def predict(self, subset=1, verbose=1):
def softmax(predicted_output):
a = np.exp(predicted_output)
b = np.sum(a, 1).reshape(-1, 1)
return a / b
prediction_model = self.prediction_model
all_answer_options_intseq = self.data.cache.all_answer_options_intseq
explain_intseq = self.data.exp_intseq
questions_intseq = self.data.questions_intseq
answers = self.data.cache.answers
indices = self.data.indices
int_ans = np.array(
[self.data.convert_to_int(letter) for letter, ans in answers])
if subset == 1:
train_indices, val_indices, test_indices = self.data.indices
train_indices = train_indices[0:150]
val_indices = val_indices[0:150]
test_indices = test_indices[0:150]
indices = [train_indices, val_indices, test_indices]
prediction_model.compile(optimizer='adam',
loss=_loss_tensor,
metrics=[keras.metrics.categorical_accuracy])
all_answer_options_intseq = np.array(all_answer_options_intseq)
acc = []
for i in range(3):
ind = indices[i]
input1 = explain_intseq[ind]
input2 = questions_intseq[ind]
input3 = all_answer_options_intseq[:, 0, :][ind]
input4 = all_answer_options_intseq[:, 1, :][ind]
input5 = all_answer_options_intseq[:, 2, :][ind]
input6 = all_answer_options_intseq[:, 3, :][ind]
predicted_output = prediction_model.predict(
[input1, input2, input3, input4, input5, input6],
batch_size=64,
verbose=verbose)
predicted_output_softmax = softmax(predicted_output)
predicted_ans = np.argmax(predicted_output, axis=1)
accuracy = np.mean(predicted_ans == int_ans[ind])
acc.append(accuracy)
print('train,val,test accuracies: {:.2f}/{:.2f}/{:.2f}'.format(
acc[0], acc[1], acc[2]))
cache = Struct()
cache.predicted_output = predicted_output
cache.predicted_output_softmax = predicted_output_softmax
cache.predicted_ans = predicted_ans
cache.int_ans = int_ans
self.predictions_cache = cache
self.acc = acc
return cache
