def dense_block(x, stage, nb_layers, nb_filter, growth_rate, dropout_rate=None, weight_decay=1e-4, grow_nb_filters=True):
''' Build a dense_block where the output of each conv_block is fed to subsequent ones
# Arguments
x: input tensor
stage: index for dense block
nb_layers: the number of layers of conv_block to append to the model.
nb_filter: number of filters
growth_rate: growth rate
dropout_rate: dropout rate
weight_decay: weight decay factor
grow_nb_filters: flag to decide to allow number of filters to grow
'''
eps = 1.1e-5
concat_feat = x
for i in range(nb_layers):
branch = i+1
x = conv_block(concat_feat, stage, branch, growth_rate, dropout_rate, weight_decay)
concat_feat = merge([concat_feat, x], mode='concat', concat_axis=concat_axis, name='concat_'+str(stage)+'_'+str(branch))
if grow_nb_filters:
nb_filter += growth_rate
return concat_feat, nb_filter
python类merge()的实例源码
get_final_test_dense121_feature.py 文件源码
项目：Baidu-_contest
作者: DeepLJH0001
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get_final_test_inceptionv4_feature.py 文件源码
项目：Baidu-_contest
作者: DeepLJH0001
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def dense_block(x, stage, nb_layers, nb_filter, growth_rate, dropout_rate=None, weight_decay=1e-4, grow_nb_filters=True):
''' Build a dense_block where the output of each conv_block is fed to subsequent ones
# Arguments
x: input tensor
stage: index for dense block
nb_layers: the number of layers of conv_block to append to the model.
nb_filter: number of filters
growth_rate: growth rate
dropout_rate: dropout rate
weight_decay: weight decay factor
grow_nb_filters: flag to decide to allow number of filters to grow
'''
eps = 1.1e-5
concat_feat = x
for i in range(nb_layers):
branch = i+1
x = conv_block(concat_feat, stage, branch, growth_rate, dropout_rate, weight_decay)
concat_feat = merge([concat_feat, x], mode='concat', concat_axis=concat_axis, name='concat_'+str(stage)+'_'+str(branch))
if grow_nb_filters:
nb_filter += growth_rate
return concat_feat, nb_filter
def build_mod5(opt=adam()):
n = 3 * 1024
in1 = Input((128,), name='x1')
x1 = fc_block1(in1, n)
x1 = fc_identity(x1, n)
in2 = Input((1024,), name='x2')
x2 = fc_block1(in2, n)
x2 = fc_identity(x2, n)
x = merge([x1, x2], mode='concat', concat_axis=1)
x = fc_identity(x, n)
out = Dense(4716, activation='sigmoid', name='output')(x)
model = Model(input=[in1, in2], output=out)
model.compile(optimizer=opt, loss='categorical_crossentropy')
# model.summary()
# plot(model=model, show_shapes=True)
return model
inception_resnet_v2.py 文件源码
项目：keras-inception-resnetV2
作者: kentsommer
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def block17(input, scale=1.0, activation_fn='relu'):
if K.image_dim_ordering() == "th":
channel_axis = 1
else:
channel_axis = -1
shortcut = input
tower_conv = conv2d_bn(input, 192, 1, 1, activ_fn=activation_fn)
tower_conv1_0 = conv2d_bn(input, 128, 1, 1, activ_fn=activation_fn)
tower_conv1_1 = conv2d_bn(tower_conv1_0, 160, 1, 7, activ_fn=activation_fn)
tower_conv1_2 = conv2d_bn(tower_conv1_1, 192, 7, 1, activ_fn=activation_fn)
mixed = merge([tower_conv, tower_conv1_2], mode='concat', concat_axis=channel_axis)
up = conv2d_bn(mixed, 1088, 1, 1, activ_fn=False, normalize=False)
up = Lambda(do_scale, output_shape=K.int_shape(up)[1:], arguments={'scale':scale})(up)
net = merge([shortcut, up], mode='sum')
if activation_fn:
net = Activation(activation_fn)(net)
return net
inception_resnet_v2.py 文件源码
项目：keras-inception-resnetV2
作者: kentsommer
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def block8(input, scale=1.0, activation_fn='relu'):
