我们从Python开源项目中，提取了以下12个代码示例，用于说明如何使用keras.layers.Add()。
def _shortcut(inputs, x): # shortcut path _, inputs_w, inputs_h, inputs_ch = K.int_shape(inputs) _, x_w, x_h, x_ch = K.int_shape(x) stride_w = int(round(inputs_w / x_w)) stride_h = int(round(inputs_h / x_h)) equal_ch = inputs_ch == x_ch if stride_w>1 or stride_h>1 or not equal_ch: shortcut = Conv2D(x_ch, (1, 1), strides = (stride_w, stride_h), kernel_initializer = init, padding = 'valid')(inputs) else: shortcut = inputs merged = Add()([shortcut, x]) return merged
def ResidualBlock1D_helper(layers, kernel_size, filters, final_stride=1): def f(_input): basic = _input for ln in range(layers): #basic = BatchNormalization()( basic ) # triggers known keras bug w/ TimeDistributed: https://github.com/fchollet/keras/issues/5221 basic = ELU()(basic) basic = Conv1D(filters, kernel_size, kernel_initializer='he_normal', kernel_regularizer=l2(1.e-4), padding='same')(basic) # note that this strides without averaging return AveragePooling1D(pool_size=1, strides=final_stride)(Add()([_input, basic])) return f
def deflating_convolution(inputs, n_deflation_layers, n_filters_init=32, noise=None, name_prefix=None): def add_linear_noise(x, eps, ind): flattened_deflated = Reshape((-1,), name=name_prefix + '_conv_flatten_{}'.format(ind))(x) deflated_shape = ker.int_shape(x) deflated_size = deflated_shape[1] * deflated_shape[2] * deflated_shape[3] noise_transformed = Dense(deflated_size, activation=None, name=name_prefix + '_conv_noise_dense_{}'.format(ind))(eps) added_noise = Add(name=name_prefix + '_conv_add_noise_{}'.format(ind))([noise_transformed, flattened_deflated]) x = Reshape((deflated_shape[1], deflated_shape[2], deflated_shape[3]), name=name_prefix + '_conv_backreshape_{}'.format(ind))(added_noise) return x deflated = Conv2D(filters=n_filters_init, kernel_size=(5, 5), strides=(2, 2), padding='same', activation='relu', name=name_prefix + '_conv_0')(inputs) if noise is not None: deflated = add_linear_noise(deflated, noise, 0) for i in range(1, n_deflation_layers): deflated = Conv2D(filters=n_filters_init * (2**i), kernel_size=(5, 5), strides=(2, 2), padding='same', activation='relu', name=name_prefix + '_conv_{}'.format(i))(deflated) # if noise is not None: # deflated = add_linear_noise(deflated, noise, i) return deflated
def synthetic_adaptive_prior_discriminator(data_dim, latent_dim): data_input = Input(shape=(data_dim,), name='disc_internal_data_input') # center the data around 0 in [-1, 1] as it is in [0, 1]. centered_data = Lambda(lambda x: 2 * x - 1, name='disc_centering_data_input')(data_input) discriminator_body_data = repeat_dense(centered_data, n_layers=2, n_units=256, name_prefix='disc_body_data') theta = Dense(4*256, activation='relu', name='disc_theta')(discriminator_body_data) discriminator_body_data_t = repeat_dense(centered_data, n_layers=2, n_units=256, name_prefix='disc_body_data_t') discriminator_body_data_t = Dense(1, activation=None, name='disc_data_squash')(discriminator_body_data_t) latent_input = Input(shape=(latent_dim,), name='disc_internal_latent_input') discriminator_body_latent = repeat_dense(latent_input, n_layers=2, n_units=256, name_prefix='disc_body_latent') sigma = Dense(4*256, activation='relu', name='disc_sigma')(discriminator_body_latent) discriminator_body_latent_t = repeat_dense(latent_input, n_layers=2, n_units=256, name_prefix='disc_body_latent_t') discriminator_body_latent_t = Dense(1, activation=None, name='disc_latent_squash')(discriminator_body_latent_t) merged_data_latent = Multiply(name='disc_mul_sigma_theta')([theta, sigma]) merged_data_latent = Lambda(lambda x: ker.sum(x, axis=-1), name='disc_add_activ_sig_the')(merged_data_latent) discriminator_output = Add(name='disc_add_data_latent_t')([discriminator_body_data_t, discriminator_body_latent_t, merged_data_latent]) collapsed_noise = Lambda(lambda x: 0.5 * ker.sum(x ** 2, axis=-1), name='disc_noise_addition')(latent_input) discriminator_output = Add(name='disc_add_all_toghether')([discriminator_output, collapsed_noise]) discriminator_model = Model(inputs=[data_input, latent_input], outputs=discriminator_output, name='disc_internal_model') return discriminator_model
