如何使用權重手動預測神經網絡中的數據與karas

Question

我已經盡力遵循關於神經網絡結構的在線指南，但我必須缺少一些基本的東西。給定一組經過訓練的權重及其偏差，我想簡單地用這些權重手動預測輸入，而不使用預測方法。

使用帶有keras的MNIST圖像我試圖手動編輯我的數據以包含偏差的額外特徵，但是這種努力似乎沒有提供比沒有偏差更好的圖像準確性，使用keras預測方法。我的代碼與我的嘗試一起在下面。

請注意接近底部的兩條評論，用於將keras方法預測用於準確的圖像表示，然後我嘗試通過手動獲取權重和添加偏差來嘗試。

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 

Answer 1

手動預測計算似乎比在最終的層的第一層和activation='sigmoid'後像activation='relu'缺少激活函數正確其他。

執行以下操作手冊更改預測編碼和預測應該很好地工作：

from scipy.stats import logistic 

weights=np.vstack([model.get_weights()[0],model.get_weights()[1]]) 
prediction=np.dot(x_train,weights) 

prediction[prediction<0]=0    ### RELU after 1st 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=logistic.cdf(prediction)  ### Sigmoid after 2nd layer 

prediction=prediction.reshape(priorShape_x_train)