if K.image_dim_ordering() == "th":
channel_axis = 1
else:
channel_axis = -1
shortcut = input
tower_conv = conv2d_bn(input, 192, 1, 1, activ_fn=activation_fn)
tower_conv1_0 = conv2d_bn(input, 192, 1, 1, activ_fn=activation_fn)
tower_conv1_1 = conv2d_bn(tower_conv1_0, 224, 1, 3, activ_fn=activation_fn)
tower_conv1_2 = conv2d_bn(tower_conv1_1, 256, 3, 1, activ_fn=activation_fn)
mixed = merge([tower_conv, tower_conv1_2], mode='concat', concat_axis=channel_axis)
up = conv2d_bn(mixed, 2080, 1, 1, activ_fn=False, normalize=False)
up = Lambda(do_scale, output_shape=K.int_shape(up)[1:], arguments={'scale':scale})(up)
net = merge([shortcut, up], mode='sum')
if activation_fn:
net = Activation(activation_fn)(net)
return net
def fire_module(x, squeeze=16, expand=64):
x = Convolution2D(squeeze, 1, 1, border_mode='valid')(x)
x = Activation('relu')(x)
left = Convolution2D(expand, 1, 1, border_mode='valid')(x)
left = Activation('relu')(left)
right= ZeroPadding2D(padding=(1, 1))(x)
right = Convolution2D(expand, 3, 3, border_mode='valid')(right)
right = Activation('relu')(right)
y = merge([left, right], mode='concat', concat_axis=1)
return y
# Original SqueezeNet from paper. Global Average Pool implemented manually with Average Pooling Layer
def fire_module(x, squeeze=16, expand=64):
x = Convolution2D(squeeze, 1, 1, border_mode='valid')(x)
x = Activation('relu')(x)
left = Convolution2D(expand, 1, 1, border_mode='valid')(x)
left = Activation('relu')(left)
right= ZeroPadding2D(padding=(1, 1))(x)
right = Convolution2D(expand, 3, 3, border_mode='valid')(right)
right = Activation('relu')(right)
x = merge([left, right], mode='concat', concat_axis=1)
return x
# Original SqueezeNet from paper. Global Average Pool implemented manually with Average Pooling Layer
def block_inception_a(input):
if K.image_dim_ordering() == "th":
channel_axis = 1
else:
channel_axis = -1
branch_0 = conv2d_bn(input, 96, 1, 1)
branch_1 = conv2d_bn(input, 64, 1, 1)
branch_1 = conv2d_bn(branch_1, 96, 3, 3)
branch_2 = conv2d_bn(input, 64, 1, 1)
branch_2 = conv2d_bn(branch_2, 96, 3, 3)
branch_2 = conv2d_bn(branch_2, 96, 3, 3)
branch_3 = AveragePooling2D((3,3), strides=(1,1), border_mode='same')(input)
branch_3 = conv2d_bn(branch_3, 96, 1, 1)
x = merge([branch_0, branch_1, branch_2, branch_3], mode='concat', concat_axis=channel_axis)
return x
def block_reduction_a(input):
if K.image_dim_ordering() == "th":
channel_axis = 1
else:
channel_axis = -1
branch_0 = conv2d_bn(input, 384, 3, 3, subsample=(2,2), border_mode='valid')
branch_1 = conv2d_bn(input, 192, 1, 1)
branch_1 = conv2d_bn(branch_1, 224, 3, 3)
branch_1 = conv2d_bn(branch_1, 256, 3, 3, subsample=(2,2), border_mode='valid')
branch_2 = MaxPooling2D((3,3), strides=(2,2), border_mode='valid')(input)
x = merge([branch_0, branch_1, branch_2], mode='concat', concat_axis=channel_axis)
return x
def block_reduction_b(input):
if K.image_dim_ordering() == "th":
channel_axis = 1
else:
channel_axis = -1
branch_0 = conv2d_bn(input, 192, 1, 1)
branch_0 = conv2d_bn(branch_0, 192, 3, 3, subsample=(2, 2), border_mode='valid')
branch_1 = conv2d_bn(input, 256, 1, 1)
branch_1 = conv2d_bn(branch_1, 256, 1, 7)
branch_1 = conv2d_bn(branch_1, 320, 7, 1)
branch_1 = conv2d_bn(branch_1, 320, 3, 3, subsample=(2,2), border_mode='valid')
branch_2 = MaxPooling2D((3, 3), strides=(2, 2), border_mode='valid')(input)
x = merge([branch_0, branch_1, branch_2], mode='concat', concat_axis=channel_axis)
return x
def create_model(self, height=32, width=32, channels=3, load_weights=False, batch_size=128):
"""
Creates a model to be used to scale images of specific height and width.
