# initial the gradients
gradWeights = np.zeros_like(Weights)
gradBiases = np.zeros_like(Biases)
print 'numCon', numCon
print 'numDis', numDis
print 'numOut', numOut
print 'Weights', len(Weights)
print 'Bias', len(Biases)
# dwadwad
# 17940
# 1544
#
paramFile = 'paramTest_GCNNPool'
write_param.write_binary(params.xypath + '../' + paramFile, Weights, Biases)
print 'Parameters have been saved at: ', params.xypath + '../' + paramFile
problem = ''
def InitByNodes(graph, word_dict):
# Embedding ---> Mapping ---> Conv1 ---> .... ---> Convn ---> Pooling ---> Fully-Connected ---> Output
# ConstructGraphConvolution(graph, word_dict, numView, numFea, numMap, numCon, numDis, numOut, \
# Wmap, Bmap,
# Wconv_root, Wconv_in, Wconv_out, Bconv, \
# Wdis, Woutput, Bdis, Boutput
# ):
# word_dict: [dict_view1, dict_view2]
# numView, numFea, numCon, numDis, numOut, \
# Wconv_root [View1:[conv1, conv2, ...], View2:[conv1, conv2, ...]]
Weights, Wconv_right = InitParam(Weights, num=numFea * numCon)
Biases, Bconv = InitParam(Biases, num=numCon)
# discriminative layer
Weights, Wdis = InitParam(Weights, num=numPool * numCon * numDis)
Biases, Bdis = InitParam(Biases, num=numDis)
# output layer
Weights, Wout = InitParam(Weights,
num=numDis * numOut,
upper=.0002,
lower=-.0002)
Biases, Bout = InitParam(Biases, newWeights=np.zeros((numOut, 1)))
Weights = Weights.reshape((-1, 1))
Biases = Biases.reshape((-1, 1))
print Weights[1], Weights[2], Weights[3], Weights[4], Weights[5]
print 'numDis', numDis
print 'numCon', numCon
print 'numOut', numOut
print 'Weights', len(Weights)
print 'Bias', len(Biases)
# dwadwad
# 17940
# 1544
write_param.write_binary(
'../paramTest_TBCNN_Conv' + str(gl.numCon) + '_Dis' + str(gl.numDis),
Weights, Biases)
print 'Done!'
# output layer
Weights, Wout = InitParam(Weights,
num=numDis * numOut,
upper=.0002,
lower=-.0002)
Biases, Bout = InitParam(Biases, newWeights=np.zeros((numOut, 1)))
Weights = Weights.reshape((-1, 1))
Biases = Biases.reshape((-1, 1))
print len(Weights)
print len(Biases)
print Biases[0, 0], ' ', Biases[1, 0]
write_param.write_binary('../param_pretrain', Weights, Biases)
dsadssd
tobegin = 0
elif Config == 'load':
#Weights = CP.load(file('param/param_30_Weights'))
#Biases = CP.load(file('param/param_30_Biases' ))
Weights = p.load(
file('param_rollback1/param_' + str(tobegin) + '_Weights'))
Biases = p.load(
file('param_rollback1/param_' + str(tobegin) + '_Biases'))
##################################
##################################
print Biases_pretrain[0]
print Biases_pretrain[1]
Biases, Bpretrain = InitParam(Biases, newWeights = Biases_pretrain)
Weights, Wpretrain = InitParam(Weights, newWeights = Weights_pretrain)
Weights, Wconv_root = InitParam(Weights, num = numFea*numCon)
Weights, Wconv_left = InitParam(Weights, num = numFea*numCon)
Weights, Wconv_right= InitParam(Weights, num = numFea*numCon)
Biases, Bconv = InitParam(Biases, num = numCon)
# discriminative layer
Weights, Wdis = InitParam(Weights, num = numPool*numCon*numDis)
Biases, Bdis = InitParam(Biases, num = numDis)
# output layer
Weights, Wout = InitParam(Weights, num = numDis*numOut, upper = .0002, lower = -.0002)
Biases, Bout = InitParam(Biases, newWeights = np.zeros( (numOut,1) ) )
Weights = Weights.reshape((-1,1))
Biases = Biases.reshape((-1,1))
write_param.write_binary('../param', Weights, Biases)
print len(Weights)
print len(Biases)
fingerprint = 'QC_LSTM'
write_nets_in_one_file(X_train, './', 'preprocessed/train_nets_'+fingerprint)
write_nets_in_one_file(X_CV, './', 'preprocessed/CV_nets_'+ fingerprint )
write_nets_in_one_file(X_test, './', 'preprocessed/test_nets_' + fingerprint)
serialize.write_labels('preprocessed/labels', y_train, y_CV, y_test)
print "writing para"
##############################
# initialize weights
#np.random.seed(123)
np.random.seed(435)
#np.random.seed(126)
#np.random.seed(127)
#np.random.seed(787)
Weights, Biases = construct.generateParam(False)
Weights = Weights.reshape((-1,1))
Biases = Biases.reshape((-1,1))
print len(Weights), len(Biases)
write_param.write_binary('preprocessed/para_'+fingerprint, Weights, Biases)
print 'done!'
Biases, Bleft = InitParam(Biases, num=numLeft)
Biases, Bright = InitParam(Biases, num=numRight)
# joint layer
Weights, Wjoint_left = InitParam(Weights, num=numLeft * numJoint)
Weights, Wjoint_right = InitParam(Weights, num=numRight * numJoint)
Biases, Bjoint = InitParam(Biases, num=numJoint)
# discriminative layer
Weights, Wdis = InitParam(Weights, num=numJoint * numDis)
Biases, Bdis = InitParam(Biases, num=numDis)
# output layer
Weights, Wout = InitParam(Weights,
num=numDis * numOut,
upper=.0002,
lower=-.0002)
Biases, Bout = InitParam(Biases, newWeights=np.zeros((numOut, 1)))
Weights = Weights.reshape((-1, 1))
Biases = Biases.reshape((-1, 1))
print 'Weights', len(Weights)
print 'Bias', len(Biases)
# dwadwad
# 17940
# 1544
#
write_param.write_binary('../MLP_paramTest', Weights, Biases)
print 'Done!'
