feed_dict = {stories[0]: trainX[0],
queries[0]: trainX[1],
stories[1]: trainX[2],
queries[1]: trainX[3],
answers[0]: trainY[0],
answers[1]: trainY[1]}
return sess.run(loss_op, feed_dict=feed_dict)
def predictf(trainX):
feed_dict = {stories[0]: trainX[0],
queries[0]: trainX[1],
stories[1]: trainX[2],
queries[1]: trainX[3]}
return sess.run(logits, feed_dict=feed_dict)
def savef(filepath):
return modelsaver.save(sess, filepath)
def restoref(filepath):
modelsaver.restore(sess, filepath)
return model(fitf=fitf, testf=testf, predictf=predictf, savef=savef, restoref=restoref)
ver, qas, repeat = handlesysargs('en/hn', None, 10)
#embedding_matrix, word_idx = pre_train_embedding(EMBEDDING_SIZE, ver)
def wmethod():
global word_idx
return word_idx
evaluate_model(compile_model, ver, qas, pad=1, wmethod='concat', flatten=0, repeat=repeat, E2E=1);
# Using last C as W per adjacent weight tying
# func = lambda x:tf.matmul(x, tf.transpose(embedlayer.get_weights()[0], [1,0]))
# dl = Lambda(func)(newu)
pred = Activation('softmax')(dl)
model = Model(input=[story_input, query_input], output=[pred])
# opt = Adam(lr=0.001,
# beta_1=0.9,
# beta_2=0.999,
# epsilon=1e-08,
# decay=0.0)
opt = SGD(lr=0.0,
momentum=0.0,
decay=0.0,
nesterov=False)
model.compile(optimizer=opt,
loss='categorical_crossentropy',
metrics=['accuracy'])
return model, [LearningRateScheduler(step_decay)]
# return model, None
ver = 'en'
qas = None
ver, qas, repeat = handlesysargs(ver, qas)
evaluate_model(compile_model, ver, qas, pad=1, wmethod=None, flatten=0, word=0, repeat=repeat);