"""
init = super(ExpantionSuperResolution, self).create_model(height, width, channels, load_weights, batch_size)
x = Convolution2D(self.n1, self.f1, self.f1, activation='relu', border_mode='same', name='level1')(init)
x1 = Convolution2D(self.n2, self.f2_1, self.f2_1, activation='relu', border_mode='same', name='lavel1_1')(x)
x2 = Convolution2D(self.n2, self.f2_2, self.f2_2, activation='relu', border_mode='same', name='lavel1_2')(x)
x3 = Convolution2D(self.n2, self.f2_3, self.f2_3, activation='relu', border_mode='same', name='lavel1_3')(x)
x = merge([x1, x2, x3], mode='ave')
out = Convolution2D(channels, self.f3, self.f3, activation='relu', border_mode='same', name='output')(x)
model = Model(init, out)
adam = optimizers.Adam(lr=1e-3)
model.compile(optimizer=adam, loss='mse', metrics=[PSNRLoss])
if load_weights: model.load_weights(self.weight_path)
self.model = model
return model
def first_block(tensor_input,filters,kernel_size=3,pooling_size=1,dropout=0.5):
k1,k2 = filters
out = Conv1D(k1,1,padding='same')(tensor_input)
out = BatchNormalization()(out)
out = Activation('relu')(out)
out = Dropout(dropout)(out)
out = Conv1D(k2,kernel_size,padding='same')(out)
pooling = MaxPooling1D(pooling_size,padding='same')(tensor_input)
# out = merge([out,pooling],mode='sum')
out = add([out,pooling])
return out
def repeated_block(x,filters,kernel_size=3,pooling_size=1,dropout=0.5):
k1,k2 = filters
out = BatchNormalization()(x)
out = Activation('relu')(out)
out = Conv1D(k1,kernel_size,padding='same')(out)
out = BatchNormalization()(out)
out = Activation('relu')(out)
out = Dropout(dropout)(out)
out = Conv1D(k2,kernel_size,padding='same')(out)
pooling = MaxPooling1D(pooling_size,padding='same')(x)
out = add([out, pooling])
#out = merge([out,pooling])
return out
def first_2d_block(tensor_input,filters,kernel_size=3,pooling_size=1,dropout=0.5):
k1,k2 = filters
out = Conv2D(k1,1,padding='same',data_format='channels_last')(tensor_input)
out = BatchNormalization()(out)
out = Activation('relu')(out)
out = Dropout(dropout)(out)
out = Conv2D(k2,kernel_size,padding='same',data_format='channels_last')(out)
pooling = MaxPooling2D(pooling_size,padding='same',data_format='channels_last')(tensor_input)
# out = merge([out,pooling],mode='sum')
out = add([out,pooling])
return out
def repeated_2d_block(x,filters,kernel_size=3,pooling_size=1,dropout=0.5):
k1,k2 = filters
out = BatchNormalization()(x)
out = Activation('relu')(out)
out = Conv2D(k1,kernel_size,padding='same',data_format='channels_last')(out)
out = BatchNormalization()(out)
out = Activation('relu')(out)
out = Dropout(dropout)(out)
out = Conv2D(k2,kernel_size,padding='same',data_format='channels_last')(out)
pooling = MaxPooling2D(pooling_size,padding='same',data_format='channels_last')(x)
out = add([out, pooling])
#out = merge([out,pooling])
return out
def first_block(tensor_input,filters,kernel_size=3,pooling_size=1,dropout=0.5):
k1,k2 = filters
out = Conv1D(k1,1,padding='same')(tensor_input)
out = BatchNormalization()(out)
out = Activation('relu')(out)
out = Dropout(dropout)(out)
out = Conv1D(k2,kernel_size,padding='same')(out)
pooling = MaxPooling1D(pooling_size,padding='same')(tensor_input)
# out = merge([out,pooling],mode='sum')
out = add([out,pooling])
return out
def first_2d_block(tensor_input,filters,kernel_size=3,pooling_size=1,dropout=0.5):
k1,k2 = filters
out = Conv2D(k1,1,padding='same',data_format='channels_last')(tensor_input)
out = BatchNormalization()(out)
out = Activation('relu')(out)
out = Dropout(dropout)(out)
out = Conv2D(k2,kernel_size,padding='same',data_format='channels_last')(out)
pooling = MaxPooling2D(pooling_size,padding='same',data_format='channels_last')(tensor_input)
# out = merge([out,pooling],mode='sum')
out = add([out,pooling])
return out