def mnist_adaptive_prior_discriminator(data_dim, latent_dim): data_input = Input(shape=(data_dim,), name='disc_internal_data_input') # center the data around 0 in [-1, 1] as it is in [0, 1]. centered_data = Lambda(lambda x: 2 * x - 1, name='disc_centering_data_input')(data_input) discriminator_body_data = repeat_dense(centered_data, n_layers=3, n_units=512, name_prefix='disc_body_data') theta = Dense(4*512, activation='relu', name='disc_theta')(discriminator_body_data) discriminator_body_data_t = repeat_dense(centered_data, n_layers=3, n_units=512, name_prefix='disc_body_data_t') discriminator_body_data_t = Dense(1, activation=None, name='disc_data_squash')(discriminator_body_data_t) latent_input = Input(shape=(latent_dim,), name='disc_internal_latent_input') discriminator_body_latent = repeat_dense(latent_input, n_layers=3, n_units=512, name_prefix='disc_body_latent') sigma = Dense(4*512, activation='relu', name='disc_sigma')(discriminator_body_latent) discriminator_body_latent_t = repeat_dense(latent_input, n_layers=3, n_units=512, name_prefix='disc_body_latent_t') discriminator_body_latent_t = Dense(1, activation=None, name='disc_latent_squash')(discriminator_body_latent_t) merged_data_latent = Multiply(name='disc_mul_sigma_theta')([theta, sigma]) merged_data_latent = Lambda(lambda x: ker.sum(x, axis=-1), name='disc_add_activ_sig_the')(merged_data_latent) discriminator_output = Add(name='disc_add_data_latent_t')([discriminator_body_data_t, discriminator_body_latent_t, merged_data_latent]) collapsed_noise = Lambda(lambda x: 0.5 * ker.sum(x**2, axis=-1), name='disc_noise_addition')(latent_input) discriminator_output = Add(name='disc_add_all_toghether')([discriminator_output, collapsed_noise]) discriminator_model = Model(inputs=[data_input, latent_input], outputs=discriminator_output, name='disc_internal_model') return discriminator_model
def sample_standard_normal_noise(inputs, **kwargs): from keras.backend import shape, random_normal n_samples = kwargs.get('n_samples', shape(inputs)[0]) n_basis_noise_vectors = kwargs.get('n_basis', -1) data_dim = kwargs.get('data_dim', 1) noise_dim = kwargs.get('noise_dim', data_dim) seed = kwargs.get('seed', 7) if n_basis_noise_vectors > 0: samples_isotropic = random_normal(shape=(n_samples, n_basis_noise_vectors, noise_dim), mean=0, stddev=1, seed=seed) else: samples_isotropic = random_normal(shape=(n_samples, noise_dim), mean=0, stddev=1, seed=seed) op_mode = kwargs.get('mode', 'none') if op_mode == 'concatenate': concat = Concatenate(axis=1, name='enc_noise_concatenation')([inputs, samples_isotropic]) return concat elif op_mode == 'add': resized_noise = Dense(data_dim, activation=None, name='enc_resized_noise_sampler')(samples_isotropic) added_noise_data = Add(name='enc_adding_noise_data')([inputs, resized_noise]) return added_noise_data return samples_isotropic
def mnist_encoder_simple(data_dim, noise_dim, latent_dim=8): data_input = Input(shape=(data_dim,), name='enc_internal_data_input') noise_input = Input(shape=(noise_dim,), name='enc_internal_noise_input') # center the input around 0 # centered_data = Lambda(lambda x: 2 * x - 1, name='enc_centering_data_input')(data_input) # concat_input = Concatenate(axis=-1, name='enc_noise_data_concat')([centered_data, noise_input]) enc_body = repeat_dense(data_input, n_layers=2, n_units=256, activation='relu', name_prefix='enc_body') enc_output = Dense(100, activation='relu', name='enc_dense_before_latent')(enc_body) enc_output = Dense(latent_dim, name='enc_latent_features')(enc_output) noise_resized = Dense(latent_dim, activation=None, name='enc_noise_resizing')(noise_input) enc_output = Add(name='enc_add_noise_data')([enc_output, noise_resized]) latent_factors = Model(inputs=[data_input, noise_input], outputs=enc_output, name='enc_internal_model') return latent_factors
def test_delete_channels_merge_others(channel_index, data_format): layer_test_helper_merge_2d(Add(), channel_index, data_format) layer_test_helper_merge_2d(Multiply(), channel_index, data_format) layer_test_helper_merge_2d(Average(), channel_index, data_format) layer_test_helper_merge_2d(Maximum(), channel_index, data_format)
