我们从Python开源项目中，提取了以下10个代码示例，用于说明如何使用keras.layers.Deconvolution2D()。
def decode(y, relu_max): assert len(y._keras_shape) == 2 latent_dim = y._keras_shape[-1] x = Reshape((1, 1, latent_dim))(y) # 1, 1, latent_dim if relu_max: x = Activation(utils.scale_up(relu_max))(x) # not good? x = BN(mode=2, axis=3)(x) batch_size = tf.shape(x)[0] x = Deconv2D(40, 7, 7, output_shape=[batch_size, 7, 7, 40], activation='relu', border_mode='same', subsample=(7,7))(x) x = BN(mode=2, axis=3)(x) # 7, 7, 40 x = Deconv2D(20, 3, 3, output_shape=[batch_size, 14, 14, 20], activation='relu', border_mode='same', subsample=(2,2))(x) x = BN(mode=2, axis=3)(x) # 14, 14, 20 x = Deconv2D(1, 3, 3, output_shape=[batch_size, 28, 28, 1], activation='sigmoid', border_mode='same', subsample=(2,2))(x) # 28, 28, 1 return x
def model_EES(input_col, input_row): _input = Input(shape=(input_col, input_row, 1), name='input') EES = Conv2D(nb_filter=8, nb_row=3, nb_col=3, init='he_normal', activation='relu', border_mode='same', bias=True)(_input) EES = Deconvolution2D(nb_filter=16, nb_row=14, nb_col=14, output_shape=(None, input_col * 2, input_row * 2, 16), subsample=(2, 2), border_mode='same', init='glorot_uniform', activation='relu')(EES) out = Conv2D(nb_filter=1, nb_row=5, nb_col=5, init='glorot_uniform', activation='relu', border_mode='same')(EES) model = Model(input=_input, output=out) # sgd = SGD(lr=0.0001, decay=0.005, momentum=0.9, nesterov=True) Adam = adam(lr=0.001) model.compile(optimizer=Adam, loss='mean_squared_error', metrics=['mean_squared_error']) return model
def decode(y, relu_max): print 'decoder input shape:', y._keras_shape assert len(y._keras_shape) == 2 if relu_max: x = GaussianNoise(0.2)(y) # x = Activation(utils.relu_n(1))(x) else: x = y x = Reshape((1, 1, LATENT_DIM))(x) # 1, 1, LATENT_DIM if relu_max: print 'in decode: relu_max:', relu_max x = Activation(utils.scale_up(relu_max))(x) # x = BN(mode=2, axis=3)(x) # this bn seems not good? NOT VERIFIED # why use 512 instead of 256 here? batch_size = keras.backend.shape(x)[0] x = Deconv2D(512, 4, 4, output_shape=[batch_size, 4, 4, 512], activation='relu', border_mode='same', subsample=(4,4))(x) x = BN(mode=2, axis=3)(x) # 4, 4, 512 x = Deconv2D(256, 5, 5, output_shape=[batch_size, 8, 8, 256], activation='relu', border_mode='same', subsample=(2,2))(x) x = BN(mode=2, axis=3)(x) # 8, 8, 256 x = Deconv2D(128, 5, 5, output_shape=(batch_size, 16, 16, 128), activation='relu', border_mode='same', subsample=(2,2))(x) x = BN(mode=2, axis=3)(x) # 16, 16, 256 x = Deconv2D(64, 5, 5, output_shape=(batch_size, 32, 32, 64), activation='relu', border_mode='same', subsample=(2,2))(x) x = BN(mode=2, axis=3)(x) # 32, 32, 64 x = Deconv2D(3, 5, 5, output_shape=(batch_size, 32, 32, 3), activation='linear', border_mode='same', subsample=(1,1))(x) # 32, 32, 3 x = BN(mode=2, axis=3)(x) return x
