def f(dat):
ds = []
for row in dat:
s, a = self.forge(row)
ds.append((s, a))
ds = np.array(ds)
return DataSet(np.vstack(ds[:, 0]).reshape((-1, h, w, c)), np.vstack(ds[:, 1]))
def adapt(self, filename):
ds = []
dat = self.load_dataset(filename)
for row in dat:
s, a = self.forge(row)
ds.append((s, a))
ds = np.array(ds)
np.random.shuffle(ds)
size = ds.shape[0]
train_size = int(size * 0.8)
train = ds[:train_size, :]
test = ds[train_size:, :]
validation_size = int(train.shape[0] * 0.2)
validation = train[:validation_size, :]
train = train[validation_size:, :]
h, w, c = self.get_input_shape()
train = DataSet(
np.vstack(train[:, 0]).reshape((-1, h, w, c)),
np.vstack(train[:, 1]))
validation = DataSet(
np.vstack(validation[:, 0]).reshape((-1, h, w, c)),
np.vstack(validation[:, 1]))
test = DataSet(
np.vstack(test[:, 0]).reshape((-1, h, w, c)), np.vstack(test[:,
1]))
print(train.images.shape, train.labels.shape)
print(validation.images.shape, validation.labels.shape)
print(test.images.shape, test.labels.shape)
self.ds_train = train
self.ds_valid = validation
self.ds_test = test