def generator(dataset_path, is_linear, shape):
train, test = load_dict_data(dataset_path, is_linear, shape)
sys.stdout.write('building model ...')
cp = CheckPoint()
v, model = cp.load_model()
print v
if v == -1:
raise IOError('model has not been trained.')
model.build_generator(-2)
def batch_generator(tt, model, batch_size, comment):
sample_num = tt['data'].shape[0]
batch_num = sample_num / batch_size
result = []
for i in range(batch_num):
sys.stdout.write('\rgenerate %s of batch %d' % (comment, i))
sys.stdout.flush()
start_i = batch_size * i
end_i = batch_size * (i+1)
batch_result = model.generate(tt['data'][start_i: end_i])
result.append(batch_result)
result = np.vstack(result)
print ''
return result
def batch_test(tt, model, batch_size, comment):
sample_num = tt['data'].shape[0]
batch_num = sample_num / batch_size
result = []
for i in range(batch_num):
sys.stdout.write('\rtesting %s of batch %d' % (comment, i))
sys.stdout.flush()
start_i = batch_size * i
end_i = batch_size * (i+1)
batch_result = model.testing(tt['data'][start_i: end_i], tt['labels'][start_i: end_i], -1)
result.append(batch_result)
result = np.mean(result)
print ''
return result
batch_size = 500
result_X_train = batch_generator(train, model, batch_size, 'train')
result_X_test = batch_generator(test, model, batch_size, 'test')
print batch_test(train, model, batch_size, 'train')
print batch_test(test, model, batch_size, 'test')
print result_X_train.shape
print result_X_test.shape
train['data'] = result_X_train
test['data'] = result_X_test
return train, test
def build_model():
sys.stdout.write('building model ...')
cp = CheckPoint()
v, model = cp.load_model()
print v
if v == -1:
raise IOError('model has not been trained.')
p_y_given_x = model.layers[-1].get_output()
y_pred = T.argmax(p_y_given_x, axis=1)
if isinstance(model, DropoutModel):
predict = theano.function(inputs = [model.x, model.is_train],
outputs = y_pred)
return predict, True
elif isinstance(model, ConvModel):
predict = theano.function(inputs = [model.x],
outputs = y_pred)
return predict, False
return None, True