def deep_decoder1(input_shape): encoded = Input(shape=input_shape) print 'decoder input shape:', encoded._keras_shape batch_size = tf.shape(encoded)[0] x = BatchNormalization(mode=2, axis=3)(encoded) h, w, _ = encoded._keras_shape[1:] x = Deconv2D(32, 1, 1, output_shape=[batch_size, h, w, 32], activation='relu', border_mode='same')(x) x = BatchNormalization(mode=2, axis=3)(x) x = Deconv2D(32, 3, 3, output_shape=[batch_size, h, w, 32], activation='relu', border_mode='same')(x) x = BatchNormalization(mode=2, axis=3)(x) h *= 2; w *= 2 x = Deconv2D(64, 3, 3, output_shape=(batch_size, h, w, 64), activation='relu', border_mode='same', subsample=(2, 2))(x) x = BatchNormalization(mode=2, axis=3)(x) x = Deconv2D(64, 3, 3, output_shape=(batch_size, h, w, 64), activation='relu', border_mode='same', subsample=(1, 1))(x) x = BatchNormalization(mode=2, axis=3)(x) h *= 2; w *= 2 x = Deconv2D(32, 3, 3, output_shape=(batch_size, h, w, 32), activation='relu', border_mode='same', subsample=(2, 2))(x) x = BatchNormalization(mode=2, axis=3)(x) x = Deconv2D(32, 3, 3, output_shape=(batch_size, h, w, 32), activation='relu', border_mode='same', subsample=(1, 1))(x) x = BatchNormalization(mode=2, axis=3)(x) x = Deconv2D(3, 3, 3, output_shape=(batch_size, 32, 32, 3), activation='linear', border_mode='same', subsample=(1, 1))(x) x = BatchNormalization(mode=2, axis=3)(x) decoded = x return Model(encoded, decoded)
def deep_decoder2(input_shape): encoded = Input(shape=input_shape) print 'encoded shape:', encoded.get_shape().as_list() x = encoded # x = BatchNormalization(mode=2, axis=3)(encoded) # batch_size, h, w, _ = tf.shape(x) batch_size = tf.shape(x)[0] # dim: (1, 1, 512) x = Deconv2D(512, 4, 4, output_shape=[batch_size, 4, 4, 512], activation='relu', border_mode='same', subsample=(4, 4))(encoded) x = BatchNormalization(mode=2, axis=3)(x) # (4, 4, 512) x = Deconv2D(256, 5, 5, output_shape=[batch_size, 8, 8, 256], activation='relu', border_mode='same', subsample=(2, 2))(x) x = BatchNormalization(mode=2, axis=3)(x) # dim: (8, 8, 236) # h *= 2; w *= 2 x = Deconv2D(128, 5, 5, output_shape=(batch_size, 16, 16, 128), activation='relu', border_mode='same', subsample=(2, 2))(x) x = BatchNormalization(mode=2, axis=3)(x) # dim: (16, 16, 256) x = Deconv2D(64, 5, 5, output_shape=(batch_size, 32, 32, 64), activation='relu', border_mode='same', subsample=(2, 2))(x) x = BatchNormalization(mode=2, axis=3)(x) # dim: (32, 32, 64) x = Deconv2D(3, 5, 5, output_shape=(batch_size, 32, 32, 3), activation='linear', border_mode='same', subsample=(1, 1))(x) decoded = BatchNormalization(mode=2, axis=3)(x) return Model(encoded, decoded)
def model_EED(input_col, input_row): _input = Input(shape=(input_col, input_row, 1), name='input') Feature = Conv2D(nb_filter=64, nb_row=3, nb_col=3, init='glorot_uniform', activation='relu', border_mode='same', bias=True)(_input) Feature = Conv2D(nb_filter=64, nb_row=3, nb_col=3, init='glorot_uniform', activation='relu', border_mode='same', bias=True)(Feature) Feature3 = Conv2D(nb_filter=64, nb_row=3, nb_col=3, init='glorot_uniform', activation='relu', border_mode='same', bias=True)(Feature) Feature_out = merge(inputs=[Feature, Feature3], mode='sum') # Upsampling Upsampling1 = Conv2D(nb_filter=8, nb_row=1, nb_col=1, init='glorot_uniform', activation='relu', border_mode='same', bias=True)(Feature_out) Upsampling2 = Deconvolution2D(nb_filter=8, nb_row=14, nb_col=14, output_shape=(None, input_col * 2, input_row * 2, 8), subsample=(2, 2), border_mode='same', init='glorot_uniform', activation='relu')(Upsampling1) Upsampling3 = Conv2D(nb_filter=64, nb_row=1, nb_col=1, init='glorot_uniform', activation='relu', border_mode='same', bias=True)(Upsampling2) # Mulyi-scale