def repeated_2d_block(x,filters,kernel_size=3,pooling_size=1,dropout=0.5):
k1,k2 = filters
out = BatchNormalization()(x)
out = Activation('relu')(out)
out = Conv2D(k1,kernel_size,padding='same',data_format='channels_last')(out)
out = BatchNormalization()(out)
out = Activation('relu')(out)
out = Dropout(dropout)(out)
out = Conv2D(k2,kernel_size,padding='same',data_format='channels_last')(out)
pooling = MaxPooling2D(pooling_size,padding='same',data_format='channels_last')(x)
out = add([out, pooling])
#out = merge([out,pooling])
return out
def first_block(tensor_input,filters,kernel_size=3,pooling_size=1,dropout=0.5):
k1,k2 = filters
out = Conv1D(k1,1,padding='same')(tensor_input)
out = BatchNormalization()(out)
out = Activation('relu')(out)
out = Dropout(dropout)(out)
out = Conv1D(k2,kernel_size,strides=2,padding='same')(out)
pooling = MaxPooling1D(pooling_size,strides=2,padding='same')(tensor_input)
# out = merge([out,pooling],mode='sum')
out = add([out,pooling])
return out
def repeated_block(x,filters,kernel_size=3,pooling_size=1,dropout=0.5):
k1,k2 = filters
out = BatchNormalization()(x)
out = Activation('relu')(out)
out = Conv1D(k1,kernel_size,padding='same')(out)
out = BatchNormalization()(out)
out = Activation('relu')(out)
out = Dropout(dropout)(out)
out = Conv1D(k2,kernel_size,strides=2,padding='same')(out)
pooling = MaxPooling1D(pooling_size,strides=2,padding='same')(x)
out = add([out, pooling])
#out = merge([out,pooling])
return out
def first_2d_block(tensor_input,filters,kernel_size=3,pooling_size=2,dropout=0.5):
k1,k2 = filters
out = Conv2D(k1,1,padding='same',data_format='channels_last')(tensor_input)
out = BatchNormalization()(out)
out = Activation('relu')(out)
out = Dropout(dropout)(out)
out = Conv2D(k2,kernel_size,2,padding='same',data_format='channels_last')(out)
pooling = MaxPooling2D(pooling_size,padding='same',data_format='channels_last')(tensor_input)
# out = merge([out,pooling],mode='sum')
out = add([out,pooling])
return out
def dense_block(x, stage, nb_layers, nb_filter, growth_rate, dropout_rate=None, weight_decay=1e-4, grow_nb_filters=True):
''' Build a dense_block where the output of each conv_block is fed to subsequent ones
# Arguments
x: input tensor
stage: index for dense block
nb_layers: the number of layers of conv_block to append to the model.
nb_filter: number of filters
growth_rate: growth rate
dropout_rate: dropout rate
weight_decay: weight decay factor
grow_nb_filters: flag to decide to allow number of filters to grow
'''
eps = 1.1e-5
concat_feat = x
for i in range(nb_layers):
branch = i+1
x = conv_block(concat_feat, stage, branch, growth_rate, dropout_rate, weight_decay)
concat_feat = merge([concat_feat, x], mode='concat', concat_axis=concat_axis, name='concat_'+str(stage)+'_'+str(branch))
if grow_nb_filters:
nb_filter += growth_rate
return concat_feat, nb_filter
def double_conv_layer(x, size, dropout, batch_norm):
from keras.models import Model
from keras.layers import Input, merge, Convolution2D, MaxPooling2D, UpSampling2D
from keras.layers.normalization import BatchNormalization
from keras.layers.core import Dropout, Activation
conv = Convolution2D(size, 3, 3, border_mode='same')(x)
if batch_norm == True:
conv = BatchNormalization(mode=0, axis=1)(conv)
conv = Activation('relu')(conv)
conv = Convolution2D(size, 3, 3, border_mode='same')(conv)
if batch_norm == True:
conv = BatchNormalization(mode=0, axis=1)(conv)
conv = Activation('relu')(conv)
if dropout > 0:
conv = Dropout(dropout)(conv)
return conv
def dense_block(x, stage, nb_layers, nb_filter, growth_rate, dropout_rate=None, weight_decay=1e-4, grow_nb_filters=True):
''' Build a dense_block where the output of each conv_block is fed to subsequent ones
# Arguments
x: input tensor
stage: index for dense block
nb_layers: the number of layers of conv_block to append to the model.
