Posted in Python onJune 22, 2020
1、模型结果设计
2、代码
from keras import Input, Model from keras.layers import Dense, Concatenate import numpy as np from keras.utils import plot_model from numpy import random as rd samples_n = 3000 samples_dim_01 = 2 samples_dim_02 = 2 # 样本数据 x1 = rd.rand(samples_n, samples_dim_01) x2 = rd.rand(samples_n, samples_dim_02) y_1 = [] y_2 = [] y_3 = [] for x11, x22 in zip(x1, x2): y_1.append(np.sum(x11) + np.sum(x22)) y_2.append(np.max([np.max(x11), np.max(x22)])) y_3.append(np.min([np.min(x11), np.min(x22)])) y_1 = np.array(y_1) y_1 = np.expand_dims(y_1, axis=1) y_2 = np.array(y_2) y_2 = np.expand_dims(y_2, axis=1) y_3 = np.array(y_3) y_3 = np.expand_dims(y_3, axis=1) # 输入层 inputs_01 = Input((samples_dim_01,), name='input_1') inputs_02 = Input((samples_dim_02,), name='input_2') # 全连接层 dense_01 = Dense(units=3, name="dense_01", activation='softmax')(inputs_01) dense_011 = Dense(units=3, name="dense_011", activation='softmax')(dense_01) dense_02 = Dense(units=6, name="dense_02", activation='softmax')(inputs_02) # 加入合并层 merge = Concatenate()([dense_011, dense_02]) # 分成两类输出 --- 输出01 output_01 = Dense(units=6, activation="relu", name='output01')(merge) output_011 = Dense(units=1, activation=None, name='output011')(output_01) # 分成两类输出 --- 输出02 output_02 = Dense(units=1, activation=None, name='output02')(merge) # 分成两类输出 --- 输出03 output_03 = Dense(units=1, activation=None, name='output03')(merge) # 构造一个新模型 model = Model(inputs=[inputs_01, inputs_02], outputs=[output_011, output_02, output_03 ]) # 显示模型情况 plot_model(model, show_shapes=True) print(model.summary()) # # 编译 # model.compile(optimizer="adam", loss='mean_squared_error', loss_weights=[1, # 0.8, # 0.8 # ]) # # 训练 # model.fit([x1, x2], [y_1, # y_2, # y_3 # ], epochs=50, batch_size=32, validation_split=0.1) # 以下的方法可灵活设置 model.compile(optimizer='adam', loss={'output011': 'mean_squared_error', 'output02': 'mean_squared_error', 'output03': 'mean_squared_error'}, loss_weights={'output011': 1, 'output02': 0.8, 'output03': 0.8}) model.fit({'input_1': x1, 'input_2': x2}, {'output011': y_1, 'output02': y_2, 'output03': y_3}, epochs=50, batch_size=32, validation_split=0.1) # 预测 test_x1 = rd.rand(1, 2) test_x2 = rd.rand(1, 2) test_y = model.predict(x=[test_x1, test_x2]) # 测试 print("测试结果:") print("test_x1:", test_x1, "test_x2:", test_x2, "y:", test_y, np.sum(test_x1) + np.sum(test_x2))
补充知识:Keras多输出(多任务)如何设置fit_generator
在使用Keras的时候,因为需要考虑到效率问题,需要修改fit_generator来适应多输出
# create model model = Model(inputs=x_inp, outputs=[main_pred, aux_pred]) # complie model model.compile( optimizer=optimizers.Adam(lr=learning_rate), loss={"main": weighted_binary_crossentropy(weights), "auxiliary":weighted_binary_crossentropy(weights)}, loss_weights={"main": 0.5, "auxiliary": 0.5}, metrics=[metrics.binary_accuracy], ) # Train model model.fit_generator( train_gen, epochs=num_epochs, verbose=0, shuffle=True )
看Keras官方文档:
generator: A generator or an instance of Sequence (keras.utils.Sequence) object in order to avoid duplicate data when using multiprocessing. The output of the generator must be either
a tuple (inputs, targets)
a tuple (inputs, targets, sample_weights).
Keras设计多输出(多任务)使用fit_generator的步骤如下:
根据官方文档,定义一个generator或者一个class继承Sequence
class Batch_generator(Sequence): """ 用于产生batch_1, batch_2(记住是numpy.array格式转换) """ y_batch = {'main':batch_1,'auxiliary':batch_2} return X_batch, y_batch # or in another way def batch_generator(): """ 用于产生batch_1, batch_2(记住是numpy.array格式转换) """ yield X_batch, {'main': batch_1,'auxiliary':batch_2}
重要的事情说三遍(亲自采坑,搜了一大圈才发现滴):
如果是多输出(多任务)的时候,这里的target是字典类型
如果是多输出(多任务)的时候,这里的target是字典类型
如果是多输出(多任务)的时候,这里的target是字典类型
以上这篇Keras-多输入多输出实例(多任务)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三水点靠木。
Keras-多输入多输出实例(多任务)
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