具有多个输入的 Keras 网格搜索

问题描述：

我正在尝试对我的超参数进行网格搜索，以调整深度学习架构.我有多个模型输入选项，我正在尝试使用 sklearn 的网格搜索 api.问题是，网格搜索api只接受单个数组作为输入，代码在检查数据大小维度时失败.(我的输入维度是5*数据点数，而根据sklearn api，它应该是数据点数*特征维度).我的代码看起来像这样:

I am trying to do a grid search over my hyperparameters for tuning a deep learning architecture. I have multiple input options to the model and I am trying to use sklearn's grid search api. The problem is, grid search api only takes single array as input and the code fails while it checks for the data size dimension.(My input dimension is 5*number of data points while according to sklearn api, it should be number of data points*feature dimension). My code looks something like this:

from keras.layers import Concatenate, Reshape, Input, Embedding, Dense, Dropout
from keras.models import Model
from keras.wrappers.scikit_learn import KerasClassifier

def model(hyparameters):
    a = Input(shape=(1,))
    b = Input(shape=(1,))
    c = Input(shape=(1,))
    d = Input(shape=(1,))
    e = Input(shape=(1,))

    //Some operations and I get a single output -->out
    model = Model([a, b, c, d, e], out)
    model.compile(optimizer='rmsprop',
                               loss='categorical_crossentropy',
                               metrics=['accuracy'])
    return model

k_model = KerasClassifier(build_fn=model, epochs=150, batch_size=512, verbose=2)
# define the grid search parameters
param_grid = hyperparameter options dict
grid = GridSearchCV(estimator=k_model, param_grid=param_grid, n_jobs=-1)
grid_result = grid.fit([a_input, b_input, c_input, d_input, e_input], encoded_outputs)

答

这是使用具有多个输入的 GridSearch 和 Keras 模型的解决方法.诀窍在于将所有输入合并到一个数组中.我创建了一个接收单一输入的虚拟模型，然后使用 Lambda 层将其拆分为所需的部分.程序可以根据自己的数据结构轻松修改

this is workaround to use GridSearch and Keras model with multiple inputs. the trick consists in merge all the inputs in a single array. I create a dummy model that receives a SINGLE input and then split it into the desired parts using Lambda layers. the procedure can be easily modified according to your own data structure

def createMod(optimizer='Adam'):
    
    combi_input = Input((3,)) # (None, 3)
    a_input = Lambda(lambda x: tf.expand_dims(x[:,0],-1))(combi_input) # (None, 1) 
    b_input = Lambda(lambda x: tf.expand_dims(x[:,1],-1))(combi_input) # (None, 1)
    c_input = Lambda(lambda x: tf.expand_dims(x[:,2],-1))(combi_input) # (None, 1)
    
    ## do something
    c = Concatenate()([a_input, b_input, c_input])
    x = Dense(32)(c)

    out = Dense(1,activation='sigmoid')(x)
    model = Model(combi_input, out)

    model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics='accuracy')

    return model


## recreate multiple inputs
n_sample = 1000
a_input, b_input, c_input = [np.random.uniform(0,1, n_sample) for _ in range(3)]
y = np.random.randint(0,2, n_sample)

## merge inputs
combi_input = np.stack([a_input, b_input, c_input], axis=-1)


model = tf.keras.wrappers.scikit_learn.KerasClassifier(build_fn=createMod, verbose=0)
batch_size = [10, 20]
epochs = [10, 5]
optimizer = ['adam','SGD']
param_grid = dict(batch_size=batch_size, epochs=epochs)
grid = GridSearchCV(estimator=model, param_grid=param_grid, n_jobs=-1, cv=3)
grid_result = grid.fit(combi_input, y)

另一个简单而有价值的解决方案

具有多个输入的 Keras 网格搜索

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