3, 1, pad=1, input_sample_shape=sample_shape)) net.add(layer.BatchNormalization(name + '_2')) net.add(layer.Activation(name + '_3'))
def ConvBnReLUPool(net, name, nb_filers, sample_shape=None): net.add( layer.Conv2D(name + '_conv', nb_filers, 3, 1, pad=1, input_sample_shape=sample_shape)) net.add(layer.BatchNormalization(name + '_bn')) net.add(layer.Activation(name + '_relu')) net.add(layer.MaxPooling2D(name + '_pool', 2, 2, border_mode='valid'))
def create_net(use_cpu=False): if use_cpu: layer.engine = 'singacpp' net = ffnet.FeedForwardNet(loss.SoftmaxCrossEntropy(), metric.Accuracy()) net.add( layer.Conv2D("conv1", 16, 3, 1, pad=1, input_sample_shape=(3, 32, 32))) net.add(layer.BatchNormalization("bn1")) net.add(layer.Activation("relu1")) Block(net, "2a", 16, 1) Block(net, "2b", 16, 1) Block(net, "2c", 16, 1) Block(net, "3a", 32, 2) Block(net, "3b", 32, 1) Block(net, "3c", 32, 1) Block(net, "4a", 64, 2) Block(net, "4b", 64, 1) Block(net, "4c", 64, 1) net.add(layer.AvgPooling2D("pool4", 8, 8, border_mode='valid')) net.add(layer.Flatten('flat')) net.add(layer.Dense('ip5', 10)) print 'Start intialization............' for (p, name) in zip(net.param_values(), net.param_names()): # print name, p.shape if 'mean' in name or 'beta' in name: p.set_value(0.0) elif 'var' in name: p.set_value(1.0) elif 'gamma' in name: initializer.uniform(p, 0, 1) elif len(p.shape) > 1: if 'conv' in name: # initializer.gaussian(p, 0, math.sqrt(2.0/p.shape[1])) initializer.gaussian(p, 0, 9.0 * p.shape[0]) else: initializer.uniform(p, p.shape[0], p.shape[1]) else: p.set_value(0) # print name, p.l1() return net
def build_net(self): if self.use_cpu: layer.engine = 'singacpp' else: layer.engine = 'cudnn' self.net = ffnet.FeedForwardNet(loss.SoftmaxCrossEntropy(), metric.Accuracy()) self.net.add( Reshape('reshape1', (self.vocab_size, ), input_sample_shape=(self.maxlen, self.vocab_size))) self.net.add(layer.Dense('embed', self.embed_size)) # output: (embed_size, ) self.net.add(layer.Dropout('dropout')) self.net.add(Reshape('reshape2', (1, self.maxlen, self.embed_size))) self.net.add( layer.Conv2D('conv', self.filters, (self.kernel_size, self.embed_size), border_mode='valid')) # output: (filter, embed_size) if self.use_cpu == False: self.net.add(layer.BatchNormalization('batchNorm')) self.net.add(layer.Activation('activ')) # output: (filter, embed_size) self.net.add(layer.MaxPooling2D('max', stride=self.pool_size)) self.net.add(layer.Flatten('flatten')) self.net.add(layer.Dense('dense', 2))
def create_net(input_shape, use_cpu=False): if use_cpu: layer.engine = 'singacpp' net = ffnet.FeedForwardNet(loss.SoftmaxCrossEntropy(), metric.Accuracy()) net.add( layer.Conv2D('conv1', nb_kernels=32, kernel=7, stride=3, pad=1, input_sample_shape=input_shape)) net.add(layer.Activation('relu1')) net.add(layer.BatchNormalization('bn1')) net.add(layer.MaxPooling2D('pool1', 2, 2, border_mode='valid')) net.add(layer.Conv2D('conv2', nb_kernels=64, kernel=5, stride=3)) net.add(layer.Activation('relu2')) net.add(layer.BatchNormalization('bn2')) net.add(layer.MaxPooling2D('pool2', 2, 2, border_mode='valid')) net.add(layer.Conv2D('conv3', nb_kernels=128, kernel=3, stride=1, pad=2)) net.add(layer.Activation('relu3')) net.add(layer.BatchNormalization('bn3')) net.add(layer.MaxPooling2D('pool3', 2, 2, border_mode='valid')) net.add(layer.Conv2D('conv4', nb_kernels=256, kernel=3, stride=1)) net.add(layer.Activation('relu4')) net.add(layer.BatchNormalization('bn4')) net.add(layer.MaxPooling2D('pool4', 2, 2, border_mode='valid')) net.add(layer.Conv2D('conv5', nb_kernels=512, kernel=3, stride=1, pad=1)) net.add(layer.Activation('relu5')) net.add(layer.BatchNormalization('bn5')) net.add(layer.MaxPooling2D('pool5', 