nb_filter: number of filters
growth_rate: growth rate
dropout_rate: dropout rate
weight_decay: weight decay factor
grow_nb_filters: flag to decide to allow number of filters to grow
'''
eps = 1.1e-5
concat_feat = x
for i in range(nb_layers):
branch = i+1
x = conv_block(concat_feat, stage, branch, growth_rate, dropout_rate, weight_decay)
concat_feat = merge([concat_feat, x], mode='concat', concat_axis=concat_axis, name='concat_'+str(stage)+'_'+str(branch))
if grow_nb_filters:
nb_filter += growth_rate
return concat_feat, nb_filter
def fire_module(x, fire_id, squeeze=16, expand=64, dim_ordering='th'):
s_id = 'fire' + str(fire_id) + '/'
if dim_ordering is 'tf':
c_axis = 3
else:
c_axis = 1
x = Convolution2D(squeeze, 1, 1, border_mode='valid', name=s_id + sq1x1)(x)
x = Activation('relu', name=s_id + relu + sq1x1)(x)
left = Convolution2D(expand, 1, 1, border_mode='valid', name=s_id + exp1x1)(x)
left = Activation('relu', name=s_id + relu + exp1x1)(left)
right = Convolution2D(expand, 3, 3, border_mode='same', name=s_id + exp3x3)(x)
right = Activation('relu', name=s_id + relu + exp3x3)(right)
x = merge([left, right], mode='concat', concat_axis=c_axis, name=s_id + 'concat')
return x
# Original SqueezeNet from paper.
def res_block(input_tensor, nb_filters=16, block=0, subsample_factor=1):
subsample = (subsample_factor, subsample_factor, subsample_factor)
x = BatchNormalization(axis=4)(input_tensor)
x = Activation('relu')(x)
x = Convolution3D(nb_filters, 3, 3, 3, subsample=subsample, border_mode='same')(x)
x = BatchNormalization(axis=4)(x)
x = Activation('relu')(x)
x = Convolution3D(nb_filters, 3, 3, 3, subsample=(1, 1, 1), border_mode='same')(x)
if subsample_factor > 1:
shortcut = Convolution3D(nb_filters, 1, 1, 1, subsample=subsample, border_mode='same')(input_tensor)
else:
shortcut = input_tensor
x = merge([x, shortcut], mode='sum')
return x
def res_block(input_tensor, nb_filters=16, block=0, subsample_factor=1):
subsample = (subsample_factor, subsample_factor)
x = BatchNormalization(axis=3)(input_tensor)
x = Activation('relu')(x)
x = Convolution2D(nb_filters, 3, 3, subsample=subsample, border_mode='same')(x)
x = BatchNormalization(axis=3)(x)
x = Activation('relu')(x)
x = Convolution2D(nb_filters, 3, 3, subsample=(1, 1), border_mode='same')(x)
if subsample_factor > 1:
shortcut = Convolution2D(nb_filters, 1, 1, subsample=subsample, border_mode='same')(input_tensor)
else:
shortcut = input_tensor
x = merge([x, shortcut], mode='sum')
return x
def res_block(input_tensor, nb_filters=16, block=0, subsample_factor=1):
subsample = (subsample_factor, subsample_factor)
x = BatchNormalization(axis=3)(input_tensor)
x = Activation('relu')(x)
x = Convolution2D(nb_filters, 3, 3, subsample=subsample, border_mode='same')(x)
x = BatchNormalization(axis=3)(x)
x = Activation('relu')(x)
x = Convolution2D(nb_filters, 3, 3, subsample=(1, 1), border_mode='same')(x)
if subsample_factor > 1:
shortcut = Convolution2D(nb_filters, 1, 1, subsample=subsample, border_mode='same')(input_tensor)
else:
shortcut = input_tensor
x = merge([x, shortcut], mode='sum')
return x
def res_block(input_tensor, nb_filters=16, block=0, subsample_factor=1):
subsample = (subsample_factor, subsample_factor, subsample_factor)
x = BatchNormalization(axis=4)(input_tensor)
x = Activation('relu')(x)
x = Convolution3D(nb_filters, 3, 3, 3, subsample=subsample, border_mode='same')(x)
x = BatchNormalization(axis=4)(x)
x = Activation('relu')(x)
x = Convolution3D(nb_filters, 3, 3, 3, subsample=(1, 1, 1), border_mode='same')(x)
if subsample_factor > 1:
shortcut = Convolution3D(nb_filters, 1, 1, 1, subsample=subsample, border_mode='same')(input_tensor)
else:
shortcut = input_tensor
x = merge([x, shortcut], mode='sum')
return x
def res_block(input_tensor, nb_filters=16, block=0, subsample_factor=1):
subsample = (subsample_factor, subsample_factor)
x = BatchNormalization(axis=3)(input_tensor)
x = Activation('relu')(x)
x = Convolution2D(nb_filters, 3, 3, subsample=subsample, border_mode='same')(x)
x = BatchNormalization(axis=3)(x)
x = Activation('relu')(x)
x = Convolution2D(nb_filters, 3, 3, subsample=(1, 1), border_mode='same')(x)
if subsample_factor > 1:
shortcut = Convolution2D(nb_filters, 1, 1, subsample=subsample, border_mode='same')(input_tensor)
else:
shortcut = input_tensor
x = merge([x, shortcut], mode='sum')
return x
