(x_train, t_train), (x_test, t_test) = load_mnist(normalize=True,
one_hot_label=True)
# settings of the neuralnet
form = [784, 50, 10]
activ_func = ['relu', 'softmax']
loss_func = 'cross_entropy'
# settings of the batch prediction
test_size = x_test.shape[0]
net = NeuralNet(form, activ_func, loss_func)
# settings of the weights parameter
f = open('weights.pkl', 'rb')
net.W = pickle.load(f)
f.close()
def check_prediction():
i = np.random.randint(0, len(x_test))
img = x_test[i]
label = np.argmax(t_test[i])
z = np.argmax(net.forprop(img))
img_show(img.reshape(28, 28) * 255)
print('label : {}'.format(label))
print('prediction : {}'.format(z))
def prediction_accuracy(it_num=100, batch_size=100):
acc = 0
