网格搜索具有多个输入的Keras [英] Grid Search for Keras with multiple inputs

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。我对该模型有多个输入选项，并且正在尝试使用sklearn的网格搜索API。问题是，网格搜索api仅将单个数组作为输入，并且在检查数据大小维度时代码失败。（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*功能尺寸）。我的代码如下所示：

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)

推荐答案

这是使用带有多个输入的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。我创建了一个虚拟模型，该模型接收一个输入，然后使用Lambda层将其拆分为所需的部分。该过程可以根据您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

Another simple and valuable solution

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