构建具有多个输出的子类的模型,使用数据集tensorflow作为输入。自定义的数据集。
使用keras适合训练模式。
当我只用训练集,它可以运行。但是,一旦我使用相同类型的数据集和验证输入,出了问题,如:“检查模式的目标时错误:预期没有数据,却得到了:”
数据类型是像 '元组(数据,(目标[0],目标[1]))'
tensorflow-GPU == 1.12,tensorflow.keras
错误信息
File "/home/god/anaconda3/envs/tensorflow_n/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py", line 1574, in fit
steps=validation_steps)
File "/home/god/anaconda3/envs/tensorflow_n/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py", line 992, in _standardize_user_data
class_weight, batch_size)
File "/home/god/anaconda3/envs/tensorflow_n/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py", line 1154, in _standardize_weights
exception_prefix='target')
File "/home/god/anaconda3/envs/tensorflow_n/lib/python3.6/site-packages/tensorflow/python/keras/engine/training_utils.py", line 257, in standardize_input_data
'expected no data, but got:', data)
ValueError: ('Error when checking model target: expected no data, but got:', (<tf.Tensor 'IteratorGetNext_1:1' shape=(16, 16, 513) dtype=float32>, <tf.Tensor 'IteratorGetNext_1:2' shape=(16, 16, 513) dtype=float32>))
简化代码原因相同的错误
import tensorflow as tf
class Model(tf.keras.Model):
def __init__(self):
super().__init__()
self.dense1 = tf.keras.layers.Dense(units=16)
self.dense2 = tf.keras.layers.Dense(units=16)
def compute_output_shape(self, input_shape):
return input_shape, input_shape
def call(self, inputs, training=None, mask=None):
out1 = self.dense1(inputs)
out2 = self.dense2(inputs)
return out1, out2
train_dataset = tf.data.Dataset.from_tensor_slices(
(tf.constant(0., shape=[1024, 16]), (tf.constant(0., shape=[1024, 16]), tf.constant(0., shape=[1024, 16])))).repeat().batch(32)
valid_dataset = tf.data.Dataset.from_tensor_slices(
(tf.constant(0., shape=[128, 16]), (tf.constant(0., shape=[128, 16]), tf.constant(0., shape=[128, 16])))).repeat(1).batch(32)
model = Model()
model.compile(
optimizer=tf.train.AdamOptimizer(learning_rate=1e-4),
loss=[tf.keras.losses.mse, tf.keras.losses.mse],
loss_weights=[1, 1]
)
model.fit(
train_dataset,
validation_data=valid_dataset,
epochs=10,
steps_per_epoch=30,
validation_steps=4,
)
Keras不PyTorch,你不能没有一个非常先进的原因继承模式。
inputs = Input(input_shape)
out1 = Dense(16)(inputs)
out2 = Dense(16)(inputs)
model = tf.keras.Model(inputs, [out1,out2])
独立x
和y
当训练:
x_train = your_tuple[0]
y_train = your_tuple[1]
model.fit(x_train, y_train, ....)