我想创建多个tf.data.Dataset
使用from_generator()
功能。我想向生成器函数发送一个参数(raw_data_gen
)。这个想法是生成器函数将根据发送的参数产生不同的数据。这样我想raw_data_gen
能够提供训练、验证或测试数据。
training_dataset = tf.data.Dataset.from_generator(raw_data_gen, (tf.float32, tf.uint8), ([None, 1], [None]), args=([1]))
validation_dataset = tf.data.Dataset.from_generator(raw_data_gen, (tf.float32, tf.uint8), ([None, 1], [None]), args=([2]))
test_dataset = tf.data.Dataset.from_generator(raw_data_gen, (tf.float32, tf.uint8), ([None, 1], [None]), args=([3]))
当我尝试致电时收到的错误消息from_generator()
这样就是:
TypeError: from_generator() got an unexpected keyword argument 'args'
这里是raw_data_gen
函数,虽然我不确定你是否需要这个,因为我的预感是问题出在调用from_generator()
:
def raw_data_gen(train_val_or_test):
if train_val_or_test == 1:
#For every filename collected in the list
for filename, lab in training_filepath_label_dict.items():
raw_data, samplerate = soundfile.read(filename)
try: #assume the audio is stereo, ready to be sliced
raw_data = raw_data[:,0] #raw_data is a np.array, just take first channel with slice
except IndexError:
pass #this must be mono audio
yield raw_data, lab
elif train_val_or_test == 2:
#For every filename collected in the list
for filename, lab in validation_filepath_label_dict.items():
raw_data, samplerate = soundfile.read(filename)
try: #assume the audio is stereo, ready to be sliced
raw_data = raw_data[:,0] #raw_data is a np.array, just take first channel with slice
except IndexError:
pass #this must be mono audio
yield raw_data, lab
elif train_val_or_test == 3:
#For every filename collected in the list
for filename, lab in test_filepath_label_dict.items():
raw_data, samplerate = soundfile.read(filename)
try: #assume the audio is stereo, ready to be sliced
raw_data = raw_data[:,0] #raw_data is a np.array, just take first channel with slice
except IndexError:
pass #this must be mono audio
yield raw_data, lab
else:
print("generator function called with an argument not in [1, 2, 3]")
raise ValueError()