Keras set_session in for loop problem, not run and stop

Question

I want to reproduce result for keras , the backend is tensorflow . So I use ParameterGrid for check that. When I run K.set_session(Session) second time in a loop, the program stop, and nothing to print. No error! How to fix it? Thanks. Here is my code.

'''Trains a simple convnet on the MNIST dataset.
Gets to 99.25% test accuracy after 12 epochs
(there is still a lot of margin for parameter tuning).
16 seconds per epoch on a GRID K520 GPU.
'''

from __future__ import print_function
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K
import numpy
import tensorflow
from sklearn.model_selection import ParameterGrid

##  TuneParameter
TuneParameter = {}
TuneParameter["Batch"] = [50, 50, 50, 50, 50, 50]
TuneParameter["Epoch"] = [2]
TuneParameter = ParameterGrid(TuneParameter)

##  For each pair of parameter
for p in TuneParameter:

    ##  Initial session
    numpy.random.seed(2018)
    tensorflow.set_random_seed(2018)
    Session = tensorflow.Session(graph=tensorflow.get_default_graph())
    K.set_session(Session)

    # input image dimensions
    img_rows, img_cols = 28, 28

    # the data, split between train and test sets
    (x_train, y_train), (x_test, y_test) = mnist.load_data()

    if K.image_data_format() == 'channels_first':
        x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
        x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
        input_shape = (1, img_rows, img_cols)
    else:
        x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
        x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
        input_shape = (img_rows, img_cols, 1)

        x_train = x_train.astype('float32')
        x_test = x_test.astype('float32')
        x_train /= 255
        x_test /= 255
        print('x_train shape:', x_train.shape)
        print(x_train.shape[0], 'train samples')
        print(x_test.shape[0], 'test samples')

    # convert class vectors to binary class matrices
    y_train = keras.utils.to_categorical(y_train, 10)
    y_test = keras.utils.to_categorical(y_test, 10)

    model = Sequential()
    model.add(Conv2D(32, kernel_size=(3, 3),
                     activation='relu',
                     input_shape=input_shape))
    model.add(Conv2D(64, (3, 3), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.25))
    model.add(Flatten())
    model.add(Dense(128, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(10, activation='softmax'))

    model.compile(loss=keras.losses.categorical_crossentropy,
                  optimizer=keras.optimizers.Adadelta(),
                  metrics=['accuracy'])

    model.fit(x_train, y_train,
              batch_size=p["Batch"],
              epochs=p["Epoch"],
              verbose=1,
              validation_data=(x_test, y_test))
    score = model.evaluate(x_test, y_test, verbose=0)

    K.clear_session()
    print('Test loss:', score[0])
    print('Test accuracy:', score[1])

Answer 1

Try

session = K.get_session()

Removing the:

Session = tensorflow.Session(graph=tensorflow.get_default_graph())
K.set_session(Session)