如何使用权重通过keras手动预测神经网络中的数据

问：

我已经尽了最大努力遵循有关神经网络结构的在线指南，但我一定缺少一些基本的东西。给定一组经过训练的权重及其偏差，我想简单地使用这些权重手动预测输入，而不使用 predict 方法。

使用带有 keras 的 MNIST 图像，我尝试手动编辑我的数据以包含额外的偏差特征，但是这种努力似乎没有提供比完全不使用偏差更好的图像准确性，而且绝对比使用 keras 预测方法的准确性低得多。我的代码和我的尝试在下面。

请注意底部附近的两条评论，使用 keras 方法预测来获得准确的图像表示，然后是我手动获取权重并添加偏差的糟糕尝试。

from keras.datasets import mnist
import numpy as np
import time
from keras.models import Sequential
from keras.layers import Dense
import tensorflow as tf
from matplotlib import pyplot as plt

comptime=time.time()
with tf.device('/cpu:0'):
    tf.placeholder(tf.float32, shape=(None, 20, 64))

    seed = 7
    np.random.seed(seed)
    model = Sequential()
    (x_train, _), (x_test, _) = mnist.load_data()
    x_train = x_train.astype('float32') / 255.
    priorShape_x_train=x_train.shape #prior shape of training set
    x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))
    x_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))
    x_train_shaped=x_train
    model.add(Dense(32, input_dim=784, init='uniform', activation='relu'))
    model.add(Dense(784, init='uniform', activation='sigmoid'))
    model.compile(loss='binary_crossentropy', optimizer='adadelta', metrics=['accuracy'])
    model.fit(x_train[1:2500], x_train[1:2500], nb_epoch=10)

#proper keras prediction
prediction_real=model.predict(x_train[57:58])
prediction_real=prediction_real.reshape((28,28))

#manual weight prediction attempt
x_train=np.hstack([x_train,np.zeros(x_train.shape[0]).reshape(x_train.shape[0],1)]) #add extra column for bias
x_train[:,-1]=1 #add placeholder as 1
weights=np.vstack([model.get_weights()[0],model.get_weights()[1]]) #add trained weights as extra row vector
prediction=np.dot(x_train,weights) #now take dot product.. repeat pattern for next layer
prediction=np.hstack([prediction,np.zeros(prediction.shape[0]).reshape(prediction.shape[0],1)])
prediction[:,-1]=1
weights=np.vstack([model.get_weights()[2],model.get_weights()[3]])
prediction=np.dot(prediction,weights)
prediction=prediction.reshape(priorShape_x_train)

plt.imshow(prediction[57], interpolation='nearest',cmap='gray')
plt.savefig('myprediction.png') #my prediction, not accurate
plt.imshow(prediction_real,interpolation='nearest',cmap='gray')
plt.savefig('realprediction.png') #in-built keras method, accurate