GridSearchCVを用いたハイパーパラメータチューニング

Question

Python 3.6.6
Windows7
tensorflow 1.14.0

GridSearchCVを用いたハイパーパラメータチューニングを行おうと考え、ためしにweb上のコードを動かしてみたのですが、下記のようなエラーが出ました。

TypeError: in converted code:

    TypeError: tf__initialize_variables() missing 1 required positional argument: 'self'

このサイトやスタックオーバーフローなどで調べてみたところクラスをインスタンス化していないのが問題のようでしたが、インスタンス化自体はしてあるように思えます。他の部分に問題があるのでしょうか？

以下コード全文

import numpy as np
from sklearn import datasets, preprocessing
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Activation
from tensorflow.python.keras import utils
from tensorflow.keras import backend as K
from tensorflow.keras.wrappers.scikit_learn import KerasClassifier

# import data and divided it into training and test purposes
iris = datasets.load_iris()
x = preprocessing.scale(iris.data)
y = np_utils.to_categorical(iris.target)
x_tr, x_te, y_tr, y_te = train_test_split(x, y, train_size  = 0.7)
num_classes = y_te.shape[1]


# Define model for iris classification
def iris_model(activation="relu", optimizer="adam", out_dim=100):
    model = Sequential()
    model.add(Dense(out_dim, input_dim=4, activation=activation))
    model.add(Dense(out_dim, activation=activation))   
    model.add(Dense(num_classes, activation="softmax"))
    model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])
    return model

# Define options for parameters
activation = ["relu", "sigmoid"]
optimizer = ["adam", "adagrad"]
out_dim = [100, 200]
nb_epoch = [10, 25]
batch_size = [5, 10]


# Retrieve model and parameter into GridSearchCV
model = KerasClassifier(build_fn=iris_model, verbose=0)
param_grid = dict(activation=activation, 
                  optimizer=optimizer, 
                  out_dim=out_dim, 
                  nb_epoch=nb_epoch, 
                  batch_size=batch_size)
grid = GridSearchCV(estimator=model, param_grid=param_grid)


# Run grid search
grid_result = grid.fit(x_tr, y_tr)


# Get the best score and the optimized mode
print (grid_result.best_score_)
print (grid_result.best_params_)

# Evaluate the model with test data
grid_eval = grid.predict(x_te)
def y_binary(i):
    if   i == 0: return [1, 0, 0]
    elif i == 1: return [0, 1, 0]
    elif i == 2: return [0, 0, 1]
y_eval = np.array([y_binary(i) for i in grid_eval])
accuracy = (y_eval == y_te)
print (np.count_nonzero(accuracy == True) / (accuracy.shape[0] * accuracy.shape[1]))


# Now see the optimized model
model = iris_model(activation=grid_result.best_params_['activation'], 
                   optimizer=grid_result.best_params_['optimizer'], 
                   out_dim=grid_result.best_params_['out_dim'])
model.summary()