Reconstruction Reslayer1 = Conv2D(nb_filter=64, nb_row=3, nb_col=3, init='glorot_uniform', activation='relu', border_mode='same', bias=True)(Upsampling3) Reslayer2 = Conv2D(nb_filter=64, nb_row=3, nb_col=3, init='glorot_uniform', activation='relu', border_mode='same', bias=True)(Reslayer1) Block1 = merge(inputs=[Reslayer1, Reslayer2], mode='sum') Reslayer3 = Conv2D(nb_filter=64, nb_row=3, nb_col=3, init='glorot_uniform', activation='relu', border_mode='same', bias=True)(Block1) Reslayer4 = Conv2D(nb_filter=64, nb_row=3, nb_col=3, init='glorot_uniform', activation='relu', border_mode='same', bias=True)(Reslayer3) Block2 = merge(inputs=[Reslayer3, Reslayer4], mode='sum') # ***************// Multi_scale1 = Conv2D(nb_filter=16, nb_row=1, nb_col=1, init='glorot_uniform', activation='relu', border_mode='same', bias=True)(Block2) Multi_scale2a = Conv2D(nb_filter=16, nb_row=1, nb_col=1, init='glorot_uniform', activation='relu', border_mode='same', bias=True)(Multi_scale1) Multi_scale2b = Conv2D(nb_filter=16, nb_row=3, nb_col=3, init='glorot_uniform', activation='relu', border_mode='same', bias=True)(Multi_scale1) Multi_scale2c = Conv2D(nb_filter=16, nb_row=5, nb_col=5, init='glorot_uniform', activation='relu', border_mode='same', bias=True)(Multi_scale1) Multi_scale2d = Conv2D(nb_filter=16, nb_row=7, nb_col=7, init='glorot_uniform', activation='relu', border_mode='same', bias=True)(Multi_scale1) Multi_scale2 = merge(inputs=[Multi_scale2a, Multi_scale2b, Multi_scale2c, Multi_scale2d], mode='concat') out = Conv2D(nb_filter=1, nb_row=1, nb_col=1, init='glorot_uniform', activation='relu', border_mode='same', bias=True)(Multi_scale2) model = Model(input=_input, output=out) Adam = adam(lr=0.001) model.compile(optimizer=Adam, loss='mean_squared_error', metrics=['mean_squared_error']) return model
def deep_model2(input_shape): input_img = Input(shape=input_shape) print 'input shape:', input_img._keras_shape # 32, 32 x = Conv2D(32, 3, 3, activation='relu', border_mode='same', subsample=(2, 2))(input_img) x = BatchNormalization(mode=2, axis=3)(x) # 16, 16 x = Conv2D(64, 3, 3, activation='relu', border_mode='same', subsample=(2, 2))(x) x = BatchNormalization(mode=2, axis=3)(x) # 8, 8 x = Conv2D(128, 3, 3, activation='relu', border_mode='same', subsample=(2, 2))(x) x = BatchNormalization(mode=2, axis=3)(x) # 4, 4 # latent_dim = (1, 1, 1024) x = Conv2D(1024, 4, 4, activation='linear', border_mode='same', subsample=(4, 4))(x) x = GaussianNoise(0.1)(x) encoded = Activation('sigmoid')(x) print 'encoded shape:', encoded.get_shape().as_list() # x = BatchNormalization(mode=2, axis=3)(encoded) # batch_size, h, w, _ = tf.shape(x) batch_size = tf.shape(encoded)[0] # dim: (1, 1, 512) x = Deconv2D(512, 4, 4, output_shape=[batch_size, 4, 4, 512], activation='relu', border_mode='same', subsample=(4, 4))(encoded) x = BatchNormalization(mode=2, axis=3)(x) # (4, 4, 512) x = Deconv2D(256, 5, 5, output_shape=[batch_size, 8, 8, 256], activation='relu', border_mode='same', subsample=(2, 2))(x) x = BatchNormalization(mode=2, axis=3)(x) # dim: (8, 8, 256) x = Deconv2D(128, 5, 5, output_shape=(batch_size, 16, 16, 128), activation='relu', border_mode='same', subsample=(2, 