product_matching/experiments/euclidean_product_matching.py [43:65]:
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    model.params['input_shapes'] = preprocessor.context['input_shapes']
    model.params['vocab_size'] = preprocessor.context['vocab_size']
    model.params['emb_dim'] = EMB_DIM
    model.params['box_dim'] = BOX_DIM
    model.params['dropout'] = DROPOUT
    model.params['task'] = task
    print(model.params)
    print("Building and Compiling the Model")
    model.build()
    model.compile()

print("Training the Model")
train_generator = mz.PairDataGenerator(train_processed, num_dup=1, num_neg=NEG_SIZE, batch_size=BATCH_SIZE, shuffle=True)
valid_x, valid_y = test_processed.unpack()
evaluate = mz.callbacks.EvaluateAllMetrics(model, x=valid_x, y=valid_y, batch_size=BATCH_SIZE)
history = model.fit_generator(train_generator, epochs=10, callbacks=[evaluate], workers=6, use_multiprocessing=True)

import pickle
pickle.dump(history.history, open("history_"+model_name.strip()+".pkl","wb"))
try:
    model.save("model_"+model_name.strip()+".model")
except:
    model._backend.save("model_"+model_name.strip()+".model")
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product_matching/experiments/hyperboloid_product_matching.py [43:65]:
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    model.params['input_shapes'] = preprocessor.context['input_shapes']
    model.params['vocab_size'] = preprocessor.context['vocab_size']
    model.params['emb_dim'] = EMB_DIM
    model.params['box_dim'] = BOX_DIM
    model.params['dropout'] = DROPOUT
    model.params['task'] = task
    print(model.params)
    print("Building and Compiling the Model")
    model.build()
    model.compile()

print("Training the Model")
train_generator = mz.PairDataGenerator(train_processed, num_dup=1, num_neg=NEG_SIZE, batch_size=BATCH_SIZE, shuffle=True)
valid_x, valid_y = test_processed.unpack()
evaluate = mz.callbacks.EvaluateAllMetrics(model, x=valid_x, y=valid_y, batch_size=BATCH_SIZE)
history = model.fit_generator(train_generator, epochs=10, callbacks=[evaluate], workers=6, use_multiprocessing=True)

import pickle
pickle.dump(history.history, open("history_"+model_name.strip()+".pkl","wb"))
try:
    model.save("model_"+model_name.strip()+".model")
except:
    model._backend.save("model_"+model_name.strip()+".model")
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