Problema com gridSearch

ghnunes
(usa Linux Mint)

Enviado em 23/08/2022 - 11:22h

Olá pessoal, estou fazendo alguns testes com o gridSearchCV, mas estou com esse erro, vocês sabem me dizer o motivo ?

in clone raise TypeError("Cannot clone object '%s' (type %s): "
TypeError: Cannot clone object '<keras.engine.functional.Functional object at 0x7f330fe610a0>' (type <class 'keras.engine.functional.Functional'>): it does not seem to be a scikit-learn estimator as it does not implement a 'get_params' method.

Modelo:

from keras import backend as K, regularizers

from keras.engine.training import Model

from keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense, \

    BatchNormalization, Activation, Input

import ModelLib

 

 

class Cifar100_Model(ModelLib.ModelLib):

    def build_classifier_model(self, dataset, n_classes=5,

                               activation='elu', dropout_1_rate=0.25,

                               dropout_2_rate=0.5,

                               reg_factor=50e-4, bias_reg_factor=None, batch_norm=False):

         

        n_classes = dataset.n_classes

        print(n_classes)

        print("----------------------------------------------------------------------------")

        l2_reg = regularizers.l2(reg_factor) #K.variable(K.cast_to_floatx(reg_factor))

        l2_bias_reg = None

        if bias_reg_factor:

            l2_bias_reg = regularizers.l2(bias_reg_factor) #K.variable(K.cast_to_floatx(bias_reg_factor))

 

        # input image dimensions

        h, w, d = 32, 32, 3

 

        if K.image_data_format() == 'channels_first':

            input_shape = (d, h, w)

        else:

            input_shape = (h, w, d)

 

        # input image dimensions

        x = input_1 = Input(shape=input_shape)

 

        x = Conv2D(filters=32, kernel_size=(3, 3), padding='same', kernel_regularizer=l2_reg, bias_regularizer=l2_bias_reg)(x)

        if batch_norm:

            x = BatchNormalization()(x)

        x = Activation(activation=activation)(x)

        x = Conv2D(filters=32, kernel_size=(3, 3), padding='same', kernel_regularizer=l2_reg, bias_regularizer=l2_bias_reg)(x)

        if batch_norm:

            x = BatchNormalization()(x)

        x = Activation(activation=activation)(x)

        x = MaxPooling2D(pool_size=(2, 2))(x)

        x = Dropout(rate=dropout_1_rate)(x)

 

        x = Conv2D(filters=64, kernel_size=(3, 3), padding='same', kernel_regularizer=l2_reg, bias_regularizer=l2_bias_reg)(x)

        if batch_norm:

            x = BatchNormalization()(x)

        x = Activation(activation=activation)(x)

        x = Conv2D(filters=64, kernel_size=(3, 3), padding='same', kernel_regularizer=l2_reg, bias_regularizer=l2_bias_reg)(x)

        if batch_norm:

            x = BatchNormalization()(x)

        x = Activation(activation=activation)(x)

        x = MaxPooling2D(pool_size=(2, 2))(x)

        x = Dropout(rate=dropout_1_rate)(x)

 

        x = Conv2D(filters=128, kernel_size=(3, 3), padding='same', kernel_regularizer=l2_reg, bias_regularizer=l2_bias_reg)(x)

        if batch_norm:

            x = BatchNormalization()(x)

        x = Activation(activation=activation)(x)

        x = Conv2D(filters=128, kernel_size=(3, 3), padding='same', kernel_regularizer=l2_reg, bias_regularizer=l2_bias_reg)(x)

        if batch_norm:

            x = BatchNormalization()(x)

        x = Activation(activation=activation)(x)

        x = MaxPooling2D(pool_size=(2, 2))(x)

        x = Dropout(rate=dropout_1_rate)(x)

 

        x = Conv2D(filters=256, kernel_size=(2, 2), padding='same', kernel_regularizer=l2_reg, bias_regularizer=l2_bias_reg)(x)

        if batch_norm:

            x = BatchNormalization()(x)

        x = Activation(activation=activation)(x)

        x = Conv2D(filters=256, kernel_size=(2, 2), padding='same', kernel_regularizer=l2_reg, bias_regularizer=l2_bias_reg)(x)

        if batch_norm:

            x = BatchNormalization()(x)

        x = Activation(activation=activation)(x)

        x = MaxPooling2D(pool_size=(2, 2))(x)

        x = Dropout(rate=dropout_1_rate)(x)

 

        x = Flatten()(x)

        x = Dense(units=512, kernel_regularizer=l2_reg, bias_regularizer=l2_bias_reg)(x)

        if batch_norm:

            x = BatchNormalization()(x)

        x = Activation(activation=activation)(x)

 

 

        x = Dropout(rate=dropout_2_rate)(x)

        x = Dense(units=n_classes, kernel_regularizer=l2_reg, bias_regularizer=l2_bias_reg)(x)

        if batch_norm:

            x = BatchNormalization()(x)

        x = Activation(activation='softmax')(x)

 

        model = Model(inputs=[input_1], outputs=[x])

        return model



 

Código teste:

import models.cifar100_model

 

def load_model():

    return models.cifar100_model.Cifar100_Model()

 

def get_params(self, deep = True):

    return {"learning rate" self.learning_rate}

 

 

model_lib = load_model() 

         

model = model_lib.build_classifier_model(dataset)

 

from sklearn.model_selection import GridSearchCV

x_train = dataset.x_train

y_train = dataset.y_train_labels

learning_rate = [0.01, 0.1]

param_grid = dict(learning_rate = learning_rate)

grid = GridSearchCV(estimator = model, param_grid=param_grid, n_jobs=-1, cv=3, scoring='accuracy') 

gridResult = grid.fit(x_train,y_train)