2))(x) x = BatchNormalization(mode=2, axis=3)(x) # dim: (16, 16, 256) x = Deconv2D(64, 5, 5, output_shape=(batch_size, 32, 32, 64), activation='relu', border_mode='same', subsample=(2, 2))(x) x = BatchNormalization(mode=2, axis=3)(x) # dim: (32, 32, 64) x = Deconv2D(3, 5, 5, output_shape=(batch_size, 32, 32, 3), activation='linear', border_mode='same', subsample=(1, 1))(x) decoded = BatchNormalization(mode=2, axis=3)(x) print 'decoded shape:', decoded.get_shape().as_list() autoencoder = Model(input_img, decoded) return autoencoder
def relu_deep_model1(input_shape, relu_max): input_img = Input(shape=input_shape) print 'input shape:', input_img._keras_shape # 32, 32 x = Conv2D(64, 3, 3, activation='relu', border_mode='same', subsample=(2, 2))(input_img) x = BatchNormalization(mode=2, axis=3)(x) # 16, 16 x = Conv2D(128, 3, 3, activation='relu', border_mode='same', subsample=(2, 2))(x) x = BatchNormalization(mode=2, axis=3)(x) # 8, 8 x = Conv2D(256, 3, 3, activation='relu', border_mode='same', subsample=(2, 2))(x) x = BatchNormalization(mode=2, axis=3)(x) # 4, 4 # latent_dim = (1, 1, 1024) x = Conv2D(1024, 4, 4, activation='linear', border_mode='same', subsample=(4, 4))(x) x = GaussianNoise(0.2)(x) # encoded = Activation('relu')(x) encoded = Activation(relu_n(relu_max))(x) print 'encoded shape:', encoded.get_shape().as_list() # in the origianl design, no BN as the first layer of decoder because of bug x = encoded # x = BatchNormalization(mode=2, axis=3)(encoded) # batch_size, h, w, _ = tf.shape(x) batch_size = tf.shape(x)[0] # dim: (1, 1, 512) x = Deconv2D(512, 4, 4, output_shape=[batch_size, 4, 4, 512], activation='relu', border_mode='same', subsample=(4, 4))(x) x = BatchNormalization(mode=2, axis=3)(x) # (4, 4, 512) x = Deconv2D(256, 5, 5, output_shape=[batch_size, 8, 8, 256], activation='relu', border_mode='same', subsample=(2, 2))(x) x = BatchNormalization(mode=2, axis=3)(x) # dim: (8, 8, 256) x = Deconv2D(128, 5, 5, output_shape=(batch_size, 16, 16, 128), activation='relu', border_mode='same', subsample=(2, 2))(x) x = BatchNormalization(mode=2, axis=3)(x) # dim: (16, 16, 256) x = Deconv2D(64, 5, 5, output_shape=(batch_size, 32, 32, 64), activation='relu', border_mode='same', subsample=(2, 2))(x) x = BatchNormalization(mode=2, axis=3)(x) # dim: (32, 32, 64) x = Deconv2D(3, 5, 5, output_shape=(batch_size, 32, 32, 3), activation='linear', border_mode='same', subsample=(1, 1))(x) decoded = BatchNormalization(mode=2, axis=3)(x) print 'decoded shape:', decoded.get_shape().as_list() autoencoder = Model(input_img, decoded) return autoencoder
def deep_model1(input_shape): input_img = Input(shape=input_shape) print 'input shape:', input_img._keras_shape # 32, 32 x = Conv2D(32, 3, 3, activation='relu', border_mode='same', subsample=(2, 2))(input_img) x = BatchNormalization(mode=2, axis=3)(x) # 16, 16 x = Conv2D(64, 3, 3, activation='relu', border_mode='same', subsample=(2, 2))(x) x = BatchNormalization(mode=2, axis=3)(x) # 8, 8 x = Conv2D(128, 3, 3, activation='relu', border_mode='same', subsample=(2, 2))(x) x = BatchNormalization(mode=2, axis=3)(x) # 4, 4 latent_dim = (1, 1, 1024) z_mean = Conv2D(1024, 4, 4, activation='linear', border_mode='same', subsample=(4, 4))(x) # z_mean = GaussianNoise(0.1)(z_mean) # TODO: the next layer use 16K parameters, will it be a problem? z_log_var = Conv2D(1024, 4, 4, activation='linear', border_mode='same', subsample=(4, 4))(x) z = Lambda(sampling_gaussian, output_shape=latent_dim)([z_mean, z_log_var]) print 'encoded shape:', z._keras_shape # x = BatchNormalization(mode=2, axis=3)(z) batch_size = tf.shape(z)[0] h, w, _ = z._keras_shape[1:] # dim: (1, 1, 512) x = Deconv2D(512, 4, 4, output_shape=[batch_size, 4, 4, 512], activation='relu', border_mode='same', subsample=(4, 4))(z) x = BatchNormalization(mode=2, axis=3)(x) # (4, 4, 512) x = Deconv2D(256, 5, 5, output_shape=[batch_size, 8, 8, 256], activation='relu', border_mode='same', subsample=(2, 2))(x) x = BatchNormalization(mode=2, axis=3)(x) # dim: (8, 8, 256) x = Deconv2D(128, 5, 5, output_shape=(batch_size, 16, 16, 128), activation='relu', border_mode='same', subsample=(2, 2))(x) x = BatchNormalization(mode=2, axis=3)(x) # dim: (16, 16, 256) x = Deconv2D(64, 5, 5, output_shape=(batch_size, 32, 32, 64), activation='relu', border_mode='same', subsample=(2, 2))(x) x = BatchNormalization(mode=2, axis=3)(x) # dim: (32, 32, 64) x = Deconv2D(3, 5, 5, output_shape=(batch_size, 32, 32, 3), activation='linear', border_mode='same', subsample=(1, 1))(x) decoded = BatchNormalization(mode=2, axis=3)(x) print 'decoded shape:', decoded._keras_shape autoencoder = Model(input_img, decoded) # define vae loss def vae_loss(y, y_pred): # TODO: generalize this function recon_loss = K.sum(K.square(y_pred - y), axis=[1, 2, 3]) kl_loss = -0.5 * K.sum(1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis=[1, 2, 3]) print ('pre average loss shape:', recon_loss.get_shape().as_list(), kl_loss.get_shape().as_list()) return K.mean(recon_loss + kl_loss) return autoencoder, vae_loss
def decode(y, relu_max): print 'decoder input shape:', y._keras_shape assert len(y._keras_shape) == 2 if relu_max: x = GaussianNoise(0.2)(y) x = Activation(utils.relu_n(1))(x) else: x = y x = Reshape((1, 1, LATENT_DIM))(x) # 1, 1, LATENT_DIM if relu_max: print 'in decode: relu_max:', relu_max x = Activation(utils.scale_up(relu_max))(x) # x = BN(mode=2, axis=3)(x) # this bn seems not good? NOT VERIFIED # why use 512 instead of 256 here? batch_size = keras.backend.shape(x)[0] x = Deconv2D(512, 6, 6, output_shape=[batch_size, 6, 6, 512], activation='relu', border_mode='same', subsample=(6,6))(x) x = BN(mode=2, axis=3)(x) # 6, 6, 512 x = Deconv2D(256, 5, 5, output_shape=[batch_size, 12, 12, 256], activation='relu', border_mode='same', subsample=(2,2))(x) x = BN(mode=2, axis=3)(x) # 12, 12, 256 x = Deconv2D(128, 5, 5, output_shape=(batch_size, 24, 24, 128), activation='relu', border_mode='same', subsample=(2,2))(x) x = BN(mode=2, axis=3)(x) # 24, 24, 128 x = Deconv2D(64, 5, 5, output_shape=(batch_size, 48, 48, 64), activation='relu', border_mode='same', subsample=(2,2))(x) x = BN(mode=2, axis=3)(x) # 48, 48, 64 x = Deconv2D(32, 5, 5, output_shape=(batch_size, 96, 96, 32), activation='relu', border_mode='same', subsample=(2,2))(x) x = BN(mode=2, axis=3)(x) # 96, 96, 32 x = Deconv2D(3, 5, 5, output_shape=(batch_size, 96, 96, 3), activation='linear', border_mode='same', subsample=(1,1))(x) # 32, 32, 3 x = BN(mode=2, axis=3)(x) return x
