为什么SoftMax总是提供1.0的概率?

我一直在尝试简单的mnist例子。很抱歉,如果这个问题是最基本的。

from keras.datasets import mnist
from keras.layers import Input, Conv2D, Dense
from keras.models import Sequential
from keras.utils import np_utils

def myModel():

    model= Sequential()
    layer1 = Dense(1024, input_shape=(784,), activation='relu')
    layer2 = Dense(512, activation='relu')
    layer3 = Dense(10, activation='softmax')
    model.add (layer1)
    model.add (layer2)
    model.add(layer3)
    model.compile(loss='categorical_crossentropy', optimizer='adam')
    return model


if __name__ == '__main__':
    print "Inside the main function "
    model = myModel()
    (trainX, trainY), (testX, testY) = mnist.load_data()
    print ("TrainX shape is ", trainX.shape)
    trainX = trainX.reshape(trainX.shape[0], trainX.shape[1] * trainX.shape[2])
    trainY = np_utils.to_categorical(trainY, 10)
    model.fit(trainX, trainY, batch_size=200, epochs=1)

    print ("Let's predict now..")
    print ("Shae of x and shape of 100" , trainX.shape, trainX[10].shape)
    result = model.predict(trainX[100].reshape(1,784 ))
    print result

    import matplotlib.pyplot as plt 
    plt.subplot(2,2,1)
    plt.imshow(trainX[1100].reshape(28,28))
    plt.show()

最后一层的输出值为

[[0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]]

我该如何解释这个结果?.这不是结果的概率分布吗?如果不是,我该怎么做?