Keras使⽤Lambda层详解
我就废话不多说了，⼤家还是直接看代码吧！
from tensorflow.dels import Sequential, Model
from tensorflow.python.keras.layers import Dense, Flatten, Conv2D, MaxPool2D, Dropout, Conv2DTranspose, Lambda, Input, Reshape, Add, Multiply from tensorflow.python.keras.optimizers import Adam
def deconv(x):
height = x.get_shape()[1].value
width = x.get_shape()[2].value
new_height = height*2
new_width = width*2
x_resized = size_images(x, [new_height, new_width], tf.image.ResizeMethod.NEAREST_NEI
GHBOR)
return x_resized
def Generator(scope='generator'):
imgs_noise = Input(shape=inputs_shape)
x = Conv2D(filters=32, kernel_size=(9,9), strides=(1,1), padding='same', activation='relu')(imgs_noise)
x = Conv2D(filters=64, kernel_size=(3,3), strides=(2,2), padding='same', activation='relu')(x)
x = Conv2D(filters=128, kernel_size=(3,3), strides=(2,2), padding='same', activation='relu')(x)
x1 = Conv2D(filters=128, kernel_size=(3,3), strides=(1,1), padding='same', activation='relu')(x)
x1 = Conv2D(filters=128, kernel_size=(3,3), strides=(1,1), padding='same', activation='relu')(x1)
x2 = Add()([x1, x])
x3 = Conv2D(filters=128, kernel_size=(3,3), strides=(1,1), padding='same', activation='relu')(x2)
x3 = Conv2D(filters=128, kernel_size=(3,3), strides=(1,1), padding='same', activation='relu')(x3)
x4 = Add()([x3, x2])
x5 = Conv2D(filters=128, kernel_size=(3,3), strides=(1,1), padding='same', activation='relu')(x4)
x5 = Conv2D(filters=128, kernel_size=(3,3), strides=(1,1), padding='same', activation='relu')(x5)
x6 = Add()([x5, x4])
x = MaxPool2D(pool_size=(2,2))(x6)
x = Lambda(deconv)(x)
x = Conv2D(filters=64, kernel_size=(3, 3), strides=(1,1), padding='same',activation='relu')(x)
x = Lambda(deconv)(x)
x = Conv2D(filters=32, kernel_size=(3, 3), strides=(1,1), padding='same',activation='relu')(x)
x = Lambda(deconv)(x)
resizedx = Conv2D(filters=3, kernel_size=(3, 3), strides=(1, 1), padding='same',activation='tanh')(x)
x = Lambda(lambda x: x+1)(x)
y = Lambda(lambda x: x*127.5)(x)
model = Model(inputs=imgs_noise, outputs=y)
model.summary()
return model
my_generator = Generator()
my_generatorpile(loss='binary_crossentropy', optimizer=Adam(0.7, decay=1e-3), metrics=['accuracy'])
补充知识：含有Lambda⾃定义层keras模型，保存遇到的问题及解决⽅案
⼀，许多应⽤，keras含有的层已经不能满⾜要求，需要透过Lambda⾃定义层来实现⼀些layer，这个情况下，只能保存模型的权重，⽆法使⽤model.save来保存模型。保存时会报
TypeError: can't pickle _thread.RLock objects
⼆，解决⽅案，为了便于后续的部署，可以转成tensorflow的PB进⾏部署。
dels import load_model
import tensorflow as tf
import os, sys
from keras import backend as K
from tensorflow.python.framework import graph_util, graph_io
def h5_to_pb(h5_weight_path, output_dir, out_prefix="output_", log_tensorboard=True):
if not ists(output_dir):
os.mkdir(output_dir)
h5_model = build_model()
h5_model.load_weights(h5_weight_path)
out_nodes = []
for i in range(len(h5_model.outputs)):
out_nodes.append(out_prefix + str(i + 1))
tf.identity(h5_model.output[i], out_prefix + str(i + 1))
model_name = os.path.splitext(os.path.split(h5_weight_path)[-1])[0] + '.pb'
sess = K.get_session()
init_graph = aph.as_graph_def()
main_graph = vert_variables_to_constants(sess, init_graph, out_nodes)
graph_io.write_graph(main_graph, output_dir, name=model_name, as_text=False)
if log_tensorboard:
from ls import import_pb_to_tensorboard
import_pb_to_tensorboard.import_to_tensorboard(os.path.join(output_dir, model_name), output_dir)
def build_model():
inputs = Input(shape=(784,), name='input_img')
x = Dense(64, activation='relu')(inputs)
x = Dense(64, activation='relu')(x)
y = Dense(10, activation='softmax')(x)
h5_model = Model(inputs=inputs, outputs=y)
return h5_model
if __name__ == '__main__':
if len(sys.argv) == 3:
# usage: python3 h5_to_pb.py h5_weight_path output_dir
h5_to_pb(h5_weight_path=sys.argv[1], output_dir=sys.argv[2])
以上这篇Keras 使⽤ Lambda层详解就是⼩编分享给⼤家的全部内容了，希望能给⼤家⼀个参考，也希望⼤家多多⽀持。

版权声明：本站内容均来自互联网，仅供演示用，请勿用于商业和其他非法用途。如果侵犯了您的权益请与我们联系QQ:729038198，我们将在24小时内删除。