def adapter(ps, feats, x):
d = tf.parse_example(x, feats)
img = tf.to_dense(d['flt_img'])
# img = tf.cast(d['int_img'], tf.float32) / 255.
lbl = d['int_lbl']
return img, lbl
def load(path, feat):
ds = dataset(path).take(1)
for d in ds.map(lambda x: tf.parse_single_example(x, feat)):
return tf.to_dense(d['toks']).numpy()