def identity_block(input_tensor, kernel_size, filters, stage, block, trainable=True): nb_filter1, nb_filter2, nb_filter3 = filters if K.image_dim_ordering() == 'tf': bn_axis = 3 else: bn_axis = 1 conv_name_base = 'res' + str(stage) + block + '_branch' bn_name_base = 'bn' + str(stage) + block + '_branch' x = Convolution2D(nb_filter1, (1, 1), name=conv_name_base + '2a', trainable=trainable)(input_tensor) x = FixedBatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x) x = Activation('relu')(x) x = Convolution2D(nb_filter2, (1, kernel_size), padding='same', name=conv_name_base + '2b', trainable=trainable)(x) x = FixedBatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x) x = Activation('relu')(x) x = Convolution2D(nb_filter3, (1, 1), name=conv_name_base + '2c', trainable=trainable)(x) x = FixedBatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x) x = Add()([x, input_tensor]) x = Activation('relu')(x) return x
def identity_block_td(input_tensor, kernel_size, filters, stage, block, trainable=True): # identity block time distributed nb_filter1, nb_filter2, nb_filter3 = filters if K.image_dim_ordering() == 'tf': bn_axis = 3 else: bn_axis = 1 conv_name_base = 'res' + str(stage) + block + '_branch' bn_name_base = 'bn' + str(stage) + block + '_branch' x = TimeDistributed(Convolution2D(nb_filter1, (1, 1), trainable=trainable, kernel_initializer='normal'), name=conv_name_base + '2a')(input_tensor) x = TimeDistributed(FixedBatchNormalization(axis=bn_axis), name=bn_name_base + '2a')(x) x = Activation('relu')(x) x = TimeDistributed(Convolution2D(nb_filter2, (1, kernel_size), trainable=trainable, kernel_initializer='normal',padding='same'), name=conv_name_base + '2b')(x) x = TimeDistributed(FixedBatchNormalization(axis=bn_axis), name=bn_name_base + '2b')(x) x = Activation('relu')(x) x = TimeDistributed(Convolution2D(nb_filter3, (1, 1), trainable=trainable, kernel_initializer='normal'), name=conv_name_base + '2c')(x) x = TimeDistributed(FixedBatchNormalization(axis=bn_axis), name=bn_name_base + '2c')(x) x = Add()([x, input_tensor]) x = Activation('relu')(x) return x
def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(1, 2), trainable=True): nb_filter1, nb_filter2, nb_filter3 = filters if K.image_dim_ordering() == 'tf': bn_axis = 3 else: bn_axis = 1 conv_name_base = 'res' + str(stage) + block + '_branch' bn_name_base = 'bn' + str(stage) + block + '_branch' x = Convolution2D(nb_filter1, (1, 1), strides=strides, name=conv_name_base + '2a', trainable=trainable)(input_tensor) x = FixedBatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x) x = Activation('relu')(x) x = Convolution2D(nb_filter2, (1, kernel_size), padding='same', name=conv_name_base + '2b', trainable=trainable)(x) x = FixedBatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x) x = Activation('relu')(x) x = Convolution2D(nb_filter3, (1, 1), name=conv_name_base + '2c', trainable=trainable)(x) x = FixedBatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x) shortcut = Convolution2D(nb_filter3, (1, 1), strides=strides, name=conv_name_base + '1', trainable=trainable)(input_tensor) shortcut = FixedBatchNormalization(axis=bn_axis, name=bn_name_base + '1')(shortcut) x = Add()([x, shortcut]) x = Activation('relu')(x) return x
def conv_block_td(input_tensor, kernel_size, filters, stage, block, input_shape, strides=(2, 2), trainable=True): # conv block time distributed nb_filter1, nb_filter2, nb_filter3 = filters if K.image_dim_ordering() == 'tf': bn_axis = 3 else: bn_axis = 1 conv_name_base = 'res' + str(stage) + block + '_branch' bn_name_base = 'bn' + str(stage) + block + '_branch' x = TimeDistributed(Convolution2D(nb_filter1, (1, 1), strides=strides, trainable=trainable, kernel_initializer='normal'), input_shape=input_shape, name=conv_name_base + '2a')(input_tensor) x = TimeDistributed(FixedBatchNormalization(axis=bn_axis), name=bn_name_base + '2a')(x) x = Activation('relu')(x) x = TimeDistributed(Convolution2D(nb_filter2, (1, kernel_size), padding='same', trainable=trainable, kernel_initializer='normal'), name=conv_name_base + '2b')(x) x = TimeDistributed(FixedBatchNormalization(axis=bn_axis), name=bn_name_base + '2b')(x) x = Activation('relu')(x) x = TimeDistributed(Convolution2D(nb_filter3, (1, 1), kernel_initializer='normal'), name=conv_name_base + '2c', trainable=trainable)(x) x = TimeDistributed(FixedBatchNormalization(axis=bn_axis), name=bn_name_base + '2c')(x) shortcut = TimeDistributed(Convolution2D(nb_filter3, (1, 1), strides=strides, trainable=trainable, kernel_initializer='normal'), name=conv_name_base + '1')(input_tensor) shortcut = TimeDistributed(FixedBatchNormalization(axis=bn_axis), name=bn_name_base + '1')(shortcut) x = Add()([x, shortcut]) x = Activation('relu')(x) return x