2, 2, border_mode='valid')) net.add(layer.Flatten('flat')) net.add(layer.Dense('ip6', 2048)) net.add(layer.Activation('relu6')) net.add(layer.BatchNormalization('bn6')) net.add(layer.Dropout('dropout6', 0.5)) net.add(layer.Dense('ip7', 128)) net.add(layer.Activation('relu7')) net.add(layer.BatchNormalization('bn7')) net.add(layer.Dropout('dropout7', 0.2)) net.add(layer.Dense('ip8', 2)) print 'Parameter intialization............' for (p, name) in zip(net.param_values(), net.param_names()): print name, p.shape if 'mean' in name or 'beta' in name: p.set_value(0.0) elif 'var' in name: p.set_value(1.0) elif 'gamma' in name: initializer.uniform(p, 0, 1) elif len(p.shape) > 1: if 'conv' in name: initializer.gaussian(p, 0, p.size()) else: p.gaussian(0, 0.02) else: p.set_value(0) print name, p.l1() return net
def test_batch_normalization(self): in_sample_shape = (3, 224, 224) bn = layer.BatchNormalization('bn', input_sample_shape=in_sample_shape) out_sample_shape = bn.get_output_sample_shape() self.check_shape(out_sample_shape, in_sample_shape)
def create_net(input_shape, use_cpu=False): if use_cpu: layer.engine = 'singacpp' net = ffnet.FeedForwardNet(loss.SoftmaxCrossEntropy(), metric.Accuracy()) ConvBnReLU(net, 'conv1_1', 64, input_shape) #net.add(layer.Dropout('drop1', 0.3)) net.add(layer.MaxPooling2D('pool0', 2, 2, border_mode='valid')) ConvBnReLU(net, 'conv1_2', 128) net.add(layer.MaxPooling2D('pool1', 2, 2, border_mode='valid')) ConvBnReLU(net, 'conv2_1', 128) net.add(layer.Dropout('drop2_1', 0.4)) ConvBnReLU(net, 'conv2_2', 128) net.add(layer.MaxPooling2D('pool2', 2, 2, border_mode='valid')) ConvBnReLU(net, 'conv3_1', 256) net.add(layer.Dropout('drop3_1', 0.4)) ConvBnReLU(net, 'conv3_2', 256) net.add(layer.Dropout('drop3_2', 0.4)) ConvBnReLU(net, 'conv3_3', 256) net.add(layer.MaxPooling2D('pool3', 2, 2, border_mode='valid')) ConvBnReLU(net, 'conv4_1', 256) net.add(layer.Dropout('drop4_1', 0.4)) ConvBnReLU(net, 'conv4_2', 256) net.add(layer.Dropout('drop4_2', 0.4)) ConvBnReLU(net, 'conv4_3', 256) net.add(layer.MaxPooling2D('pool4', 2, 2, border_mode='valid')) ConvBnReLU(net, 'conv5_1', 512) net.add(layer.Dropout('drop5_1', 0.4)) ConvBnReLU(net, 'conv5_2', 512) net.add(layer.Dropout('drop5_2', 0.4)) ConvBnReLU(net, 'conv5_3', 512) net.add(layer.MaxPooling2D('pool5', 2, 2, border_mode='valid')) #ConvBnReLU(net, 'conv6_1', 512) #net.add(layer.Dropout('drop6_1', 0.4)) #ConvBnReLU(net, 'conv6_2', 512) #net.add(layer.Dropout('drop6_2', 0.4)) #ConvBnReLU(net, 'conv6_3', 512) #net.add(layer.MaxPooling2D('pool6', 2, 2, border_mode='valid')) #ConvBnReLU(net, 'conv7_1', 512) #net.add(layer.Dropout('drop7_1', 0.4)) #ConvBnReLU(net, 'conv7_2', 512) #net.add(layer.Dropout('drop7_2', 0.4)) #ConvBnReLU(net, 'conv7_3', 512) #net.add(layer.MaxPooling2D('pool7', 2, 2, border_mode='valid')) net.add(layer.Flatten('flat')) net.add(layer.Dense('ip1', 256)) net.add(layer.BatchNormalization('bn1')) net.add(layer.Activation('relu1')) net.add(layer.Dropout('dropout1', 0.2)) net.add(layer.Dense('ip2', 16)) net.add(layer.BatchNormalization('bn2')) net.add(layer.Activation('relu2')) net.add(layer.Dropout('dropout2', 0.2)) net.add(layer.Dense('ip3', 2)) print 'Parameter intialization............' for (p, name) in zip(net.param_values(), net.param_names()): print name, p.shape if 'mean' in name or 'beta' in name: p.set_value(0.0) elif 'var' in name: p.set_value(1.0) elif 'gamma' in name: initializer.uniform(p, 0, 1) elif len(p.shape) > 1: if 'conv' in name: initializer.gaussian(p, 0, p.size()) else: p.gaussian(0, 0.02) else: p.set_value(0) print name, p.l1() return net