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
python类Activation()的实例源码
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
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)
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)
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)
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
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 prep_model(inputs, N, s0pad, s1pad, c, granlevels=1):
# LSTM
lstm = LSTM(N, return_sequences=True, implementation=2,
kernel_regularizer=l2(c['l2reg']), recurrent_regularizer=l2(c['l2reg']),
bias_regularizer=l2(c['l2reg']))
x1 = inputs[0]
x2 = inputs[1]
h1 = lstm(x1)
h2 = lstm(x2)
W_x = Dense(N, kernel_initializer='glorot_uniform', use_bias=True,
kernel_regularizer=l2(c['l2reg']))
W_h = Dense(N, kernel_initializer='orthogonal', use_bias=True,
kernel_regularizer=l2(c['l2reg']))
sigmoid = Activation('sigmoid')
a1 = multiply([x1, sigmoid( add([W_x(x1), W_h(h1)]) )])
a2 = multiply([x2, sigmoid( add([W_x(x2), W_h(h2)]) )])
# Averaging
avg = Lambda(function=lambda x: K.mean(x, axis=1),
output_shape=lambda shape: (shape[0], ) + shape[2:])
gran1 = avg(a1)
gran2 = avg(a2)
return [gran1, gran2], N
def prep_model(inputs, N, s0pad, s1pad, c, granlevels=1):
# LSTM
lstm = LSTM(N, return_sequences=True, implementation=2,
kernel_regularizer=l2(c['l2reg']), recurrent_regularizer=l2(c['l2reg']),
bias_regularizer=l2(c['l2reg']))
x1 = inputs[0]
x2 = inputs[1]
h1 = lstm(x1)
h2 = lstm(x2)
W_x = Dense(N, kernel_initializer='glorot_uniform', use_bias=True,
kernel_regularizer=l2(c['l2reg']))
W_h = Dense(N, kernel_initializer='orthogonal', use_bias=True,
kernel_regularizer=l2(c['l2reg']))
sigmoid = Activation('sigmoid')
a1 = multiply([x1, sigmoid( add([W_x(x1), W_h(h1)]) )])
a2 = multiply([x2, sigmoid( add([W_x(x2), W_h(h2)]) )])
# Averaging
avg = Lambda(function=lambda x: K.mean(x, axis=1),
output_shape=lambda shape: (shape[0], ) + shape[2:])
gran1 = avg(a1)
gran2 = avg(a2)
return [gran1, gran2], N
def model_cnn(net_layers, input_shape):
inp = Input(shape=input_shape)
model = inp
for cl in net_layers['conv_layers']:
model = Conv2D(filters=cl[0], kernel_size=cl[1], activation='relu')(model)
if cl[4]:
model = MaxPooling2D()(model)
if cl[2]:
model = BatchNormalization()(model)
if cl[3]:
model = Dropout(0.2)(model)
model = Flatten()(model)
for dl in net_layers['dense_layers']:
model = Dense(dl[0])(model)
model = Activation('relu')(model)
if dl[1]:
model = BatchNormalization()(model)
if dl[2]:
model = Dropout(0.2)(model)
model = Dense(1)(model)
model = Activation('sigmoid')(model)
model = Model(inp, model)
return model
# %%
# LSTM architecture
# conv_layers -> [(filters, kernel_size, BatchNormaliztion, Dropout, MaxPooling)]
# dense_layers -> [(num_neurons, BatchNormaliztion, Dropout)]
